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Comparative analysis of web-based programs for single amino acid substitutions in proteins

Arunabh ChoudhuryDepartment of Computer Science, Jamia Millia Islamia, Jamia Nagar, New Delhi, INDIATaj MohammadCentre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi, INDIAFarah AnjumDepartment of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi ArabiaAlaa ShafieDepartment of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi ArabiaIndrakant K. SinghMolecular Biology Research Lab, Department of Zoology, Deshbandhu College, University of Delhi, Kalkaji, New Delhi, IndiaBekhzod AbdullaevScientific Department, Akfa University, Tashkent, UzbekistanVisweswara Rao PasupuletiCentre for International Collaboration and Research, Reva University, Rukmini Knowledge Park, Kattigenahalli, Yelahanka, Bangalore, Karnataka, IndiaMohd AdnanDepartment of Biology, College of Science, University of Hail, Hail, Saudi ArabiaDharmendra Kumar YadavCollege of Pharmacy, Gachon University of Medicine and Science, Yeonsu-gu, Incheon City, KoreaMd. Imtaiyaz HassanCentre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi, INDIA
PLoS ONEjournal2022en
ABI

Аннотация

Single amino-acid substitution in a protein affects its structure and function. These changes are the primary reasons for the advent of many complex diseases. Analyzing single point mutations in a protein is crucial to see their impact and to understand the disease mechanism. This has given many biophysical resources, including databases and web-based tools to explore the effects of mutations on the structure and function of human proteins. For a given mutation, each tool provides a score-based outcomes which indicate deleterious probability. In recent years, developments in existing programs and the introduction of new prediction algorithms have transformed the state-of-the-art protein mutation analysis. In this study, we have performed a systematic study of the most commonly used mutational analysis programs (10 sequence-based and 5 structure-based) to compare their prediction efficiency. We have carried out extensive mutational analyses using these tools for previously known pathogenic single point mutations of five different proteins. These analyses suggested that sequence-based tools, PolyPhen2, PROVEAN, and PMut, and structure-based web tool, mCSM have a better prediction accuracy. This study indicates that the employment of more than one program based on different approaches should significantly improve the prediction power of the available methods.

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