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| Format: | Artículo Open Access |
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Wiley
2025
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| Online Access: | https://onlinelibrary.wiley.com/doi/10.1002/prot.26821 |
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| _version_ | 1867012290379776000 |
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| author | Olga O. Lebedenko Mikhail S. Polovinkin Anastasiia A. Kazovskaia Nikolai R. Skrynnikov |
| author_facet | Olga O. Lebedenko Mikhail S. Polovinkin Anastasiia A. Kazovskaia Nikolai R. Skrynnikov Olga O. Lebedenko Mikhail S. Polovinkin Anastasiia A. Kazovskaia Nikolai R. Skrynnikov |
| collection | Wiley Open Access |
| contents | PCANN Program for Structure‐Based Prediction of Protein–Protein Binding Affinity: Comparison With Other Neural‐Network Predictors Olga O. Lebedenko Mikhail S. Polovinkin Anastasiia A. Kazovskaia Nikolai R. Skrynnikov Proteins: Structure, Function, and Bioinformatics ABSTRACTIn this communication, we introduce a new structure‐based affinity predictor for protein–protein complexes. This predictor, dubbed PCANN (Protein Complex Affinity by Neural Network), uses the ESM‐2 language model to encode the information about protein binding interfaces and graph attention network (GAT) to parlay this information into predictions. In the tests employing two previously unused literature‐extracted datasets, PCANN performed better than the best of the publicly available predictors, BindPPI, with mean absolute error (MAE) of 1.3 versus 1.4 kcal/mol. Further progress in the development of predictors using deep learning models is faced with two problems: (i) the amount of experimental data available to train and test new predictors is limited and (ii) the available data are often not very accurate and lack internal consistency with respect to measurement conditions. These issues can be potentially addressed through an AI‐leveraged literature search followed by careful human curation and by introducing additional parameters to account for variations in experimental conditions. 10.1002/prot.26821 http://creativecommons.org/licenses/by-nc/4.0/ |
| doi_str_mv | 10.1002/prot.26821 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_prot_26821 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | PCANN Program for Structure‐Based Prediction of Protein–Protein Binding Affinity: Comparison With Other Neural‐Network Predictors Olga O. Lebedenko Mikhail S. Polovinkin Anastasiia A. Kazovskaia Nikolai R. Skrynnikov Proteins: Structure, Function, and Bioinformatics PCANN Program for Structure‐Based Prediction of Protein–Protein Binding Affinity: Comparison With Other Neural‐Network Predictors Olga O. Lebedenko Mikhail S. Polovinkin Anastasiia A. Kazovskaia Nikolai R. Skrynnikov Proteins: Structure, Function, and Bioinformatics ABSTRACTIn this communication, we introduce a new structure‐based affinity predictor for protein–protein complexes. This predictor, dubbed PCANN (Protein Complex Affinity by Neural Network), uses the ESM‐2 language model to encode the information about protein binding interfaces and graph attention network (GAT) to parlay this information into predictions. In the tests employing two previously unused literature‐extracted datasets, PCANN performed better than the best of the publicly available predictors, BindPPI, with mean absolute error (MAE) of 1.3 versus 1.4 kcal/mol. Further progress in the development of predictors using deep learning models is faced with two problems: (i) the amount of experimental data available to train and test new predictors is limited and (ii) the available data are often not very accurate and lack internal consistency with respect to measurement conditions. These issues can be potentially addressed through an AI‐leveraged literature search followed by careful human curation and by introducing additional parameters to account for variations in experimental conditions. 10.1002/prot.26821 http://creativecommons.org/licenses/by-nc/4.0/ |
| title | PCANN Program for Structure‐Based Prediction of Protein–Protein Binding Affinity: Comparison With Other Neural‐Network Predictors |
| topic | Proteins: Structure, Function, and Bioinformatics |
| url | https://onlinelibrary.wiley.com/doi/10.1002/prot.26821 |