Statistical mechanical models that afford an intermediate resolution between macroscopic chemical models and all-atom simulations have been successful in capturing folding behaviors of many small single-domain proteins. However, the applicability of one such successful approach, the Wako-Saitô-Muñoz-Eaton (WSME) model, is limited by the size of the protein as the number of conformations grows exponentially with protein length. In this work, we surmount this size limitation by introducing a novel approximation that treats stretches of 3 or 4 residues as blocks, thus reducing the phase space by nearly three orders of magnitude. The performance of the ‘bWSME’ model is validated by comparing the predictions for a globular enzyme (RNase H) and a repeat protein (IκBα), against experimental observables and the model without block approximation. Finally, as a proof of concept, we predict the free-energy surface of the 370-residue, multi-domain maltose binding protein and identify an intermediate in good agreement with single-molecule force-spectroscopy measurements. The bWSME model can thus be employed as a quantitative predictive tool to explore the conformational landscapes of large proteins, extract the structural features of putative intermediates, identify parallel folding paths, and thus aid in the interpretation of both ensemble and single-molecule experiments.
While AlphaFold2 is rapidly being adopted as a new standard
in
protein structure predictions, it is limited to single structures.
This can be insufficient for the inherently dynamic world of biomolecules.
In this Letter, we propose AlphaFold2-RAVE, an efficient protocol
for obtaining Boltzmann-ranked ensembles from sequence. The method
uses structural outputs from AlphaFold2 as initializations for artificial
intelligence-augmented molecular dynamics. We release the method as
an open-source code and demonstrate results on different proteins.
In the short time since it has appeared, AlphaFold2 (AF2) has been widely adopted as a new standard in accurate and fast protein structure prediction starting from any arbitrary sequence of amino acids. However, AF2 maps a single sequence to a single structure, and even with recently proposed modifications that add conformational diversity, it is arguably devoid of thermodynamics. In this working paper we demonstrate an efficient protocol that uses the structural diversity from AF2 as a starting point to perform Artificial Intelligence augmented enhanced molecular dynamics simulations. Specifically we use the “Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE)” method as post-processing on AF2, and thus the protocol shown here is called AlphaFold2-RAVE. These simulations expand upon the results from AF2 ranking them as per their correct Boltzmann weights. This schema for going from sequence to Boltzmann weighted ensemble of structures is demonstrated here for a small cold-shock protein, and will be expanded to include many more sequences together with an easy-to-use open-source code.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.