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MINI REVIEW article

Front. Bioinform.
Sec. Protein Bioinformatics
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1520382
This article is part of the Research Topic Computational protein function prediction based on sequence and/or structural data View all 4 articles

MACHINE LEARNING APPROACHES FOR PROTEIN-LIGAND BINDING SITES PREDICTION FROM SEQUENCES DATA

Provisionally accepted
Orhun Vural Orhun Vural *Leon Jololian Leon Jololian
  • University of Alabama at Birmingham, Birmingham, United States

The final, formatted version of the article will be published soon.

    Proteins, composed of amino acids, are crucial for a wide range of biological functions. Proteins have various interaction sites, one of which is the protein-ligand binding site, essential for molecular interactions and biochemical reactions. These sites enable proteins to bind with other molecules, facilitating key biological functions. Accurate prediction of these binding sites is pivotal in computational drug discovery, helping to identify therapeutic targets and facilitate treatment development. Machine learning has made significant contributions to this field by improving the prediction of protein-ligand interactions. This paper reviews studies that use machine learning to predict protein-ligand binding sites from sequence data, focusing on recent advancements. The review examines various embedding methods and machine learning architectures, addressing current challenges and the ongoing debates in the field. Additionally, research gaps in the existing literature are highlighted, and potential future directions for advancing the field are discussed. This study provides a thorough overview of sequence-based approaches for predicting protein-ligand binding sites, offering insights into the current state of research and future possibilities.

    Keywords: Protein-ligand binding sites, Computational Drug Discovery, sequence-based protein, deep learning, Binding prediction

    Received: 31 Oct 2024; Accepted: 10 Jan 2025.

    Copyright: © 2025 Vural and Jololian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Orhun Vural, University of Alabama at Birmingham, Birmingham, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.