期刊
NUCLEIC ACIDS RESEARCH
卷 50, 期 W1, 页码 W392-W397出版社
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkac323
关键词
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资金
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (CAPES) [23038.004007/2014-82, 051/2013]
- Conselho Nacional de Desenvolvimento Cient'ifico e Tecnologico (CNPq)
- Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG)
- European Bioinformatics Institute
- National Institutes of Health [AA123456, BB123456]
- Alcohol & Education Research Council [abcde123456]
- National Institutes of Health
Proteins are important macromolecules for living systems, often interacting in binding sites with other molecules. GRaSP-web is a graph-based method using machine learning to predict putative ligand binding site residues, outperforming other state-of-the-art methods with a MCC of 0.61. It is scalable and quick, making consistent predictions for bound/unbound structures and a large dataset of proteins.
Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fundamental importance to determine protein function, being a fundamental step in processes such as drug design and discovery. However, identifying such binding regions is not trivial due to the drawbacks of experimental methods, which are costly and time-consuming. Here we propose GRaSP-web, a web server that uses GRaSP (Graph-based Residue neighborhood Strategy to Predict binding sites), a residue-centric method based on graphs that uses machine learning to predict putative ligand binding site residues. The method outperformed 6 state-of-the-art residue-centric methods (MCC of 0.61). Also, GRaSP-web is scalable as it takes 10-20 seconds to predict binding sites for a protein complex (the state-of-the-art residue-centric method takes 2-5h on the average). It proved to be consistent in predicting binding sites for bound/unbound structures (MCC 0.61 for both) and for a large dataset of multi-chain proteins (4500 entries, MCC 0.61). GRaSPWeb is freely available at https://grasp.ufv.br .
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