ArticlePLoS computational biology2018
Coevolving residues inform protein dynamics profiles and disease susceptibility of nSNVs.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Long-range coupling regulates stator dynamics in the bacterial flagellar motor.bioRxiv : the preprint server for biology · 2025Article
- Dynamics-based protein network features accurately discriminate neutral and rheostat positions.Biophysical journal · 2024Article
- Dissecting Allosteric Mutations for Antibiotic Resistance by Time-Dependent Linear Response Theory.Journal of chemical theory and computation · 2024Article
- Evidence that the cold- and menthol-sensing functions of the human TRPM8 channel evolved separately.Science advances · 2024Article
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- Engineering gain-of-function mutants of a WW domain by dynamics and structural analysis.Protein science : a publication of the Protein Society · 2023Article
- Allosteric regulatory control in dihydrofolate reductase is revealed by dynamic asymmetry.Protein science : a publication of the Protein Society · 2023Article
- Protein dynamics provide mechanistic insights about epistasis among common missense polymorphisms.Biophysical journal · 2023Article
- Dynamic coupling of residues within proteins as a mechanistic foundation of many enigmatic pathogenic missense variants.PLoS computational biology · 2022Article
- Clinical pharmacogenomics in action: design, assessment and implementation of a novel pharmacogenetic panel supporting drug selection for diseases of the central nervous system (CNS).Journal of translational medicine · 2021Article
- Hinge-shift mechanism as a protein design principle for the evolution of β-lactamases from substrate promiscuity to specificity.Nature communications · 2021Article
- Not Enough Natural Data? Sequence and Ye Shall Find.Frontiers in molecular biosciences · 2020Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
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.
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