Hashi: Bridging Statistical Model Derived 1D Microstate Encodings and Protein 3D Structural Ensembles

Abstract

The functioning of proteins is intimately linked to the conformational states they sample within the native ensemble. Generating ensembles from a single static structure is therefore a research domain receiving considerable attention. In this application note, we introduce Hashi, a pipeline to rapidly generate realistic structural ensembles from the outputs of the structure-based Wako–Saitô–Muñoz–Eaton (WSME) statistical mechanical model of protein folding. This approach relies on integrating the block WSME model outputs─strings of zeros and ones describing the conformational status of every residue over thousands or millions of microstates, each assigned a statistical weight derived from physically grounded energy-entropy terms, and free energy profiles─with the RANCH module of the ensemble optimization method from the ATSAS software suite, providing three-dimensional views of the structural ensembles within the model framework. It is applicable to a variety of single-chain monomeric systems with lengths ranging from 30 to 500 residues, including globular and repeat proteins. Ensembles can be generated within seconds to minutes on a standard computer, without recourse to high-performance computational clusters. The generated structural ensembles can also be rank-ordered according to their free energies within a given macrostate or a range of reaction coordinate values. Since the statistical weights of the WSME model microstates can be reweighted or calibrated with experiments, the ensembles shed light on not just the folding mechanism but also on the structural excursions that determine function and opening of otherwise buried binding pockets.