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PreprintCited by 2,598Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

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Fast and accurate protein structure search with Foldseek

Open access
van Kempen, Michel, Kim, Stephanie S., Tumescheit, Charlotte, Mirdita, Milot, Lee, Jeongjae, Gilchrist, Cameron L.M., Söding, Johannes, Steinegger, Martin·2022·Nature Biotechnology
DOI 10.1101/2022.02.07.479398PMC PMC10869269

Preprint — published as 10.1038/s41587-023-01773-0 · Paper page

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Abstract

As structure prediction methods are generating millions of publicly available protein structures, searching these databases is becoming a bottleneck. Foldseek aligns the structure of a query protein against a database by describing the amino acid backbone of proteins as sequences over a structural alphabet. Foldseek decreases computation times by four to five orders of magnitude with 86%, 88% and 133% of the sensitivities of DALI, TM-align and CE, respectively.

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Fig. 1 Foldseek workflow. a, Foldseek searches a set of query structures through a set of target structures. (1) Query and target structures are discretized into 3Di sequences (see b). To detect candidate structures, we apply the fast and sensitive k-mer and ungapped alignment prefilter of MMseqs2 to the 3Di sequences, (2) followed by vectorized Smith–Waterman local alignment combining 3Di and amino acid substitution scores. Alternatively, a global alignment is computed with a 1.7-times accelerated TM-align version (Supplementary Fig. 12). b, Learning the 3Di alphabet. (1) 3Di states describe tertiary interaction between a residue i and its nearest neighbor j. Nearest neighbors have the closest virtual center distance (yellow). Virtual center positions (Supplementary Fig. 1) were optimized for maximum search sensitivity. (2) To describe the interaction geometry of residues i and j, we extract seven angles, the Euclidean Cα distance and two sequence distance features from the six Cα coordinates of the two backbone fragments (blue and red). (3) These 10 features are used to define 20 3Di states by training a VQ-VAE28 modified to learn states that are maximally evolutionary conserved. For structure searches, the encoder predicts the best-matching 3Di state for each residue.
Fig. 1 Foldseek workflow. a, Foldseek searches a set of query structures through a set of target structures. (1) Query and target structures are discretized into 3Di sequences (see b). To detect candidate structures, we apply the fast and sensitive k-mer and ungapped alignment prefilter of MMseqs2 to the 3Di sequences, (2) followed by vectorized Smith–Waterman local alignment combining 3Di and amino acid substitution scores. Alternatively, a global alignment is computed with a 1.7-times accelerated TM-align version (Supplementary Fig. 12). b, Learning the 3Di alphabet. (1) 3Di states describe tertiary interaction between a residue i and its nearest neighbor j. Nearest neighbors have the closest virtual center distance (yellow). Virtual center positions (Supplementary Fig. 1) were optimized for maximum search sensitivity. (2) To describe the interaction geometry of residues i and j, we extract seven angles, the Euclidean Cα distance and two sequence distance features from the six Cα coordinates of the two backbone fragments (blue and red). (3) These 10 features are used to define 20 3Di states by training a VQ-VAE28 modified to learn states that are maximally evolutionary conserved. For structure searches, the encoder predicts the best-matching 3Di state for each residue.CC BY · PubMed Central
Fig. 2 Foldseek reaches similar sensitivities as structural aligners at thousands of times their speed. a, Cumulative distributions of sensitivity for homology detection on the SCOPe40 database of single-domain structures. TPs are matches within the same superfamily; FPs are matches between different folds. Sensitivity is the area under the ROC (AUROC) curve up to the first FP (see Supplementary Fig. 4 for family and fold). b, Precision-recall curve of SCOPe40 superfamilies (see Supplementary Fig. 4 for family and fold). c, Average sensitivity up to the first FP for family, superfamily and fold versus total runtime on an AMD EPYC 7702P 64-core CPU for the all-versus-all searches of 11,211 structures of SCOPe40. d, Search sensitivity on multi-domain, full-length AlphaFold2 protein models. One hundred queries, randomly selected from AlphaFoldDB (version 1), were searched against this database. Per-residue query coverage (y axis) is the fraction of residues covered by at least x (x axis) TP matches ranked before the first FP match. e, Alignment quality for alignments of AlphaFoldDB (version 1) protein models (top panel), averaged over the top five matches of each of the 100 queries. Sensitivity = TP residues in alignment / query length; precision = TP residues / alignment length. Reference-based alignment quality benchmark on HOMSTRAD alignments. f, Alignment quality comparison between Foldseek and Dali for each HOMSTRAD family. The F1 score is the harmonic mean between sensitivity and precision.
Fig. 2 Foldseek reaches similar sensitivities as structural aligners at thousands of times their speed. a, Cumulative distributions of sensitivity for homology detection on the SCOPe40 database of single-domain structures. TPs are matches within the same superfamily; FPs are matches between different folds. Sensitivity is the area under the ROC (AUROC) curve up to the first FP (see Supplementary Fig. 4 for family and fold). b, Precision-recall curve of SCOPe40 superfamilies (see Supplementary Fig. 4 for family and fold). c, Average sensitivity up to the first FP for family, superfamily and fold versus total runtime on an AMD EPYC 7702P 64-core CPU for the all-versus-all searches of 11,211 structures of SCOPe40. d, Search sensitivity on multi-domain, full-length AlphaFold2 protein models. One hundred queries, randomly selected from AlphaFoldDB (version 1), were searched against this database. Per-residue query coverage (y axis) is the fraction of residues covered by at least x (x axis) TP matches ranked before the first FP match. e, Alignment quality for alignments of AlphaFoldDB (version 1) protein models (top panel), averaged over the top five matches of each of the 100 queries. Sensitivity = TP residues in alignment / query length; precision = TP residues / alignment length. Reference-based alignment quality benchmark on HOMSTRAD alignments. f, Alignment quality comparison between Foldseek and Dali for each HOMSTRAD family. The F1 score is the harmonic mean between sensitivity and precision.CC BY · PubMed Central

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Research works printed but not linked: 16

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Metadata

Authors
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M.
Affiliations
  • Max Planck Institute for Multidisciplinary Sciences (DE)
  • Seoul National University (KR)
Corresponding author
  • Söding, Johannes
  • Steinegger, Martin
ORCID
Venue
Nature Biotechnology
Publisher
openRxiv
Published
2022-02-09
Volume
42
Issue
2
Pages
243-246
ISSN
0733-222X (ISSN-L)
DOI
10.1101/2022.02.07.479398
PMC
PMC10869269
OpenAlex
W4375858802
Cited by
2,598
References
48
Citations by year
2026: 830 · 2025: 917 · 2024: 592 · 2023: 211 · 2022: 44
FWCI
226.70
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Nature Biotechnology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
  • GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
  • Seoul National University Open Repository (Seoul National University) (submittedVersion)
Funders
  • National Research Foundation
  • Bundesministerium für Bildung und Forschung
  • Seoul National University
  • National Research Foundation of Korea (2021-R1C1-C102065, 2019R1A6A1A10073437, 2020M3A9G7103933, 2021R1C1C102065, 2021M3A9I4021220, 2021-M3A9-I4021220, 2020M3‐A9G7‐103933, 2019R1A6A1, 2019R1-A6A1-A10073437)
  • Max-Planck-Gesellschaft
  • Samsung
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 structure search, Foldseek, structural alignment, structural alphabet, protein structure database, computational efficiency
MeSH terms
Algorithms, Amino Acids, Proteins, Software, Databases, Protein

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