scholarly journals Enhanced Sampling of Protein Conformational Transitions via Dynamically Optimized Collective Variables

2018 ◽  
Author(s):  
Z. Faidon Brotzakis ◽  
Michele Parrinello
Keyword(s):  
Degrees Of Freedom ◽  
Conformational Transition ◽  
Conformational Dynamics ◽  
Computational Cost ◽  
Md Simulations ◽  
Conformational Transitions ◽  
Enhanced Sampling ◽  
Collective Variables ◽  
Simulation Based ◽  
Combined Data

AbstractProtein conformational transitions often involve many slow degrees of freedom. Their knowledge would give distinctive advantages since it provides chemical and mechanistic insight and accelerates the convergence of enhanced sampling techniques that rely on collective variables. In this study, we implemented a recently developed variational approach to conformational dynamics metadynamics to the conformational transition of the moderate size protein, L99A T4 Lysozyme. In order to find the slow modes of the system we combined data coming from NMR experiments as well as short MD simulations. A Metadynamics simulation based on these information reveals the presence of two intermediate states, at an affordable computational cost.

scholarly journals Accelerating the Calculation of Protein-Ligand Binding Free Energy and Residence Times using Dynamically Optimized Collective Variables

2018 ◽  
Author(s):  
Z. Faidon Brotzakis ◽  
Vittorio Limongelli ◽  
Michele Parrinello
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Degrees Of Freedom ◽  
Binding Free Energy ◽  
Conformational Dynamics ◽  
Computational Cost ◽  
Binding Pocket ◽  
Binding Mode ◽  
Collective Variables ◽  
Binding Interaction

AbstractElucidation of the ligand/protein binding interaction is of paramount relevance in pharmacology to increase the success rate of drug design. To this end a number of computational methods have been proposed, however all of them suffer from limitations since the ligand binding/unbinding transitions to the molecular target involve many slow degrees of freedom that hamper a full characterization of the binding process. Being able to express this transition in simple and general slow degrees of freedom, would give a distinctive advantage, since it would require minimal knowledge of the system under study, while in turn it would elucidate its physics and accelerate the convergence speed of enhanced sampling methods relying on collective variables. In this study we pursuit this goal by combining for the first time Variation Approach to Conformational dynamics with Funnel-Metadynamics. In so doing, we predict for the benzamidine/trypsin system the ligand binding mode, and we accurately compute the absolute protein-ligand binding free energy and unbinding rate at unprecedented low computational cost. Finally, our simulation protocol reveals the energetics and structural details of the ligand binding mechanism and shows that water and binding pocket solvation/desolvation are the dominant slow degrees of freedom.

scholarly journals Making high-dimensional molecular distribution functions tractable through Belief Propagation on Factor Graphs

2021 ◽  
Author(s):  
Zachary Smith ◽  
Pratyush Tiwary
Keyword(s):  
Belief Propagation ◽  
Conformational Dynamics ◽  
Joint Probability ◽  
Md Simulations ◽  
Distribution Functions ◽  
Factor Graph ◽  
High Dimensional ◽  
Enhanced Sampling ◽  
Small Peptides ◽  
Approximate Probability

Molecular dynamics (MD) simulations provide a wealth of high-dimensional data at all-atom and femtosecond resolution but deciphering mechanistic information from this data is an ongoing challenge in physical chemistry and biophysics. Theoretically speaking, joint probabilities of the equilibrium distribution contain all thermodynamic information, but they prove increasingly difficult to compute and interpret as the dimensionality increases. Here, inspired by tools in probabilistic graphical modeling, we develop a factor graph trained through belief propagation that helps factorize the joint probability into an approximate tractable form that can be easily visualized and used. We validate the study through the analysis of the conformational dynamics of two small peptides with 5 and 9 residues. Our validations include testing the conditional dependency predictions through an intervention scheme inspired by Judea Pearl. Secondly we directly use the belief propagation based approximate probability distribution as a high-dimensional static bias for enhanced sampling, where we achieve spontaneous back-and-forth motion between metastable states that is up to 350 times faster than unbiased MD. We believe this work opens up useful ways to thinking about and dealing with high-dimensional molecular simulations.

scholarly journals A Virtual Reality Ensemble Molecular Dynamics Workflow to Study Complex Conformational Changes in Proteins

2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Reality ◽  
Conformational Changes ◽  
Large Scale ◽  
A Priori ◽  
Computational Cost ◽  
Md Simulations ◽  
Collective Variables ◽  
Sampling Protocols ◽  
State Models

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>

scholarly journals Deep learning the slow modes for rare events sampling

2021 ◽  
Vol 118 (44) ◽  
pp. e2113533118
Author(s):  
Luigi Bonati ◽  
GiovanniMaria Piccini ◽  
Michele Parrinello
Keyword(s):  
Sampling Method ◽  
Rare Events ◽  
Conformational Transition ◽  
Transfer Operator ◽  
Atomistic Simulations ◽  
Enhanced Sampling ◽  
Collective Variables ◽  
Long Time ◽  
Computational Resources

The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational resources. Many such methods rely on the identification of an appropriate set of collective variables. These are meant to describe the system’s modes that most slowly approach equilibrium under the action of the sampling algorithm. Once identified, the equilibration of these modes is accelerated by the enhanced sampling method of choice. An attractive way of determining the collective variables is to relate them to the eigenfunctions and eigenvalues of the transfer operator. Unfortunately, this requires knowing the long-term dynamics of the system beforehand, which is generally not available. However, we have recently shown that it is indeed possible to determine efficient collective variables starting from biased simulations. In this paper, we bring the power of machine learning and the efficiency of the recently developed on the fly probability-enhanced sampling method to bear on this approach. The result is a powerful and robust algorithm that, given an initial enhanced sampling simulation performed with trial collective variables or generalized ensembles, extracts transfer operator eigenfunctions using a neural network ansatz and then accelerates them to promote sampling of rare events. To illustrate the generality of this approach, we apply it to several systems, ranging from the conformational transition of a small molecule to the folding of a miniprotein and the study of materials crystallization.

scholarly journals Combining Virtual Reality Visualization with Ensemble Molecular Dynamics to Study Complex Protein Conformational Changes

2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Reality ◽  
Conformational Changes ◽  
Large Scale ◽  
Computational Cost ◽  
Md Simulations ◽  
Collective Variables ◽  
Protein Conformational Changes ◽  
Sampling Protocols ◽  
State Models

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>

scholarly journals Conformational Transition Pathways in Major Facilitator Superfamily Transporters

2019 ◽  
Author(s):  
Dylan Ogden ◽  
Kalyan Immadisetty ◽  
Mahmoud Moradi
Keyword(s):  
Conformational Changes ◽  
Glucose Transporter ◽  
Conformational Transition ◽  
Conformational Dynamics ◽  
Salt Bridge ◽  
Md Simulations ◽  
Major Facilitator Superfamily ◽  
Glucose Transporter 1 ◽  
Major Facilitator ◽  
Mfs Transporters

AbstractMajor facilitator superfamily (MFS) of transporters consists of three classes of membrane transporters: symporters, uniporters, and antiporters. Despite such diverse functions, MFS transporters are believed to undergo similar conformational changes within their distinct transport cycles. While the similarities between conformational changes are noteworthy, the differences are also important since they could potentially explain the distinct functions of symporters, uniporters, and antiporters of MFS superfamily. We have performed a variety of equilibrium, non-equilibrium, biased, and unbiased all-atom molecular dynamics (MD) simulations of bacterial proton-coupled oligopeptide transporter GkPOT, glucose transporter 1 (GluT1), and glycerol-3-phosphate transporter (GlpT) to compare the similarities and differences of the conformational dynamics of three different classes of transporters. Here we have simulated the apo protein in an explicit membrane environment. Our results suggest a very similar conformational transition involving interbundle salt-bridge formation/disruption coupled with the orientation changes of transmembrane (TM) helices, specifically H1/H7 and H5/H11, resulting in an alternation in the accessibility of water at the cyto- and periplasmic gates.

scholarly journals Combining Virtual Reality Visualization with Ensemble Molecular Dynamics to Study Complex Protein Conformational Changes

2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Reality ◽  
Conformational Changes ◽  
Large Scale ◽  
Computational Cost ◽  
Md Simulations ◽  
Collective Variables ◽  
Protein Conformational Changes ◽  
Sampling Protocols ◽  
State Models

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>

scholarly journals The role of water in host-guest interaction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Valerio Rizzi ◽  
Luigi Bonati ◽  
Narjes Ansari ◽  
Michele Parrinello
Keyword(s):  
Ligand Binding ◽  
Degrees Of Freedom ◽  
Enhanced Sampling ◽  
Free Energies ◽  
Collective Variables ◽  
Complex Behaviour ◽  
Field Quality ◽  
Non Local ◽  
Binding Free Energies

AbstractOne of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system’s degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.

scholarly journals Hydrogen-deuterium exchange mass spectrometry captures distinct dynamics upon substrate and inhibitor binding to a transporter

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruyu Jia ◽  
Chloe Martens ◽  
Mrinal Shekhar ◽  
Shashank Pant ◽  
Grant A. Pellowe ◽  
...  
Keyword(s):  
Conformational Changes ◽  
Conformational Transition ◽  
Substrate Binding ◽  
Conformational Dynamics ◽  
Md Simulations ◽  
Allosteric Coupling ◽  
Exchange Mass Spectrometry ◽  
Hydrogen Deuterium ◽  
Deuterium Exchange Mass Spectrometry ◽  
Hdx Ms

AbstractProton-coupled transporters use transmembrane proton gradients to power active transport of nutrients inside the cell. High-resolution structures often fail to capture the coupling between proton and ligand binding, and conformational changes associated with transport. We combine HDX-MS with mutagenesis and MD simulations to dissect the molecular mechanism of the prototypical transporter XylE. We show that protonation of a conserved aspartate triggers conformational transition from outward-facing to inward-facing state. This transition only occurs in the presence of substrate xylose, while the inhibitor glucose locks the transporter in the outward-facing state. MD simulations corroborate the experiments by showing that only the combination of protonation and xylose binding, and not glucose, sets up the transporter for conformational switch. Overall, we demonstrate the unique ability of HDX-MS to distinguish between the conformational dynamics of inhibitor and substrate binding, and show that a specific allosteric coupling between substrate binding and protonation is a key step to initiate transport.

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