Statistical analysis of disease‐causing and neutral mutations in human membrane proteins

Mutations in transmembrane proteins (TMPs) have diverse effects on their structure and functions, which may lead to various diseases. In this present study, we have investigated variations in human membrane proteins and found that negatively charged to positively charged/polar and nonpolar to nonpol...

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Published inProteins, structure, function, and bioinformatics Vol. 87; no. 6; pp. 452 - 466
Main Authors Kulandaisamy, A., Priya, S. Binny, Sakthivel, R., Frishman, Dmitrij, Gromiha, M. Michael
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 01.06.2019
Wiley Subscription Services, Inc
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Summary:Mutations in transmembrane proteins (TMPs) have diverse effects on their structure and functions, which may lead to various diseases. In this present study, we have investigated variations in human membrane proteins and found that negatively charged to positively charged/polar and nonpolar to nonpolar changes are dominant in disease‐causing and neutral mutations, respectively. Further, we analyzed the top 10 preferred mutations in 14 different disease classes and found that each class has at least two Arg mutations. Moreover, in cardiovascular diseases and congenital disorders of metabolism, Cys mutations occur more frequently in single‐pass proteins, whereas Arg and nonpolar residues are more frequently substituted in multi‐pass membrane proteins. The immune system diseases are enriched in C → R and C → Y mutations in inside and outside regions. On the other hand, in the membrane region, E → K and R → Q mutations are prevalent. The comparison of mutations in topologically similar regions of globular and membrane proteins showed that Ser and Thr mutations cause deleterious effects in membrane regions, whereas Cys and charged residues, Asp and Arg are prevalent in the buried regions of globular proteins. Our comprehensive analysis of disease‐associated mutations in transmembrane proteins will be useful for developing prediction tools.
Bibliography:Funding information
Department of Science and Technology, Ministry of Science and Technology, Grant/Award Number: INT/RUS/RSF/P‐09; Ministry of Human Resource and Development, India; Russian Science Foundation, Grant/Award Number: 16‐44‐02002
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ISSN:0887-3585
1097-0134
DOI:10.1002/prot.25667