StarPep Toolbox: an open-source software to assist chemical space analysis of bioactive peptides and their functions using complex networks.

MOTIVATION: Antimicrobial peptides (AMPs) are promising molecules to treat infectious diseases caused by multi-drug resistance pathogens, some types of cancer, and other conditions. Computer-aided strategies are efficient tools for the high-throughput screening of AMPs. RESULTS: This report highlights StarPep Toolbox, an open-source and user-friendly software to study the bioactive chemical space of AMPs using complex network-based representations, clustering, and similarity-searching models. The novelty of this research lies in the combination of network science and similarity-searching techniques, distinguishing it from conventional methods based on machine learning and other computational approaches. The network-based representation of the AMP chemical space presents promising opportunities for peptide drug repurposing, development, and optimization. This approach could serve as a baseline for the discovery of a new generation of therapeutics peptides. AVAILABILITY AND IMPLEMENTATION: All underlying code and installation files are accessible through GitHub (https://github.com/Grupo-Medicina-Molecular-y-Traslacional/StarPep) under the Apache 2.0 license.

ISCB Student Council Symposium 2021, a virtual global venue: challenges and lessons learned.

Since 2004, the ISCB Student Council has been organizing different symposia worldwide, gathering together the community of young computational biologists. Due to the coronavirus disease 2019 (COVID-19) pandemic situation, the world scientific community was forced to cancel in-person meetings for almost two years, imposing the adoption of virtual formats instead. After the successful editions of our continental symposia in 2020 in the USA, Latin America, and Europe, we organized our flagship global event, the Student Council Symposium (SCS) 2021, trying to apply all previous lessons learned and to exploit the advantages that virtuality has to offer.

Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides.

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 1065 unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.

Highlights of the 1st Ecuadorian-Venezuelan Symposium of Young Researchers in Bioinformatics (1SEVJIB).

The COVID-19 pandemic profoundly changed how scientific conferences are organized, fostering virtual meetings. These online events have allowed students and researchers to overcome geographical, administrative and economic barriers to attend and organize high-quality international symposiums. Moreover, these virtual conferences have contributed to the creation of inclusive activities that strengthen scientific communities. Here, we summarize the main activities and learnings from the 1st Ecuadorian-Venezuelan Symposium of Young Bioinformatics Researchers (1SEVJIB), organized by the Ecuadorian and Venezuelan ISCB-SC Regional Student Groups (RSGs). This symposium aimed to provide an opportunity for undergraduate and postgraduate students from Ecuador, Venezuela, and other Latin American countries to share their Bioinformatics research. The 1SEVJIB was the first bi-national conference organized by two RSGs from Latin America (LatAm). This symposium was a two-day virtual meeting with five activities: 1) oral student presentations, 2) poster session, 3) keynote lectures, 4) workshop, and 5) round table. This conference promoted the scientific exchange and cooperation networks between young Bioinformatics researchers and students from Ecuador, Venezuela, and LatAm, giving them opportunities to boost their scientific careers.

The Molecular Basis of JAZ-MYC Coupling, a Protein-Protein Interface Essential for Plant Response to Stressors.

The jasmonic acid (JA) signaling pathway is one of the primary mechanisms that allow plants to respond to a variety of biotic and abiotic stressors. Within this pathway, the JAZ repressor proteins and the basic helix-loop-helix (bHLH) transcription factor MYC3 play a critical role. JA is a volatile organic compound with an essential role in plant immunity. The increase in the concentration of JA leads to the decoupling of the JAZ repressor proteins and the bHLH transcription factor MYC3 causing the induction of genes of interest. The primary goal of this study was to identify the molecular basis of JAZ-MYC coupling. For this purpose, we modeled and validated 12 JAZ-MYC3 3D in silico structures and developed a molecular dynamics/machine learning pipeline to obtain two outcomes. First, we calculated the average free binding energy of JAZ-MYC3 complexes, which was predicted to be -10.94 +/-2.67 kJ/mol. Second, we predicted which ones should be the interface residues that make the predominant contribution to the free energy of binding (molecular hotspots). The predicted protein hotspots matched a conserved linear motif SL••FL•••R, which may have a crucial role during MYC3 recognition of JAZ proteins. As a proof of concept, we tested, both in silico and in vitro, the importance of this motif on PEAPOD (PPD) proteins, which also belong to the TIFY protein family, like the JAZ proteins, but cannot bind to MYC3. By mutating these proteins to match the SL••FL•••R motif, we could force PPDs to bind the MYC3 transcription factor. Taken together, modeling protein-protein interactions and using machine learning will help to find essential motifs and molecular mechanisms in the JA pathway.

4th ISCB Latin American Student Council Symposium: a virtual and inclusive experience during COVID-19 times.

Since 2014, the ISCB Latin American Student Council Symposium (LA-SCS) serves as the main biannual activity where students from all levels, postdocs and early researchers from the entire Latin American region can gather to discuss recent advances in the fields of bioinformatics and computational biology. This time we faced a major unexpected obstacle, a worldwide pandemic that has completely disrupted human activities at a planetary scale. Countless conferences have been either canceled, reprogrammed for the next year or moved to a virtual format. However, thanks to an important strengthening of the Latin American student network and the creation of several new RSGs in the continent, we were able to get together a fearless team that aimed to overcome the pandemic obstacles and still organise the 4th LA-SCS. Here we summarize our experiences in our first virtual symposium.

A putative antimicrobial peptide from Hymenoptera in the megaplasmid pSCL4 of Streptomyces clavuligerus ATCC 27064 reveals a singular case of horizontal gene transfer with potential applications.

Streptomyces clavuligerus is a Gram-positive bacterium that is a high producer of secondary metabolites with industrial applications. The production of antibiotics such as clavulanic acid or cephamycin has been extensively studied in this species; nevertheless, other aspects, such as evolution or ecology, have received less attention. Furthermore, genes that arise from ancient events of lateral transfer have been demonstrated to be implicated in important functions of host species. This approximation discovered relevant genes that genomic analyses overlooked. Thus, we studied the impact of horizontal gene transfer in the S. clavuligerus genome. To perform this task, we applied whole-genome analysis to identify a laterally transferred sequence from different domains. The most relevant result was a putative antimicrobial peptide (AMP) with a clear origin in the Hymenoptera order of insects. Next, we determined that two copies of these genes were present in the megaplasmid pSCL4 but absent in the S. clavuligerus ATCC 27064 chromosome. Additionally, we found that these sequences were exclusive to the ATCC 27064 strain (and so were not present in any other bacteria) and we also verified the expression of the genes using RNAseq data. Next, we used several AMP predictors to validate the original annotation extracted from Hymenoptera sequences and explored the possibility that these proteins had post-translational modifications using peptidase cleavage prediction. We suggest that Hymenoptera AMP-like proteins of S. clavuligerus ATCC 27064 may be useful for both species adaptation and as an antimicrobial molecule with industrial applications.