Inferring the Effects of Protein Variants on Protein–Protein Interactions with Interpretable Transformer Representations
Identifying pathogenetic variants and inferring their impact on protein–protein interactions sheds light on their functional consequences on diseases. Limited by the availability of experimental data on the consequences of protein interaction, most existing methods focus on building models to predic...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10494974/ https://www.ncbi.nlm.nih.gov/pubmed/37701056 http://dx.doi.org/10.34133/research.0219 |
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author | Liu, Zhe Qian, Wei Cai, Wenxiang Song, Weichen Wang, Weidi Maharjan, Dhruba Tara Cheng, Wenhong Chen, Jue Wang, Han Xu, Dong Lin, Guan Ning |
author_facet | Liu, Zhe Qian, Wei Cai, Wenxiang Song, Weichen Wang, Weidi Maharjan, Dhruba Tara Cheng, Wenhong Chen, Jue Wang, Han Xu, Dong Lin, Guan Ning |
author_sort | Liu, Zhe |
collection | PubMed |
description | Identifying pathogenetic variants and inferring their impact on protein–protein interactions sheds light on their functional consequences on diseases. Limited by the availability of experimental data on the consequences of protein interaction, most existing methods focus on building models to predict changes in protein binding affinity. Here, we introduced MIPPI, an end-to-end, interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from IMEx. MIPPI was specifically trained to determine the types of variant impact (increasing, decreasing, disrupting, and no effect) on protein–protein interactions. We demonstrate the accuracy of MIPPI and provide interpretation through the analysis of learned attention weights, which exhibit correlations with the amino acids interacting with the variant. Moreover, we showed the practicality of MIPPI in prioritizing de novo mutations associated with complex neurodevelopmental disorders and the potential to determine the pathogenic and driving mutations. Finally, we experimentally validated the functional impact of several variants identified in patients with such disorders. Overall, MIPPI emerges as a versatile, robust, and interpretable model, capable of effectively predicting mutation impacts on protein–protein interactions and facilitating the discovery of clinically actionable variants. |
format | Online Article Text |
id | pubmed-10494974 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-104949742023-09-12 Inferring the Effects of Protein Variants on Protein–Protein Interactions with Interpretable Transformer Representations Liu, Zhe Qian, Wei Cai, Wenxiang Song, Weichen Wang, Weidi Maharjan, Dhruba Tara Cheng, Wenhong Chen, Jue Wang, Han Xu, Dong Lin, Guan Ning Research (Wash D C) Research Article Identifying pathogenetic variants and inferring their impact on protein–protein interactions sheds light on their functional consequences on diseases. Limited by the availability of experimental data on the consequences of protein interaction, most existing methods focus on building models to predict changes in protein binding affinity. Here, we introduced MIPPI, an end-to-end, interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from IMEx. MIPPI was specifically trained to determine the types of variant impact (increasing, decreasing, disrupting, and no effect) on protein–protein interactions. We demonstrate the accuracy of MIPPI and provide interpretation through the analysis of learned attention weights, which exhibit correlations with the amino acids interacting with the variant. Moreover, we showed the practicality of MIPPI in prioritizing de novo mutations associated with complex neurodevelopmental disorders and the potential to determine the pathogenic and driving mutations. Finally, we experimentally validated the functional impact of several variants identified in patients with such disorders. Overall, MIPPI emerges as a versatile, robust, and interpretable model, capable of effectively predicting mutation impacts on protein–protein interactions and facilitating the discovery of clinically actionable variants. AAAS 2023-09-11 /pmc/articles/PMC10494974/ /pubmed/37701056 http://dx.doi.org/10.34133/research.0219 Text en Copyright © 2023 Zhe Liu et al. https://creativecommons.org/licenses/by/4.0/Exclusive licensee Science and Technology Review Publishing House. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Liu, Zhe Qian, Wei Cai, Wenxiang Song, Weichen Wang, Weidi Maharjan, Dhruba Tara Cheng, Wenhong Chen, Jue Wang, Han Xu, Dong Lin, Guan Ning Inferring the Effects of Protein Variants on Protein–Protein Interactions with Interpretable Transformer Representations |
title | Inferring the Effects of Protein Variants on Protein–Protein Interactions with Interpretable Transformer Representations |
title_full | Inferring the Effects of Protein Variants on Protein–Protein Interactions with Interpretable Transformer Representations |
title_fullStr | Inferring the Effects of Protein Variants on Protein–Protein Interactions with Interpretable Transformer Representations |
title_full_unstemmed | Inferring the Effects of Protein Variants on Protein–Protein Interactions with Interpretable Transformer Representations |
title_short | Inferring the Effects of Protein Variants on Protein–Protein Interactions with Interpretable Transformer Representations |
title_sort | inferring the effects of protein variants on protein–protein interactions with interpretable transformer representations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10494974/ https://www.ncbi.nlm.nih.gov/pubmed/37701056 http://dx.doi.org/10.34133/research.0219 |
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