Accurate prediction of protein function using statistics-informed graph networks
Abstract Understanding protein function is pivotal in comprehending the intricate mechanisms that underlie many crucial biological activities, with far-reaching implications in the fields of medicine, biotechnology, and drug development. However, more than 200 million proteins remain uncharacterized...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-50955-0 |
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| author | Yaan J. Jang Qi-Qi Qin Si-Yu Huang Arun T. John Peter Xue-Ming Ding Benoît Kornmann |
| author_facet | Yaan J. Jang Qi-Qi Qin Si-Yu Huang Arun T. John Peter Xue-Ming Ding Benoît Kornmann |
| author_sort | Yaan J. Jang |
| collection | DOAJ |
| description | Abstract Understanding protein function is pivotal in comprehending the intricate mechanisms that underlie many crucial biological activities, with far-reaching implications in the fields of medicine, biotechnology, and drug development. However, more than 200 million proteins remain uncharacterized, and computational efforts heavily rely on protein structural information to predict annotations of varying quality. Here, we present a method that utilizes statistics-informed graph networks to predict protein functions solely from its sequence. Our method inherently characterizes evolutionary signatures, allowing for a quantitative assessment of the significance of residues that carry out specific functions. PhiGnet not only demonstrates superior performance compared to alternative approaches but also narrows the sequence-function gap, even in the absence of structural information. Our findings indicate that applying deep learning to evolutionary data can highlight functional sites at the residue level, providing valuable support for interpreting both existing properties and new functionalities of proteins in research and biomedicine. |
| format | Article |
| id | doaj-art-bcf4a929b2d748e5abbab148c6535708 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-bcf4a929b2d748e5abbab148c65357082025-08-20T03:45:35ZengNature PortfolioNature Communications2041-17232024-08-0115111210.1038/s41467-024-50955-0Accurate prediction of protein function using statistics-informed graph networksYaan J. Jang0Qi-Qi Qin1Si-Yu Huang2Arun T. John Peter3Xue-Ming Ding4Benoît Kornmann5Department of Biochemistry, University of OxfordAmoAi TechnologiesAmoAi TechnologiesInstitute of Biochemistry, ETH ZürichSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and TechnologyDepartment of Biochemistry, University of OxfordAbstract Understanding protein function is pivotal in comprehending the intricate mechanisms that underlie many crucial biological activities, with far-reaching implications in the fields of medicine, biotechnology, and drug development. However, more than 200 million proteins remain uncharacterized, and computational efforts heavily rely on protein structural information to predict annotations of varying quality. Here, we present a method that utilizes statistics-informed graph networks to predict protein functions solely from its sequence. Our method inherently characterizes evolutionary signatures, allowing for a quantitative assessment of the significance of residues that carry out specific functions. PhiGnet not only demonstrates superior performance compared to alternative approaches but also narrows the sequence-function gap, even in the absence of structural information. Our findings indicate that applying deep learning to evolutionary data can highlight functional sites at the residue level, providing valuable support for interpreting both existing properties and new functionalities of proteins in research and biomedicine.https://doi.org/10.1038/s41467-024-50955-0 |
| spellingShingle | Yaan J. Jang Qi-Qi Qin Si-Yu Huang Arun T. John Peter Xue-Ming Ding Benoît Kornmann Accurate prediction of protein function using statistics-informed graph networks Nature Communications |
| title | Accurate prediction of protein function using statistics-informed graph networks |
| title_full | Accurate prediction of protein function using statistics-informed graph networks |
| title_fullStr | Accurate prediction of protein function using statistics-informed graph networks |
| title_full_unstemmed | Accurate prediction of protein function using statistics-informed graph networks |
| title_short | Accurate prediction of protein function using statistics-informed graph networks |
| title_sort | accurate prediction of protein function using statistics informed graph networks |
| url | https://doi.org/10.1038/s41467-024-50955-0 |
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