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 10 65 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-tovery-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.

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.

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.

Antimicrobial peptides (AMPs) are small bioactive chemicals that have appeared as promising compounds to treat a wide range of diseases. The effectiveness of AMPs resides in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the AMPs’ chemical space (AMPCS) is huge, it is estimated that there exist more than 1065 unique sequences of peptides with 50 residues or fewer, which represent a big challenge for the discovery of new promising sequences and the identification of common features, motifs, or relevant biological functions shared by these peptides. Therefore, we present a new approach based on network science and similarity searching to discover new potential AMPs, specifically antiparasitic peptides (APPs). We have taken advantage of network-based representation of APPs’ chemical space (APPCS) to retrieve valuable information, using three types of networks: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify the most important and non-redundant nodes, and these peptides were taken as queries (Qs) against the graph database starPepDB to discover new potential APPs with similarity searching by group fusion (MAX-SIM rule) models. We evaluated the multi-query similarity searching models (mQSSMs) performance with five benchmarking data sets of APP/non-APPs. It can be stated that the predictions performed by the best mQSSMs present a strong-to-very strong predictive agreement since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding outcomes were attained by the mQSSM with 219 Qs from both networks CSN and HSPN (219Q_0.5_HB-HC-Singletons_CSN-HSPN) and by using 0.5 as similarity threshold, with MCC values greater than 0.85 in external datasets. Then, we compared the performance metrics of our mQSSMs with APPs prediction servers AMPDiscover and AMPFun. The model proposed in this report outperformed the machine learning approaches with statistically significant differences, showing the enormous potential of this method. After applying our method and additional filters, we proposed 95 repurposed leads as potential APPs, which have not been associated with this activity until now. In addition, we explored sequence similarities and motifs shared by these peptides, which can serve as templates for searching and designing new promising APPs. The analyses show that the similarity models proposed in this study could contribute to identifying APPs with high effectivity and reliability. Our models and pipeline are freely available through the starPep toolbox software at http://mobiosd-hub.com/starpep.

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.