ConforFold Recovers Alternative Protein Conformations Beyond MSA Subsampling

Abstract

Conformational changes underlie many aspects of protein function, yet current structure prediction tools remain limited in their ability to systematically sample structural ensembles. Here, we present ConforPSSP and ConforFold, a combined framework that integrates secondary-structure sampling into deep learning-based prediction to recover multiple protein conformational states. ConforPSSP employs a transformer model trained on multi-residue fragments to generate diverse 8-state protein secondary structure predictions (PSSPs), which are then used to condition a retrained OpenFold model (ConforFold). ConforFold achieved state-of-the-art performance in conformer recovery. On our test dataset of protein samples with two alternative conformations, it correctly identified both conformers in 84% of cases at TM-scores[≥]0.8, outperforming AlphaFlow (75.4%), which uses diffusion-based sampling, and Cfold, which relies on MSA clustering. Combining ConforFold with AlphaFlow further improved recovery rates while retaining the complementary strengths of both approaches. These results establish ConforFold as a broadly applicable framework for modeling structural ensembles. By explicitly integrating secondary structure it recovers conformations inaccessible to MSA-based subsampling or diffusion models, offering a new avenue for investigating conformational heterogeneity, mechanistic transitions, and the structural basis of protein function.