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.

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.

Improving predictions of protein-protein interfaces by combining amino acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages.

Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR).

Analysis of binding properties and specificity through identification of the interface forming residues (IFR) for serine proteases in silico docked to different inhibitors.

Background: Enzymes belonging to the same super family of proteins in general operate on variety of substrates and are inhibited by wide selection of inhibitors. In this work our main objective was to expand the scope of studies that consider only the catalytic and binding pocket amino acids while analyzing enzyme specificity and instead, include a wider category which we have named the Interface Forming Residues (IFR). We were motivated to identify those amino acids with decreased accessibility to solvent after docking of different types of inhibitors to sub classes of serine proteases and then create a table (matrix) of all amino acid positions at the interface as well as their respective occupancies.

PDB-Metrics: a web tool for exploring the PDB contents.

PDB-Metrics (http://sms.cbi.cnptia.

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%.

Building multiple sequence alignments with a flavor of HSSP alignments.

Homology-derived secondary structure of proteins (HSSP) is a well-known database of multiple sequence alignments (MSAs) which merges information of protein sequences and their three-dimensional structures. It is available for all proteins whose structure is deposited in the PDB. It is also used by STING and (Java)Protein Dossier to calculate and present relative entropy as a measure of the degree of conservation for each residue of proteins whose structure has been solved and deposited in the PDB.