Accessible and Robust Machine Learning Approaches to Improve the Opsin Genotype-Phenotype Map

Abstract Predicting phenotypes from genetic variation is a central challenge in biology. Linking genotypes and phenotypes using machine learning (ML) offers great promise, but its use is limited by poor accessibility, overestimated performance, 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. Second, we show that encoding sequences with amino-acid physicochemical properties improves predictive performance and reveals mechanistic relationships. Third, we develop Phylogenetically Weighted Cross-Validation (PW-CV), a method that accounts for non-independence among related sequences, providing more realistic assessments of model generalizability. Finally, we present the Mine-N-Match (MNM) pipeline, which systematically links published opsin sequences to compiled in-vivo λmax data, expanding genotype–phenotype coverage and improving prediction, especially for invertebrate opsins with undersampled heterologous data. By integrating accessible software, biologically informed encoding, phylogeny-aware evaluation, and data harmonization, our framework improves confidence, accuracy, and interpretability of genotype–phenotype prediction. An accurate genotype-phenotype map allows 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. Competing Interest Statement The authors have declared no competing interest.