Ensemble Architecture for Prediction of Enzyme‐ligand Binding Residues Using Evolutionary Information

Abstract

Enzyme interactions with ligands are crucial for various biochemical reactions governing life. Over many years attempts to identify these residues for biotechnological manipulations have been made using experimental and computational techniques. The computational approaches have gathered impetus with the accruing availability of sequence and structure information, broadly classified into template-based and de novo methods. One of the predominant de novo methods using sequence information involves application of biological properties for supervised machine learning. Here, we propose a support vector machines-based ensemble for prediction of protein-ligand interacting residues using one of the most important discriminative contributing properties in the interacting residue neighbourhood, i. e., evolutionary information in the form of position-specific- scoring matrix (PSSM). The study has been performed on a non-redundant dataset comprising of 9269 interacting and 91773 non-interacting residues for prediction model generation and further evaluation. Of the various PSSM-based models explored, the proposed method named ROBBY (pRediction Of Biologically relevant small molecule Binding residues on enzYmes) shows an accuracy of 84.0 %, Matthews Correlation Coefficient of 0.343 and F-measure of 39.0 % on 78 test enzymes. Further, scope of adding domain knowledge such as pocket information has also been investigated; results showed significant enhancement in method precision. Findings are hoped to boost the reliability of small-molecule ligand interaction prediction for enzyme applications and drug design.

Exploring Impact of Protein Sequence Local Information to Predict Enzyme-Ligand Binding Residues Using Machine Learning

“…In this study, we considered the biochemically diverse enzymes which interact with small molecules such as drugs and drug-like molecules. We selected the non-redundant dataset investigated and studied by [17]. It contains 311 protein chains, 8682 of which are interacting residues, and 87430 are non-interacting.…”

“…There are 17 enzymes in Dtestset17 testing dataset, with 587 interacting residues and 4343 non-interacting residues. Work discussed [17] outlines the process for creating detailed non-redundant datasets.…”

“…Researchers have made continuous effort to analyze and predict protein-ligand complexes using evolutionary information, physicochemical properties or properties related to structural information. Some of them are enzyme specific, or ligand specific approaches, but still precise prediction is challenging due to multiple factors [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Developing a suitable predictor needs meticulous understanding of numerous fundamental assumptions with respect to varied functional architecture for their usage [18].…”

Binding Site Detection Remastered: Enabling Fast, Robust, and Reliable Binding Site Detection and Descriptor Calculation with DoGSite3

“…Sequence-based methods are based on the assumption that residues belonging to a ligand binding site are conserved in the evolution of a protein family as they are indispensable for the protein’s function. ROBBY employs features derived from the protein sequence. Through a multiple sequence alignment performed by PSI-BLAST, evolutionary information is retrieved from the LigASite database.…”

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

“…A variety of protein functions can be attributed to or regulated by these interactions [1]. Understanding how protein-ligand interactions take place has been the goal of many research studies [2][3][4][5], as molecular recognition is *Correspondence: sabrina@ufv.br 1 Department of Computer Science, Universidade Federal de Viçosa, 36570-900 Viçosa, Brazil 8 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), CB10 1SD Hinxton, UK Full list of author information is available at the end of the article pivotal in biological processes, including signal transduction, catalysis and the regulation of biological function, to name a few examples.…”