Protein loop prediction via sequential Monte Carlo and simultaneous side-chain filtering

Using computation to predict a protein's 3D structure from its amino acid sequence remains a highly challenging problem in biology. This talk focuses on one particular subproblem: the structure prediction of the variable loop regions in proteins that connect the more ordered helices and sheets. The primary difficulty of this task is the lack of efficient sampling algorithms to explore the low-energy conformational space of the loop region. Our new method, inspired by sequential Monte Carlo techniques, simultaneously explores both the backbone and side-chain space of the loop region as it places one amino acid at a time. Amino acid placements are filtered at each step to retain the most promising candidates, while maintaining a diverse set of samples. This produces an ensemble of low-energy conformation proposals for the loop region of interest, from which a final prediction can be chosen according to selection criteria. We show that the method improves sampling and prediction accuracy on benchmark datasets compared to previous approaches.