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Schaeffer RD, Zhang J, Medvedev KE, Kinch LN, Cong Q, Grishin NV. ECOD domain classification of 48 whole proteomes from AlphaFold Structure Database using DPAM2. PLoS Comput Biol 2024; 20:e1011586. [PMID: 38416793 PMCID: PMC10927120 DOI: 10.1371/journal.pcbi.1011586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/11/2024] [Accepted: 02/20/2024] [Indexed: 03/01/2024] Open
Abstract
Protein structure prediction has now been deployed widely across several different large protein sets. Large-scale domain annotation of these predictions can aid in the development of biological insights. Using our Evolutionary Classification of Protein Domains (ECOD) from experimental structures as a basis for classification, we describe the detection and cataloging of domains from 48 whole proteomes deposited in the AlphaFold Database. On average, we can provide positive classification (either of domains or other identifiable non-domain regions) for 90% of residues in all proteomes. We classified 746,349 domains from 536,808 proteins comprised of over 226,424,000 amino acid residues. We examine the varying populations of homologous groups in both eukaryotes and bacteria. In addition to containing a higher fraction of disordered regions and unassigned domains, eukaryotes show a higher proportion of repeated proteins, both globular and small repeats. We enumerate those highly populated domains that are shared in both eukaryotes and bacteria, such as the Rossmann domains, TIM barrels, and P-loop domains. Additionally, we compare the sampling of homologous groups from this whole proteome set against our stable ECOD reference and discuss groups that have been enriched by structure predictions. Finally, we discuss the implication of these results for protein target selection for future classification strategies for very large protein sets.
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Affiliation(s)
- R. Dustin Schaeffer
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Jing Zhang
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Kirill E. Medvedev
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Lisa N. Kinch
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Qian Cong
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Nick V. Grishin
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
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Lau AM, Kandathil SM, Jones DT. Merizo: a rapid and accurate protein domain segmentation method using invariant point attention. Nat Commun 2023; 14:8445. [PMID: 38114456 PMCID: PMC10730818 DOI: 10.1038/s41467-023-43934-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 11/24/2023] [Indexed: 12/21/2023] Open
Abstract
The AlphaFold Protein Structure Database, containing predictions for over 200 million proteins, has been met with enthusiasm over its potential in enriching structural biological research and beyond. Currently, access to the database is precluded by an urgent need for tools that allow the efficient traversal, discovery, and documentation of its contents. Identifying domain regions in the database is a non-trivial endeavour and doing so will aid our understanding of protein structure and function, while facilitating drug discovery and comparative genomics. Here, we describe a deep learning method for domain segmentation called Merizo, which learns to cluster residues into domains in a bottom-up manner. Merizo is trained on CATH domains and fine-tuned on AlphaFold2 models via self-distillation, enabling it to be applied to both experimental and AlphaFold2 models. As proof of concept, we apply Merizo to the human proteome, identifying 40,818 putative domains that can be matched to CATH representative domains.
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Affiliation(s)
- Andy M Lau
- Department of Computer Science, University College London, London, WC1E 6BT, UK
| | - Shaun M Kandathil
- Department of Computer Science, University College London, London, WC1E 6BT, UK
| | - David T Jones
- Department of Computer Science, University College London, London, WC1E 6BT, UK.
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Bruley A, Bitard-Feildel T, Callebaut I, Duprat E. A sequence-based foldability score combined with AlphaFold2 predictions to disentangle the protein order/disorder continuum. Proteins 2023; 91:466-484. [PMID: 36306150 DOI: 10.1002/prot.26441] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 10/14/2022] [Accepted: 10/18/2022] [Indexed: 11/11/2022]
Abstract
Order and disorder govern protein functions, but there is a great diversity in disorder, from regions that are-and stay-fully disordered to conditional order. This diversity is still difficult to decipher even though it is encoded in the amino acid sequences. Here, we developed an analytic Python package, named pyHCA, to estimate the foldability of a protein segment from the only information of its amino acid sequence and based on a measure of its density in regular secondary structures associated with hydrophobic clusters, as defined by the hydrophobic cluster analysis (HCA) approach. The tool was designed by optimizing the separation between foldable segments from databases of disorder (DisProt) and order (SCOPe [soluble domains] and OPM [transmembrane domains]). It allows to specify the ratio between order, embodied by regular secondary structures (either participating in the hydrophobic core of well-folded 3D structures or conditionally formed in intrinsically disordered regions) and disorder. We illustrated the relevance of pyHCA with several examples and applied it to the sequences of the proteomes of 21 species ranging from prokaryotes and archaea to unicellular and multicellular eukaryotes, for which structure models are provided in the AlphaFold protein structure database. Cases of low-confidence scores related to disorder were distinguished from those of sequences that we identified as foldable but are still excluded from accurate modeling by AlphaFold2 due to a lack of sequence homologs or to compositional biases. Overall, our approach is complementary to AlphaFold2, providing guides to map structural innovations through evolutionary processes, at proteome and gene scales.
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Affiliation(s)
- Apolline Bruley
- Sorbonne Université, Muséum National d'Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France
| | - Tristan Bitard-Feildel
- Sorbonne Université, Muséum National d'Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France
| | - Isabelle Callebaut
- Sorbonne Université, Muséum National d'Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France
| | - Elodie Duprat
- Sorbonne Université, Muséum National d'Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France
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Schaeffer RD, Zhang J, Kinch LN, Pei J, Cong Q, Grishin NV. Classification of domains in predicted structures of the human proteome. Proc Natl Acad Sci U S A 2023; 120:e2214069120. [PMID: 36917664 PMCID: PMC10041065 DOI: 10.1073/pnas.2214069120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 02/06/2023] [Indexed: 03/16/2023] Open
Abstract
Recent advances in protein structure prediction have generated accurate structures of previously uncharacterized human proteins. Identifying domains in these predicted structures and classifying them into an evolutionary hierarchy can reveal biological insights. Here, we describe the detection and classification of domains from the human proteome. Our classification indicates that only 62% of residues are located in globular domains. We further classify these globular domains and observe that the majority (65%) can be classified among known folds by sequence, with a smaller fraction (33%) requiring structural data to refine the domain boundaries and/or to support their homology. A relatively small number (966 domains) cannot be confidently assigned using our automatic pipelines, thus demanding manual inspection. We classify 47,576 domains, of which only 23% have been included in experimental structures. A portion (6.3%) of these classified globular domains lack sequence-based annotation in InterPro. A quarter (23%) have not been structurally modeled by homology, and they contain 2,540 known disease-causing single amino acid variations whose pathogenesis can now be inferred using AF models. A comparison of classified domains from a series of model organisms revealed expansions of several immune response-related domains in humans and a depletion of olfactory receptors. Finally, we use this classification to expand well-known protein families of biological significance. These classifications are presented on the ECOD website (http://prodata.swmed.edu/ecod/index_human.php).
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Affiliation(s)
- R. Dustin Schaeffer
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX75390
| | - Jing Zhang
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX75390
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX75390
| | - Lisa N. Kinch
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX75390
- HHMI, University of Texas Southwestern Medical Center, Dallas, TX75390
| | - Jimin Pei
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX75390
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX75390
| | - Qian Cong
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX75390
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX75390
| | - Nick V. Grishin
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX75390
- Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX75390
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