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PreprintCited by 16Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

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Mining folded proteomes in the era of accurate structure prediction

Open access
Bayly-Jones, Charles, Whisstock, James·2021·PLoS Computational Biology
DOI 10.1101/2021.08.24.457439PMC PMC8986115

Preprint — published as 10.1371/journal.pcbi.1009930 · Paper page

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Abstract

Protein structure fundamentally underpins the function and processes of numerous biological systems. Fold recognition algorithms offer a sensitive and robust tool to detect structural, and thereby functional, similarities between distantly related homologs. In the era of accurate structure prediction owing to advances in machine learning techniques, previously curated sequence databases have become a rich source of biological information. Here, we use bioinformatic fold recognition algorithms to scan the entire AlphaFold structure database to identify novel protein family members, infer function and group predicted protein structures. As an example of the utility of this approach, we identify novel, previously unknown members of various pore-forming protein families, including MACPFs, GSDMs and aerolysin-like proteins. Further, we explore the use of structure-based mining for functional inference.

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Fig 1 Conceptual overview of structure-guided fold recognition against the AlphaFold database. a. Entire proteome sequence databases are converted by (b) machine-learning methods into high-accuracy (c) structural model predictions. d. Curation of these structural databases into a unified resource of structures (S1 Text). e. DALI based fold matching to perform functional inference, identification of unknown members and structural classification. f. Searching the foldome in (d) for matches of murine GSDM-D N-terminal domain yields MACPF/GSMD family members, including C11orf42 –an unknown member of the GSDM family. g. Example phylogenetic analysis of perforin-like proteins identified from the foldome by DALI. Alignment of the central MACPF/GSDM fold is possible by extracting domain boundaries based on AlphaFold prediction allowing specific comparison between members without interfering ancillary domains.
Fig 1 Conceptual overview of structure-guided fold recognition against the AlphaFold database. a. Entire proteome sequence databases are converted by (b) machine-learning methods into high-accuracy (c) structural model predictions. d. Curation of these structural databases into a unified resource of structures (S1 Text). e. DALI based fold matching to perform functional inference, identification of unknown members and structural classification. f. Searching the foldome in (d) for matches of murine GSDM-D N-terminal domain yields MACPF/GSMD family members, including C11orf42 –an unknown member of the GSDM family. g. Example phylogenetic analysis of perforin-like proteins identified from the foldome by DALI. Alignment of the central MACPF/GSDM fold is possible by extracting domain boundaries based on AlphaFold prediction allowing specific comparison between members without interfering ancillary domains.CC BY · PubMed Central
Fig 2 Identification of uncharacterised human proteins by fold-recognition. a. Uncharacterised human proteins were curated from the AlphaFold database. b. Low-confidence regions of AlphaFold models were excluded based on pLDDT criteria. c. All models were screened against the rest of the human AlphaFold database. d. Four examples (C12orf56, Q8IXR9; C22orf9, Q6ICG6; C11orf16, Q9NQ32; C6orf136, Q5SQH8) of uncharacterised proteins where fold-matching enabled the assignment of domain composition (labelled in various colours). Furthermore, homologs or similar proteins (blue label) provide insight into potential function (black dot points).
Fig 2 Identification of uncharacterised human proteins by fold-recognition. a. Uncharacterised human proteins were curated from the AlphaFold database. b. Low-confidence regions of AlphaFold models were excluded based on pLDDT criteria. c. All models were screened against the rest of the human AlphaFold database. d. Four examples (C12orf56, Q8IXR9; C22orf9, Q6ICG6; C11orf16, Q9NQ32; C6orf136, Q5SQH8) of uncharacterised proteins where fold-matching enabled the assignment of domain composition (labelled in various colours). Furthermore, homologs or similar proteins (blue label) provide insight into potential function (black dot points).CC BY · PubMed Central
Fig 3 Proteome-wide search and classification of Pfam groups. a. Overview of analysis, firstly search models representing pfam entries were curated from either trRosetta or the PDB. These were each searched against the entire S. aureus foldome to identify matches. The final output was filtered by expectation value. b. Select examples of S. aureus enzymes classified into Pfam category PF04481. This Pfam category previously had unknown role or function. c. The uncategorised protein (Q2G056) is homologous to different Pfam groups with similar enzymatic function (PF06259, PF15609, PF19742).
Fig 3 Proteome-wide search and classification of Pfam groups. a. Overview of analysis, firstly search models representing pfam entries were curated from either trRosetta or the PDB. These were each searched against the entire S. aureus foldome to identify matches. The final output was filtered by expectation value. b. Select examples of S. aureus enzymes classified into Pfam category PF04481. This Pfam category previously had unknown role or function. c. The uncategorised protein (Q2G056) is homologous to different Pfam groups with similar enzymatic function (PF06259, PF15609, PF19742).CC BY · PubMed Central

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Citations

Cites

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Metadata

Authors
Bayly-Jones, C., & Whisstock, J.
Affiliations
Monash University (AU)
Corresponding author
Whisstock, James
ORCID
Venue
PLoS Computational Biology
Publisher
openRxiv
Published
2021-08-25
Volume
18
Issue
3
Pages
e1009930-e1009930
ISSN
1553-734X (ISSN-L)
DOI
10.1101/2021.08.24.457439
PMC
PMC8986115
OpenAlex
W4221071868
Cited by
16
References
1
Citations by year
2026: 2 · 2024: 4 · 2023: 4 · 2022: 6
FWCI
1.14
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Australian Research Data Commons
  • Medical Research Council
  • Australian Research Council
  • National Health and Medical Research Council
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
protein fold recognition, AlphaFold, protein structure prediction, pore-forming proteins, protein function prediction, structural similarity
MeSH terms
Machine Learning, Algorithms, Computational Biology, Proteome, Databases, Protein

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