Protein oligomer modeling guided by predicted interchain contacts in CASP14
Open accessAbstract
For CASP14, we developed deep learning‐based methods for predicting homo‐oligomeric and hetero‐oligomeric contacts and used them for oligomer modeling. To build structure models, we developed an oligomer structure generation method that utilizes predicted interchain contacts to guide iterative restrained minimization from random backbone structures. We supplemented this gradient‐based fold‐and‐dock method with template‐based and ab initio docking approaches using deep learning‐based subunit predictions on 29 assembly targets. These methods produced oligomer models with summed Z‐scores 5.5 units higher than the next best group, with the fold‐and‐dock method having the best relative performance. Over the eight targets for which this method was used, the best of the five submitted models had average oligomer TM‐score of 0.71 (average oligomer TM‐score of the next best group: 0.64), and explicit modeling of inter‐subunit interactions improved modeling of six out of 40 individual domains (ΔGDT‐TS > 2.0).
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At least one group (Baker) already has a 'fold and dock' algorithm intended to model conformational change on binding.
“Third, at least one group (Baker) already has a ‘fold and dock’ algorithm intended to model conformational change on binding22.”
From Critical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp> (2021) · cites this paper as [22]
A number of groups augmented classical docking with interface residue-residue contact prediction, often using deep-learning methods, producing some of the best results, although gains are fairly modest.
“A number of groups have now augmented these with the prediction in interface residue-residue contacts, often employing deep-learning methods, for example22, producing some of the best results, although so far, the gains are fairly modest.”
From Critical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp> (2021) · cites this paper as [22]
The Baker group have successfully incorporated deep learning prediction of inter-residue distance errors into their refinement procedures, allowing computational effort to be focused on the parts of the structure most needing it.
“First, the Baker group have successfully incorporated deep learning prediction of inter-residue distance errors into their refinement procedures22 allowing computational effort to be focused on the parts of the structure most needing it.”
From Critical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp> (2021) · cites this paper as [22]
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- Authors
- Baek, M., Anishchenko, I., Park, H., Humphreys, I. R., & Baker, D.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Baker, David
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2021-08-23
- Volume
- 89
- Issue
- 12
- Pages
- 1824-1833
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC8616806
- OpenAlex
- W3186985836
- Cited by
- 18
- References
- 39
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 2 · 2022: 7 · 2021: 5
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- Amgen
- Division of Biological Infrastructure (DBI 1937533)
- Microsoft
- National Institute of Allergy and Infectious Diseases (HHSN272201700059C)
- The Schmidt Family Foundation
- Open Philanthropy Project
- Howard Hughes Medical Institute
- National Science Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein oligomerization, template-based docking, deep learning, CASP14
- MeSH terms
- Deep Learning, Models, Molecular, Protein Binding, Protein Conformation, Proteins, Software, Computational Biology, Sequence Analysis, Protein, Protein Subunits, Databases, Protein
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