Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data.
Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-10-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011521&type=printable |
| _version_ | 1827587751106576384 |
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| author | Lorenzo Posani Francesca Rizzato Rémi Monasson Simona Cocco |
| author_facet | Lorenzo Posani Francesca Rizzato Rémi Monasson Simona Cocco |
| author_sort | Lorenzo Posani |
| collection | DOAJ |
| description | Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear. Here, based on a theoretical analysis of the prediction error, we propose descriptors of the sequence data, characterizing their quantity and relevance relative to the model. Our theoretical framework identifies a trade-off between these two quantities, and determines the optimal subset of data for the prediction task, showing that simple models can outperform complex ones when inferred from adequately-selected sequences. We also show how repeated subsampling of the sequence data is informative about how much epistasis in the fitness landscape is not captured by the computational model. Our approach is illustrated on several protein families, as well as on in silico solvable protein models. |
| first_indexed | 2024-03-09T00:23:28Z |
| format | Article |
| id | doaj.art-93d74210f67a445588adcc33de68db4f |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-09T00:23:28Z |
| publishDate | 2023-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-93d74210f67a445588adcc33de68db4f2023-12-12T05:31:52ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-10-011910e101152110.1371/journal.pcbi.1011521Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data.Lorenzo PosaniFrancesca RizzatoRémi MonassonSimona CoccoPredicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear. Here, based on a theoretical analysis of the prediction error, we propose descriptors of the sequence data, characterizing their quantity and relevance relative to the model. Our theoretical framework identifies a trade-off between these two quantities, and determines the optimal subset of data for the prediction task, showing that simple models can outperform complex ones when inferred from adequately-selected sequences. We also show how repeated subsampling of the sequence data is informative about how much epistasis in the fitness landscape is not captured by the computational model. Our approach is illustrated on several protein families, as well as on in silico solvable protein models.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011521&type=printable |
| spellingShingle | Lorenzo Posani Francesca Rizzato Rémi Monasson Simona Cocco Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data. PLoS Computational Biology |
| title | Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data. |
| title_full | Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data. |
| title_fullStr | Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data. |
| title_full_unstemmed | Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data. |
| title_short | Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data. |
| title_sort | infer global predict local quantity relevance trade off in protein fitness predictions from sequence data |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011521&type=printable |
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