Protein-peptide interactions play a fundamental role in wide variety of biological processes, such as cell signaling, regulatory networks, immune responses, and enzyme inhibition. Peptides are characterized by low toxicity small interface areas; therefore, they good targets for therapeutic strategies, rational drug planning protein Approximately 10% the ethical pharmaceutical market is protein/peptide-based. Furthermore, it estimated that 40% mediated peptides. Despite fast increase volume...

Essential roles in biological systems depend on protein-ligand recognition, which is mostly driven by specific non-covalent interactions. Consequently, investigating these interactions contributes to understanding how molecular recognition occurs. Nowadays, a large-scale data set of complexes available the Protein Data Bank, what led several tools be proposed as an effort elucidate Nonetheless, there not all-in-one tool that couples statistical, visual, and interactive analysis conserved...

Identifying binding sites is crucial for expanding our knowledge of various biological processes, supporting drug discovery, and repositioning strategies, particularly in the early research phases target hopping. This especially important neglected diseases, which predominantly impact vulnerable populations developing regions. To address this, we created BENDER DB, a database designed to map predicted protein within proteomes pathogens associated with diseases. Utilizing AlphaFold-predicted...

The 2019 pandemic of coronavirus disease (COVID-19) caused by SARS-CoV-2 led to millions deaths worldwide since its emergence. viral genomic material can code structural and non-structural proteins including the main protease or 3CLpro, a cysteine that cleavages polyprotein generating 11 participate in pre-replication. Thus, 3CLpro is promising therapeutic target for inhibition new drugs drug repositioning because dissimilar human proteases. We conducted vitro assays demonstrating modulation...

The discovery of protein-ligand-binding sites is a major step for elucidating protein function and investigating new functional roles. Detecting experimentally time-consuming expensive. Thus, variety in silico methods to detect predict binding was proposed as they can be scalable, fast present low cost.We Graph-based Residue neighborhood Strategy Predict (GRaSP), novel residue centric scalable method ligand-binding site residues. It based on supervised learning strategy that models the...

Abstract Proteins are essential macromolecules for the maintenance of living systems. Many them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization these fundamental importance to determine protein function, being a step processes such as drug design discovery. However, identifying is not trivial due drawbacks experimental methods, which costly time-consuming. Here we propose GRaSP-web, web server that uses GRaSP...

The development of new drugs is a very complex and time-consuming process, for this reason, researchers have been resorting heavily to drug repurposing techniques as an alternative the treatment various diseases. This approach especially interesting when it comes emerging diseases with high rates infection, because lack quickly cure brings many human losses until mitigation epidemic, case COVID-19. In work, we combine in-house developed machine learning strategy docking, MM-PBSA...

Metals are present in >30% of proteins found nature and assist them to perform important biological functions, including storage, transport, signal transduction enzymatic activity. Traditional experimental techniques for metal-binding site prediction usually costly time-consuming, making computational tools that can these predictions significant importance. Here we Genetic Active Site Search (GASS)-Metal, a new method protein prediction. The relies on parallel genetic algorithm find...

A huge amount of data about genomes and sequence variation is available continues to grow on a large scale, which makes experimentally characterizing these mutations infeasible regarding disease association effects protein structure function. Therefore, reliable computational approaches are needed support the understanding their impacts. Here, we present VERMONT 2.0, visual interactive platform that combines structural parameters with visualizations make impact point more understandable. We...

In this paper, we propose an interactive visualization called ADVISe (Annotation Dynamics Visualization), which tackles the problem of visualizing evolutions in enzyme annotations across several releases UniProt/SwissProt database. More specifically, visualize dynamics Enzyme Commission numbers (EC numbers), are a numerical and hierarchical classification scheme for enzymes based on chemical reactions they catalyze. An EC number consists four separated by periods represents progressively...

In this paper, we propose an interactive visualization called VERMONT which tackles the problem of visualizing mutations and infers their possible effects on conservation physicochemical topological properties in protein families. More specifically, visualize a set structure-based sequence alignments integrate several structural parameters that should aid biologists gaining insight into consequences mutations. allowed us to identify patterns position-specific as well exceptions may help...

The volume and diversity of biological data are increasing at very high rates. Vast amounts protein sequences structures, genetic interactions phenotype studies have been produced. majority generated by high-throughput devices is automatically annotated because manually annotating them not possible. Thus, efficient precise automatic annotation methods required to ensure the quality reliability both associated annotations. We proposed ENZYMatic Annotation Predictor (ENZYMAP), a technique...

Interactions between proteins and ligands are relevant in many biological processes. In the last years, such interactions have gained even more attention as comprehension of protein-ligand molecular recognition is an important step to ligand prediction, target identificantion, drug design, among others. This article presents GReMLIN (Graph Mining strategy infer protein-Ligand INteraction patterns), a search for conserved set related proteins, based on frequent subgraph mining, that able...

Computing contacts in proteins is important to several types of studies from Bioinformatics Structural Biology. An accurate computation essential the correctness and reliability applications involving folding prediction, protein structure quality assessment structures, network analysis, thermodynamic stability protein-protein protein-ligand interactions, docking so forth. In this work, we built an extensive database using about 45,000 structures PDB compare three paradigms for contact...

Interactions between proteins and non-proteic small molecule ligands play important roles in the biological processes of living systems. Thus, development computational methods to support our understanding ligand-receptor recognition process is fundamental importance since these are a major step towards ligand prediction, target identification, lead discovery, more. This article presents visGReMLIN, web server that couples graph mining-based strategy detect motifs at protein-ligand interface...

Protein-protein interactions (PPIs) are fundamental in many biological processes and understanding these is key for a myriad of applications including drug development, peptide design identification targets. The data deluge demands efficient scalable methods to characterize understand protein-protein interfaces. In this paper, we present ppiGReMLIN, graph based strategy infer interaction patterns set complexes. Our method combines an unsupervised learning with frequent subgraph mining order...

Cell surface receptors play essential roles in perceiving and processing external internal signals at the cell of plants animals. The receptor-like protein kinases (RLK) proteins (RLPs), two major classes with membrane receptor configuration, a crucial role plant development disease defense. Although RLPs RLKs share similar single-pass transmembrane harbor short divergent C-terminal regions instead conserved kinase domain RLKs. This RLP structural design precludes sequence comparison...

Contacts, defined as interand intramolecular interactions predicted computationally, are typically detected using Euclidean distance and atom types. However, traditional methods can be computationally expensive limit scalability. We introduce COCαDA (Contact Optimization by Cα Distance Analysis), a novel method that incorporates domain knowledge of amino acids to optimize cutoffs, simplifying implementation enhancing efficiency. outperforms such all-against-all, static cutoff (SC),...

Background: The last decade was marked by increased neuroscience research involving machine Learning (ML) and medical images such as functional magnetic resonance electroencephalogram (EEG). There are many challenges in this field, including the need for more data a standard presenting results. Since ML models tend to be sensitive input data, different strategies acquisition, preprocessing, feature selection, validation significantly impact results achieved. Therefore, significant variation...

Protein-ligand interaction (PLI) networks show how proteins interact with small non-protein ligands through noncovalent bonding. Understanding such interactions is a crucial step towards ligand prediction, target identification and drug design. We propose CALI (Complex network-based Analysis of protein-Ligand Interactions), graph-based, visual strategy coupled complex network topological properties to summarize detect frequent patterns in PLIs. Patterns obtained were compared experimentally...

Abstract Summary GAPIN is a web-based application for structural interaction network analysis among any type of PDB molecules, regardless whether their interfaces are between chain-chain or chain-ligand. A special emphasis given to graph clustering, allowing users scrutinize target contexts ligand candidates. We show how can be used unveil underlying hydrophobic patterns on set peptidase-inhibitor complexes. In another experiment, we there positive correlation cluster sizes and the presence...