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| Natura: | Artículo científico |
| Lingua: | en |
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Molecular biology and evolution
2026
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| Accesso online: | https://pubmed.ncbi.nlm.nih.gov/42301139/ |
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| _version_ | 1868266037049294848 |
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| author | Frazer, Seth A Oakley, Todd H |
| author_facet | Frazer, Seth A Oakley, Todd H Frazer, Seth A Oakley, Todd H |
| collection | PubMed - marine biology |
| contents | Accessible and Robust Machine Learning Approaches to Improve the Opsin Genotype-Phenotype Map. Frazer, Seth A Oakley, Todd H Predicting phenotypes from genetic variation is a central challenge in biology. Here, machine learning (ML) offers great promise, but its use is often limited by poor accessibility, difficulty with interpretability, and a "data-cliff"-a gap between abundant sequences and scarce functional measurements. To develop more robust methods for genotype-phenotype prediction, an outstanding model system is opsin genes, visual pigments with extensive phenotypic information that strongly influence animal spectral sensitivity. Here we advance ML characterization of the opsin genotype-phenotype map through four main contributions. First, we introduce the Opsin Phenotype Tool for Inference of Color Sensitivity (OPTICS), a user-friendly platform for predicting maximum wavelength sensitivity (λmax) from amino-acid sequences, featuring integrated modules for SHapley Additive exPlanations (SHAP) and 3D structural mapping to reveal sequence-specific mechanistic drivers. Second, we show that encoding sequences with amino-acid physicochemical properties improves predictive performance and interpretability over standard encoding methods, and performs competitively with state-of-the-art protein language models, while retaining biological explainability. Finally, we present the Mine-N-Match (MNM) pipeline, which systematically links published opsin sequences to compiled data on in-vivo λmax values, expanding genotype-phenotype coverage and improving prediction, especially for undersampled taxa. By integrating accessible software, biologically informed encoding, and data harmonization, our framework improves confidence, accuracy, and interpretability of genotype-phenotype predictions for animal opsins. An accurate genotype-phenotype map will allow simulating molecular evolution of function, reconstructing the history of visual phenotypes, designing functional proteins, and generating new hypotheses that can be tested with heterologous phenotyping. |
| format | Artículo científico |
| id | pubmed_42301139 |
| institution | PubMed |
| language | en |
| publishDate | 2026 |
| publisher | Molecular biology and evolution |
| record_format | pubmed |
| spellingShingle | Accessible and Robust Machine Learning Approaches to Improve the Opsin Genotype-Phenotype Map. Frazer, Seth A Oakley, Todd H Accessible and Robust Machine Learning Approaches to Improve the Opsin Genotype-Phenotype Map. Frazer, Seth A Oakley, Todd H Predicting phenotypes from genetic variation is a central challenge in biology. Here, machine learning (ML) offers great promise, but its use is often limited by poor accessibility, difficulty with interpretability, and a "data-cliff"-a gap between abundant sequences and scarce functional measurements. To develop more robust methods for genotype-phenotype prediction, an outstanding model system is opsin genes, visual pigments with extensive phenotypic information that strongly influence animal spectral sensitivity. Here we advance ML characterization of the opsin genotype-phenotype map through four main contributions. First, we introduce the Opsin Phenotype Tool for Inference of Color Sensitivity (OPTICS), a user-friendly platform for predicting maximum wavelength sensitivity (λmax) from amino-acid sequences, featuring integrated modules for SHapley Additive exPlanations (SHAP) and 3D structural mapping to reveal sequence-specific mechanistic drivers. Second, we show that encoding sequences with amino-acid physicochemical properties improves predictive performance and interpretability over standard encoding methods, and performs competitively with state-of-the-art protein language models, while retaining biological explainability. Finally, we present the Mine-N-Match (MNM) pipeline, which systematically links published opsin sequences to compiled data on in-vivo λmax values, expanding genotype-phenotype coverage and improving prediction, especially for undersampled taxa. By integrating accessible software, biologically informed encoding, and data harmonization, our framework improves confidence, accuracy, and interpretability of genotype-phenotype predictions for animal opsins. An accurate genotype-phenotype map will allow simulating molecular evolution of function, reconstructing the history of visual phenotypes, designing functional proteins, and generating new hypotheses that can be tested with heterologous phenotyping. |
| title | Accessible and Robust Machine Learning Approaches to Improve the Opsin Genotype-Phenotype Map. |
| url | https://pubmed.ncbi.nlm.nih.gov/42301139/ |