Ligand binding free energy and kinetics calculation in 2020

2020 ◽  
Vol 10 (4) ◽  
Author(s):  
Vittorio Limongelli
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Free Energy

Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

2020 ◽  
Author(s):  
E. Prabhu Raman ◽  
Thomas J. Paul ◽  
Ryan L. Hayes ◽  
Charles L. Brooks III
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Combinatorial Libraries ◽  
Free Energy Calculations ◽  
Lead Optimization ◽  
Efficient Estimation ◽  
Lead Compound

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>

scholarly journals Alchemical Absolute Protein-Ligand Binding Free Energies for Drug Design

2021 ◽  
Author(s):  
Yuriy Khalak ◽  
Gary Tresdern ◽  
Matteo Aldeghi ◽  
Hannah Magdalena Baumann ◽  
David L. Mobley ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Drug Design ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains...

Estimation of the ligand-binding free energy of checkpoint kinase 1 via non-equilibrium MD simulations

2020 ◽  
Vol 100 ◽  
pp. 107648 ◽  
Author(s):  
Nguyen Thi Mai ◽  
Ngo Thi Lan ◽  
Thien Y Vu ◽  
Phuong Thi Mai Duong ◽  
Nguyen Thanh Tung ◽  
...  
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Md Simulations ◽  
Checkpoint Kinase ◽  
Checkpoint Kinase 1 ◽  
Non Equilibrium

scholarly journals A Benchmark of Popular Free Energy Approaches Revealing the Inhibitors Binding to SARS-CoV2 Mpro

2020 ◽  
Author(s):  
Son Tung Ngo ◽  
Nguyen Minh Tam ◽  
Pham Minh Quan ◽  
Trung Hai Nguyen
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Drug Target ◽  
Binding Free Energy ◽  
Essential Elements ◽  
Dominant Factor ◽  
Linear Interaction ◽  
Energy Perturbation ◽  
Binding Cleft

COVID-19 pandemic has killed millions of people worldwide since its outbreak in Dec 2019. The pandemic is caused by the SARS-CoV-2 virus whose main protease (Mpro) is a promising drug target since it plays a key role in viral proliferation and replication. Currently, designing an effective therapy is an urgent task, which requires accurately estimating ligand-binding free energy to the SARS-CoV-2 Mpro. However, it should be noted that the accuracy of a free energy method probably depends on the protein target. A highly accurate approach for some targets may fail to produce a reasonable correlation with experiment when a novel enzyme is considered as a drug target. Therefore, in this context, the ligand-binding affinity to SARS-CoV-2 Mpro was calculated via various approaches. The Autodock Vina (Vina) and Autodock4 (AD4) packages were manipulated to preliminary investigate the ligand-binding affinity and pose to the SARS-CoV-2 Mpro. The binding free energy was then refined using the fast pulling of ligand (FPL), linear interaction energy (LIE), molecular mechanics-Poission Boltzmann surface area (MM-PBSA), and free energy perturbation (FEP) methods. The benchmark results indicated that for docking calculations, Vina is more accurate than AD4 and for free energy methods, FEP is the most accurate followed by LIE, FPL and MM-PBSA (FEP > LIE > FPL > MM-PBSA). Moreover, the binding mechanism was also revealed by atomistic simulations. The vdW interaction is the dominant factor. The residues <i>Thr25</i>, <i>Thr26</i>, <i>His41</i>, <i>Ser46</i>, <i>Asn142</i>, <i>Gly143</i>, <i>Cys145</i>, <i>Glu166</i>, and <i>Gln189</i> are essential elements affecting on the binding process. Furthermore, the <i>Ser46</i> and related residues probably are important elements affecting the enlarge/dwindle of the SARS-CoV-2 Mpro binding cleft. The benchmark probably guide for further investigations using computational approaches.

Modelling flexible protein–ligand binding in p38α MAP kinase using the QUBE force field

2020 ◽  
Vol 56 (6) ◽  
pp. 932-935 ◽  
Author(s):  
Joshua T. Horton ◽  
Alice E. A. Allen ◽  
Daniel J. Cole
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Force Field ◽  
Map Kinase ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Quantum Mechanical ◽  
Flexible Protein ◽  
Binding Free Energy Calculations ◽  
P38α Map Kinase

The accuracy of quantum mechanical bespoke (QUBE) force fields for protein–ligand binding free energy calculations are benchmarked against experiment.

scholarly journals Imaging G protein–coupled receptors while quantifying their ligand-binding free-energy landscape

2015 ◽  
Vol 12 (9) ◽  
pp. 845-851 ◽  
Author(s):  
David Alsteens ◽  
Moritz Pfreundschuh ◽  
Cheng Zhang ◽  
Patrizia M Spoerri ◽  
Shaun R Coughlin ◽  
...  
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
G Protein ◽  
Binding Free Energy ◽  
Energy Landscape ◽  
G Protein Coupled Receptors ◽  
Free Energy Landscape ◽  
G Protein Coupled
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