An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein–ligand binding affinity

Nanoscale ◽  
2020 ◽  
Vol 12 (19) ◽  
pp. 10737-10750 ◽  
Author(s):  
Kaifang Huang ◽  
Song Luo ◽  
Yalong Cong ◽  
Susu Zhong ◽  
John Z. H. Zhang ◽  
...  
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Energy Term ◽  
Entropic Contribution

Modifying the energy term and considering the entropic contribution by IE method significantly improve the accuracy of predicted binding free energy in MM/PBSA method.

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 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.

scholarly journals Effects of Water Models on Binding Affinity: Evidence from All-Atom Simulation of Binding of Tamiflu to A/H5N1 Neuraminidase

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Trang Truc Nguyen ◽  
Man Hoang Viet ◽  
Mai Suan Li
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Force Fields ◽  
Binding Free Energy ◽  
Molecular Mechanic ◽  
Water Density ◽  
Wild Type ◽  
Oseltamivir Carboxylate ◽  
Water Models

The influence of water models SPC, SPC/E, TIP3P, and TIP4P on ligand binding affinity is examined by calculating the binding free energyΔGbindof oseltamivir carboxylate (Tamiflu) to the wild type of glycoprotein neuraminidase from the pandemic A/H5N1 virus.ΔGbindis estimated by the Molecular Mechanic-Poisson Boltzmann Surface Area method and all-atom simulations with different combinations of these aqueous models and four force fields AMBER99SB, CHARMM27, GROMOS96 43a1, and OPLS-AA/L. It is shown that there is no correlation between the binding free energy and the water density in the binding pocket in CHARMM. However, for three remaining force fieldsΔGbinddecays with increase of water density. SPC/E provides the lowest binding free energy for any force field, while the water effect is the most pronounced in CHARMM. In agreement with the popular GROMACS recommendation, the binding score obtained by combinations of AMBER-TIP3P, OPLS-TIP4P, and GROMOS-SPC is the most relevant to the experiments. For wild-type neuraminidase we have found that SPC is more suitable for CHARMM than TIP3P recommended by GROMACS for studying ligand binding. However, our study for three of its mutants reveals that TIP3P is presumably the best choice for CHARMM.

scholarly journals Application of ESMACS Binding Free Energy Protocols to Diverse Datasets: Bromodomain-Containing Protein 4

2019 ◽  
Author(s):  
David Wright ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
Gary Tresadern ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Water Molecules ◽  
Calculation Data ◽  
Simulation Input ◽  
Explicit Water ◽  
Entropic Contribution

<div>We investigate the robustness of our ensemble molecular dynamics binding free energy protocols, known as ESMACS, to different choices of forcefield, starting structure and analysis. ESMACS is based on MMPBSA and we examinge the influence of multiple trajectories, explicit water molecules and estimates of the entropic contribution to the binding free energy.</div><div><br></div><div>Simulation input and binding affinity calculation data:</div>https://doi.org/10.5281/zenodo.1484050

scholarly journals Application of ESMACS Binding Free Energy Protocols to Diverse Datasets: Bromodomain-Containing Protein 4

2019 ◽  
Author(s):  
David Wright ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
Gary Tresadern ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Water Molecules ◽  
Calculation Data ◽  
Simulation Input ◽  
Explicit Water ◽  
Entropic Contribution

<div>We investigate the robustness of our ensemble molecular dynamics binding free energy protocols, known as ESMACS, to different choices of forcefield, starting structure and analysis. ESMACS is based on MMPBSA and we examinge the influence of multiple trajectories, explicit water molecules and estimates of the entropic contribution to the binding free energy.</div><div><br></div><div>Simulation input and binding affinity calculation data:</div>https://doi.org/10.5281/zenodo.1484050

scholarly journals Overview of the SAMPL6 pKa Challenge: Evaluating small molecule microscopic and macroscopic pKa predictions

2020 ◽  
Author(s):  
Mehtap Işık ◽  
Ariën S. Rustenburg ◽  
Andrea Rizzi ◽  
M. R. Gunner ◽  
David L. Mobley ◽  
...  
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Small Molecule ◽  
Kinase Inhibitors ◽  
Dissociation Constants ◽  
Binding Free Energy ◽  
Acid Dissociation ◽  
Prediction Methods ◽  
Protonation State

AbstractThe prediction of acid dissociation constants (pKa) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (log D), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pKa Challenge was the first time that a separate challenge was conducted for evaluating pKa predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 log D Challenge, caused by protonation state and pKa prediction issues. The goal of the pKa challenge was to assess the performance of contemporary pKa prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 pKa distinct prediction methods. In addition to blinded submissions, four widely used pKa prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic pKa predictions allowed in-depth evaluation of pKa prediction performance. This article highlights deficiencies of typical pKa prediction evaluation approaches when the distinction between microscopic and macroscopic pKas is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic pKa predictions guided by the available experimental data. Top-performing submissions for macroscopic pKa predictions achieved RMSE of 0.7-1.0 pKa units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic pKas predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1-3 pKa units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic pKa predictions, especially errors in charged tautomers. The degree of inaccuracy in pKa predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand pKa by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.

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 ◽  
Urgent Task ◽  
Energy Perturbation

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>Thr26</i>, <i>His41</i>, <i>Ser46</i>, <i>Asn142</i>, <i>Gly143</i>, <i>Cys145</i>, <i>His164</i>, <i>Glu166</i>, and <i>Gln189</i> are essential elements affecting on the binding process. The benchmark probably guide for further investigations using computational approaches.

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 Application of ESMACS Binding Free Energy Protocols to Diverse Datasets: Bromodomain-Containing Protein 4

2018 ◽  
Author(s):  
David Wright ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
Gary Tresadern ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Water Molecules ◽  
Calculation Data ◽  
Simulation Input ◽  
Explicit Water ◽  
Entropic Contribution

<div>We investigate the robustness of our ensemble molecular dynamics binding free energy protocols, known as ESMACS, to different choices of forcefield, starting structure and analysis. ESMACS is based on MMPBSA and we examinge the influence of multiple trajectories, explicit water molecules and estimates of the entropic contribution to the binding free energy.</div><div><br></div><div>Simulation input and binding affinity calculation data:</div>https://doi.org/10.5281/zenodo.1484050

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 ◽  
Urgent Task ◽  
Energy Perturbation

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>Thr26</i>, <i>His41</i>, <i>Ser46</i>, <i>Asn142</i>, <i>Gly143</i>, <i>Cys145</i>, <i>His164</i>, <i>Glu166</i>, and <i>Gln189</i> are essential elements affecting on the binding process. The benchmark probably guide for further investigations using computational approaches.

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