EVORESOLVE: Preserving Evolutionary Heterogeneity in Protein Conformation Prediction

Evolution preserves a protein family's capacity for multiple functional conformations, leaving partially distinct structural constraints across its homologs. However, AlphaFold3 (AF3)-style folding models merge these signals by averaging MSA-row outer products, leaving the folding model with single evolutionary summary. Therefore, we introduce EVORESOLVE, which learns multiple evolutionary summaries before MSA aggregation. Motivated by the richer row-wise variation observed in multi-conformation proteins, EVORESOLVE organizes homolog evidence into compact, query-conditioned branches using sliced wasserstein transport. Multi-conformation training thus supervises the new aggregation process, encouraging branches to emphasize different evidence in support of alternative states. The resulting representations drive a shared folding backbone, producing multiple predictions from one searched MSA without target-specific alignment editing. Across various conformational benchmarks, EVORESOLVE achieves the lowest reported RMSD among the compared methods, indicating that it can retain state-relevant differences while preserving the evolutionary constraints across homologs.