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PreprintCited by 25Protein Structure and DynamicsGenomics and Rare DiseasesEnzyme Structure and Function

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Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues

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Sen, Neeladri, Anishchenko, Ivan, Bordin, Nicola, Sillitoe, Ian, Velankar, Sameer, Baker, David, Orengo, Christine·2021·Briefings in Bioinformatics
DOI 10.1101/2021.11.17.468998PMC PMC9294430

Preprint — published as 10.1093/bib/bbac187 · Paper page

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Abstract

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologues. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologues in the Protein Databank (PDB). We noticed that the model quality was higher and the RMSD lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces, conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, if they destabilized the protein structure based on ddG calculations or if they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms a larger percentage of disease associated missense mutations were buried, closer to predicted functional sites, predicted as destabilising and/or pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

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Metadata

Authors
Sen, N., Anishchenko, I., Bordin, N., Sillitoe, I., Velankar, S., Baker, D., & Orengo, C.
Affiliations
  • Institute of Structural and Molecular Biology (GB)
  • University of Washington (US)
  • European Bioinformatics Institute (GB)
  • Howard Hughes Medical Institute (US)
Corresponding author
  • Velankar, Sameer
  • Baker, David
  • Orengo, Christine
ORCID
Venue
Briefings in Bioinformatics
Publisher
openRxiv
Published
2021-11-19
Volume
23
Issue
4
ISSN
1467-5463 (ISSN-L)
DOI
10.1101/2021.11.17.468998
PMC
PMC9294430
OpenAlex
W4225516213
Cited by
25
References
1
Citations by year
2026: 2 · 2025: 6 · 2024: 3 · 2023: 8 · 2022: 6
FWCI
1.88
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Briefings in Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • Padua Research Archive (University of Padova) (submittedVersion, cc-by)
Funders
  • National Science Foundation (1937533)
  • Directorate for Biological Sciences
  • Biotechnology and Biological Sciences Research Council (BB/R014892/1, BB/R009597/1, BB/S017135/1, BB/S016007/1, BB/S020144/1)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Keywords
disease-associated mutations, missense mutations, protein structure prediction, AlphaFold, RoseTTAFold, Pfam domains, ligand binding sites, protein-protein interfaces, conserved residues, protein stability, DDG, mutation pathogenicity, polymorphism
MeSH terms
Humans, Models, Molecular, Mutation, Proteins, Mutation, Missense, Databases, Protein

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