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

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 (lambda-max) 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.