Short Abstract: Independent Researcher (CONICET), Professor (UNSAM) Head of the Protein Structure, Function and Plasticity Laboratory at IIBIO, UNSAM Dr Chemes' main expertise is in the study of structure-function relationships in proteins, using both experimental and bioinformatics techniques. Their work focuses on small modular elements in proteins called “Linear Motifs” which are present within flexible, intrinsically disordered regions of proteins. They also study how protein flexibility contributes to functional outputs in disordered proteins. They focus our work on viral pathogenic proteins, which exploit disorder and linear motifs to hijack cell regulation. They recently became part of an international consortium dedicated to developing computational resources for intrinsically disordered proteins and contribute to bioinformatic databases such as ELM (Eukaryotic Linear Motif Database), Disprot (Disordered Proteins Database) and PED (Protein Ensembles Database). They work in close collaboration with international partners including Toby Gibson at the European Molecular Biology Laboratory (Heidelberg, Germany), Silvio Tosatto at University of Padova (Padova, Italy), Gary Daughdrill at University of South Florida (Tampa, USA), Pau Bernadó at CNRS (Montpellier, France) and Ignacio Sánchez (Protein Physiology Lab, FCEN-UBA). She was trained as a Biologist at University of Buenos Aires (Undergraduate degree), later obtaining a Master in Science (M.Sc.) degree from Rockefeller University (New York, USA) and a PhD degree from University of Buenos Aires in 2010. During her postdoc period, she collaborated closely with Toby Gibson (EMBL) during several stays in his group. She currently lead a laboratory at the Institute for Biotechnological Investigations, UNSAM, Argentina. They are funded by Agencia Nacional de Promoción Científica y Tecnológica (Arg), Canadian Institutes of Health and Research (CIHR) and European Commission (Marie Curie RISE Horizon 2020 action).

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Short Abstract: Leishmaniasis is a public health disease that requires the development of more effective treatments and the identification of novel molecular targets. Since blocking PI3K/AKT pathway has been successfully studied as an effective anti-cancer strategy for decades, we examined whether the same approach would also be feasible in Leishmania due to their high amount and diverse set of annotated proteins. Here we used a best reciprocal hits protocol to identify potential protein kinase homologues in an annotated human PI3K/AKT pathway. We modelled their 3D-structures to estimate the druggability of their binding pockets, and used the models to run a virtual screening method based on molecular docking. We found and studied five protein kinases in five different Leishmania species, which are AKT, GSK3, AMPK, mTOR and CDK homologues from the studied pathways. The compounds found for different enzymes and species were analyzed and suggested as starting point scaffolds for the design of inhibitors. We studied the kinases participation in protein-protein interaction networks, and supported with the literature potential deleterious effects if inhibited. The analysis contributes to improve the knowledge about the presence of similar signaling pathways in Leishmania, as well as the discovery of compounds acting against any of these kinases as potential molecular targets in the parasite.

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Short Abstract: Alignments of discrete objects can be constructed in a very general setting as super-objects from which the constituent objects are recovered by means of projections. Here we focus on contact maps, i.e., undirected graphs with an ordered set of vertices. These serve as natural discretizations of RNA and protein structures. In the general case, the alignment problem for vertex ordered graphs is NP-complete. In the special case of RNA secondary structures, i.e., crossing-free matchings, however, the alignments have a recursive structure. The alignment problem then can be solved by a variant of the Sankoff algorithm in polynomial time.

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Short Abstract: Currently, we live in times where the availability and transfers of molecular test kits for the determination of the SARS-CoV-2 virus are decisive and fundamental, especially for critical care areas and (LMIC) countries. At the moment, in Costa Rica, as in most Latin American countries, this procedure is done manually (or with electronic sheets) and there is no Information System that includes inventories, geographic factors, patterns, movements and occurrences that monitors in real-time application and availability based on data sent from clinical laboratories. Likewise, having a geospatial location and quantification system is essential and necessary for resource planning, patient care and public health actions. Therefore, the proposal exposes the development of a functional prototype that controls the two types of molecular tests implemented, according to WHO recommendations: Open Cycle and Closed Cycle through quantitative PCR in clinical laboratories. With the Information System, control is carried out in real time, and the tests (positive and negative cases) can be annotated on Web forms and geolocated in the attention of districts, cantons and provinces for epidemiological control. For this purpose, an App and a Web Application were developed with aeospatial system that includes Artificial Intelligence modules. The developed prototype can be of great support to Latin American countries, where there are many resource and personnel limitations in the current SARS-CoV-2 virus pandemic. In a more advanced version of the platform, the information of the virus SARS-CoV-2 and its genome will be determined and integrated by nanotechnological devices.

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Short Abstract: Pyrazinamide (PZA) is one of the most important drugs used in first and second-line treatments against tuberculosis. It is especially useful to eliminate bacteria in a latent state and to reduce relapse rates. Pyrazinoic acid (POA) is the active form of PZA, and is generated by hydrolysis of PZA by the enzyme pyrazinamidase (PZase). The importance of an acidic environment for sterilizing activity was discovered by the high in vivo and poor in vitro activity. It is suggested that the acidic environment experienced by M. tuberculosis inside granulomes is responsible for in vivo lethal effect. It is accepted in consensus that not a single target of PZA exists, but several potential targets are thought to be present. One of the proposed models for POA accumulation states that PZA enters to the cell through passive diffusion, is hydrolyzed to POA, and expelled to the extracellular environment by efflux pumps. Outside, an acidic extracellular environment protons a fraction of the expelled POA to HPOA and in this form it re-enters to the cytoplasm by a membrane potential gradient. In this way, the external acidic environment helps to recover and accumulate intracellular POA, setting up a cycle that leads to acidification of the cytoplasm. At this moment, the intracellular accumulation of POA and the cytoplasmic acidification, acts on several potential targets and metabolic pathways. Several studies have shown that overexpression of PZase, the presence of efflux pump inhibitors, or a reduction of temperature eliminates the requirement of an acidic environment for in vitro testing. In addition, it has been shown that susceptibility does not necessarily need an internal acidification of the cytoplasm. To better explain the real contribution of an acidic extracellular environment to internal accumulation of POA and to better undestand alternative scenarios of resistance/susceptibility, we modelled the PZA/POA metabolic pathway using a system of coupled non-linear differential equations. Our results indicate that in the equilibrium, POA accumulation is independent of external pH and only depends on the ratio between the rates of POA production and efflux. In addition, the acidic environment has a significant contribution in the internal accumulation of total-POA (POA + HPOA) only when the ratio between the efflux rate and diffusion constant of external POA/HPOA is greater than 1. Interestingly, the rate of POA production could help to increase the total-POA accumulation independently of efflux rate and external pH.

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Short Abstract: The Covid-19 pandemic, caused by SARS-CoV-2 virus, still keeps the scientific community in an exhaustive race to find effective treatments to control this disease. One of the fundamental processes of this virus is viral transcription and replication, which allow the synthesis of genetic material and the consequent multiplication of the virus to infect other cells or organisms. These are performed by a multi-subunity machinery of various nonstructural proteins (nsp); among which the RNA-dependent RNA polymerase (RdRp or nsp12) is the most important, and conserved among coronaviruses. The structure of this protein (PDB ID: 6M71) was used as a target in the application of computational strategies for drug search, such as virtual screening and molecular docking. The region considered for virtual screening was three important amino acids for protein catalysis: T680 (in Motif A), N691 and D623 (in Motif B), where a grid box was located. In turn, applying the concept of drug repositioning, considered as a quick response in the treatment of sudden outbreaks of diseases, it was used the Pathogen box, a database of chemical compounds, analyzed for the treatment against malaria, which were filtered under the criteria of selecting those that do not present any violation of Lipinski's rule of five. Remdesivir, Beclabuvir and Sofosbuvir, previously used in in silico and in vitro studies for inhibition of nsp12, were used as positive controls. On the other hand, it was used Fafdrugs4 program to filter drugs considered false positives or “pains”. The results showed a top10 of potential target inhibitors, with a delta-G higher than those of the positive controls, of which TCMDC-135052 and TCMDC-125802, both with a delta-G of -7.53, present interactions with the three important residues of the nsp12 catalytic site. At the same time, these drugs were not identified as “pains”. We prepared six systems (considering nsp12, cofactors proteins and ligands) for validation by molecular dynamics in Gromacs. This study proposes two ligands, which can be considered as potential inhibitor drugs of replication machinery for the development of effective treatments against this pandemic.

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Short Abstract: The current emergency state produced by the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) has highlighted the need of tools to anticipate the actions required to control new infectious agents. For instance, anticipating the virus transmissibility would allow to prepare health systems. This transmissibility depends on multiple factors, including among others, the efficiency of assembly of viral particles, a process that depends on the self protein interactions. We study the relationship of three coronavirus (SARS-CoV-1, SARS-CoV-2 and Middle East Respiratory Sindrome (MERS) CoV) transmissibility number (R0) with the self-interactome completeness. Six machine-learning algorithms trained with residue cluster classes derived from protein three-dimensional structure, were used to estimate the fraction of complete self-interactome (FraCoSI) for each virus. These six algorithms were evaluated in: i) predicting known self-interactome for SARS-CoV-1 and two ribosome self-interactomes, ii) comparing protein-protein interaction predictions derived from real structures with those obtained with structural models. One algorithm consistently outperforms the other five in these tests. An inverse relationship between FraCoSI and R0 was observed: the more transmissible a virus is, the lower its FraCoSI. This constitutes the first evidence supporting the prediction of the transmissibility of viruses from the virus protein structures.

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Short Abstract: Background: The interaction of different transcription factors (TFs) and DNA is a key mechanism for gene expression regulation. While the binding properties of many individual TFs are well known, there is a limited understanding on how different TFs interact with DNA cooperatively, either forming dimers or co-binding the same region. This cooperativity gives rise to a combinatorial binding of TFs (motif grammar) which is key for cell differentiation, development and response to external stimuli. Discovering this motif grammar is essential for understanding context-specific TF binding properties. Results: We present a method based on non-negative matrix factorization (NMF) to factorize DNA sequences and discover TF-cobinder pairs. The only input is a set of TF binding sites (TFBSs) for the same TF (anchor). The TFBS flank sequences are converted into one-hot-encoding format and then we applied NMF to both sides of the anchor TFBSs, independently, with an increasing number of k factors (from 2 to 5), trying to group the N input sequences into k factors (clusters), where each factor corresponds to a TF-cobinder configuration (e.g., separated by a fixed spacer and considering the motif orientation). For each factor, we select the individual sequences that have a high contribution (weight) and we use them to build position frequency matrices (PFMs). The PFMs are trimmed to keep only the relevant positions with high information content (IC) and then are clustered to remove redundancy and annotate the cobinder motifs. Finally, the method calculates the spacer between the TFBS anchor and the cobinder, resulting in a comprehensive list considering all the relevant spacers, their motifs, and the frequency of such configuration in its input dataset. We applied this method to individual TFs and TF families, revealing TF-cobinder pairs with varying spacers in multiple conditions. We analysed TFBSs from the UniBind database (1,532 ChIP-seq experiments encompassing 231 human TFs). We identified motifs of already known co-binders, such as TAL1 with GATA1, or POU5F1 with SOX2. Furthermore, we identified homo-dimerization events for various TFs, including GATA TFs and FOXA1. Conclusions: The presented method applied on genomic sequences discovers co-binder motifs associated with TFBSs. Unlike motif discovery tools, this method reveals motifs that have a fixed spacer relative to the anchor TFBS, showing a strict motif grammar that may differ among datasets of the same TF. We plan to systematically apply this method in predicted humans and mouse TFBSs to compare conservation of TF-cobinder configurations.

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Short Abstract: Motivation: The first step in any genome research after obtaining the read data, is to perform a due quality control of the sequenced reads. In a de novo genome assembly project, the second step is to estimate two important features, the genome size and “best k-mer”, to start the assembly tests with different de novo assembly software and its parameters. However, the quality control of the sequenced genome libraries as a whole, instead of focusing on the reads only, is frequently overlooked, and realized to be important only when the assembly tests did not render the expected results. Results: We have developed GSER, a Genome Size Estimator using R, a pipeline to evaluate the relationship between k-mers and genome size, as a means for quality assessment of the sequenced genome libraries. GSER generates a set of charts that allow the analyst to evaluate the library datasets before starting the assembly. Availability: The script which runs the pipeline can be downloaded from http://www.mobilomics.org/GSER/downloads or http://github.com/mobilomics/GSER

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