EvoStructCLIP: A Mutation-Centered Multimodal Embedding Model for CAGI7 Variant Effect Prediction

Abstract

AO_SCPLOWBSTRACTC_SCPLOWWe present EvoStructCLIP, a mutation-centered multimodal embedding model that integrates local 3D structural windows and evolutionary constraints to predict missense variant effects. EvoStructCLIP combines two encoders: a structure voxel encoder derived from AlphaFold residue neighborhoods and an MSA-based evolutionary encoder. It aligns the modalities through CLIP-style contrastive learning, with FuseMix regularization and an auxiliary pathogenicity loss trained on 153,787 ClinVar variants. Evaluations using lightweight regressors demonstrate that EvoStructCLIP embeddings capture highly transferable predictive signals across diverse phenotypes, including gene-specific functional readouts of BRCA1, KCNQ4, and PTEN/TPMT. This transferability is further supported in the CAGI7 blind competition setting, where models generalized to predicting different gene-specific readouts for BARD1, FGFR, and TSC2 without target-specific retraining and achieved competitive performance across heterogeneous biological tasks.