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Posters - Schedules

Poster presentations at ISMB/ECCB 2021 will be presented virtually. Authors will pre-record their poster talk (5-7 minutes) and will upload it to the virtual conference platform site along with a PDF of their poster beginning July 19 and no later than July 23. All registered conference participants will have access to the poster and presentation through the conference and content until October 31, 2021. There are Q&A opportunities through a chat function and poster presenters can schedule small group discussions with up to 15 delegates during the conference.

Information on preparing your poster and poster talk are available at: https://www.iscb.org/ismbeccb2021-general/presenterinfo#posters

Ideally authors should be available for interactive chat during the times noted below:

View Posters By Category

Session A: Sunday, July 25 between 15:20 - 16:20 UTC
Session B: Monday, July 26 between 15:20 - 16:20 UTC
Session C: Tuesday, July 27 between 15:20 - 16:20 UTC
Session D: Wednesday, July 28 between 15:20 - 16:20 UTC
Session E: Thursday, July 29 between 15:20 - 16:20 UTC
Agglomerative clustering of fragment 3D structures based on pairwise RMSD
COSI: 3DSIG
  • Antoine Moniot, LORIA, France
  • Isaure Chauvot de Beauchene, CAPSID team, CNRS, LORIA, France
  • Yann Guermeur, CNRS, France

Short Abstract: In structural biology, many fragment-based 3D modeling methods require fragment libraries. They represent the whole set of possible 3D structures (conformations) observed experimentally for each fragment, with a chosen precision.
In docking, for this precision, it is important to have as few prototypes as possible inside the libraries.
One way to create a library is to cluster all observed conformations in order to retain only the representative prototypes. The most common measure of 3D similarity is the Root Mean Squared Deviation (RMSD) applied after a structural superposition. But this RMSD after alignment is not a metric, which means that distance-based clustering is not applicable.
Current alternative methods, based on an approximation of the RMSD or internal coordinates, retrieve too many prototypes.
We propose a new type of clustering which meets our needs, based on hierarchical agglomerative clustering. The linkage criterion for agglomerating two clusters is the radius of the minimal ball enclosing them. The prototypes are the centers of the balls at the end of the clustering process. They constitute a cover of all possible conformations within a given RMSD. We discuss the complexity issues associated with solving the quadratic programming problems that produce the minimal enclosing balls.

All-Atom Molecular Simulations of a Type II DNA Topoisomerase Molecular Motor
COSI: 3DSIG
  • Andrej Perdih, National Institute of Chemistry, Slovenia
  • Matic Pavlin, University of Ljubljana, Faculty of Electrical Engineering, Slovenia
  • Katja Valjavec, National Institute of Chemistry, Slovenia
  • Barbara Herlah, National Institute of Chemistry, Slovenia

Short Abstract: Type II DNA topoisomerases are complex molecular motors, which manage the topological states of the DNA in the cell and are crucial players in fundamental cellular processes such as cell division. Since the human type II topoisomerase isoform α has a higher expression level in rapidly proliferating cells, including cancer cells, targeting these molecular motors is also considered as an attractive approach in cancer chemotherapy.
By applying all-atom molecular dynamics (MD) simulations, we investigated the full-length type II topoisomerase molecular motor to increase the understanding of the structure - function relationship. Starting from the available crystal structure of a the topoisomerase IIα from Saccharomyces cerevisiae (PDB ID 4GFH), we additionally constructed two more configurations of this molecular motor and performed a μs-long simulations of each system and subsequently comprehensively analyzed the observed behavior of all three states. The results provide us with new basic understanding of a variety of possible conformational changes these huge systems can undergo. Finally, based on the obtained results we mapped the simulated configurations of all three structures to the steps of the hypothetical catalytic cycle via which type II topoisomerases perform its function.

ASSESSING THE CONSERVATION OF LARGE-SCALE CONFORMATIONAL MOVEMENTS IN HOMOLOGOUS PROTEINS USING A NOVEL METRIC BASED ON DIFFERENCE DISTANCE MAPS
COSI: 3DSIG
  • Mallika Iyer, Sanford Burnham Prebys Medical Discovery Institute, United States
  • Lukasz Jaroszewski, Biosciences Division, University of California Riverside School of Medicine, United States
  • Zhanwen Li, Biosciences Division, University of California Riverside School of Medicine, United States
  • Mayya Sedova, Biosciences Division, University of California Riverside School of Medicine, United States
  • Adam Godzik, Biosciences Division, University of California Riverside School of Medicine, United States

Short Abstract: We know that homologous proteins share similar sequences, structures and (often) functions. However, proteins exist in not one, but a multitude of conformations. Transitions between these conformations occur via large- and small-scale movements and are intrinsic to protein function. However, the relationship between evolutionary distance and the similarity of large-scale conformational movements in homologous proteins has not been systematically assessed. Here, we begin to do so using X-ray crystal structures deposited in the Protein Data Bank (PDB). For many individual proteins, the PDB contains multiple coordinate sets representing their distinct conformations that can be used to study their large-scale movements. Therefore, for each protein with two conformations deposited in the PDB, we created a difference distance map (DDM) representing the conformational difference/movement between them. We then compared the DDMs of homologous protein pairs and calculated the correlation between them to quantify their similarity. We found that as sequence identity increases the DDM correlation, and thus the similarity in the conformational movements, also increases. These results can be used to inform structure modeling methods, where instead of modeling just a single conformation of the target protein, we can model functionally relevant conformational movements based on those of its homologs.

Assessing the Impact of Missense Variants in Giant Sarcomeric Proteins using Computational Methods
COSI: 3DSIG
  • Franca Fraternali, King's College London, United Kingdom
  • Timir Weston, King's College London, United Kingdom

Short Abstract: Giant sarcomeric proteins are structural and signalling elements that maintain the integrity of the sarcomere. Missense variants in these proteins have been associated with skeletal and cardiac myopathies; however, the sheer size of these proteins renders the mapping of genotype to phenotype a difficult task. Computational approaches are able to augment experimental approaches by transferring knowledge from variants with known effects to those without, and identifying which features are most important to the prediction of phenotypic consequences. Using recent biophysical characterisations of domains in titin in concert with data generated from molecular dynamics simulations, we aimed to improve predictors of missense variants in sarcomeric proteins and delineate the different interpretations of phenotypic consequence present in the literature. We show that prediction algorithms such as Rhapsody, which make use of a wider range of information, do better than common sequence-based methods in predicting the impacts of missense variants in the characterised titin data sets and can highlight possible differences in similar domains that localise to different regions of the sarcomere. We also show the potential of atomistic MD features to identify pathogenic variants classified incorrectly by conventional classifiers.

BetAware-Deep for the discrimination and topology prediction of prokaryotic transmembrane beta-barrels
COSI: 3DSIG
  • Giovanni Madeo, University of Bologna - Biocomputing Group, Italy
  • Castrense Savojardo, University of Bologna - Biocomputing Group, Italy
  • Pier Luigi Martelli, University of Bologna - Biocomputing Group, Italy
  • Rita Casadio, University of Bologna - Biocomputing Group, Italy

Short Abstract: Transmembrane β-Barrel (TMBB) proteins of Gram-negative bacteria are located in outer membranes and meditate all the communication between the environment and the cell, playing therefore a crucial role in many biological processes. The experimental resolution of their structure is still challenging and computational approaches can help in discriminating them from other proteins and in determining their topology (i.e. the number and orientation of membrane-spanning segments). We developed BetAware-Deep, and we released it as a web server available at busca.biocomp.unibo.it/betaware2. BetAware-Deep is trained on 58 TMBB proteins and it exploits new features: (i) the application of a deep recurrent network scanning input sequences followed by a probabilistic graphical model predicting the labelling; (ii) the definition of a new input feature based on profile-weighted hydrophobic moment; (iii) the adoption of an extended labelling schema which takes into consideration non-barrel regions as well as ambiguity around borders of transmembrane β-strand segments. On an independent set of 15 proteins, BetAware-Deep correctly predicts 10 topologies, outperforming state-of-the-art methods. Discrimination results on a large set of 1009 TMBB and 7571 non TMBB proteins show that BetAware-Deep performs at the level of other state-of-the-art methods, with a MCC of 0.91.

Comprehensive analysis of genetic variations on human SLC transporters
COSI: 3DSIG
  • Kengo Kinoshita, Tohoku University, Japan
  • Hafumi Nishi, Tohoku University, Japan
  • Yuya Hanazono, National Institutes for Quantum and Radiological Science and Technology, Japan

Short Abstract: SoLute Carrier (SLC) transporters are proteins conveying small molecules and ions across membranes. The transporter family is one of the most prominent protein families in the human genome and consists of more than 50 subfamilies. We have developed an integrated SLC transporter database, CASLcDB (Comprehensive Annotation of human SLc transporter DataBase, caslc.sb.ecei.tohoku.ac.jp/) that includes biochemical and biological information of all human SLC subfamilies. CASLcDB allows users to browse each SLC protein information such as modeled three-dimensional structures, predicted secondary structures, genetic variants, biological networks (protein-protein, coexpression, text-mining), and phylogenetic trees. Here we focused on genetic variants of SLC transporters, mainly their allele frequency and structural locations. Based on the transmembrane regions predicted from amino acid sequences, the variants obtained from the gnomAD database were classified into five categories: extracellular (EC), transmembrane (TM), intracellular (IC), and the two marginal regions between EC and TM, and TM and IC. We revealed that the two marginal regions had different trends in terms of normalized variant counts, particularly among variants with higher allele frequency (>5%). Furthermore, the substitution pattern analyses revealed that arginine has unique characteristics compared to other amino acids.

Computational prediction for comprehensive mutagenesis on different TP53 PDB structures using Delaunay tessellation in Automute2.
COSI: 3DSIG
  • Shaimaa Sait, George Mason University, United States

Short Abstract: Mutation in TP53 is found in 50% of human cancer, where this mutation affects the protein structure conformation that further impacts the function. The computational tools for predicting the structure or phenotypes are time and cost-efficiency. Many X-ray crystallography structures for each protein in the Protein Data Bank (PDB) do not present an average structure. This study exhibits the structure conformation change for protein P53 when mutagenesis is introduced to two structures of the P53 sequence with different resolutions. This study describes a computational geometry technique based on the Delaunay tessellation of protein structure, which explores a statistical potential to calculate residues environmental changes compared to the wild type. Automute2 is a machine learning-based method using a comparison between wild type and mutant amino acid. This computation tool predicts the protein stability using different approaches such as energy, activity, thermal change and disease potential. This research indicates the result of the comprehensive mutagenesis prediction for the two different structural resolutions for a certain protein that was not significantly altered.

Contribution of bioinformatics to blood transfusion: database and 3D intraprotein interaction studies
COSI: 3DSIG
  • Aline Floch, Univ Paris Est Creteil, INSERM, IMRB, EFS, France
  • Stéphane Téletchéa, Nantes Université, CNRS UMR 6286, UFIP, France
  • Alexandre G. De Brevern, INSERM UMR-S 1134, DSIMB, University of Paris, France

Short Abstract: In transfusion medicine, blood group antigens from the Rhesus system play a crucial role. We have provided a more complete and integrative view of this system.
Thanks to an intense curation (>500 articles), a modern database was developed for RhD variants. RHeference (www.rheference.org/) contains 2-3 more entries than related databases and multiple queries can be done. It contains more than 10.000 individual pieces of data. It is useful for medical doctors and researchers. (Floch et al, Transf MedRev, 2021)
We have proposed a RhD model, highlighting the interest of 3D structural models to apprehend mutations involved in transfusion problems (de Brevern et al, Transfusion, 2018).
Modeling of trimers provided arguments for the benignity of some known RhD variants in transfusion medicine. (Floch et al, Transfusion, 2021). With Molecular dynamics, we were able to show that the stoichiometry (still unknown) does not influence the dynamics of the monomers. Different parts of the complexes were categorized with classical approaches and more structural alphabet (Melarkode Vattekatte et al, J Struct Biol, 2020), some loops are disordered regions (ms in preparation).
These works provide a baseline of the structural behavior of these proteins to study the hundreds of genetic variants relevant in transfusion medicine.

Correlation between CYP2D6 variants and their metabolic activity by Molecular Dynamics Simulations
COSI: 3DSIG
  • Alexandros Kanterakis, Foundation for Research and Technology, Heraklion (FORTH), Greece
  • George Potamias, Foundation for Research and Technology, Heraklion (FORTH), Greece
  • Danai Maria Kotzampasi, University of Crete, School of Medicine, Greece
  • Vangelis Daskalakis, Cyprus University of Technology, Department of Chemical Engineering, Greece

Short Abstract: Cytochrome P450s is an enzyme superfamily of hemoglobin responsible for metabolizing more than 90% of clinical drugs. One of the most significant enzymes in this family, CYP2D6, metabolizes ~25% of the clinically used drugs including crucial and commonly administered drugs such as antidepressants, chemotherapeutics, beta-blockers and opioids. Variations in CYP2D6, a highly polymorphic loci in the genome, could alter its activity influencing the efficacy and toxicity of numerous drugs resulting in wide intraindividual variability in drug metabolism activity and changes of the drug plasma concentration. Our main objective was to investigate the key factors that determine the metabolizer phenotype. The complete connecting link between the genetic variants and the metabolizer phenotype is still an open and challenging question. To this end, we have probed the dynamics of numerous CYP2D6 variants, as enzyme models, with normal function and no function, at an all-atom resolution. With this approach we aimed at filling-in the gaps of missing information and provide detailed schemes that connect the fluctuations and crucial conformational changes of the different variants to their function, related to drug metabolism. Results are of great importance for areas like Adverse Drug Reaction (ADR) prediction, drug repurposing and drug discovery.

Cross-Modality and Self-Supervised Protein Embedding for Compound-Protein Affinity and Contact Prediction
COSI: 3DSIG
  • Yang Shen, Texas A&M University, United States
  • Yuning You, Texas A&M University, United States

Short Abstract: In silico prediction of compound-protein interaction is important to accelerate drug discovery. Current sequence-based methods for the prediction of compound-protein affinity and contact (CPAC), while aiming at contact prediction as mechanistic interpretation of affinity prediction, only rely on learning from the lone structure-unaware 1D protein sequences. We for the first time adopt cross-modality learning in CPAC to introduce structure-awareness into protein embeddings. We treat proteins as multi-modal data available in both modalities of 1D amino-acid sequences and 2D residue-residue contact maps, where 2D modality provides complementary structure knowledge for utilization. To integrate the information from both modalities, two cross-modality schemes, concatenation and cross interaction, are proposed for combining hierarchical recurrent neural networks (HRNN) as sequence encoder and graph attention networks (GAT) as structure encoder. Moreover, we leverage the promising self-supervised pre-training techniques for embedding 1D sequences and 2D graphs on top of cross-modality models, to address supervision starvation via exploiting rich unpaired unlabelled protein domain data. Numerical results demonstrate that our cross-modality and self-supervised protein embedding can improve the generalizability of affinity and contact prediction for unseen proteins.

Data-Driven Analysis of Single Point Mutations through Rapid Scan of 3D Micro-Environments
COSI: 3DSIG
  • Jochen Sieg, Universität Hamburg, Germany
  • Matthias Rarey, Universität Hamburg, Germany

Short Abstract: The way mutations change a protein structure is fundamental to understand and modulate protein functionality. Especially, the structural changes that a single mutation introduces into the local micro-environment can provide valuable insights. While modeling such changes is essential for rational design and predicting mutation effects experimental protein structures for both wild type and mutant are rarely directly available. However, if we limit our focus to the direct proximity of a mutation, the PDB holds a wealth of experimental examples. We developed an efficient algorithm based on the SIENA technology for the fast retrieval of mutations from the PDB based on the similarity of amino acid 3D micro-environments. The algorithm recovers 90% of known mutant structures in the ProTherm data set. A search for all amino acids in one input structure against the entire PDB is performed in <10 seconds for most cases and ensembles of the mutated structures are generated automatically. On average we find 208 mutants per query protein of which only 11% are available in ProTherm illustrating the large amount of experimental mutant structures that can be mined for protein design and method development. The new method will be made available as part of our online platform proteins.plus.

DDTNBI: de novo target prediction using a social network-derived method
COSI: 3DSIG
  • Carlos Vigil, Pontificia Universidad Católica de Chile, Chile
  • Andreas Schueller, Pontificia Universida Católica de Chile, Chile

Short Abstract: Target identification is a key step in the discovery and development of novel drugs. Computationalmethodologies have been developed to be able to predict possible interactions between a drug candidate and a biological target of interest, generally using information obtained from either the drugs chemical structure or the target three-dimensional structure. Network based methodologies have shown to have anexcellent performance in this area of studies, allowing for the integration of different sources of information at the moment of predicting the targets for a given drug candidate, however this generallylacks the ability to generate predictions for novel chemical entities, making their use limited. Here wepresent a hybrid method that integrates naive drug-target topology mining with chemical similarity for de novo target prediction of novel chemical compounds. This method is characterized by using a trilayered drug-drug-target network constructed from the protein-ligand interaction annotations and the drug-drugchemical similarity, on which a resource-spreading algorithm derived from traditional social networkmethod predicts the potential biological targets for both known drugs and new chemical entities or failed drugs in clinical trials. We identified that this new hybrid method is capable of reaching and, even, exceeding the performance of state-of-the-art network-based methods.

DINC-COVID: A Webserver for Ensemble Docking with SARS-CoV-2 proteins
COSI: 3DSIG
  • Mauricio M. Rigo, Rice University, United States
  • Sarah Hall-Swan, Rice University, United States
  • Dinler A. Antunes, University of Houston, United States
  • Didier Devaurs, University of Edinburgh, United Kingdom
  • Lydia E. Kavraki, Rice University, United States
  • Geancarlo Zanatta, Federal University of Ceara, Brazil

Short Abstract: The COVID-19 pandemic has been causing disastrous effects worldwide and efforts have been undertaken to mitigate this disease. This includes the determination of new crystallographic structures of SARS-CoV-2 proteins. These structures were crucial to numerous virtual screening (VS) projects searching for potential drug inhibitors. However, the approach in producing effective inhibitors has been shown to be inefficient. The underlying reason may be because receptor flexibility has not been taken into account. To address this issue we have implemented a computational tool for ensemble docking with SARS-CoV-2 proteins. Representative ensembles of protein conformations were extracted from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations are available for ensemble docking via a user-friendly webserver called DINC-COVID (dinc-covid.kavrakilab.org). DINC-COVID was validated using data on tested inhibitors of two SARS-CoV-2 proteins. We obtained good correlations between docking-derived binding energies and experimentally determined binding affinities. Some of the best results have been obtained on a dataset of large ligands determined through room temperature crystallography, therefore capturing alternative receptor conformations. This work highlights the importance of accounting for protein flexibility in docking studies and provides a platform for the identification of new SARS-CoV-2 inhibitors.

DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins
COSI: 3DSIG
  • Sutanu Bhattacharya, Auburn University, United States
  • Rahmatullah Roche, Auburn University, United States
  • Debswapna Bhattacharya, Auburn University, United States

Short Abstract: Threading a query protein sequence onto a library of weakly homologous structural templates remains challenging, even when sequence-based predicted contact or distance information is used. Contact- or distance-assisted threading methods utilize only the spatial proximity of the interacting residue pairs for template selection and alignment, ignoring their orientation. Moreover, existing threading methods fail to consider the neighborhood effect induced by the query-template alignment.

We present a new distance- and orientation-based covariational threading method called DisCovER by effectively integrating information from inter-residue distance and orientation along with the topological network neighborhood of a query-template alignment. Our method first selects a subset of templates using standard profile-based threading coupled with topological network similarity terms to account for the neighborhood effect and subsequently performs distance- and orientation-based query-template alignment using an iterative double dynamic programming framework. Multiple large-scale benchmarking results on query proteins classified as weakly homologous from the Continuous Automated Model Evaluation (CAMEO) experiment and from the current literature show that our method outperforms several existing state-of-the-art threading approaches; and that the integration of the neighborhood effect with the inter-residue distance and orientation information synergistically contributes to the improved performance of DisCovER.

The open-source DisCovER software package is freely available at github.com/Bhattacharya-Lab/DisCovER.

DLAB - Deep learning methods for structure-based virtual screening of antibodies
COSI: 3DSIG
  • Constantin Schneider, University of Oxford, United Kingdom
  • Andrew Buchanan, AstraZeneca, United Kingdom
  • Bruck Taddese, AstraZeneca, United Kingdom
  • Charlotte Deane, University of Oxford, United Kingdom

Short Abstract: Antibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases.
Despite this importance, the discovery and development process for antibody therapeutics remains reliant on high-throughput experimental screens, which are both cost- and time-intensive.

Recently, several machine learning studies on antibody sequence data have demonstrated the potential of machine learning approaches for the development of computational tools to support the antibody therapeutics discovery pipeline. Here, we describe the development of a framework for structure-based deep learning for antibodies (DLAB), which can be used to learn from structural data on antibody-antigen complexes. We demonstrate that DLAB can be used both to improve antibody-antigen docking and for structure-based virtual screening of antibody drug candidates.

Dynamic networks improve protein structural classification
COSI: 3DSIG
  • Khalique Newaz, University of Notre Dame, United States
  • Jacob Piland, University of Notre Dame, United States
  • Patricia Clark, University of Notre Dame, United States
  • Scott Emrich, Univerisity of Tennessee, United States
  • Jun Li, University of Notre Dame, United States
  • Tijana Milenkovic, University of Notre Dame, United States

Short Abstract: Protein structural classification (PSC) is a supervised problem of assigning proteins into pre-defined structural (e.g., CATH or SCOPe) classes based on the proteins' sequence or 3D structural features. We recently proposed PSC approaches that model protein 3D structures as protein structure networks (PSNs) and analyze PSN-based protein features, which performed better than or comparable to state-of-the-art sequence or other 3D structure-based approaches in the task of PSC. However, existing PSN-based PSC approaches model the whole 3D structure of a protein as a static PSN. Because folding of a protein is a dynamic process, where some parts of a protein fold before others, modeling the 3D structure of a protein as a dynamic PSN can further help improve the existing PSC performance. Here, we propose a novel way to model 3D structures of proteins as dynamic PSNs, with the hypothesis that this will improve upon the current state-of-the-art PSC approaches that are based on static PSNs (and thus upon the existing state-of-the-art sequence and other 3D structural approaches). Indeed, we confirm this on 71 datasets spanning a large set of ~44,000 protein domains from CATH and SCOPe.

Embed structure-awareness into protein language models for mutation fitness prediction
COSI: 3DSIG
  • Yang Shen, Texas A&M University, United States
  • Yuanfei Sun, Texas A&M University, United States

Short Abstract: Self-attention pretrained protein sequence models have demonstrated capability of learning informative representations for various downstream prediction and generation tasks. The attention mechanism, presumed essential factor, has proven to recover protein structure and property features to a certain level from sequences solely. From these observations, we further studied the embedding ability of protein pretrained models that: (1) model trained over a small yet diverse sequence set gives competitive sequence modeling performance compared to large training sets, (2) attentions learnt in sequence only models are still deficient to discover medium and long range interactions between residues, (3) structure awareness in attention can be increased by supervising attention weights with structure information, and best structure awareness is acquired under multi-task optimization of masked token modeling and attention supervision, (4) finally, we compared the quality of structure-naive vs structure-aware representations over an integrated task, mutation fitness prediction, to showcase the advantage of structure awareness.

Energetic Local Frustration across NMR Structures in the Protein Data Bank
COSI: 3DSIG
  • Atilio O Rausch, Facultad de Ingenieria, Universidad Nacional de Entre Rios, Oro Verde, Argentina, Argentina
  • Alexander M. Monzon, Department of Biomedical Sciences, University of Padua, Padua, Italy, Italy
  • Leandro G. Radusky, Center for Genomic Regulation, Barcelona Institute for Science and Technology, Barcelona, Spain, Spain
  • R. Gonzalo Parra, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany, Germany

Short Abstract: Natural Proteins spontaneously fold by globally minimizing their internal conflicts. However, 10-15% of their residue-residue interactions are in strong energetic conflict or “highly frustrated”. Such frustration sculpts protein dynamics and allows proteins to explore the basin of their energy landscapes. NMR spectroscopy provides information on proteins in solution and hence, enables the study of the ensemble of structures that constitute the native state. Thanks to our brand new FrustratometeR (Rausch et al, Bioinformatics 2021), we have been able to analyse all NMR entries in the PDB to study the links between local frustration and structural flexibility.

Results:
We have analysed 3191 non redundant proteins with a median number of 20 models per PDB entry. We have computed local frustration for all ensembles and analysed which are the typical frustration variations to be expected in NMR structures. We correlated local frustration values with amino acid identity as well as with many structural properties such as RMSF, disorder propensity, amino acid identity and others.

Significance:
We present the first work that studies local frustration in the context of conformational dynamics of the native state. Our work contributes to better understand the importance of local frustration for protein motion and function.

Epitope profiling of coronavirus-binding antibodies using computational structural modelling
COSI: 3DSIG
  • Sarah Robinson, University of Oxford, United Kingdom
  • Matthew Raybould, University of Oxford, United Kingdom
  • Constantin Schneider, University of Oxford, United Kingdom
  • Wing Ki Wong, University of Oxford, United Kingdom
  • Claire Marks, University of Oxford, United Kingdom
  • Charlotte Deane, University of Oxford, United Kingdom

Short Abstract: Identifying the epitope of an antibody is a key step in understanding its function and its potential as a therapeutic. Sequence-based clonal clustering can identify antibodies with similar epitope complementarity, however, antibodies from markedly different lineages but with similar structures can engage the same epitope. We describe a novel computational method for epitope profiling based on structural modelling and clustering. The method identifies sequence-dissimilar but functionally-similar antibodies across the Coronavirus Antibody Database, and achieves accuracy (92% of antibodies in multiple-occupancy structural clusters bind to consistent domains). Our approach functionally links antibodies with distinct genetic lineages, species origins, and coronavirus specificities. This indicates greater convergence exists in the immune responses to coronaviruses than is suggested by sequence-based approaches. Our results show that applying structural analytics to large class-specific antibody databases will enable high confidence structure-function relationships to be drawn, yielding new opportunities to identify functional convergence hitherto missed by sequence-only analysis.

Exploring human population variation and three-dimensional structures in the Armadillo repeat family
COSI: 3DSIG
  • Maxim Tsenkov, University of Dundee, United Kingdom
  • Javier Sánchez Utgés, University of Dundee, United Kingdom
  • Stuart MacGowan, University of Dundee, United Kingdom
  • Geoff Barton, University of Dundee, United Kingdom

Short Abstract: Armadillo repeats (ARs) are 41-amino acids long that fold into a conserved three alpha-helical structure. ARs are organised in tandem arrays that form a superhelical Armadillo-Domain (AD) structure. ADs mediate protein-protein interactions on the concave surface and assemble multi-protein complexes.

We conducted a sequence analysis of 2270 ARs across 57 organisms and explored the human population variant data. We studied the different structural and functional pressures at each position in the ARs, as determined by amino acid conservation, and constraints in the human population data. We integrated this with a quantitative structural analysis of 265 ARs across 162 PDBe structures to identify positions important for the structural fold of the AD and the binding sites involved with protein-substrate interactions.

Positions inferred from sequence and variant analysis reflect the same positions in our structural analysis that have high contact numbers and sites enriched for substrate interactions. Conserved positions constrained in the human population highlight residues in the hydrophobic core of ADs. Unconserved sites depleted for missense variants represent binding-specificity sites or may be involved in the structural maintenance of ADs. We demonstrate how conservation and variation features are constrained by structure and thus, can be used to predict structural features.

Exploring the microbiome protein structure space using simulations and deep learning
COSI: 3DSIG
  • Pawel Szczerbiak, Malopolska Centre of Biotechnology, Jagiellonian University, Poland
  • Julia Koehler Leman, Flatiron Institute, United States
  • Douglas Renfrew, Flatiron Institute, United States
  • Vladimir Gligorijevic, Flatiron Institute, United States
  • Daniel Berenberg, New Your University, United States
  • Chris Chandler, Flatiron Institute, United States
  • Richard Bonneau, Flatiron Institute, United States
  • Tomasz Kosciolek, Malopolska Centre of Biotechnology, Jagiellonian University, Poland

Short Abstract: The human gut microbiome comprises about 3 million unique bacterial genes which is ~10 more than the number of human body genes. Exploring them would give us a possibility to treat diseases that originate in or are influenced by the microbiome. The main goal of the Microbiome Immunity Project is to understand the role played by the various bacteria in the human microbiome. In the first stage of the project we focused on GEBA genomes that cover the microbial part of the tree of life. For this purpose we prepared a dataset consisting of ~250,000 unique newly predicted microbial protein domain structures (in the second stage we will concentrate solely on human gut microbiome genes coming from the UHGP project, reaching ~1,000,000 structures in total). In order to make our analysis more robust, we used two methods: Rosetta and DMPFold which utilize different approaches to the protein structure prediction problem. In the poster we are showing the difference between both methods with special emphasis on structural and functional annotations, new folds identification and structure space visualization. We will also shed some light on sequence-structure-function relationships and compare our dataset with experimental ones (CATH, PDB).

FoldHSphere: Deep Hyperspherical Embeddings for Protein Fold Recognition
COSI: 3DSIG
  • Amelia Villegas-Morcillo, University of Granada, Spain
  • Victoria Sánchez, University of Granada, Spain
  • Angel M. Gomez, University of Granada, Spain

Short Abstract: Current state-of-the-art deep learning approaches for protein fold recognition learn protein embeddings that improve prediction performance at the fold level. However, there still exists a performance gap at the fold level and the (relatively easier) family level, suggesting that it might be possible to learn an embedding space that better represents the protein folds. In this paper, we propose the FoldHSphere method towards this goal through a two-stage learning procedure. We first obtain prototype vectors for each fold class that are maximally separated in hyperspherical space. We then train a neural network by minimizing the angular large margin cosine loss (LMCL) to learn protein embeddings clustered around the corresponding hyperspherical fold prototypes. Our network architectures, ResCNN-GRU and ResCNN-BGRU, process the input protein sequences by applying several residual-convolutional blocks followed by a gated recurrent unit-based recurrent layer. Evaluation results on the LINDAHL dataset indicate that the use of our hyperspherical embeddings effectively bridges the performance gap at the family and fold levels. Furthermore, our FoldHSpherePro ensemble method outperforms the current state-of-the-art. In conclusion, our methodology is efficient in learning discriminative and fold-representative embeddings for the protein domains. The proposed hyperspherical embeddings are effective at identifying the protein fold class by pairwise comparison, even when amino acid sequence similarities are low.

FoldRecNet, a protein fold recognition algorithm for shallow-alignment proteins
COSI: 3DSIG
  • Evgeny Tankhilevich, Imperial College Loncon, United Kingdom
  • Suhail Islam, Imperial College London, United Kingdom
  • Michael Sternberg, Imperial College London, United Kingdom

Short Abstract: This poster presents FoldRecNet, a protein fold recognition algorithm for shallow-alignment proteins. These proteins do not have a deep multiple sequence alignment (MSA) available. They comprise up to 18% of human proteome and play an important biological role. In order to handle such proteins well, FoldRecNet does not use any information from MSA as input features. This sets it apart from the current state of the art in protein structure prediction, that relies heavily on MSA information.

FoldRecNet works by using deep neural networks to build context-dependent sequence encodings from physico-chemical properties of amino acids, and soft-aligning the encodings of the query and the template. When the templates for the given query are ranked by FoldRecNet alignment score, the structural match is detected among the top 10 hits for 60% of the test set. When using off-the-shelf tools for comparative modelling and model quality assessment on the top 100 templates ranked by FoldRecNet score, the match is detected among the top 5 hits for 63% of the test set, which is on par with the current state of the art in protein structure prediction.

Future applications of FoldRecNet may include using its sequence encoding for template free protein structure prediction.

Gaussian process regression predicts mutant protein binding affinity
COSI: 3DSIG
  • David R. Bell, Frederick National Lab for Cancer Research, United States
  • Serena H. Chen, Oak Ridge National Lab, United States

Short Abstract: Protein-protein interactions underlie numerous physiological processes. One way to probe these interactions is to mutate protein residues and determine the difference in binding affinity. Comprehensive mutagenesis is costly; here, we investigate a way to minimize this expense and predict the relative binding affinity of point mutations from minimal prior data. We use Gaussian process (GP) regression across residue volume and hydrophobicity to predict the relative binding affinities of point mutations from a small subset of residue affinity data. We apply this GP regression technique to predict peptide-MHC class II (pMHCII) binding affinities from experimental and computational datasets. We find that the proposed GP regression technique can predict the relative binding affinities of 9 residues from a 6-residue subset with an average R2 coefficient of determination value of 0.62 ± 0.04 (±95% CI), and average error of 0.09 ± 0.01 kcal/mol (±95% CI), and with an ROC AUC value of 0.92 for binary classification of enhanced or diminished binding affinity. We find that this prediction method is most accurate for neutral residues at anchor residue sites without register shifts. We further briefly discuss how this technique can be applied to broader protein-protein interaction systems.

Graph embeddings for protein structural comparison
COSI: 3DSIG
  • Vladimir Gligorijevic, Flatiron Institute, United States
  • Daniel Berenberg, New York University, United States
  • Richard Bonneau, New York University, United States

Short Abstract: In the age of big data, protein fold recognition methods are required to behave reliably and efficiently at scale. Algorithmic developments in Deep Learning, namely graph- and 2D- convolutional networks, are celebrated in a variety of different contexts including automatic feature extraction of biological data. While empirically these methods have shown state-of-the-art performance amongst one another, there remains a distinct lack of comparison to earlier methods relying on manually engineered features. We present a comparison study of existing deep learning-based methods to simpler graphlet-based approaches in order to fairly assess the general utility of deemed state-of-the-art machine learning algorithms. Doing so will provide a definitive categorization of structural comparison methods and alert researchers to current limitations in the most recent approaches.

Graphical Models For Identifying Pore-forming Family Proteins In The Twilight Zone
COSI: 3DSIG
  • Nan Xu, USC, United States
  • Ted Kahn, BASF, United States
  • Theju Jacob, BASF, United States
  • Yan Liu, USC, United States

Short Abstract: Pore-forming toxins (PFTs) are proteins that form lesions in biological membranes. Understanding structures of PFTs benefits us in developing biotechnological applications like antimicrobial drug development, cancer gene therapy and DNA sequencing. However, some existing approaches like HMMs only consider sequence information, which makes them fail to recognize new toxins of similar functions but from low homology (twilight zones) sequences. Meanwhile, available structure information from toxins of interesting functions is too limited to develop well-performed data-hungry models like deep learning approaches. To solve the challenging pore-forming family proteins identification problem, we propose a sample-efficient graphical model, where a protein structure graph is firstly constructed according to consensus secondary structures and a Semi-Markov CRFs model is then developed to perform protein sequence segmentation. We collect toxins of similar functions from 3 twilight zones and observe high rankings of positive proteins for each zone compared with negative proteins from Culled PDB Database. To extract toxins of interesting functions from genome-wide protein database for further study, we develop an efficient framework on 43 million proteins from UniRef50, where a deep learning module is introduced to perform PFTs classification and the proposed graphical model is incorporated for structural similarity ranking among the most likely PFTs.

How good are protein structure prediction methods at predicting folding pathways?
COSI: 3DSIG
  • Carlos Outeiral Rubiera, University of Oxford, United Kingdom
  • Charlotte Deane, University of Oxford, United Kingdom

Short Abstract: Deep learning has achieved unprecedented success in predicting a protein's crystal structure, but whether this achievement relates to a better modelling of the folding process is an open question. In this work, we compare the dynamic pathways from six state-of-the-art protein structure prediction methods to experimental folding data. We find evidence of a weak correlation between simulated dynamics and formal kinetics; however, many of the structures of the predicted intermediates are incompatible with available hydrogen-deuterium exchange experiments. These results suggest that recent advances in protein structure prediction do not provide an enhanced understanding of the principles underpinning protein folding.

How sticky are your proteins?
COSI: 3DSIG
  • Dea Gogishvili, Vrije Universiteit Amsterdam, Netherlands
  • Juami van Gils, Vrije Universiteit Amsterdam, Netherlands
  • Jan van Eck, Vrije Universiteit Amsterdam, Netherlands
  • Robbin Bouwmeester, Vrije Universiteit Amsterdam, Netherlands
  • Erik van Dijk, Vrije Universiteit Amsterdam, Netherlands
  • Sanne Abeln, Vrije Universiteit Amsterdam, Netherlands

Short Abstract: Proteins tend to bury hydrophobic residues inside their core during the folding process to provide stability to the protein structure and to prevent aggregation. Nevertheless, many proteins do expose such ’sticky’ hydrophobic residues to the solvent. Hydrophobic residues may play an important functional role, for example in protein-protein interactions and ligand binding. Here, we investigated how hydrophobic/sticky proteins should be defined in terms of surface hydrophobicity and trained a machine learning model that predicts these hydrophobicity measures from the primary sequence. Firstly, we define structure-based measures: the total and relative hydrophobic surface area(T/RHSA), and - using our MolPatch method - the largest hydrophobic patch(LHP). Secondly, by adapting solvent accessibility predictions from NetsurfP2.0 we obtain well-performing sequence-based prediction methods for the THSA(R2 = 0.75), and RHSA(R2 = 0.49), while the LHP is more difficult to predict(R2 = 0.12). Finally, sticky proteins were mapped to the human proteome by considering tissues, pathways, and diseases in which such proteins occur. We show that very hydrophobic proteins are typically not highly expressed, suggesting there is evolutionary pressure against overabundant sticky proteins. Despite this, we show that sticky proteins are surprisingly abundant in the human brain and that such proteins are associated with neurodegenerative pathways.

In silico study of peptide-drugs as inhibitors of Aβ oligomerization in Alzheimer’s Disease
COSI: 3DSIG
  • Marilena Theodoropoulou, National and Kapodistrian University of Athens, Greece
  • Nikolaos Papandreou, National and Kapodistrian University of Athens, Greece
  • Georgia Nasi, National and Kapodistrian University of Athens, Greece
  • Panagiotis Spatharas, National and Kapodistrian University of Athens, Greece
  • Vassiliki Iconomidou, National and Kapodistrian University of Athens, Greece

Short Abstract: Alzheimer’s Disease (AD) is a neurodegenerative disorder, which is characterized by the presence of amyloid plaques, whose main building block is the amyloid-beta peptide (Αβ). Apart from Aβ, a variety of proteins are co-localized in the amyloid plaques. We aimed to design potential peptide-inhibitors of Αβ oligomerization, derived from aggregation-prone regions of such proteins. Recent studies indicate that oligomers of Aβ constitute the most toxic intermediates that interfere with vital functions of neuronal cells. These studies led our group to focus on computationally studying the interactions between peptide-inhibitors and oligomeric Aβ species, in order to evaluate their potential as pharmacological agents for AD treatment. Aggregation-prone regions were predicted with AMYLPRED2, Molecular Docking was performed using ClusPro, and Molecular Dynamics Simulations were performed via GROMACS. Analysis of the simulations revealed that the oligomers partially adopt the conformation of soluble Αβ. The results of our study indicate that these peptides might be suitable candidates for AD treatment.

Investigating the Interaction between SARS-CoV-2 NSP15 and Human RNF41 Using In Silico Methods
COSI: 3DSIG
  • Annika Viswesh, Palo Alto High School, USA and Stanford University, USA, United States
  • Soichi Wakatsuki, Stanford University, United States

Short Abstract: Patients with acute SARS-CoV-2 infection exhibit hyper-inflammatory response and Type 1 Interferon (IFN-1) deficiency. Studies show that SARS-CoV-2 NSP15 suppresses the immune response; however, this has not been investigated at a molecular level. RNF41 controls inflammation and IFN-1 production by binding to MYD88 and TBK1 in the immune signaling pathways. We hypothesized that SARS-CoV-2 NSP15 binds to RNF41 and inhibits RNF41 from regulating the immune signaling pathways. Molecular docking of RNF41 C-terminal domain (CTD) to five NSP15 poses, MYD88, TBK1, and USP8, were performed. Previously unknown structure of RNF41 Zinc-finger domain (ZFD) was generated using homology modeling and docked to different NSP15 poses after determining the RNF41 ZFD active sites using computational techniques. Results show NSP15, TBK1, MYD88, and USP8 bind to the same residues of RNF41 CTD. NSP15 has the highest binding affinity to RNF41 CTD. Preliminary MD simulations support the docking results confirming our hypothesis that binding between RNF41 CTD and NSP15 could cause the immune system's disruption. Further, NSP15’s binding sites were > ~8 Å away from its catalytic sites, indicating that NSP15’s cleaving function can continue even when NSP15 binds to RNF41. These results set the direction for researching drugs to target SARS-CoV-2 NSP15’s binding sites.

MODAMDH: identification of diverse Amine Dehydrogenases by screening biodiversity using sequence and structure-based approaches
COSI: 3DSIG
  • Eddy Elisée, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, France
  • Raphaël Meheust, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, France
  • Laurine Ducrot, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, France
  • Mark Stam, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, France
  • Eric Pelletier, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, France
  • Karine Bastard, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, France
  • Jean-Louis Petit, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, France
  • Megan Bennett, York Structural Biology Laboratory, Department of Chemistry, University of York, United Kingdom
  • Gideon Grogan, York Structural Biology Laboratory, Department of Chemistry, University of York, United Kingdom
  • Véronique de Berardinis, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, France
  • Anne Zaparucha, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, France
  • David Vallenet, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, France
  • Carine Vergne-Vaxelaire, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, France

Short Abstract: The current boom in environmental genomics data provides a huge resource of new sequences of potential biocatalysts. Through the MODAMDH project, we focus on one of the key biocatalysts named amine dehydrogenases (AmDHs) which enable the access to amines that are important entities in the chemical industry.
We started from a previously described NAD(P)H-dependent AmDH family from which several members were experimentally characterized. This family was first expanded, up to 27k sequences, by mining very large metagenomic databanks in search of the conserved catalytic domain. We then applied structural modelling and active site classification to define subfamilies. We also generated a pool of ~100k candidate families containing more than 20M NAD(P)-binding protein sequences from which we found, using HMM-HMM profile comparison, >30 families sharing distant homology with the reference AmDH family. Furthermore, catalophores (i.e. minimal active site topologies) will be designed from native AmDH structures and used to find active site analogs in the candidate NAD(P)-dependent families. Most interesting enzymes will be experimentally characterized through enzymatic and crystallographic assays.
This work is ongoing and the presented workflow could be applied to other enzyme families in the quest for new structures and activities.

Molecular interplay between SARS-CoV-2 & human proteins for viral activation and entry, potential drugs for combat & scope for new therapeutics
COSI: 3DSIG
  • Naveen Vankadari, Monash University, Australia

Short Abstract: The pandemic Coronavirus Disease 2019 (COVID19) caused by SARS-CoV-2 resulted a global morbidity of over 115 million and a mortality of 2.5 million. Whilst the vaccination been administered in many countries, there several antiviral treatments are being clinically evaluated to fill the “therapeutic gap” in parallel. Development of potential drugs or potential vaccines requires an understanding of SARS-CoV-2 pathogenicity and mechanism of action. Thus, it is essential to understand the full repertoire of viral proteins and their interplay with host factors. Here, we show how the SARS-CoV-2 spike protein undergoes 3 stages of processing to allow virion activation and host cell infection. Our comprehensive structural and computational studies reveal why COVID19 is hypervirulent and incites the possible reason for the failure of several antibody treatments. In addition, our resolved complex structures of spike protein with different host cell receptors shows the complexity of entry. We also demonstrate via experimental, biophysical and molecular dynamics studies that how the host proteins CD26 (DPP4), CD147, Furin and TMPRSS2 process the viral spike glycoprotein and assist in the viral entry in addition to ACE2. These results cognise the detailed mechanism of spike-glycoprotein for viral entry and reveal new avenues for potential therapeutic.

Molecular simulations of the HLA-E/β2m/peptide/NKG2A/CD94 immune complexes
COSI: 3DSIG
  • Eva Prašnikar, National Institute of Chemistry, Slovenia
  • Andrej Perdih, National Institute of Chemistry, Slovenia
  • Jure Borišek, National Institute of Chemistry, Slovenia

Short Abstract: Senescent cells are a promising new target for the treatment of age-related diseases. Recently, an increased expression of HLA-E ligand has been reported on the surface of senescent cells derived from human skin. HLA-E acts as a ligand for the NK cell inhibitory receptor, NKG2A/CD94, and is expressed as a heterotrimer of the nonameric peptide, HLA-E heavy and light chain (β2m). Blocking this receptor-ligand interaction enhanced the immune response against senescent cells in vitro, suggesting a novel strategy to eliminate senescent cells.

We performed several microsecond all-atom molecular dynamics simulations of immune complexes to elucidate the intricacies of receptor (NKG2A/CD94)-ligand (HLA-E/β2m/peptide) molecular recognition mediating NK cell protection. We identified key differences in the interactions between the receptor-ligand complexes, which are fine-tuned by a ligand-specific nonameric peptide, via an entangled network of hydrogen bonds. We further show that the receptor NKG2A protein could regulate NK cell activity by forming key contacts with the CD94 protein, that comprise most of the energetically important interactions with the ligand. This knowledge rationalizes the atomistic details of the fundamental NK cell protective mechanism and may enable new opportunities in rational-based drug discovery for diverse pathologies such as viral infections, cancer, and elimination of senescent cells.

Multi-task learning to leverage partially annotated data for PPI Interface prediction
COSI: 3DSIG
  • Henriette Capel, Vrije Universiteit Amsterdam, Netherlands
  • K. Anton Feenstra, Vrije Universiteit Amsterdam, Netherlands
  • Sanne Abeln, Vrije Universiteit Amsterdam, Netherlands

Short Abstract: Protein interactions are crucial for protein functioning. However, protein-protein interaction (PPI) interface annotations are scarce. We overcome the problem of the limited size datasets by effectively training a deep neural net predicting PPI interfaces using a multi-task learning strategy. We start with the model architecture of OPUS-TASS by Xu et al. Only for one-third of their structural annotated dataset residue-based PPI annotations were available. Our results show that the multi-task learning strategy significantly outperforms the single-task learner, including secondary structure, solvent accessibility, and buried residues as related learning tasks. Moreover, extending the training dataset with structural data labels of only these structural annotations - but not the PPI annotations - further increases the model performance. The multi-task setup and data extension become even more important for smaller annotated datasets. Therefore, we show that the multi-task learning strategy can be beneficial for a small set of protein structural annotated training data.

PREDICTING CONFORMATIONAL B-CELL EPITOPES USING GRAPH-BASED SIGNATURES
COSI: 3DSIG
  • Bruna Moreira da Silva, The University of Melbourne, Australia
  • David B. Ascher, The University of Melbourne, Australia
  • Douglas E. V. Pires, The University of Melbourne, Australia

Short Abstract: Accurate identification of B-cell epitopes is crucial for disease control, diagnostics and vaccine development, but in general experimental approaches are expensive, time consuming and low throughput.

Here we present epitope3D, a new machine learning method trained and validated on the largest conformational epitope data set collected to date, outperforming available alternative approaches, achieving MCC and F1-scores of 0.55 and 0.57 on cross-validation and 0.45 and 0.36 during independent blind tests, respectively.

epitope3D uses the concept of graph-based structural signatures to better model and distinguish epitope from non-epitope regions.

Prediction of Drug Induced genotoxicity of FDA approved Anti-Cancerous drugs using Molecular docking and QSAR approach
COSI: 3DSIG
  • Rishabh Panesar, Amity University Mumbai, India
  • Sagarika Sahoo, Amity University Mumbai, India
  • Jean Bernard, Insight Biosolutions, France
  • Sarra Akermi, Annotation Analytics pvt. ltd., India
  • Deepak Kumar, Indian science and Technology Foundation, India
  • Anshul Nigam, Amity University Mumbai, India

Short Abstract: Objective: In-silico modelling of Drug/DNA interactions between anti-cancerous drugs and DNAs for analyzing Drug toxicity using Molecular docking approach
Materials and Methods: We have selected 5 anti-cancerous compounds and 5 types of DNA structures and performed docking by AUTODOCK VINA for understanding the interactions. We used VEGA QSAR model for predicting the genotoxicity of the compounds and compared these results with the binding energy produced by molecular docking.
Results: Drug Vinorelbine (anti-cancerous) is found to has best affinities for all DNA’s with docking score of -10.28 kcal/mol and predicted as non-mutagenic compound by VEGA QSAR method therefore, safer to use. Drugs Axitinib and Azacitidine interacts with all DNA’s with docking energies of -9.12 kcal/mol and -7.68 kcal/mol and predicted as mutagenic compound by VEGA QSAR method (Table 1). The control compound Caffeine (Control) found low affinity for DNA’s with docking energy of -6.1 kcal/mol and predicted to be non-mutagenic by QSAR method.
Conclusion: Therefore, our analysis reveal that the best anti-cancerous drug is Vinorelbine (anti-cancerous) which has more affinity for the DNA’s and non-mutagenic to the cells. It means that this drug cannot create any genotoxic effect on the cells and safer to use.

Prediction of Protein Dimer Structures using Deep Learning and Coevolution Based Methods
COSI: 3DSIG
  • Nikita Desai, University College London, United Kingdom

Short Abstract: Recent years have seen significant advances in 3D protein structure prediction, especially of protein monomers and single domains. The last CASP experiment saw significant advances in the free-modelling prediction of protein monomers, most notable being the advances made by AlphaFold 2. Despite these advances, there remain a number of unsolved challenges in protein modelling, one of which is the modelling of protein complexes. In this work, I present a number of computational prediction methods that combine deep learning and coevolution-based methods to predict the protein homodimer and heterodimer complex structures without complex templates. A number of sub-challenges are addressed for each type of dimer. For homodimers, I therefore present a method for differentiating between inter- and intra-chain contacts, a non-trivial challenge for homodimer docking. For heterodimers, a large challenge remains in building joined multiple sequence alignments (MSAs) for the prediction of inter-chain prediction. In this work I present a few solutions for some of the challenges in dimer docking.

SARS-CoV-2 genome variants epidemiology surveillance in Ethiopia and dynamic mutational change of S Spike protein through computational analysis
COSI: 3DSIG
  • Ayele Abaysew, Technology and Innovation Institute, Ethiopia
  • Tesfaye Adisu, Technology and Innovation Institute, Ethiopia
  • Professor Rita Majumdar, Sharda University, India

Short Abstract: SARS CoV-2 virus has been a global pandemic since 2019 and 4 thousand death records were registered in Ethiopia as of June, 2021. This study aims to identify the variants of SARS CoV 2 that were circulating in Ethiopia and spot dynamic mutational changes of spike antigenicity based on genome data computational analysis. The genomes from Ethiopia were confirmed to be evolutionary related to RaTG13 and SL-bat coronavirus and Spike receptor sites were conserved. The clade distribution of the genome was reflected as GH, GR and other O and intended for new variants. 3 female samples were detected as variants of concern VUI202012/01GRY B.1.1.7. Despite 21 notable mutations, 71% D614G, 28% D614X, 35% N501Y and 21% NSP5 S284G mutation occurred predominantly in our genome samples and could be antigenicity and infectivity. Mutation on N440K was perceived in a sample and potency resist SER-52 antibody neutralization and vaccine escape.

SenseNet: A Cytoscape3-plugin for analysis of MD-based interaction networks
COSI: 3DSIG
  • Markus Schneider, Technical University of Munich, Germany
  • Iris Antes, Technical University of Munich, Germany

Short Abstract: Computational methods play a key role for investigating allosteric mechanisms in proteins, with the potential of generating valuable insights for innovative drug design. Here we present the SenseNet framework for analysis of protein structure networks, which differs from established network models by focusing on interaction timelines obtained by molecular dynamics simulations. This approach is evaluated by predicting allosteric residues reported by NMR experiments in the PDZ2 domain of hPTP1e, a reference system for which previous predictions have shown considerable variance. We applied models based on the mutual information between interaction timelines to estimate the conformational influence of each residue on its local environment. In terms of accuracy our prediction model is comparable to the top performing model published for this system, but does not rely on NMR structures as the others. Our results are complementary to experiments and the consensus of previous predictions, demonstrating the potential of our analysis tool. Biochemical interpretation of our model suggests that allosteric residues in the PDZ2 domain form two distinct clusters of contiguous sidechain surfaces. SenseNet is provided as a plugin for the network analysis software Cytoscape, contributing to a system of compatible tools bridging the fields of system and structural biology.

Sequence-based Interface Prediction for Conformational Epitopes
COSI: 3DSIG
  • Sanne Abeln, Vrije Universiteit Amsterdam, Netherlands
  • Katharina Waury, Vrije Universiteit Amsterdam, Netherlands
  • K. Anton Feenstra, Vrije Universiteit, Amsterdam, Netherlands
  • Qingzhen Hou, Shandong University, China
  • Bas Stringer, Vrije Universiteit Amsterdam, Netherlands
  • Henriette Capel, VU University Amsterdam, Netherlands
  • Reza Haydarlou, Vrije Universiteit Amsterdam, Netherlands
  • Jaap Heringa, Vrije Universiteit Amsterdam, Netherlands

Short Abstract: Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen's epitope region, as a special type of protein-protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predict epitopes from sequence in order to focus time-consuming wet-lab experiments towards most promising epitope regions. Here, we extend our previously developed sequence-based homo- & heteromeric PPI interface predictors, to predict epitope residues that have the potential to bind an antibody.

We collected and curated a high quality epitope dataset from the SAbDab database. We trained a random forest model on this epitope dataset, reaching an AUC-ROC of 0.7. This is better than the best state-of-the-art sequence-based epitope predictor BepiPred-2.0. On one solved antibody-antigen structure of the SARS-CoV-2 virus spike receptor binding domain, our predictor reaches AUC 0.778. We furthermore show our SeRenDIP-CE predictions are stable with respect to protein length, and also for transmembrane and disordered proteins.

We added the SeRenDIP-CE Conformational Epitope predictors to our webserver (www.ibi.vu.nl/programs/serendipwww/), which is simple to use and only requires a single antigen sequence as input. This will help make the method immediately applicable in a wide range of biomedical and biomolecular research.

Strategies to Improve the Description of Ligand Binding Sites in Metalloproteins for Biomolecular Simulations.
COSI: 3DSIG
  • Okke Melse, TUM Center for Protein Assemblies and TUM School of Life Sciences, Technische Universität München, Germany
  • Iris Antes, TUM Center for Protein Assemblies and TUM School of Life Sciences, Technische Universität München, Germany

Short Abstract: Metalloproteins play a pivotal role in many biological processes, and are therefore often found as drug targets. Metalloproteins are also appreciated as biocatalysts in numerous biotechnological applications because of the diverse characteristics of transition metals (TM).[1] However, biomolecular simulations of TM-containing binding sites are challenging, mainly due to the lack of the classical 12-6 Lennard-Jones potentials to describe the strong polarization of the binding site and specific coordination geometries of TMs.[2] We assessed the performance of a large variety of Zn2+ models in long-term classical molecular dynamics simulations. We performed this study in both a monometallic and a bimetallic ligand binding site, as the latter are rarely included in evaluation studies. We found serious differences in performance between the Zn2+ models, especially in the description of Zn2+-non-charged ligating atoms, which strongly affect the binding site geometry and thereby ligand binding. We hereby illustrate the importance of parameterization of metal-containing binding sites and were able to identify suitable simulation conditions depending on the aim of the simulation. The results from this study can provide guidance for biomolecular simulations, such as ligand docking and molecular dynamics simulations of metalloproteins.
[1]Riccardi et al. Nat. Rev. Chem.,2018. 2(7):100-112.
[2]Li. et al. Chem. Rev.,2017. 117(3):1564-1686.

Structural and genomic insights into pyrazinamide resistance in Mycobacterium tuberculosis underlie differences between ancient and modern lineages
COSI: 3DSIG
  • Tanushree Tunstall, London School of Hygiene and Tropical Medicine, United Kingdom
  • Jody Phelan, London School of Hygiene and Tropical Medicine, United Kingdom
  • Charlotte Eccleston, London School of Hygiene and Tropical Medicine, United Kingdom
  • Taane Clark, London School of Hygiene and Tropical Medicine, United Kingdom
  • Nicholas Furnham, London School of Hygiene and Tropical Medicine, United Kingdom

Short Abstract: Tuberculosis remains a significant public health problem, exacerbated by resistance to treatments including the crucial first line antibiotic Pyrazinamide (PZA). The multitude of non-synonymous single nucleotide polymorphisms (nsSNPs) exhibited by the pncA gene (target for PZA) necessitate computational approaches investigating the genetic and structural basis for PZA resistance. Genome-wide analysis identified nsSNPs in pncA, their association with PZA resistance, Minor Allele Frequency (MAF) and Odds Ratio (OR). A structural model of the pncA-PZA complex, and estimators of the biophysical effects of nsSNPs (DDG) on protomer stability, ligand affinity and measures of surface area, residue depth and hydrophobicity were made. In total, 424 pncA nsSNPs, derived from ~35K M. tuberculosis clinical isolates, comprising four lineages (L1-L4), sourced globally, were analysed. Over 80% nsSNPs exhibited destabilising effects on protomer stability and ligand affinity. A total of 227 nsSNPs were associated with resistance, with significantly higher MAF and OR and differences between surface area and residue depth. There were weak associations between MAF, OR and DDG. Differences based on protomer stability were found to be significant across all lineages except L3 and L4. The ancient lineage (L1) exhibits a distinct protein stability profile for mutations associated with PZA resistance, compared to modern lineages.

Structural insights of GTRs substrate specify and transport mechanism thought Fast Dynamic Docking Guided by Adaptive Electrostatic Bias
COSI: 3DSIG
  • David Ramirez, Universidad Autonoma de Chile, Chile
  • Carlos Peña, Universidad Autonoma de Chile, Chile
  • Christa Kanstrup, University of Copenhagen, Denmark
  • Osman Mirza, University of Copenhagen, Denmark
  • Ingo Dreyer, Universidad de Talca, Chile
  • Hussam Nour-Eldin, University of Copenhagen, Denmark

Short Abstract: Nitrate/peptide transporter family (NPF) is one of the largest transporter families in the plant kingdom, with members capable to transport nitrate, peptides, phytohormones and glucosinolate defense compounds. However only a few NPF transporters have been studied so far, and little is known about their interaction with substrates and physiological functions. For instance, the glucosinolate transporters (GTRs) have shown to be essential for glucosinolate uptake in Arabinosis thaliana. However, the structural determinants for substrate specify and transport mechanisms remain unknown.
Aiming to understand the transport mechanism of GTRs, we selected GTR3 member of NPF because while GTR1 and GTR3 shares a 60% of identity, GTR1 transport 4MTB and I3M glucosinolates, meanwhile GTR3 only transports I3M. GTR3 was modeled in inward-facing conformation, and then we study how both 4MTB and I3M substrates reach the binding site through an adaptive, electrostatics-inspired bias method based in MD simulations. For each system, 50 replicas were performed, and the most relevant residues influencing the entry of the substrate to the binding site were analyzed from the sampled configurations. This method provides good accuracy predictions at an affordable computational cost, allowing the identification of key molecular determinants in the transport mechanisms and specificity of GTR transporters.

TCRen: a statistical potential for residue interaction that accurately predicts TCR:peptide recognition
COSI: 3DSIG
  • Vadim Karnaukhov, Skoltech, Russia
  • Dmitrii Shcherbinin, Shemyakin–Ovchinnikov Institute of bioorganic chemistry RAS, Russia
  • Anton Chugunov, Shemyakin–Ovchinnikov Institute of bioorganic chemistry RAS, Russia
  • Ivan Zvyagin, Shemyakin–Ovchinnikov Institute of bioorganic chemistry RAS, Russia
  • Dmitrii Chudakov, Shemyakin–Ovchinnikov Institute of bioorganic chemistry RAS, Russia
  • Roman Efremov, Shemyakin–Ovchinnikov Institute of bioorganic chemistry RAS, Russia
  • Mikhail Shugay, Shemyakin–Ovchinnikov Institute of bioorganic chemistry RAS, Russia

Short Abstract: T-cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex (MHC) proteins is a crucial step in triggering the adaptive immune response. Prediction of TCR:peptide recognition is important for many clinically relevant problems: prediction of cross-reactivity of TCRs used in adoptive T-cell-based therapies, identification of targets for antigen-specific therapies of autoimmune disorders, vaccine design. In this work, we propose a knowledge-based potential TCRen that can be used to assess binding probability between TCRs and cognate antigens. TCRen is derived from statistics of amino acid residue contacts between peptides and TCRs in crystal structures of TCR-peptide-MHC complexes from PDB. We demonstrate excellent performance of TCRen for two tasks related to TCR-peptide recognition: 1) discrimination between real and mocked TCR-peptide-MHC complexes; 2) discrimination between cognate epitope and unrelated peptides in TCR-peptide-MHC crystal structures. Comparison of TCRen with potentials describing general protein-protein interaction and protein folding rules reveals the distinctive features of TCR-peptide interactions, such as intrinsic asymmetry of the interface, complex interplay between different physicochemical properties of contacting residues and lower impact of hydrophobic interactions. We suppose TCRen may be further used in development of novel tools for prediction of TCR specificity and cross-reactivity and modeling of TCR-peptide-MHC structures.

Template-based modeling of protein complexes using the PPI3D web server
COSI: 3DSIG
  • Kliment Olechnovic, Institute of Biotechnology, Life Sciences Center, Vilnius University, Lithuania
  • Ceslovas Venclovas, Institute of Biotechnology, Life Sciences Center, Vilnius University, Lithuania
  • Albertas Timinskas, Institute of Biotechnology, Life Sciences Center, Vilnius University, Lithuania
  • Migle Tomkuviene, Institute of Biotechnology, Life Sciences Center, Vilnius University, Lithuania
  • Justas Dapkunas, Institute of Biotechnology, Life Sciences Center, Vilnius University, Lithuania

Short Abstract: The PPI3D web server is user-friendly software, focused on searching, analyzing and modeling protein-protein, protein-peptide and protein-nucleic acid interactions in the context of three-dimensional structures. PPI3D uses data from the Protein Data Bank and updates weekly in order to always have the newest structures. Reducing the data redundancy by clustering and analyzing the properties of interaction interfaces using Voronoi tessellation makes this software a highly effective tool for addressing different questions related to protein interactions. In recent CASP and CAPRI experiments, PPI3D also proved to be highly effective in detecting structural templates for modeling protein complexes, thus it may be useful for anyone interested in all types of protein interactions. The web server is available at bioinformatics.lt/ppi3d.

The refinement of theoretical 3D models of proteins using the ReFOLD3 web server
COSI: 3DSIG
  • Recep Adiyaman, University of Reading, United Kingdom
  • Liam McGuffin, University of Reading, United Kingdom

Short Abstract: Recent developments in deep learning and template-based modelling have enabled the prediction of the 3D models of proteins with near-experimental accuracy. Despite these advances, predicted 3D models may still contain significant local errors, such as irregular bonds and angles. The ModFOLD server was developed by our group to evaluate the quality of 3D models and it is also capable of accurately identifying local errors on a per-residue basis. The ReFOLD3 web server was designed to fix these identified residue errors to improve the overall quality of protein structures. ReFOLD3 works by improving the accuracy and efficiency of Molecular Dynamics simulations by applying a unique gradual restraint strategy, which is based on both local model quality and predicted residue contact scores obtained from the ModFOLD8 server. The ReFOLD3 method was independently benchmarked in CASP14, where it ranked among the top ten approaches in the refinement category. Additionally, ReFOLD3 was used by our group to improve the quality of ~70% of the top server 3D models in the regular tertiary prediction category of CASP14. Finally, ReFOLD3 was also used by our group to refine 3D models of the SARS-2-CoV targets with unknown structures, as part of the CASP Commons 2020 COVID-19 initiative.

The structural landscape of transcriptional regulation network in Mus musculus embryonic myogenesis: A bioinformatics approach
COSI: 3DSIG
  • Gustavo Sandoval, Grupo de Investigacion en Bioinformatica y Biologia Estructural. Universidad Nacional Mayor de San Marcos, Peru
  • Rydberg Supo, Grupo de Investigacion en Bioinformatica y Biologia Estructural. Universidad Nacional Mayor de San Marcos, Peru

Short Abstract: In vertebrates, the embryonic development of muscle tissue is a highly complex and heterogeneous process known as myogenesis. Between all responsible agents, there are several transcription factors and signaling molecules that lead to the activation of specific genes. In this way, one important step before the construction of a mathematical model for this biological system is to describe the structural landscape and interaction profiles of the main proteins involved in this network. To perform this study, protein sequences of different protein regulators were obtained using Uniprot: Myf5 (P24699), MyoD (P10085), MyoG (P12979), Pax3 (P24610), and Pax7 (P47239). After that, 3D protein structures were obtained using SWISS-Model and validated using PDBsum and ModFold. Also, interaction profiles were retrieved using STRING database. Based on this structure analysis, proteins were classified as basic helix-loop-helix transcription factors (Myf5, MyoD, and MyoG) and paired box transcription factors (Pax3 and Pax7). According to their interaction profiles, all members of this network showed high scores which corresponds to their conserved function across vertebrates. These results constitute the first step to obtain a mathematical model to understand the molecular basis of muscle development and to propose new therapeutic targets for muscle disorders such as muscular dystrophy (Financial Support: VRIP-UNMSM Código B19101141 and Contrato N° 390-2019-FONDECYT).

Three dimensional computational visualization of a distinct chromatin loop in human lymphoblastoid cells by super resolution imaging
COSI: 3DSIG
  • Zofia Parteka, Centre of New Technologies, University of Warsaw, S. Banacha 2c, 02-097 Warsaw, Poland, Poland
  • Jacqueline Jufen Zhu, The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06030, USA, United States
  • Byoungkoo Lee, The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06030, USA, United States
  • Karolina Jodkowska, Centre of New Technologies, University of Warsaw, S. Banacha 2c, 02-097 Warsaw, Poland, Poland
  • Ping Wang, The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06030, USA, United States
  • Jesse Aaron, Advanced Imaging Center, Janelia Research Campus, Howard Hughes Medical Institute, United States
  • Teng-Leong Chew, Advanced Imaging Center, Janelia Research Campus, Howard Hughes Medical Institute, United States
  • Yijun Ruan, The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06030, USA, United States
  • Dariusz Plewczynski, Centre of New Technologies, University of Warsaw, S. Banacha 2c, 02-097 Warsaw, Poland, Poland

Short Abstract: The three-dimensional (3D) genome structure plays a fundamental role in gene regulation and cellular functions. Recent studies in 3D genomics inferred the very basic functional chromatin folding structures known as chromatin loops, the long-range chromatin interactions that are mediated by protein factors. To visualize the looping structure of chromatin, we applied interferometric photoactivated localization microscopy (iPALM) to image a specific chromatin loop in human lymphoblastoid cells. Totally, we have generated thirteen good-quality images of the target chromatin region with super-resolution. To reconstruct the chromatin structures from captured images, we modeled them as continuous looping conformations using a traveling salesman problem solver (TSP). We then compared the physical distances in image models with contact frequencies generated by ChIA-PET and Hi-C to examine the concordance. While showing a good correlation with genomic sequencing data, image models reveal heterogeneity between individuals and the fine structures within the loop in single cells.

Towards protein interface prediction using SE(3)-Transformer
COSI: 3DSIG
  • Tsukasa Nakamura, JSPS(PD)/Tohoku University, Japan

Short Abstract: Predicting protein interfaces from protein monomer structures is important for large-scale structure prediction of protein complexes. SE(3)-Transformer is one of the deep learning models that take as input a connected graph in which each node has coordinate information, such as a graph constructed from a 3D point cloud. In this model, equivariance is guaranteed for 3D rotational and translational transformations of input, and stable feature computation is possible. In this study, I applied this model to structure data of protein-protein interfaces. A structure data is a 3D point cloud of atoms, and in this sense, it is expected that using this model will have an advantage in prediction performance. However, the actual performance improvement on large datasets is unclear. A structure data is a point cloud of atomic species and coordinates, and the graph structure is created based on chemical bonds. The input is an interface substructure cut out from tertiary structure by spatial distance constraint, which may result in a disconnected graph. To handle this, I introduced virtual edges to connect disconnected graphs. Also, the model is extended to Siamese Network (two sub-networks sharing parameters and weights) because the input is a pair of graphs.

Ultra fast structural alignment using neighbor hood aware structural alphabet
COSI: 3DSIG
  • Martin Steinegger, Seoul National University, South Korea
  • Johannes Söding, Max Planck Institute for Biophysical Chemistry, Germany
  • Stephanie Kim, Seoul National University, South Korea
  • Michel van Kempen, Max Planck Institute for Biophysical Chemistry, Germany

Short Abstract: Proteins homology detection is crucial to functionally annotate or to trace back evolution. Finding distantly related proteins using sequence alignment is challenging since the protein sequence can diverge far. Protein structures are conserved for a longer period of time. Hence, structural alignment enhances remote homology detection. Since the recent breakthrough of AlphaFold2, we anticipate a rapid increase in publicly available structures. However, state-of-the-art structural aligners are not sensitive enough or do not scale well to search large structural collections. Here we propose a structural aligner that is sensitive and fast enough to scale to billions of structural comparisons.

In this study, we developed an extended method that aligns billions of protein structural alignment for classification. For fast comparison, we developed structural alphabets, 3Di, that represent not only the backbone of the structure but also local fragments through its nearest neighbor interactions.

3Di outperformed 3D-blast and CLE in classifying family, superfamily, and folds of the SCOP benchmark set. 3Di’s family and superfamily AUCs were 0.82 and 0.43, which outperformed both 3D-blast and CLE by 0.08 and 0.15. Furthermore, we aim to develop more efficient structural alignment algorithm that utilizes the 3Di structural alphabets for faster and more accurate structure comparison algorithm.

Using iCn3D and the WWW for structure-based collaborative research: example of differential molecular interactions analysis
COSI: 3DSIG
  • Philippe Youkharibache, NIH, United States
  • Thomas Madej, NCBI/NLM/NIH, United States
  • Jiyao Wang, National Institutes of Health, United States
  • Raul Cachau, Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, USA, United States

Short Abstract: We developed new key features in the iCn3D web-based software to structure-based research and data exploration to 1) Foster structure-based collaborative research via iCn3D; 2) Enable comparative analysis of receptor ligand interaction analysis
1. The rapid generation of new 3D structures and sequences demands efficient tools to expedite structural analyses and their sharing in a native 3D context. We developed the open-source software iCn3D (doi.org/10.1093/bioinformatics/btz502) pronounced ""I see in 3D"", to take advantage of web technologies and allow scientists of different backgrounds to perform and share sequence-structure analyses over the Internet and engage in collaborations through a simple mechanism of exchanging ""lifelong"" web links (URLs).
2. We apply the software to sequence-structure analyses of SARS-COV-2 and related coronaviruses and identify specific sequence-structure micro-homologies in receptor binding domains/motifs (RBD/RBM) across coronaviruses from SARS to MERS, OC43, HKU1, HKU4, and MHV that could be targeted by neutralizing antibodies or other therapeutic molecules (www.biorxiv.org/content/10.1101/2020.07.01.182964v2)
3. New features allow a first-level structural analysis of differences in the SARS-CoV-2/ACE2 interface for emerging variants of concern, now used in various viral epitope resources.

Visualization of Electrostatic Potential and Residue Mutations with iCn3D, a Web-based 3D Viewer
COSI: 3DSIG
  • Jiyao Wang, NIH/NCBI, United States
  • Philippe Youkharibache, NIH/NCI, United States
  • Aron Marchler-Bauer, NIH/NCBI, United States
  • Dachuan Zhang, NIH/NCBI, United States
  • Thomas Madej, NIH/NCBI, United States
  • Christopher Lanczycki, NIH/NCBI, United States
  • Shennan Lu, NIH/NCBI, United States
  • Gabriele Marchler, NIH/NCBI, United States

Short Abstract: "iCn3D is not only a web-based 3D viewer for sharing 1D/2D/3D representations of biomolecular structures, but also an analysis tool to show interactions, electrostatic potential, residue mutations, etc. Recently iCn3D has been integrated with a web service based on the DelPhi program to compute and display electrostatic potentials either mapped onto molecular surfaces or as potential maps for any subset of a structure. One example is shown in the sharable link: structure.ncbi.nlm.nih.gov/icn3d/share.html?XCxR6fSTmXHxR3o1A. iCn3D also displays predicted side-chain conformations for residue mutations using a web service adapted from the scap program. Users can alternate between the wild type and the mutant conformation, and observe respective changes in the interaction networks of the wild type and the mutant. One example is at structure.ncbi.nlm.nih.gov/icn3d/share.html?MrSHTgeTmEFZj8uy8. Furthermore, with the recent release of ""icn3d"" package at npm and iCn3D 3.0, users can write Node.js scripts to call functions in icn3d. These scripts can be run in the command line to process a list of 3D structures to get annotations. One example script is to calculate the change of interactions due to a mutation as shown at: github.com/ncbi/icn3d/blob/master/icn3dnode/interaction2.js.

Source code: github.com/ncbi/icn3d and www.npmjs.com/package/icn3d."

VoroContacts: a tool for the analysis of interatomic contacts in macromolecular structures
COSI: 3DSIG
  • Kliment Olechnovic, Vilnius University, Lithuania
  • Ceslovas Venclovas, Vilnius University, Lithuania

Short Abstract: VoroContacts is a versatile tool for computing and analyzing contact surface areas (CSAs) and solvent accessible surface areas (SASAs) for 3D structures of proteins, nucleic acids and their complexes at the atomic resolution. CSAs and SASAs are derived using Voronoi tessellation of 3D structure, represented as a collection of atomic balls. VoroContacts web server features a highly configurable query interface, which enables on-the-fly analysis of contacts for selected set of atoms and allows filtering interatomic contacts by their type, surface areas, distance between contacting atoms and sequence separation between contacting residues. The VoroContacts functionality is also implemented as part of the standalone Voronota package, enabling batch processing. VoroContacts is available at bioinformatics.lt/wtsam/vorocontacts.



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