Improving AlphaFold3 by Engineering MSA and Template Inputs

AlphaFold3 introduces a unified framework for predicting the structures and interaction of several biomolecules including single-chain protein monomers, multi-chain protein multimers, and protein-ligand complexes. While it achieves the state-of-the-art performance in most predictions, its prediction accuracy depends on the quality of multiple sequence alignment (MSA) and structural template inputs. There are few works of using customized MSAs and templates to improve AlphaFold3. In this work, we systematically investigate how diverse and carefully engineered MSAs and templates can be leveraged to improve AlphaFold3 predictions. We evaluate our methods on protein monomers, multimers, and protein-ligand complexes, and observe consistent, sizable gains in structure prediction accuracy for monomers (TM-score 0.937 vs 0.882), for multimers (DockQ score 0.550 vs 0.525), and for protein-ligand complexes (ligand RMSD 3.258 [A] vs 4 [A]) compared to the default AlphaFold3. Moreover, for the first time, we demonstrate that AlphaFold3 performs significantly better than AlphaFold2 when both use the same customized MSA and template inputs. The results highlight the importance and effectiveness of using diverse MSAs and templates to improve AlphaFold3.