scholarly journals Insights into Membrane Protein-Lipid Interactions from Free Energy Calculations

2019 ◽  
Author(s):  
Robin A. Corey ◽  
Owen N. Vickery ◽  
Mark S. P. Sansom ◽  
Phillip J. Stansfeld
Keyword(s):  
Free Energy ◽  
Membrane Protein ◽  
Protein Structures ◽  
Potential Of Mean Force ◽  
Free Energy Calculations ◽  
Coarse Grained ◽  
Computational Technique ◽  
Dynamic Simulations ◽  
Lipid Interactions ◽  
Energy Perturbation

AbstractIntegral membrane proteins are regulated by specific interactions with lipids from the surrounding bilayer. The structures of protein-lipid complexes can be determined through a combination of experimental and computational approaches, but the energetic basis of these interactions is difficult to resolve. Molecular dynamic simulations provide the primary computational technique to estimate the free energies of these interactions. We demonstrate that the energetics of protein-lipid interactions may be reliably and reproducibly calculated using three simulation-based approaches: potential of mean force calculations. alchemical free energy perturbation, and well-tempered metadynamics. We employ these techniques within the framework of a coarse-grained force field, and apply them to both bacterial and mammalian membrane protein-lipid systems. We demonstrate good agreement between the different techniques, providing a robust framework for their automated implementation within a pipeline for annotation of newly determined membrane protein structures.

scholarly journals Insights into Membrane Protein–Lipid Interactions from Free Energy Calculations

2019 ◽  
Vol 15 (10) ◽  
pp. 5727-5736 ◽  
Author(s):  
Robin A. Corey ◽  
Owen N. Vickery ◽  
Mark S. P. Sansom ◽  
Phillip J. Stansfeld
Keyword(s):  
Free Energy ◽  
Membrane Protein ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
Lipid Interactions

scholarly journals Insights Into Membrane Protein-Lipid Interactions from Free Energy Calculations

2020 ◽  
Vol 118 (3) ◽  
pp. 18a
Author(s):  
Robin A. Corey ◽  
Owen N. Vickery ◽  
Tanadet Pipatpolkai ◽  
Frances M. Ashcroft ◽  
Mark S. Sansom ◽  
...  
Keyword(s):  
Free Energy ◽  
Membrane Protein ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
Lipid Interactions

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

2020 ◽  
Author(s):  
Maximilian Kuhn ◽  
Stuart Firth-Clark ◽  
Paolo Tosco ◽  
Antonia S. J. S. Mey ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Free Energy Calculations ◽  
Distinct Advantage ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Energy Calculations ◽  
Kendall’S Τ ◽  
Experimental Values ◽  
Better Than

Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods. <br>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

scholarly journals How proteins open fusion pores: insights from molecular simulations

2020 ◽  
Author(s):  
H. Jelger Risselada ◽  
Helmut Grubmüller
Keyword(s):  
Free Energy ◽  
Fusion Proteins ◽  
Graphics Processing Units ◽  
Fusion Reaction ◽  
Molecular Simulations ◽  
Minimum Free Energy ◽  
Free Energy Calculations ◽  
Coarse Grained ◽  
Fusion Pore ◽  
Graphics Processing

AbstractFusion proteins can play a versatile and involved role during all stages of the fusion reaction. Their roles go far beyond forcing the opposing membranes into close proximity to drive stalk formation and fusion. Molecular simulations have played a central role in providing a molecular understanding of how fusion proteins actively overcome the free energy barriers of the fusion reaction up to the expansion of the fusion pore. Unexpectedly, molecular simulations have revealed a preference of the biological fusion reaction to proceed through asymmetric pathways resulting in the formation of, e.g., a stalk-hole complex, rim-pore, or vertex pore. Force-field based molecular simulations are now able to directly resolve the minimum free-energy path in protein-mediated fusion as well as quantifying the free energies of formed reaction intermediates. Ongoing developments in Graphics Processing Units (GPUs), free energy calculations, and coarse-grained force-fields will soon gain additional insights into the diverse roles of fusion proteins.

scholarly journals Distributions of experimental protein structures on coarse-grained free energy landscapes

2015 ◽  
Vol 143 (24) ◽  
pp. 243153 ◽  
Author(s):  
Kannan Sankar ◽  
Jie Liu ◽  
Yuan Wang ◽  
Robert L. Jernigan
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Coarse Grained ◽  
Energy Landscapes ◽  
Free Energy Landscapes

scholarly journals Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo

2020 ◽  
Author(s):  
Gregory Ross ◽  
Ellery Russell ◽  
Yuqing Deng ◽  
Chao Lu ◽  
Edward Harder ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Free Energy ◽  
Drug Discovery ◽  
Free Energy Calculations ◽  
Data Sets ◽  
Grand Canonical Monte Carlo ◽  
Enhanced Sampling ◽  
Computational Performance ◽  
Energy Perturbation ◽  
Grand Canonical

<div>The prediction of protein-ligand binding affinities using free energy perturbation (FEP) is becoming increasingly routine in structure-based drug discovery. Most FEP packages use molecular dynamics (MD) to sample the configurations of proteins and ligands, as MD is well-suited to capturing coupled motion. However, MD can be prohibitively inefficient at sampling water molecules that are buried within binding sites, which has severely limited the domain of applicability of FEP and its prospective usage in drug discovery. In this paper, we present an advancement of FEP that augments MD with grand canonical Monte Carlo (GCMC), an enhanced sampling method, to overcome the problem of sampling water. We accomplished this without degrading computational performance. On both old and newly assembled data sets of proteinligand complexes, we show that the use of GCMC in FEP is essential for accurate and robust predictions for ligand perturbations that disrupt buried water. <br></div>

scholarly journals Molecular stripping in theNF-κB/IκB/DNAgenetic regulatory network

2015 ◽  
Vol 113 (1) ◽  
pp. 110-115 ◽  
Author(s):  
Davit A. Potoyan ◽  
Weihua Zheng ◽  
Elizabeth A. Komives ◽  
Peter G. Wolynes
Keyword(s):  
Free Energy ◽  
Transcription Factor ◽  
Free Energy Calculations ◽  
Coarse Grained ◽  
Genetic Switch ◽  
Binary And Ternary Complexes ◽  
Landscape Models ◽  
Gene Switches ◽  
Dna Release ◽  
Dynamics Simulations

Genetic switches based on theNF-κB/IκB/DNAsystem are master regulators of an array of cellular responses. Recent kinetic experiments have shown thatIκBcan actively removeNF-κBbound to its genetic sites via a process called “molecular stripping.” This allows theNF-κB/IκB/DNAswitch to function under kinetic control rather than the thermodynamic control contemplated in the traditional models of gene switches. Using molecular dynamics simulations of coarse-grained predictive energy landscape models for the constituent proteins by themselves and interacting with the DNA we explore the functional motions of the transcription factorNF-κBand its various binary and ternary complexes with DNA and the inhibitorIκB. These studies show that the function of theNF-κB/IκB/DNAgenetic switch is realized via an allosteric mechanism. Molecular stripping occurs through the activation of a domain twist mode by the binding ofIκBthat occurs through conformational selection. Free energy calculations for DNA binding show that the binding ofIκBnot only results in a significant decrease of the affinity of the transcription factor for the DNA but also kinetically speeds DNA release. Projections of the free energy onto various reaction coordinates reveal the structural details of the stripping pathways.

Sign in / Sign up

Export Citation Format

Share Document