1. Coevolving residues inform protein dynamics profiles and disease susceptibility of nSNVs.
- Author
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Butler BM, Kazan IC, Kumar A, and Ozkan SB
- Subjects
- Animals, Computer Simulation, Cytochrome Reductases chemistry, Genomics, Humans, Imaging, Three-Dimensional, Molecular Conformation, Muramidase chemistry, Normal Distribution, Phenotype, Protein Conformation, ROC Curve, Rats, Acyl-CoA Dehydrogenase chemistry, Computational Biology methods, Disease Susceptibility, Neurons metabolism, Protein Isoforms, Proteins chemistry
- Abstract
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., Competing Interests: The authors have declared that no competing interests exist.
- Published
- 2018
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