scholarly journals Molecular insights from conformational ensembles via machine learning

2019 ◽  
Author(s):  
O. Fleetwood ◽  
M.A. Kasimova ◽  
A.M. Westerlund ◽  
L. Delemotte
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Ion Channel ◽  
Conformational Changes ◽  
Molecular Simulations ◽  
Principal Component ◽  
Voltage Sensitivity ◽  
Learning Tools ◽  
Biomolecular Systems ◽  
Dynamics Simulations

ABSTRACTBiomolecular simulations are intrinsically high dimensional and generate noisy datasets of ever increasing size. Extracting important features in the data is crucial for understanding the biophysical properties of molecular processes, but remains a big challenge. Machine learning (ML) provides powerful dimensionality reduction tools. However, such methods are often criticized to resemble black boxes with limited human-interpretable insight.We use methods from supervised and unsupervised ML to efficiently create interpretable maps of important features from molecular simulations. We benchmark the performance of several methods including neural networks, random forests and principal component analysis, using a toy model with properties reminiscent of macromolecular behavior. We then analyze three diverse biological processes: conformational changes within the soluble protein calmodulin, ligand binding to a G protein-coupled receptor and activation of an ion channel voltage-sensor domain, unravelling features critical for signal transduction, ligand binding and voltage sensing. This work demonstrates the usefulness of ML in understanding biomolecular states and demystifying complex simulations.STATEMENT OF SIGNIFICANCEUnderstanding how biomolecules function requires resolving the ensemble of structures they visit. Molecular dynamics simulations compute these ensembles and generate large amounts of data that can be noisy and need to be condensed for human interpretation. Machine learning methods are designed to process large amounts of data, but are often criticized for their black-box nature and have historically been modestly used in the analysis of biomolecular systems. We demonstrate how machine learning tools can provide an interpretable overview of important features in a simulation dataset. We develop a protocol to quickly perform data-driven analysis of molecular simulations. This protocol is applied to identify the molecular basis of ligand binding to a receptor and of voltage sensitivity of an ion channel.

scholarly journals Biomolecular conformational changes and ligand binding: from kinetics to thermodynamics

2017 ◽  
Vol 8 (9) ◽  
pp. 6466-6473 ◽  
Author(s):  
Yong Wang ◽  
João Miguel Martins ◽  
Kresten Lindorff-Larsen
Keyword(s):  
Ligand Binding ◽  
Conformational Changes ◽  
Molecular Simulations ◽  
Kinetic Properties ◽  
Enhanced Sampling ◽  
Biomolecular Systems

Biomolecular systems such as protein–ligand complexes are governed by thermodynamic and kinetic properties that may be estimated at the same time through enhanced-sampling molecular simulations.

scholarly journals Computational Analysis of the Soluble Form of the Intracellular Chloride Ion Channel Protein CLIC1

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Peter M. Jones ◽  
Paul M. G. Curmi ◽  
Stella M. Valenzuela ◽  
Anthony M. George
Keyword(s):  
Ion Channel ◽  
Binding Site ◽  
Conformational Changes ◽  
Active Site ◽  
Bound States ◽  
Covalent Binding ◽  
Chloride Ion ◽  
Soluble Form ◽  
Active Site Cysteine ◽  
Dynamics Simulations

The chloride intracellular channel (CLIC) family of proteins has the remarkable property of maintaining both a soluble form and an integral membrane form acting as an ion channel. The soluble form is structurally related to the glutathione-S-transferase family, and CLIC can covalently bind glutathione via an active site cysteine. We report approximately 0.6 μs of molecular dynamics simulations, encompassing the three possible ligand-bound states of CLIC1, using the structure of GSH-bound human CLIC1. Noncovalently bound GSH was rapidly released from the protein, whereas the covalently ligand-bound protein remained close to the starting structure over 0.25 μs of simulation. In the unliganded state, conformational changes in the vicinity of the glutathione-binding site resulted in reduced reactivity of the active site thiol. Elastic network analysis indicated that the changes in the unliganded state are intrinsic to the protein architecture and likely represent functional transitions. Overall, our results are consistent with a model of CLIC function in which covalent binding of glutathione does not occur spontaneously but requires interaction with another protein to stabilise the GSH binding site and/or transfer of the ligand. The results do not indicate how CLIC1 undergoes a radical conformational change to form a transmembrane chloride channel but further elucidate the mechanism by which CLICs are redox controlled.

scholarly journals Allosteric binding site in a Cys-loop receptor ligand-binding domain unveiled in the crystal structure of ELIC in complex with chlorpromazine

2016 ◽  
Vol 113 (43) ◽  
pp. E6696-E6703 ◽  
Author(s):  
Mieke Nys ◽  
Eveline Wijckmans ◽  
Ana Farinha ◽  
Özge Yoluk ◽  
Magnus Andersson ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Ion Channel ◽  
Binding Site ◽  
Binding Domain ◽  
Ligand Binding Domain ◽  
Channel Pore ◽  
X Ray ◽  
Electrophysiological Recordings ◽  
Channel Opening ◽  
Dynamics Simulations

Pentameric ligand-gated ion channels or Cys-loop receptors are responsible for fast inhibitory or excitatory synaptic transmission. The antipsychotic compound chlorpromazine is a widely used tool to probe the ion channel pore of the nicotinic acetylcholine receptor, which is a prototypical Cys-loop receptor. In this study, we determine the molecular determinants of chlorpromazine binding in the Erwinia ligand-gated ion channel (ELIC). We report the X-ray crystal structures of ELIC in complex with chlorpromazine or its brominated derivative bromopromazine. Unexpectedly, we do not find a chlorpromazine molecule in the channel pore of ELIC, but behind the β8–β9 loop in the extracellular ligand-binding domain. The β8–β9 loop is localized downstream from the neurotransmitter binding site and plays an important role in coupling of ligand binding to channel opening. In combination with electrophysiological recordings from ELIC cysteine mutants and a thiol-reactive derivative of chlorpromazine, we demonstrate that chlorpromazine binding at the β8–β9 loop is responsible for receptor inhibition. We further use molecular-dynamics simulations to support the X-ray data and mutagenesis experiments. Together, these data unveil an allosteric binding site in the extracellular ligand-binding domain of ELIC. Our results extend on previous observations and further substantiate our understanding of a multisite model for allosteric modulation of Cys-loop receptors.

scholarly journals Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning

2020 ◽  
Vol 412 (26) ◽  
pp. 7085-7097
Author(s):  
Rebecca Brendel ◽  
Sebastian Schwolow ◽  
Sascha Rohn ◽  
Philipp Weller
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Detection System ◽  
Drift Time ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Learning Tools

Abstract For the first time, a prototype HS-GC-MS-IMS dual-detection system is presented for the analysis of volatile organic compounds (VOCs) in fields of quality control of brewing hop. With a soft ionization and drift time-based ion separation in IMS and a hard ionization and m/z-based separation in MS, substance identification in the case of co-elution was improved, substantially. Machine learning tools were used for a non-targeted screening of the complex VOC profiles of 65 different hop samples for similarity search by principal component analysis (PCA) followed by hierarchical cluster analysis (HCA). Partial least square regression (PLSR) was applied to investigate the observed correlation between the volatile profile and the α-acid content of hops and resulted in a standard error of prediction of only 1.04% α-acid. This promising volatilomic approach shows clearly the potential of HS-GC-MS-IMS in combination with machine learning for the enhancement of future quality assurance of hops.

scholarly journals A Heuristic Derived from Analysis of the Ion Channel Structural Proteome Permits the Rapid Identification of Hydrophobic Gates

2018 ◽  
Author(s):  
Shanlin Rao ◽  
Gianni Klesse ◽  
Phillip J. Stansfeld ◽  
Stephen J. Tucker ◽  
Mark S.P. Sansom
Keyword(s):  
Machine Learning ◽  
Ion Channels ◽  
Ion Channel ◽  
Functional State ◽  
Conformational Changes ◽  
Rapid Identification ◽  
Ion Permeation ◽  
Ionic Flux ◽  
Easy Method ◽  
Channel Structures

AbstractIon channel proteins control ionic flux across biological membranes through conformational changes in their transmembrane pores. An exponentially increasing number of channel structures captured in different conformational states are now being determined. However, these newly-resolved structures are commonly classified as either open or closed based solely on the physical dimensions of their pore and it is now known that more accurate annotation of their conductive state requires an additional assessment of the effect of pore hydrophobicity. A narrow hydrophobic gate region may disfavour liquid-phase water, leading to local de-wetting which will form an energetic barrier to water and ion permeation without steric occlusion of the pore. Here we quantify the combined influence of radius and hydrophobicity on pore de-wetting by applying molecular dynamics simulations and machine learning to nearly 200 ion channel structures. This allows us to propose a simple simulation-free heuristic model that rapidly and accurately predicts the presence of hydrophobic gates. This not only enables the functional annotation of new channel structures as soon as they are determined, but may also facilitate the design of novel nanopores controlled by hydrophobic gates.Significance statementIon channels are nanoscale protein pores in cell membranes. An exponentially increasing number of structures for channels means that computational methods for predicting their functional state are needed. Hydrophobic gates in ion channels result in local de-wetting of pores which functionally closes them to water and ion permeation. We use simulations of water behaviour within nearly 200 different ion channel structures to explore how the radius and hydrophobicity of pores determine their hydration vs. de-wetting behaviour. Machine learning-assisted analysis of these simulations enables us to propose a simple model for this relationship. This allows us to present an easy method for the rapid prediction of the functional state of new channel structures as they emerge.

scholarly journals Enhancing Sidechain Rotamer Sampling Using Non-Equilibrium Candidate Monte Carlo

2019 ◽  
Author(s):  
Kalistyn H. Burley ◽  
Samuel C. Gill ◽  
Nathan M. Lim ◽  
David Mobley
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Ligand Binding ◽  
Electrostatic Interactions ◽  
Molecular Simulations ◽  
Adequate Sampling ◽  
Binding Pockets ◽  
Sidechain Rotamers ◽  
Dynamics Simulations ◽  
Non Equilibrium

<div>Molecular simulations are a valuable tool for studying biomolecular motions and thermodynamics. However, such motions can be slow compared to simulation timescales, yet critical. Specifically, adequate sampling of sidechain motions in protein binding pockets proves crucial for obtaining accurate estimates of ligand binding free energies from molecular simulations. The timescale of sidechain rotamer flips can range from a few ps to several hundred ns or longer, particularly in crowded environments like the interior of proteins. Here, we apply a mixed non-equilibrium candidate Monte Carlo (NCMC)/molecular dynamics (MD) method to enhance sampling of sidechain rotamers. The NCMC portion of our method applies a switching protocol wherein the steric and electrostatic interactions between target sidechain atoms and the surrounding environment are cycled off and then back on during the course of a move proposal. Between NCMC move proposals, simulation of the system continues via traditional molecular dynamics. Here, we first validate this approach on a simple, solvated valine-alanine dipeptide system and then apply it to a well-studied model ligand binding site in T4 lysozyme L99A. We compute the rate of rotamer transitions for a valine sidechain using our approach and compare it to that of traditional molecular dynamics simulations. Here, we show that our NCMC/MD method substantially enhances sidechain sampling, especially in systems where the torsional barrier to rotation is high (>10 kcal/mol). These barriers can be intrinsic torsional barriers or steric barriers imposed by the environment.</div><div>Overall, this may provide a promising strategy to selectively improve sidechain sampling in molecular simulations.</div>

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