Keywords: Peptide Protein design Molecular dynamics Monte carlo Structural bioinformatics

Poster:

Short Abstract: The in silico design of peptides and proteins as binders is useful for diagnosis and therapeutics due to their low adverse effects and major specificity. To select the most promising candidates, a key matter is to understand their interactions with protein targets. Here we present PARCE, an open source Protocol for Amino acid Refinement through Computational Evolution that implements an advanced and promising method for the design of peptides and proteins. The protocol performs a random mutation in the binder sequence, then samples the bound conformations using molecular dynamics simulations, and evaluates the protein-protein interactions from multiple scoring. Finally, it accepts or rejects the mutation by applying a consensus criterion based on binding scores. The procedure is iterated with the aim to explore efficiently novel sequences with potential better affinities toward their targets. The protocol can be applied with any protein of interest to design bound peptides or proteins of biological interest. We provide a tutorial for running and reproducing the methodology.

Keywords: Pathway analysis Gene set analysis Benchmark GSEA

Poster:

Short Abstract: Gene Set Analysis (GSA), or Pathway Analysis, is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper&[prime]s citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies. Two bioinformatics tools are introduced: Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods ("GSA3D BenchmarKING"). Results show discrepancies between the most popular and the best performing GSA methods, which calls our attention towards the nature of the tool selection procedures followed by researchers and raises doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Based on the results, suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

To ask a question to the presenter click here

Keywords: Bioprospecting Molecules Bioactivity Bibliometrics Panama

Poster:

Short Abstract: In the last 20 years, important data have been generated on the identification and discovery of biomolecules with potential for health and biotechnology. This work has been achieved mainly through research organized by the Cooperative Group for Biodiversity of Panama (ICGB). Following the spirit described in the guidelines of the Nagoya Protocol ""on access to genetic resources and fair and equitable sharing of the benefits derived from their use"", an investigation by bibliometric re-analysis is presented. First a list of molecules found in Panama through bioprospecting are described and the existence of their structures is assessed. Also, key information about publishing articles relating to them are selected for instance: title, affiliation of the authors, abstracts, body and keywords in Spanish and English. The objective of this work is to identify how these biomolecules have been reported, how much is known about them and how this information has been subsequently used and by locals or foreigner scientists. Descriptive statistics of the data were calculated, segregated by type of molecule, region of the country of origin, journal of publication, country of publication, and others. Also, an automated script to collect this bibliometric information is presented. We foresee that this analysis, using the scientific production associated with these molecules, can help bring attention to these molecules and their products. Also, be a good starting point for the organization of this information for later bigger re-analysis effort or be used as reference for various biobanking projects that are being developed in our country.

To ask a question to the presenter click here

Short Abstract: Nowadays, one of the most frequently reported causes of death is Chronic Obstructive Pulmonary Disease. Due to its slow development and lack of characteristic symptoms, it may remain undiagnosed for many years, resulting in lowering of lung function and greater susceptibility to infection. This is a serious threat to health and life. The current state of knowledge suggests that the greatest risk of COPD is among smokers and those who are exposed to air pollution. The lack of a precise diagnostic method, enabling early detection of the disease, is a great challenge in the effective fight against COPD. Here we present the results of bioinformatic analyses carried out for proteomics data obtained during the first visit from subjects included in the groups of healthy controls (N1=31) and COPD patients (N2=39), according to the classification established by The Global Initiative for Chronic Obstructive Lung Disease. We studied the relationships between the values ??obtained for selected 29 proteomics parameters and the state of the patient. By comparing the characteristics of disease patients with the control group of healthy non-smokers, we checked what significant relationships in the proteomics parameters occur for GOLD Stage groups. Further, we checked the relationships between clinical chemistry data and proteomic data. Finally, we explored the relations between patient's own subjective assessment and the objective medical evaluation. These pilot analyses are a first step in a project aimed at evaluating COPD patients' state of health longitudinaly for 5 years and at developing more robust disease state biomarkers.

Video not uploaded

Keywords: Frustration Docking molecular Binding-site predictor

Poster:

Short Abstract: Introduction While proteins fold, strong energetic conflicts are minimized as proteins adopt conformations more similar to their native states (minimum frustration principle). Local violations of this principle allow proteins to encode specific signals in their energy landscapes that are required to achieve their biological functions. Methods A non redundant data set of all monomeric enzymes with protein-ligand binding sites annotations were downloaded from the BioLiP database (n = 1007) in order to characterize protein-ligand interaction pockets. Protein structures were downloaded from the Protein Data Bank (PDB) and frustration patterns were calculated using the Frustratometer tool. We compared how well protein ligand interactions are predicted using both frustrapocket and fpocket. Results We predicted protein-ligand binding sites using fpocket and frustra-pocket in our dataset. We found that protein residues that directly interact with ligands are enriched in highly frustrated interactions.When taking frustration into account with our frustra pocket strategy, we improve the amount of predicted binding sites by 10%. Moreover, frustration permits to discriminate among multiple pocket predictions in a given protein and to give a per residue score for the constituting amino acids.

Keywords: Monogenean Transcriptome Computational drug repositioning Drug Target

Poster:

Short Abstract: The production of fish in aquaculture can be affected by monogeneans, which are parasitic flatworms. The drugs available to control these parasites are few and not always effective. This work presents a methodological framework based on the monogenean Rhabdosynochus sp. in order to identify drug targets and to identify drugs with affinity to the identified proteins (drug repositioning). For this, the transcriptome of Rhabdosynochus sp. will be sequenced and assembled, and the proteins will be predicted and annotated. Subsequently, the proteases and protease inhibitors, ligand-associated ion channels, G-protein-coupled receptors and kinases proteins will be identified by aligning the Rhabdosynochus sp. proteins against the protein databases MEROPS, LGIC, KINBASE and Pfam, using the sequence alignment programs Blast, HMMER and OMA. The identified proteins will be aligned against the ChEMBL, DrugBank and Therapeutic Targets Database, which have information of the proteins that can be used as a target. A computational drug repositioning analysis will be performed to identify any drug with affinity to the target proteins of Rhabdosynochus sp. For this, information on the three-dimensional structure of chemical compounds will be obtained from the ZINC15 database, and the three-dimensional structure of proteins will be obtained through the structural modeling of amino acid sequences using the Modeller program. The analysis of the protein-drug interaction and the virtual screening will be performed in the AutoDock Vina and UCSF Chimera programs, respectively. Finally, we will evaluate the anthelmintic activity of the compound that present the best results in computational analyzes, by means of an in vitro phenotypic screening of the whole organism.

To ask a question to the presenter click here

Keywords: Macrophages Boolean Network COVID-19 SARS-CoV-2

Poster:

Short Abstract: The virus SARS-CoV-2 has caused more than one million deaths worldwide. Part of the damage caused by this virus is associated with the host’s immune system’s dysregulation. Macrophages play an essential role in disease progression since deregulated macrophages can lead to immunologic complications such as macrophage activation syndrome, cytokine storm, and acute respiratory distress syndrome. Macrophages are innate immune cells that can detect and respond to pathogens and diverse stimuli. They differentiate into classical M1 macrophages with inflammatory properties and the alternatively activated M2 macrophages with anti-inflammatory properties, which are further sub-categorized into M2a, M2b, M2c, M2d phenotypes. To study the role of macrophages during COVID-19 we constructed a Boolean network of 32 nodes that represent cytokines, signaling pathways and TFs, and 57 interactions. The network has 2112 attractors that can be classified into M1, M2a, M2b, M2c, M2d cell types. The attractors reveal polarizations under actual biological conditions of all phenotypes that we can observe in vivo. Subsequently, we used this boolean-network to study the behavior of macrophages during SARS-CoV-2 infection. This shows that SARS-CoV-2 affects the immune balance of the host.

Keywords: Factor Xa MMPBSA FXa inhibitors Binding free energy

Poster:

Short Abstract: Cardiovascular diseases are the number one cause of death globally. Thrombosis is a pathology that can cause stroke, acute coronary syndrome (ACS) and venous thromboembolism (VTE). Factor Xa (FXa) is a serine protease that plays a key role in the clotting cascade by activating prothrombin to thrombin. Direct FXa inhibition has been proved as successful anticoagulation therapy, nevertheless approved FXa inhibitors like rivaroxaban and apixaban have adverse effects, where the risk of bleeding the major concern. Consequently, there is a need to develop new small molecules that can inhibit FXa. Here we show novel compounds with aryl piperazine peptide structure as potential FXa inhibitors. A virtual combinatorial library of 96 compounds with aryl piperazine scaffold was generated and their binding conformations to FXa estimated by molecular docking, followed by binding free energy prediction through the molecular mechanics - Poisson Boltzmann / surface area (MM-PBSA) end-point method. Relative binding free energy calculation (without entropy contributions) were performed and multivariate analysis of variance suggested a preference for chlorobenzene at the S1 site (F = 27.23, p < 0.05, N = 160) and a preference for large, aliphatic amino acids (Ile, Leu) binding to a site lined by residues Gln192, Arg143 and Gln192 (F = 14.73, p<0.001, N = 60). The binding mode of these compounds is in L-shape within the active site of FXa, similar to rivaroxaban and apixaban. The predicted Ki values for these ligands are in the subnanomolar to nanomolar range, based on a calibration curve generated from binding free energy estimates of known FXa inhibitors. These results suggest potential FXa inhibition of aryl piperazine peptide derivatives and are the first step for the further synthesis and biological validation.

Keywords: Phylogenetic Combinatorics Colored digraph Reachable sets Hierarchy Hasse diagram Rooted triples Supertrees

Poster:

Short Abstract: Comparative and evolutionary studies rely primarily on orthology prediction methods. Several of these methods are based on Reciprocal Best Hits (RBH) (also known as bidirectional best hits (BBH), reciprocal best matches (RBM), symmetric best matches (SBM), etc.) that provide the information to construct a graph. The node set of these graphs are given by the genes under study and the initial edge set is given by a transformation of the sequence similarity between the genes. The aim of these methods is to process their graphs in such a way that the final edge set represents only orthology relationships of the nodes they connect. In this work we provide a mathematical characterization of the properties that a graph that is composed only by orthology relationships should satisfy in order to represent a true evolutionary history. Let T be a phylogenetic (gene) tree and delta the mapping of the leaves (genes) of T to an element of species set. We define the best match graph (G,delta) as the digraph that contains an edge from x to y if the genes x and y reside in different species and y is one of the evolutionary closest relatives of x compared to all other genes contained in the species where y resides. Conversely, if an arbitrary (G,delta) is given, we show that it can be decided in cubic time and quadratic space whether it is derived from a tree and if that's the case, we can obtain a unique least resolved tree that explains this best match graph.

Video not uploaded