Biology subjects

Tiwary, P.

Publications and source records attributed to Tiwary, P..

3 recordsLinked to original sources

bioRxiv2018

Multi-dimensional spectral gap optimization of order parameters (SGOOP) through conditional probability factorization

Spectral gap optimization of order parameters (SGOOP) (Tiwary and Berne, Proc. Natl. Acad. Sci. 113 2839 (2016)) is a method for constructing the reaction coordinate (RC) in molecular systems, especially when they are plagued with hard to sample rare events, given a larger dictionary of order parameters or basis functions, and limited static and dynamic information about the system. In its original formulation, SGOOP is designed to construct a 1-dimensional RC. Here we extend its scope by introducing a simple but powerful extension based on the notion of conditional probability factorization where known features are washed out to learn additional and possibly hidden features of the energy landscape. We show how SGOOP can be used to proceed in a sequential and bottom-up manner to (i) systematically probe the need for extending the dimensionality of the RC, and (ii) if such a need is identified, learn additional coordinates of the RC in a computationally efficient manner. We formulate the method and demonstrate its usefulness through three illustrative examples, including the challenging and important problem of calculating the kinetics of benzene unbinding from the protein T4L99A lysozyme, where we obtain excellent agreement in terms of dissociation pathway and kinetics with other sampling methods and experiments. In this last case, starting from a larger dictionary of fairly generic and arbitrarily chosen 11 order parameters, we demonstrate how to automatically learn a 2-dimensional RC, which we then use in the infrequent metadynamics protocol to obtain 16 independent unbinding trajectories. We believe our method will be a big step in increasing the usefulness of SGOOP in performing intuition-free sampling of complex systems. Finally, we believe that the usefulness of our protocol is amplified by its applicability to not just SGOOP but also other generic methods for constructing the RC.

biophysics↗

bioRxiv2018

Achieving Reversible Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE

In this work we demonstrate how to leverage our recent iterative deep learning-all atom molecular dynamics (MD) technique \"Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)\" (Ribeiro, Bravo, Wang, Tiwary, J. Chem. Phys. 149, 072301 (2018)) for sampling protein-ligand unbinding mechanisms and calculating absolute binding affinities when plagued with difficult to sample rare events. RAVE iterates between rounds of MD and deep learning, and unlike other enhanced sampling methods, it stands out in simultaneously learning both a low-dimensional physically interpretable reaction coordinate (RC) and associated free energy. Here, we introduce a simple but powerful extension to RAVE which allows learning a position-dependent RC expressed as a superposition of piecewise linear RCs valid in different metastable states. With this approach, we retain the original physical interpretability of a RAVE-derived RC while making it applicable to a wider range of complex systems. We demonstrate how in its multi-dimensional form introduced here, RAVE can efficiently simulate the unbinding of the tightly bound benzene-lysozyme (L99A variant) complex, in all atom-precision and with minimal use of human intuition except for the choice of a larger dictionary of order parameters. These simulations had a 100 % success rate, and took between 3-50 nanoseconds for a process that takes on an average close to few hundred milliseconds, thereby reflecting a seven order of magnitude acceleration relative to straightforward MD. Furthermore, without any time-dependent biasing, the trajectories display clear back-and- forth movement between various metastable intermediates, demonstrating the reliability of the RC and its probability distribution learnt in RAVE. Our binding free energy is in good agreement with other reported simulation results. We thus believe that RAVE, especially in its multi-dimensional variant introduced here, will be a useful tool for simulating the dissociation process of practical biophysical systems with rare events in an automated manner with minimal use of human intuition.

biophysics↗

bioRxiv2017

Molecular determinants and bottlenecks in the unbinding dynamics of biotin-streptavidin

Biotin-streptavidin is a very popular system used to gain insight into protein-ligand interactions. In its tetrameric form, it is well-known for its extremely long residence times, being one of the strongest known non-covalent interactions in nature, and is heavily used across the biotechnological industry. In this work we gain understanding into the molecular determinants and bottlenecks in the unbinding of the dimeric biotinstreptavidin system in its wild type and with N23A mutation. Using new enhanced sampling methods with full atomistic resolution, we reproduce the variation caused by N23A mutation in experimentally reported residence time. We also answer a longstanding question regarding cause/effect in the coupled events of bond stretching and bond hydration during unbinding and establish that in this system, it is the bond stretching and not hydration which forms the bottleneck in the early parts of the unbinding. We believe these calculations represent a step forward in the use of atomistic simulations to study pharmacodynamics. An improved understanding of biotin-streptavidin unbinding dynamics should also have direct benefits in biotechnological and nanobiotechnological applications.

biochemistry↗