Enhanced molecular dynamics simulation of the transformation between α-helix and β-hairpin structures for peptide

Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data

Some recent advances in biomolecular simulation and global optimization have used hybrid restraint potentials, where harmonic restraints that penalize conformations inconsistent with experimental data are combined with molecular mechanics force fields. These hybrid potentials can be used to improve the performance of molecular dynamics, structure prediction, energy landscape sampling, and other computational methods that rely on the accuracy of the underlying force field. Here, we develop a hybrid restraint potential based on NapShift, an artificial neural network trained to predict protein nuclear magnetic resonance (NMR) chemical shifts from sequence and structure. In addition to providing accurate predictions of experimental chemical shifts, NapShift is fully differentiable with respect to atomic coordinates, which allows us to use it for structural refinement. By employing NapShift to predict chemical shifts from the protein conformation at each simulation step, we can compute an energy penalty and the corresponding hybrid restraint forces based on the difference between the predicted values and the experimental chemical shifts. The performance of the hybrid restraint potential was benchmarked using both basin-hopping global optimization and molecular dynamics simulations. In each case, the NapShift hybrid potential improved the accuracy, leading to better structure prediction via basin-hopping and increased local stability in molecular dynamics simulations. Our results suggest that neural network hybrid potentials based on NMR observables can enhance a broad range of molecular simulation methods, and the prediction accuracy will improve as more experimental training data become available.

A multifunnel energy landscape encodes the competing α-helix and β-hairpin conformations for a designed peptide

Depending on the amino acid sequence, as well as the local environment, some peptides have the capability to fold into multiple secondary structures. Conformational switching between such structures is a key element of protein folding and aggregation. Specifically, understanding the molecular mechanism underlying the transition from an α-helix to a β-hairpin is critical because it is thought to be a harbinger of amyloid assembly. In this study, we explore the energy landscape for an 18-residue peptide (DP5), designed by Araki and Tamura to exhibit equal propensities for the α-helical and β-hairpin forms. We find that the degeneracy is encoded in the multifunnel nature of the underlying free energy landscape. In agreement with experiment, we also observe that mutation of tyrosine at position 12 to serine shifts the equilibrium in favor of the α-helix conformation, by altering the landscape topography. The transition from the α-helix to the β-hairpin is a complex stepwise process, and occurs via collapsed coil-like intermediates. Our findings suggest that even a single mutation can tune the emergent features of the landscape, providing an efficient route to protein design. Interestingly, the transition pathways for the conformational switch seem to be minimally perturbed upon mutation, suggesting that there could be universal microscopic features that are conserved among different switch-competent protein sequences.

Enhanced QM/MM sampling for free energy calculation of chemical reactions: A case study of double proton transfer

Free energy calculations for chemical reactions with a steep energy barrier require well defined reaction coordinates (RCs). However, when multiple parallel channels exist along selected RC, the application of conventional enhanced samplings is difficult to generate correct sampling within limited simulation time and thus cannot give correct prediction about the favorable pathways, the relative stability of multiple products or intermediates. Here, we implement the selective integrated tempering sampling (SITS) method with quantum mechanical and molecular mechanical (QM/MM) potential to investigate the chemical reactions in solution. The combined SITS-QM/MM scheme is used to identify possible reaction paths, intermediate and product states, and the free energy profiles for the different reaction paths. Two double proton transfer reactions were studied to validate the implemented method and simulation protocol, from which the independent and correlated proton transfer processes are identified in two representative systems, respectively. This protocol can be generalized to various kinds of chemical reactions for both academic studies and industry applications, such as in exploration and optimization of potential reactions in DNA encoded compound library and halogen or deuterium substitution of the hit discovery and lead optimization stages of drug design via providing a better understanding of the reaction mechanism along the designed chemical reaction pathways.