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 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.

Computational approaches for deciphering the equilibrium and kinetic properties of iron transport proteins

Metallomics ◽  
2017 ◽  
Vol 9 (11) ◽  
pp. 1513-1533 ◽  
Author(s):  
H. Abdizadeh ◽  
A. R. Atilgan ◽  
C. Atilgan ◽  
B. Dedeoglu
Keyword(s):  
Conformational Changes ◽  
Iron Transport ◽  
Molecular Simulations ◽  
Transport Proteins ◽  
Kinetic Properties ◽  
Computational Approaches ◽  
Iron Transport Proteins

Conformational changes in transferrin proteins predicted by molecular simulations.

CarR, a MarR-family regulator from Corynebacterium glutamicum, modulated antibiotic and aromatic compound resistance

2019 ◽  
Vol 476 (21) ◽  
pp. 3141-3159 ◽  
Author(s):  
Meiru Si ◽  
Can Chen ◽  
Zengfan Wei ◽  
Zhijin Gong ◽  
GuiZhi Li ◽  
...  
Keyword(s):  
Stress Response ◽  
Ligand Binding ◽  
Corynebacterium Glutamicum ◽  
Conformational Changes ◽  
Cysteine Oxidation ◽  
Transcriptional Regulatory ◽  
Ligand Free ◽  
Redox Sensing

Abstract MarR (multiple antibiotic resistance regulator) proteins are a family of transcriptional regulators that is prevalent in Corynebacterium glutamicum. Understanding the physiological and biochemical function of MarR homologs in C. glutamicum has focused on cysteine oxidation-based redox-sensing and substrate metabolism-involving regulators. In this study, we characterized the stress-related ligand-binding functions of the C. glutamicum MarR-type regulator CarR (C. glutamicum antibiotic-responding regulator). We demonstrate that CarR negatively regulates the expression of the carR (ncgl2886)–uspA (ncgl2887) operon and the adjacent, oppositely oriented gene ncgl2885, encoding the hypothetical deacylase DecE. We also show that CarR directly activates transcription of the ncgl2882–ncgl2884 operon, encoding the peptidoglycan synthesis operon (PSO) located upstream of carR in the opposite orientation. The addition of stress-associated ligands such as penicillin and streptomycin induced carR, uspA, decE, and PSO expression in vivo, as well as attenuated binding of CarR to operator DNA in vitro. Importantly, stress response-induced up-regulation of carR, uspA, and PSO gene expression correlated with cell resistance to β-lactam antibiotics and aromatic compounds. Six highly conserved residues in CarR were found to strongly influence its ligand binding and transcriptional regulatory properties. Collectively, the results indicate that the ligand binding of CarR induces its dissociation from the carR–uspA promoter to derepress carR and uspA transcription. Ligand-free CarR also activates PSO expression, which in turn contributes to C. glutamicum stress resistance. The outcomes indicate that the stress response mechanism of CarR in C. glutamicum occurs via ligand-induced conformational changes to the protein, not via cysteine oxidation-based thiol modifications.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2017 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2018 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

Toward an Enhanced Sampling Molecular Dynamics Method for Studying Ligand-Induced Conformational Changes in Proteins

2015 ◽  
Vol 119 (46) ◽  
pp. 14594-14603 ◽  
Author(s):  
Ole Juul Andersen ◽  
Julie Grouleff ◽  
Perri Needham ◽  
Ross C. Walker ◽  
Frank Jensen
Keyword(s):  
Molecular Dynamics ◽  
Conformational Changes ◽  
Molecular Dynamics Method ◽  
Enhanced Sampling ◽  
Dynamics Method
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