Adaptively Iterative Multiscale Switching Simulation Strategy and Applications to Protein Folding and Structure Prediction

Structure prediction is an important means to quickly understand new protein functions. However, the prediction of effects of proteins that have no detectable templates is still to be improved. Molecular dynamics simulation is supposed to be the primary research tool for structure predictions, but it still has limitations of huge computational cost in all-atom (AA) models and rough accuracy in coarse-grained (CG) models. We propose a universal multiscale simulation strategy named AIMS in which simulations can iteratively switch among multiple resolutions in order to adaptively trade off AA accuracy and CG high-efficiency. AIMS follows the idea of CG-guided enhanced sampling so that final results always keep AA accuracy. We successfully achieve four ab initio and four data-assisted protein structure predictions using AIMS. The prediction result is an ensemble rather than a structure and provides special insights on folding metastable states. AIMS is estimated to achieve a computational speed about 40 times faster than that of conventional AA simulations.

Automated summary

Protein structure prediction is vital for understanding new protein functions, but predicting the effects of proteins with no detectable templates remains challenging. AIMS, a universal multiscale simulation strategy, allows simulations to iteratively switch among multiple resolutions to adaptively balance AA accuracy and CG efficiency. Through AIMS, faster and more accurate predictions of protein structures can be achieved, providing special insights on folding metastable states.