MmCSM-NA: Accurately predicting effects of single and multiple mutations on protein-nucleic acid binding affinity

While protein-nucleic acid interactions are pivotal for many crucial biological processes, limited experimental data has made the development of computational approaches to characterise these interactions a challenge. Consequently, most approaches to understand the effects of missense mutations on p...

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Bibliographic Details
Main Authors:	Ascher, D.B (Author), De Sá, A.G.C (Author), Myung, Y. (Author), Nguyen, T.B (Author), Pires, D.E.V (Author)
Format:	Article
Language:	English
Published:	Oxford University Press 2021
Subjects:	adaboost Article artificial neural network binding affinity classification comparative study controlled study correlation coefficient cross validation DNA protein complex double stranded DNA double stranded RNA equilibrium constant extreme gradient boosting extremely randomized tree feature selection gradient boosting k nearest neighbor kernel method missense mutation multilayer perceptron nucleic acid physical chemistry point mutation prediction protein conformation protein DNA interaction protein nucleic acid interaction protein RNA binding protein structure random forest regression analysis revertant single stranded DNA single stranded RNA supervised machine learning support vector machine
Online Access:	View Fulltext in Publisher

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View Fulltext in Publisher

MmCSM-NA: Accurately predicting effects of single and multiple mutations on protein-nucleic acid binding affinity

Internet

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