Protein allosteric site identification using machine learning and per amino acid residue reported internal protein nanoenvironment descriptors.

Allosteric regulation plays a crucial role in modulating protein functions and represents a promising strategy in drug development, offering enhanced specificity and reduced toxicity compared to traditional active site inhibition. Existing computational methods for predicting allosteric sites on proteins often rely on static protein surface pocket features, normal mode analysis or extensive molecular dynamics simulations encompassing both the protein function modulator and the protein itself. In this study, we introduce an innovative methodology that employs a per amino acid residue classifier to distinguish allosteric site-forming residues (AFRs) from non-allosteric, or free residues (FRs).

A comparison between internal protein nanoenvironments of α-helices and β-sheets.

Secondary structure elements are generally found in almost all protein structures revealed so far. In general, there are more β-sheets than α helices found inside the protein structures. For example, considering the PDB, DSSP and Stride definitions for secondary structure elements and by using the consensus among those, we found 60,727 helices in 4,376 chains identified in all-α structures and 129,440 helices in 7,898 chains identified in all-α and α + β structures.

Study of specific nanoenvironments containing α-helices in all-α and (α+β)+(α/β) proteins.

Protein secondary structure elements (PSSEs) such as α-helices, β-strands, and turns are the primary building blocks of the tertiary protein structure. Our primary interest here is to reveal the characteristics of the nanoenvironment formed by both PSSEs and their surrounding amino acid residues (AARs), which might contribute to the general understanding of how proteins fold. The characteristics of such nanoenvironments must be specific to each secondary structure element, and we have set our goal here to gather the fullest possible description of the α-helical nanoenvironment.

Citrus bioflavonoid, hesperetin, as inhibitor of two thrombin-like snake venom serine proteases isolated from Crotalus simus.

Around 5.5 million people suffer from snakebites per year, with about 400,000 cases with some type of sequelae, such as amputation, and 20,000 to 125,000 cases with the fatal end. Usually, the victim outcome depends on correct, agile and many times in situ intervention based on the proper identification of the snake venom type and its potential effects, among other factors.

Discovery of small molecule inhibitors for the snake venom metalloprotease BaP1 using in silico and in vitro tests.

Snakebites represent an important public health problem, with a great number of victims with permanent sequelae or fatal outcomes, particularly in rural, agriculturally active areas. The snake venom metalloproteases (SVMPs) are the principal proteins responsible for some clinically-relevant effects, such as local and systemic hemorrhage, dermonecrosis, and myonecrosis. Because of the difficulties in neutralizing them rapidly and locally by antivenoms, the search and design of small molecules as inhibitors of SVMPs are proposed.

Multiple structure single parameter: analysis of a single protein nano environment descriptor characterizing a shared loci on structurally aligned proteins.

Motivation: A graphical representation of physicochemical and structural descriptors attributed to amino acid residues occupying the same topological position in different, structurally aligned proteins can provide a more intuitive way to associate possible functional implications to identified variations in structural characteristics. This could be achieved by observing selected characteristics of amino acids and of their corresponding nano environments, described by the numerical value of matching descriptor. For this purpose, a web-based tool called multiple structure single parameter (MSSP) was developed and here presented.

Computational Biology Tools for Identifying Specific Ligand Binding Residues for Novel Agrochemical and Drug Design.

The term "agrochemicals" is used in its generic form to represent a spectrum of pesticides, such as insecticides, fungicides or bactericides. They contain active components designed for optimized pest management and control, therefore allowing for economically sound and labor efficient agricultural production. A "drug" on the other side is a term that is used for compounds designed for controlling human diseases.

Predicting enzyme class from protein structure using Bayesian classification.

Predicting enzyme class from protein structure parameters is a challenging problem in protein analysis. We developed a method to predict enzyme class that combines the strengths of statistical and data-mining methods. This method has a strong mathematical foundation and is simple to implement, achieving an accuracy of 45%.

The Diamond STING server.

Diamond STING is a new version of the STING suite of programs for a comprehensive analysis of a relationship between protein sequence, structure, function and stability. We have added a number of new functionalities by both providing more structure parameters to the STING Database and by improving/expanding the interface for enhanced data handling. The integration among the STING components has also been improved.