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- W4295063241 abstract "Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics." @default.
- W4295063241 created "2022-09-10" @default.
- W4295063241 creator A5003649929 @default.
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- W4295063241 creator A5060425876 @default.
- W4295063241 creator A5073630563 @default.
- W4295063241 creator A5090457657 @default.
- W4295063241 date "2022-09-06" @default.
- W4295063241 modified "2023-10-14" @default.
- W4295063241 title "Protein Function Analysis through Machine Learning" @default.
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