Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation
Abstract Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2024-01-01
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| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s44320-023-00005-6 |
| _version_ | 1797272267438161920 |
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| author | Chop Yan Lee Dalmira Hubrich Julia K Varga Christian Schäfer Mareen Welzel Eric Schumbera Milena Djokic Joelle M Strom Jonas Schönfeld Johanna L Geist Feyza Polat Toby J Gibson Claudia Isabelle Keller Valsecchi Manjeet Kumar Ora Schueler-Furman Katja Luck |
| author_facet | Chop Yan Lee Dalmira Hubrich Julia K Varga Christian Schäfer Mareen Welzel Eric Schumbera Milena Djokic Joelle M Strom Jonas Schönfeld Johanna L Geist Feyza Polat Toby J Gibson Claudia Isabelle Keller Valsecchi Manjeet Kumar Ora Schueler-Furman Katja Luck |
| author_sort | Chop Yan Lee |
| collection | DOAJ |
| description | Abstract Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly true for interactions mediated by short linear motifs occurring in disordered regions of proteins. We find that AlphaFold-Multimer predicts with high sensitivity but limited specificity structures of domain-motif interactions when using small protein fragments as input. Sensitivity decreased substantially when using long protein fragments or full length proteins. We delineated a protein fragmentation strategy particularly suited for the prediction of domain-motif interfaces and applied it to interactions between human proteins associated with neurodevelopmental disorders. This enabled the prediction of highly confident and likely disease-related novel interfaces, which we further experimentally corroborated for FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions. |
| first_indexed | 2024-03-07T14:24:57Z |
| format | Article |
| id | doaj.art-22ace84c8c9745df8c5ea1c7bef0edf3 |
| institution | Directory Open Access Journal |
| issn | 1744-4292 |
| language | English |
| last_indexed | 2024-03-07T14:24:57Z |
| publishDate | 2024-01-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj.art-22ace84c8c9745df8c5ea1c7bef0edf32024-03-06T08:06:10ZengSpringer NatureMolecular Systems Biology1744-42922024-01-01202759710.1038/s44320-023-00005-6Systematic discovery of protein interaction interfaces using AlphaFold and experimental validationChop Yan Lee0Dalmira Hubrich1Julia K Varga2Christian Schäfer3Mareen Welzel4Eric Schumbera5Milena Djokic6Joelle M Strom7Jonas Schönfeld8Johanna L Geist9Feyza Polat10Toby J Gibson11Claudia Isabelle Keller Valsecchi12Manjeet Kumar13Ora Schueler-Furman14Katja Luck15Institute of Molecular Biology (IMB) gGmbHInstitute of Molecular Biology (IMB) gGmbHDepartment of Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of JerusalemInstitute of Molecular Biology (IMB) gGmbHInstitute of Molecular Biology (IMB) gGmbHInstitute of Molecular Biology (IMB) gGmbHInstitute of Molecular Biology (IMB) gGmbHInstitute of Molecular Biology (IMB) gGmbHInstitute of Molecular Biology (IMB) gGmbHInstitute of Molecular Biology (IMB) gGmbHInstitute of Molecular Biology (IMB) gGmbHStructural and Computational Biology Unit, European Molecular Biology LaboratoryInstitute of Molecular Biology (IMB) gGmbHStructural and Computational Biology Unit, European Molecular Biology LaboratoryDepartment of Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of JerusalemInstitute of Molecular Biology (IMB) gGmbHAbstract Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly true for interactions mediated by short linear motifs occurring in disordered regions of proteins. We find that AlphaFold-Multimer predicts with high sensitivity but limited specificity structures of domain-motif interactions when using small protein fragments as input. Sensitivity decreased substantially when using long protein fragments or full length proteins. We delineated a protein fragmentation strategy particularly suited for the prediction of domain-motif interfaces and applied it to interactions between human proteins associated with neurodevelopmental disorders. This enabled the prediction of highly confident and likely disease-related novel interfaces, which we further experimentally corroborated for FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions.https://doi.org/10.1038/s44320-023-00005-6AlphaFoldProtein Interaction Interface PredictionLinear MotifsBenchmarkingExperimental Validation |
| spellingShingle | Chop Yan Lee Dalmira Hubrich Julia K Varga Christian Schäfer Mareen Welzel Eric Schumbera Milena Djokic Joelle M Strom Jonas Schönfeld Johanna L Geist Feyza Polat Toby J Gibson Claudia Isabelle Keller Valsecchi Manjeet Kumar Ora Schueler-Furman Katja Luck Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation Molecular Systems Biology AlphaFold Protein Interaction Interface Prediction Linear Motifs Benchmarking Experimental Validation |
| title | Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation |
| title_full | Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation |
| title_fullStr | Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation |
| title_full_unstemmed | Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation |
| title_short | Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation |
| title_sort | systematic discovery of protein interaction interfaces using alphafold and experimental validation |
| topic | AlphaFold Protein Interaction Interface Prediction Linear Motifs Benchmarking Experimental Validation |
| url | https://doi.org/10.1038/s44320-023-00005-6 |
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