Disease-associated mutations disrupt functionally important regions of intrinsic protein disorder.
The effects of disease mutations on protein structure and function have been extensively investigated, and many predictors of the functional impact of single amino acid substitutions are publicly available. The majority of these predictors are based on protein structure and evolutionary conservation, following the assumption that disease mutations predominantly affect folded and conserved protein regions. However, the prevalence of the intrinsically disordered proteins (IDPs) and regions (IDRs) in the human proteome together with their lack of fixed structure and low sequence conservation raise a question about the impact of disease mutations in IDRs. Here, we investigate annotated missense disease mutations and show that 21.7% of them are located within such intrinsically disordered regions. We further demonstrate that 20% of disease mutations in IDRs cause local disorder-to-order transitions, which represents a 1.7-2.7 fold increase compared to annotated polymorphisms and neutral evolutionary substitutions, respectively. Secondary structure predictions show elevated rates of transition from helices and strands into loops and vice versa in the disease mutations dataset. Disease disorder-to-order mutations also influence predicted molecular recognition features (MoRFs) more often than the control mutations. The repertoire of disorder-to-order transition mutations is limited, with five most frequent mutations (R→W, R→C, E→K, R→H, R→Q) collectively accounting for 44% of all deleterious disorder-to-order transitions. As a proof of concept, we performed accelerated molecular dynamics simulations on a deleterious disorder-to-order transition mutation of tumor protein p63 and, in agreement with our predictions, observed an increased α-helical propensity of the region harboring the mutation. Our findings highlight the importance of mutations in IDRs and refine the traditional structure-centric view of disease mutations. The results of this study offer a new perspective on the role of mutations in disease, with implications for improving predictors of the functional impact of missense mutations.
Department of Computer Science, Columbia University, New York, New York, United States of America.
Vladimir
V
Vacic
Phineus R L
PR
Markwick
Christopher J
CJ
Oldfield
Xiaoyue
X
Zhao
Chad
C
Haynes
Vladimir N
VN
Uversky
Lilia M
LM
Iakoucheva
D001120:Arginine / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology; D004194:Disease / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D056004:Molecular Dynamics Simulation; D009154:Mutation; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism; D017422:Sequence Analysis, DNA; D014157:Transcription Factors; D025521:Tumor Suppressor Proteins
D011506:Proteins; C115051:TP63 protein, human; D014157:Transcription Factors; D025521:Tumor Suppressor Proteins; D001120:Arginine