Coevolving residues inform protein dynamics profiles and disease susceptibility of nSNVs

• Published in: PLoS computational biology Vol. 14; no. 11; p. e1006626
• Main Authors: Butler, Brandon M, Kazan, I Can, Kumar, Avishek, Ozkan, S Banu
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.11.2018; Public Library of Science (PLoS)
• Subjects: Accuracy; Acyl-CoA Dehydrogenase - chemistry; Analysis; Animals; Bioinformatics; Biology and Life Sciences; Computational Biology - methods; Computer Simulation; Crystallography; Cytochrome Reductases - chemistry; Disease Susceptibility; Dynamics; Gaussian processes; Gene mutation; Genomics; Humans; Imaging, Three-Dimensional; Molecular biology; Molecular Conformation; Muramidase - chemistry; Mutation; Neurons - metabolism; Normal Distribution; Nucleotide sequence; Phenotype; Phenotypes; Physical Sciences; Physics; Protein Conformation; Protein Isoforms; Protein research; Proteins; Proteins - chemistry; Public policy; Rats; Research and Analysis Methods; Residues; ROC Curve; Sequences; Single nucleotide polymorphisms; Writing; Taiwan; United States > US; Arizona
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006626

The conformational dynamics of proteins is rarely used in methodologies used to predict the impact of genetic mutations due to the paucity of three-dimensional protein structures as compared to the vast number of available sequences. Until now a three-dimensional (3D) structure has been required to predict the conformational dynamics of a protein. We introduce an approach that estimates the conformational dynamics of a protein, without relying on structural information. This de novo approach utilizes coevolving residues identified from a multiple sequence alignment (MSA) using Potts models. These coevolving residues are used as contacts in a Gaussian network model (GNM) to obtain protein dynamics. B-factors calculated using sequence-based GNM (Seq-GNM) are in agreement with crystallographic B-factors as well as theoretical B-factors from the original GNM that utilizes the 3D structure. Moreover, we demonstrate the ability of the calculated B-factors from the Seq-GNM approach to discriminate genomic variants according to their phenotypes for a wide range of proteins. These results suggest that protein dynamics can be approximated based on sequence information alone, making it possible to assess the phenotypes of nSNVs in cases where a 3D structure is unknown. We hope this work will promote the use of dynamics information in genetic disease prediction at scale by circumventing the need for 3D structures.