Deep learning methods for 3D structural proteome and interactome modeling |
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| Institution: | 1. Department of Science and Technology, University of Sannio, via Francesco de Sanctis snc, Benevento 82100, Italy;2. Institute of Biostructures and Bioimaging, CNR, Via Mezzocannone 16, I-80134 Napoli, Italy;1. School of Computer Science and Technology, Soochow University, Suzhou 215006, China;2. School of Computer and Information, and the University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246011, China;3. Advanced Analytics Institute, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia;4. Jiangsu Provincial Key Laboratory for Information Processing Technologies, Soochow University, Suzhou 215006, China |
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| Abstract: | Bolstered by recent methodological and hardware advances, deep learning has increasingly been applied to biological problems and structural proteomics. Such approaches have achieved remarkable improvements over traditional machine learning methods in tasks ranging from protein contact map prediction to protein folding, prediction of protein–protein interaction interfaces, and characterization of protein–drug binding pockets. In particular, emergence of ab initio protein structure prediction methods including AlphaFold2 has revolutionized protein structural modeling. From a protein function perspective, numerous deep learning methods have facilitated deconvolution of the exact amino acid residues and protein surface regions responsible for binding other proteins or small molecule drugs. In this review, we provide a comprehensive overview of recent deep learning methods applied in structural proteomics. |
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