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

Abstract

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.

Cyclohexyl-α maltoside as a highly efficient tool for membrane protein studies

“…Membrane proteins (MPs) exert functions fundamental for cell physiology and, when dysregulated, contribute to disease progression ( Cournia et al., 2015 ; Kulandaisamy et al., 2019 ). Although MPs account for 20–30% of the proteins encoded by sequenced genomes ( Almen et al., 2009 ) and constitute 60% of all human drug targets ( Tiefenauer and Demarche, 2012 ), the progress with deciphering high-resolution 3-D structures of MPs is rather slow ( Moraes et al., 2014 ) and hence they remain significantly underrepresented in the RCSB Protein Data Bank ( https://www.rcsb.org/ ) ( Berman et al., 2000 ).…”

Pred‐MutHTP: Prediction of disease‐causing and neutral mutations in human transmembrane proteins

“…The topology information was retrieved from CCTOP (Dobson, Reményi, & Tusnády, ) and TOPCONS (Bernsel, Viklund, Hennerdal, & Elofsson, ) servers. In addition, we used the substitution matrices for whole human membrane proteins as well as additional ones specific to topological regions including the cytosol, TM, and extracellular space from our previous study (Kulandaisamy et al, ).…”

“…Nevertheless, it has recently become apparent that the existing methods are severely limited in their reliability for predicting the effects of variants occurring in membrane proteins (Kulandaisamy, Priya, Sakthivel, Frishman, & Gromiha, 2019). A potential reason for this is that the physiochemical requirements for embedding such polypeptides within the phospholipid bilayer make them significant outliers in the landscape of the entire cellular proteome, which serves as the training set for the universal prediction tools (Almén, Nordström, Fredriksson, & Schiöth, 2009;Gromiha & Ou, 2014).…”

TMSNP: a web server to predict pathogenesis of missense mutations in transmembrane region of membrane proteins

“…Membrane proteins represent 25% of all human proteins (Dobson, et al, 2015;Gromiha and Ou, 2014) and perform essential roles in cellular functions. Approximately 50-60% of TM proteins are drug targets for various diseases (Almeida, et al, 2017;Overington, et al, 2006) and 90% of membrane proteins present disease-associated missense mutations that may affect protein folding, stability and function (Kulandaisamy, et al, 2019). Whole genome and exome sequencing have revealed that missense mutations that are mendelian and rare disease-causing are more frequent than previously thought and collectively affect millions of patients worldwide (Chong, et al, 2015).…”