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AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms
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Abstract
Abstract Deep-learning (DL) methods like DeepMind’s AlphaFold2 (AF2) have led to substantial improvements in protein structure prediction. We analyse confident AF2 models from 21 model organisms using a new classification protocol (CATH-Assign) which exploits novel DL methods for structural comparison and classification. Of ~370,000 confident models, 92% can be assigned to 3253 superfamilies in our CATH domain superfamily classification. The remaining cluster into 2367 putative novel superfamilies. Detailed manual analysis on 618 of these, having at least one human relative, reveal extremely remote homologies and further unusual features. Only 25 novel superfamilies could be confirmed. Although most models map to existing superfamilies, AF2 domains expand CATH by 67% and increases the number of unique ‘global’ folds by 36% and will provide valuable insights on structure function relationships. CATH-Assign will harness the huge expansion in structural data provided by DeepMind to rationalise evolutionary changes driving functional divergence.
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![Fig. 6 Functional diversity between protein families revealed using Alphafold2: The HUP superfamily as an example. Members of HUP superfamily (CATH ID:3.40.50.620) possess a common structural core comprising a Rossmann αβα-sandwich fold. Several functional families within the HUP superfamily lacking a representative PDB structure now possess a representative domain from Alphafold2 enabling characterisation of putative functional sites in their associated functional families. For example, the Phosphoadenosine phosphosulfate reductase-like protein family (PPR) (CATH FunFam ID: FunFam-348; EC: 1.8.4.8) has a high-quality AF2 domain available for the poorly studied PPR protein from Leishmania infantum (af_A4I3B1_2_215; pLDDT: 94.87). This protein has no close homologue in the PDB (>30% sequence identity). We compared the AF2 model with the representative PDB structure (1zunA) from its closest Functional Family in CATH i.e., Sulphate adenylyltransferase family (SAT) [FunFam-02, representative PDB:1zunA; EC:2.7.7.4]. a Superposition of structure representatives from FunFam-348-PPR and FunFam-02-SAT. Residues conserved in both families are coloured green, FunFam-specific residues, blue (af_A4I3B1_2_215, for FunFam-348-PPR) and red (1zunA, for FunFam-02-SAT). b FunFam-2-SAT is involved in ATP hydrolysis, an essential process for its function. Analysis of conserved residues using Scorecons indicated that most active site residues (shown in red) are conserved differently between the two functional families. Moreover, there is a change in catalytic residue site (indicated as blue *) in PPR i.e., F(/L)209Y. The height of each residue indicates its degree of conservation. c Close-up view of differentially conserved positions between the families in the active site FunFam-02-SAT (red) and FunFam-348-PPR (blue). The substrate molecule (AGS) in FunFam-02-SAT (1zunA) is shown in magenta. Chemically different residues highlighted in the substrate-binding site (H61V) and catalytic site (F174Y) of FunFam-348. d Catalytic mechanisms for the two Enzyme families.](/paper-figures/686689ab9fb044f7a505ce5e80402d80/alphafold2-reveals-commonalities-and-novelties-in-protein-structure-space-for-21-figure-6.jpg)
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“As a result, numerous areas within the life sciences have experienced considerable impact due to the availability of accurately predicted protein structures. The primary domains notably affected include structure determination, structure-based drug discovery, and structural bioinformatics (11–13).”
From AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences (2024) · cites this paper as [11]
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Metadata
- Authors
- Bordin, N., Sillitoe, I., Nallapareddy, V., Rauer, C., Lam, S. D., Waman, V. P., Sen, N., Heinzinger, M., Littmann, M., Kim, S., Velankar, S., Steinegger, M., Rost, B., & Orengo, C.
- Affiliations
- Institute of Structural and Molecular Biology (GB)
- Technical University of Munich (DE)
- Seoul National University (KR)
- European Bioinformatics Institute (GB)
- Corresponding author
- Orengo, Christine
- ORCID
- Venue
- Communications Biology
- Published
- 2023-02-08
- Volume
- 6
- Issue
- 1
- Pages
- 160-160
- ISSN
- 2399-3642 (ISSN-L)
- PMC
- PMC9908985
- OpenAlex
- W4319593844
- Cited by
- 103
- References
- 57
- Citations by year
- 2026: 17 · 2025: 22 · 2024: 33 · 2023: 21
- FWCI
- 8.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Communications Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Funders
- Alexander von Humboldt-Stiftung
- Wellcome Trust (BB/R014892/1, 221327/Z/20/Z, 221327, BB/T002735/1)
- European Molecular Biology Laboratory
- DeepMind
- National Research Foundation (2019R1A6A1A10073437, 2021-R1C1-C102065)
- Deutsche Forschungsgemeinschaft (DFG-GZ: RO1320/4-1, RO1320/4‐1)
- Bundesministerium für Bildung und Forschung (01IS17049)
- Seoul National University
- National Research Foundation of Korea (2021-R1C1-C102065, 2021-M3A9-I4021220, 2020M3‐A9G7‐103933, 2019R1-A6A1-A10073437)
- Biotechnology and Biological Sciences Research Council (BB/R014892/1, BB/R009597/1, BB/V014722/1, BB/S020039/1, BB/T002735/1, 221327/Z/20/Z, BB/S020144/1)
- 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
- AlphaFold2, protein structure prediction, model organisms, remote homology, protein fold evolution, structure-function relationship
- MeSH terms
- Furylfuramide, Humans, Proteins, Databases, Protein
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