Biology subjects

Tiwary, P.

Publications and source records attributed to Tiwary, P..

26 records · Page 2Linked to original sources

bioRxiv2020

A statistical physics based approach to capture spurious solutions in AI-enhanced molecular dynamics

Artificial intelligence (AI)-based approaches have had indubitable impact across the sciences through the ability to extract relevant information from raw data. Recently AI has also seen use for enhancing the efficiency of molecular simulations, wherein AI derived slow modes are used to accelerate the simulation in targeted ways. However, while typical fields where AI is used are characterized by a plethora of data, molecular simulations per-construction suffer from limited sampling and thus limited data. As such the use of AI in molecular simulations can suffer from a dangerous situation where the AI-optimization could get stuck in spurious regimes, leading to incorrect characterization of the reaction coordinate (RC) for the problem at hand. When such an incorrect RC is then used to perform additional simulations, one could start to deviate progressively from the ground truth. To deal with this problem of spurious AI-solutions, here we report a novel and automated algorithm using ideas from statistical mechanics. It is based on the notion that a more reliable AI-solution will be one that maximizes the time-scale separation between slow and fast processes. To learn this time-scale separation even from limited data, we use a maximum caliber-based framework. We show the applicability of this automatic protocol for 3 classic benchmark problems, namely the conformational dynamics of a model peptide, ligand-unbinding from a protein, and folding/unfolding energy landscape of the C-terminal domain of protein G. We believe our work will lead to increased and robust use of trustworthy AI in molecular simulations of complex systems.

biophysics↗

bioRxiv2020

Discovering loop conformational flexibility in T4 lysozyme mutants through Artificial Intelligence aided Molecular Dynamics

Proteins sample a variety of conformations distinct from their crystal structure. These structures, their propensities, and pathways for moving between them contain enormous information about protein function that is hidden from a purely structural perspective. Molecular dynamics simulations can uncover these higher energy states but often at a prohibitively high computational cost. Here we apply our recent statistical mechanics and artificial intelligence based molecular dynamics framework for enhanced sampling of protein loops in three mutants of the protein T4 lysozyme. We are able to correctly rank these according to the stability of their excited state. By analyzing reaction coordinates, we also obtain crucial insight into why these specific perturbations in sequence space lead to tremendous variations in conformational flexibility. Our framework thus allows accurate comparison of loop conformation populations with minimal prior human bias, and should be directly applicable to a range of macromolecules in biology, chemistry and beyond.

biophysics↗

bioRxiv2019

Automatic mutual information noise omission (AMINO): generating order parameters for molecular systems

Molecular dynamics (MD) simulations generate valuable all-atom resolution trajectories of complex systems, but analyzing this high-dimensional data as well as reaching practical timescales even with powerful super-computers remain open problems. As such, many specialized sampling and reaction coordinate construction methods exist that alleviate these problems. However, these methods typically dont work directly on all atomic coordinates, and still require previous knowledge of the important distinguishing features of the system, known as order parameters (OPs). Here we present AMINO, an automated method that generates such OPs by screening through a very large dictionary of OPs, such as all heavy atom contacts in a biomolecule. AMINO uses ideas from information theory and rate distortion theory. The OPs learnt from AMINO can then serve as an input for designing a reaction coordinate which can then be used in many enhanced sampling methods. Here we outline its key theoretical underpinnings, and apply it to systems of increasing complexity. Our applications include a problem of tremendous pharmaceutical and engineering relevance, namely, calculating the binding affinity of a protein-ligand system when all that is known is the structure of the bound system. Our calculations are performed in a human-free fashion, obtaining very accurate results compared to long unbiased MD simulations on the Anton supercomputer, but in orders of magnitude less computer time. We thus expect AMINO to be useful for the calculation of thermodynamics and kinetics in the study of diverse molecular systems.

biophysics↗

bioRxiv2019

Can one trust kinetic and thermodynamic observables from biased metadynamics simulations: detailed quantitative benchmarks on millimolar drug fragment dissociation

Obtaining atomistic resolution of ligand dissociation from a protein is a much sought after experimental and computational challenge. Structural details of the dissociation process are in general hard to capture in experiments, while the relevant timescales are far beyond molecular dynamics (MD) simulations even with the most powerful super-computers. As such many different specialized enhanced sampling methods have been proposed that make it possible to efficiently calculate the dissociation mechanisms in protein-ligand systems. However, accurate benchmarks against long unbiased MD simulations are either not reported yet or simply not feasible due to the extremely long timescales. In this manuscript, we consider one such recent method "infrequent metadynamics", and benchmark in detail the various thermodynamics and kinetic information obtained from this method against extensive unbiased MD simulations for the dissociation dynamics of two different millimolar fragments from the protein FKBP in explicit water with residence times in nanoseconds to microseconds regime. We find that the metadynamics approach gives the same binding free energy profile, dissociation pathway and ligand residence time as the unbiased MD, albeit using only 6 to 50 times lower computational resources. Furthermore, we demonstrate how the metadynamics approach can self-consistently be used to ascertain whether the reweighted kinetic constants are reliable or not. We thus conclude that the answer to the question posed in the title of this manuscript is: statistically speaking, yes.

biochemistry↗

bioRxiv2018

Past-future information bottleneck framework for simultaneously sampling biomolecular reaction coordinate, thermodynamics and kinetics

The ability to rapidly learn from high-dimensional data to make reliable bets about the future outcomes is crucial in many contexts. This could be a fly avoiding predators, or the retina processing gigabytes of data almost instantaneously to guide complex human actions. In this work we draw parallels between such tasks, and the efficient sampling of complex biomolecules with hundreds of thousands of atoms. For this we use the Predictive Information Bottleneck (PIB) framework developed and used for the first two classes of problems, and re-formulate it for the sampling of biomolecular structure and dynamics, especially when plagued with rare events. Our method considers a given biomolecular trajectory expressed in terms of order parameters or basis functions, and uses a deep neural network to learn the minimally complex yet most predictive aspects of this trajectory, viz the PIB. This information is used to perform iterative rounds of biased simulations that enhance the sampling along the PIB to gradually improve its accuracy, directly obtaining associated thermodynamic and kinetic information. We demonstrate the method on two test-pieces, including benzene dissociation from the protein lysozyme, where we calculate the dissociation pathway and timescales slower than milliseconds. Finally, by performing an analysis of residues contributing to the PIB, we predict the critical mutations in the system which would be most impactful on the stability of the crucial but ephemeral transition state. We believe this work marks a big step forward in the use of predictive artificial intelligence ideas for the sampling of biomolecules.

biophysics↗

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↗