Machine learning approaches for predicting protein-ligand binding sites from sequence data
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,...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Bioinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1520382/full |
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| _version_ | 1832548295557251072 |
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| author | Orhun Vural Leon Jololian |
| author_facet | Orhun Vural Leon Jololian |
| author_sort | Orhun Vural |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d16db4f041a84ef39175fbc34bfd20e4 |
| institution | Kabale University |
| issn | 2673-7647 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj-art-d16db4f041a84ef39175fbc34bfd20e42025-02-03T06:33:29ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472025-02-01510.3389/fbinf.2025.15203821520382Machine learning approaches for predicting protein-ligand binding sites from sequence dataOrhun VuralLeon JololianProteins, 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.https://www.frontiersin.org/articles/10.3389/fbinf.2025.1520382/fullprotein-ligand binding sitescomputational drug discoverysequence-based methodsdeep learningbinding prediction |
| spellingShingle | Orhun Vural Leon Jololian Machine learning approaches for predicting protein-ligand binding sites from sequence data Frontiers in Bioinformatics protein-ligand binding sites computational drug discovery sequence-based methods deep learning binding prediction |
| title | Machine learning approaches for predicting protein-ligand binding sites from sequence data |
| title_full | Machine learning approaches for predicting protein-ligand binding sites from sequence data |
| title_fullStr | Machine learning approaches for predicting protein-ligand binding sites from sequence data |
| title_full_unstemmed | Machine learning approaches for predicting protein-ligand binding sites from sequence data |
| title_short | Machine learning approaches for predicting protein-ligand binding sites from sequence data |
| title_sort | machine learning approaches for predicting protein ligand binding sites from sequence data |
| topic | protein-ligand binding sites computational drug discovery sequence-based methods deep learning binding prediction |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1520382/full |
| work_keys_str_mv | AT orhunvural machinelearningapproachesforpredictingproteinligandbindingsitesfromsequencedata AT leonjololian machinelearningapproachesforpredictingproteinligandbindingsitesfromsequencedata |