AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

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AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

Publicated to:Commun Biol. 6 (1): 160- - 2023-12-01 6(1), DOI: 10.1038/s42003-023-04488-9

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

Alte Akad 8, TUM Sch Life Sci Weihenstephan WZW, Freising Weihenstephan, Germany - Author

European Bioinformat Inst, European Mol Biol Lab, Hinxton, England - Author

European Bioinformatics Institute - Author

Inst Adv Study TUM IAS, Lichtenbergstr 2A, D-85748 Munich, Germany - Author

School of Biological Sciences , Seoul National University - Author

Seoul Natl Univ, Artificial Intelligence Inst, Seoul, South Korea - Author

Seoul Natl Univ, Sch Biol Sci, Seoul, South Korea - Author

Technische Universität München - Author

TUM Tech Univ Munich, Dept Informat Bioinformat & Computat Biol, i12,Boltzmannstr 3, D-85748 Munich, Germany - Author

UCL, Inst Struct & Mol Biol, London WC1E 6BT, England - Author

Univ Kebangsaan Malaysia, Fac Sci & Technol, Dept Appl Phys, Bangi 43600, Selangor, Malaysia - Author

University College London - Author

University College London , Universiti Kebangsaan Malaysia - Author

Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt , Institute for Advanced Study of Technical University of Munich , Technische Universität München - Author

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.

Keywords

cathdatabaseimpactpredictionsequenceClassification

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The work has been published in the journal Commun Biol due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position 13/109, thus managing to position itself as a Q1 (Primer Cuartil), in the category Biology.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 26.87, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions May 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-05-10, the following number of citations:

WoS: 7
Scopus: 37
OpenCitations: 52

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This work has been carried out with international collaboration, specifically with researchers from: Germany; Gran Bretanya; Malaysia; Republic of Korea; United Kingdom.

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