Interaction of DisBa01 peptide from Bothrops alternatus venom with BRAF melanoma receptors: Modeling and molecular docking.

Metastatic melanoma is highly aggressive and challenging, often leading to a grim prognosis. Its progression is swift, especially when mutations like BRAFV600E continuously activate pathways vital for cell growth and survival. Although several treatments target this mutation, resistance typically emerges over time.

An Approach for Engineering Peptides for Competitive Inhibition of the SARS-COV-2 Spike Protein.

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent.

Elucidating the molecular mechanisms of essential oils' insecticidal action using a novel cheminformatics protocol.

Essential oils (EOs) are a promising source for novel environmentally safe insecticides. However, the structural diversity of their compounds poses challenges to accurately elucidate their biological mechanisms of action. We present a new chemoinformatics methodology aimed at predicting the impact of essential oil (EO) compounds on the molecular targets of commercial insecticides.

Machine learning models exploring characteristic single-nucleotide signatures in yellow fever virus.

Yellow fever virus (YFV) is the agent of the most severe mosquito-borne disease in the tropics. Recently, Brazil suffered major YFV outbreaks with a high fatality rate affecting areas where the virus has not been reported for decades, consisting of urban areas where a large number of unvaccinated people live. We developed a machine learning framework combining three different algorithms (XGBoost, random forest and regularized logistic regression) to analyze YFV genomic sequences.

VTR: A Web Tool for Identifying Analogous Contacts on Protein Structures and Their Complexes.

Evolutionarily related proteins can present similar structures but very dissimilar sequences. Hence, understanding the role of the inter-residues contacts for the protein structure has been the target of many studies. Contacts comprise non-covalent interactions, which are essential to stabilize macromolecular structures such as proteins.

Thermostabilizing mechanisms of canonical single amino acid substitutions at a GH1 β-glucosidase probed by multiple MD and computational approaches.

β-glucosidases play a pivotal role in second-generation biofuel (2G-biofuel) production. For this application, thermostable enzymes are essential due to the denaturing conditions on the bioreactors. Random amino acid substitutions have originated new thermostable β-glucosidases, but without a clear understanding of their molecular mechanisms.

Prioritizing Virtual Screening with Interpretable Interaction Fingerprints.

Machine learning-based drug discovery success depends on molecular representation. Yet traditional molecular fingerprints omit both the protein and pointers back to structural information that would enable better model interpretability. Therefore, we propose LUNA, a Python 3 toolkit that calculates and encodes protein-ligand interactions into new hashed fingerprints inspired by Extended Connectivity FingerPrint (ECFP): EIFP (Extended Interaction FingerPrint), FIFP (Functional Interaction FingerPrint), and Hybrid Interaction FingerPrint (HIFP).

Protein structural bioinformatics: An overview.

Proteins play a crucial role in organisms in nature. They are able to perform structural, catalytic, transport and defense functions in cells, among others. We understand that a variety of resources do exist to work with protein structural bioinformatics, which perform tasks such as protein modeling, protein docking, protein molecular dynamics, protein interaction, active and binding site prediction and mutation analysis.

GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs.

Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fundamental importance to determine protein function, being a fundamental step in processes such as drug design and discovery.

Computational prediction of potential inhibitors for SARS-COV-2 main protease based on machine learning, docking, MM-PBSA calculations, and metadynamics.

The development of new drugs is a very complex and time-consuming process, and for this reason, researchers have been resorting heavily to drug repurposing techniques as an alternative for the treatment of various diseases. This approach is especially interesting when it comes to emerging diseases with high rates of infection, because the lack of a quickly cure brings many human losses until the mitigation of the epidemic, as is the case of COVID-19. In this work, we combine an in-house developed machine learning strategy with docking, MM-PBSA calculations, and metadynamics to detect potential inhibitors for SARS-COV-2 main protease among FDA approved compounds.

E-Volve: understanding the impact of mutations in SARS-CoV-2 variants spike protein on antibodies and ACE2 affinity through patterns of chemical interactions at protein interfaces.

Background: The SARS-CoV-2 pandemic reverberated, posing health and social hygiene obstacles throughout the globe. Mutant lineages of the virus have concerned scientists because of convergent amino acid alterations, mainly on the viral spike protein. Studies have shown that mutants have diminished activity of neutralizing antibodies and enhanced affinity with its human cell receptor, the ACE2 protein.

GRaSP: a graph-based residue neighborhood strategy to predict binding sites.

Motivation: The discovery of protein-ligand-binding sites is a major step for elucidating protein function and for investigating new functional roles. Detecting protein-ligand-binding sites experimentally is time-consuming and expensive. Thus, a variety of in silico methods to detect and predict binding sites was proposed as they can be scalable, fast and present low cost.

Proteus: An algorithm for proposing stabilizing mutation pairs based on interactions observed in known protein 3D structures.

Background: Protein engineering has many applications for industry, such as the development of new drugs, vaccines, treatment therapies, food, and biofuel production. A common way to engineer a protein is to perform mutations in functionally essential residues to optimize their function. However, the discovery of beneficial mutations for proteins is a complex task, with a time-consuming and high cost for experimental validation.

Glutantβase: a database for improving the rational design of glucose-tolerant β-glucosidases.

Β-glucosidases are key enzymes used in second-generation biofuel production. They act in the last step of the lignocellulose saccharification, converting cellobiose in glucose. However, most of the β-glucosidases are inhibited by high glucose concentrations, which turns it a limiting step for industrial production.

visGReMLIN: graph mining-based detection and visualization of conserved motifs at 3D protein-ligand interface at the atomic level.

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

Conformational flexibility correlates with glucose tolerance for point mutations in β-glucosidases - a computational study.

β-glucosidases (EC 3.2.1.

Molecular Dynamics Gives New Insights into the Glucose Tolerance and Inhibition Mechanisms on β-Glucosidases.

β-Glucosidases are enzymes with high importance for many industrial processes, catalyzing the last and limiting step of the conversion of lignocellulosic material into fermentable sugars for biofuel production. However, β-glucosidases are inhibited by high concentrations of the product (glucose), which limits the biofuel production on an industrial scale. For this reason, the structural mechanisms of tolerance to product inhibition have been the target of several studies.

Introducing Programming Skills for Life Science Students.

The advent of the high-throughput next-generation sequencing produced a large number of biological data. Knowledge discovery from the huge amount of available biological data requires researchers to develop solid skills in biology and computer science. As the majority of the Bioinformatics professionals are either computer science or life sciences graduates, to teach biology skills to computer science students and computational skills to life science students has become usual.

A Computational Method to Propose Mutations in Enzymes Based on Structural Signature Variation (SSV).

With the use of genetic engineering, modified and sometimes more efficient enzymes can be created for different purposes, including industrial applications. However, building modified enzymes depends on several in vitro experiments, which may result in the process being expensive and time-consuming. Therefore, computational approaches could reduce costs and accelerate the discovery of new technological products.

nAPOLI: A Graph-Based Strategy to Detect and Visualize Conserved Protein-Ligand Interactions in Large-Scale.

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 protein-ligand complexes is available in the Protein Data Bank, what led several tools to be proposed as an effort to elucidate protein-ligand interactions.

Familial germline mutation defines a new human cohesinopathy.

We characterize a novel human cohesinopathy originated from a familial germline mutation of the gene encoding the cohesin subunit STAG2, which we propose to call -related X-linked Intellectual Deficiency. Five individuals carry a p.Ser327Asn (c.