The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Artificial Neural Network to Predict Structure-based Protein-protein Free Energy of Binding from Rosetta-calculated Properties
Open accessPreprint — published as 10.1039/d2cp05644e · Paper page
Abstract
The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest that allows a variety of applications in the fields of molecular and chemical biology, material sciences, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to be obtained theoretically. In this work, we devise a novel Artificial Neural Network model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model presents a root-mean-square error of 1.667 kcal/mol outperforming available state-of-art tools. Validation of the model for a variety of protein-protein complexes is showcased.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Metadata
- Authors
- Ferraz, M., Neto, J., Lins, R., & Teixeira, E.
- Affiliations
- Universidade Federal de Pernambuco (BR)
- Centro de Estudos e Sistemas Avançados do Recife (BR)
- Corresponding author
- Teixeira, Erico
- Venue
- Physical Chemistry Chemical Physics
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-08-09
- Volume
- 25
- Issue
- 10
- Pages
- 7257-7267
- ISSN
- 1463-9076 (ISSN-L)
- OpenAlex
- W4318716670
- Cited by
- 9
- References
- 1
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 2
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Physical Chemistry Chemical Physics (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Deutscher Akademischer Austauschdienst
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (425997/2018-9)
- Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (APQ‐0346‐2.09/19, APQ-0276-1.03/20)
- Fundação Oswaldo Cruz (VPPCB‐007‐FIO‐18‐2‐134)
- Instituto Nacional de Ciência e Tecnologia de Fluidos Complexos
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- artificial neural networks, Rosetta, protein-protein complex, binding affinity prediction
- MeSH terms
- Protein Binding, Proteins, Thermodynamics, Neural Networks, Computer, Entropy
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