Enhancing protein structure prediction: evaluating the role of amino acid physicochemical features in homology search

Computational models like AlphaFold2 have achieved high accuracy in protein structure prediction, but their homology search step-key to generating multiple sequence alignments (MSAs)-remains computationally expensive and prone to introducing alignment noise. We propose DIAFold, which incorporates amino acid physicochemical properties as a cost-free prefiltering strategy to improve homolog detection by prioritizing biologically meaningful MSAs over exhaustive high-sensitivity searches, using DIAMOND in a fast, single-pass setting. This yields a 5.91× speedup and reduces false positives by up to 37.7× while producing smaller yet higher-quality MSAs and preserving or improving structure prediction accuracy, particularly in low-homology regimes. These gains translate to higher TM-scores in full-chain and domain-level predictions, using fewer computational resources, highlighting the benefits of integrating physicochemical knowledge early in protein structure prediction pipelines.