scholarly journals Calculation of Binding Free Energy and Kinetic Rates with Flexible Protein Docking

2021 ◽  
Vol 61 (6) ◽  
pp. 398-399
Author(s):  
Duy Phuoc TRAN ◽  
Akio KITAO
Keyword(s):  
Free Energy ◽  
Binding Free Energy ◽  
Protein Docking ◽  
Kinetic Rates ◽  
Flexible Protein

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 Molecular mechanism of evolution and human infection with the novel coronavirus (2019-nCoV)

2020 ◽  
Author(s):  
Jiahua He ◽  
Huanyu Tao ◽  
Yumeng Yan ◽  
Sheng-You Huang ◽  
Yi Xiao
Keyword(s):  
Free Energy ◽  
Molecular Mechanisms ◽  
Vaccine Development ◽  
Binding Free Energy ◽  
Protein Docking ◽  
Human Infection ◽  
Host Cells ◽  
Spike Protein ◽  
Temperature Sensitive ◽  
Health Crisis

AbstractSince December, 2019, an outbreak of pneumonia caused by the new coronavirus (2019-nCoV) has hit the city of Wuhan in the Hubei Province. With the continuous development of the epidemic, it has become a national public health crisis and calls for urgent antiviral treatments or vaccines. The spike protein on the coronavirus envelope is critical for host cell infection and virus vitality. Previous studies showed that 2019-nCoV is highly homologous to human SARS-CoV and attaches host cells though the binding of the spike receptor binding domain (RBD) domain to the angiotensin-converting enzyme II (ACE2). However, the molecular mechanisms of 2019-nCoV binding to human ACE2 and evolution of 2019-nCoV remain unclear. In this study, we have extensively studied the RBD-ACE2 complex, spike protein, and free RBD systems of 2019-nCoV and SARS-CoV using protein-protein docking and molecular dynamics (MD) simulations. It was shown that the RBD-ACE2 binding free energy for 2019-nCoV is significantly lower than that for SARS-CoV, which is consistent with the fact that 2019-nCoV is much more infectious than SARS-CoV. In addition, the spike protein of 2019-nCoV shows a significantly lower free energy than that of SARS-CoV, suggesting that 2019-nCoV is more stable and able to survive a higher temperature than SARS-CoV. This may also provide insights into the evolution of 2019-nCoV because SARS-like coronaviruses are thought to have originated in bats that are known to have a higher body-temperature than humans. It was also revealed that the RBD of 2019-nCoV is much more flexible especially near the binding site and thus will have a higher entropy penalty upon binding ACE2, compared to the RBD of SARS-CoV. That means that 2019-nCoV will be much more temperature-sensitive in terms of human infection than SARS-CoV. With the rising temperature, 2019-nCoV is expected to decrease its infection ability much faster than SARS-CoV, and get controlled more easily. The present findings are expected to be helpful for the disease prevention and control as well as drug and vaccine development of 2019-nCoV.

scholarly journals Absolute Binding Free Energy Calculations for Highly Flexible Protein MDM2 and Its Inhibitors

2020 ◽  
Vol 21 (13) ◽  
pp. 4765 ◽  
Author(s):  
Nidhi Singh ◽  
Wenjin Li
Keyword(s):  
Free Energy ◽  
Binding Free Energy ◽  
Absolute Error ◽  
Free Energy Calculations ◽  
Binding Affinities ◽  
Energy Calculations ◽  
Murine Double Minute ◽  
Flexible Protein ◽  
Binding Free Energy Calculations ◽  
Absolute Binding Free Energy

Reliable prediction of binding affinities for ligand-receptor complex has been the primary goal of a structure-based drug design process. In this respect, alchemical methods are evolving as a popular choice to predict the binding affinities for biomolecular complexes. However, the highly flexible protein-ligand systems pose a challenge to the accuracy of binding free energy calculations mostly due to insufficient sampling. Herein, integrated computational protocol combining free energy perturbation based absolute binding free energy calculation with free energy landscape method was proposed for improved prediction of binding free energy for flexible protein-ligand complexes. The proposed method is applied to the dataset of various classes of p53-MDM2 (murine double minute 2) inhibitors. The absolute binding free energy calculations for MDMX (murine double minute X) resulted in a mean absolute error value of 0.816 kcal/mol while it is 3.08 kcal/mol for MDM2, a highly flexible protein compared to MDMX. With the integration of the free energy landscape method, the mean absolute error for MDM2 is improved to 1.95 kcal/mol.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Author(s):  
Christina Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Large Scale Assessment ◽  
New Public ◽  
Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

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>

Application of the ESMACS Binding Free Energy Protocol to a Highly Varied Ligand Dataset: Lactate Dehydogenase A

2019 ◽  
Author(s):  
David Wright ◽  
Fouad Husseini ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
...  
Keyword(s):  
Free Energy ◽  
Surface Area ◽  
Binding Free Energy ◽  
Normal Mode Analysis ◽  
Binding Mode ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Mode Analysis ◽  
Energy Calculation ◽  
Accessible Surface

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>

Modelling Flexible Protein-Ligand Binding in p38α MAP Kinase using the QUBE Force Field

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Map Kinase ◽  
Binding Free Energy ◽  
Quantum Mechanical ◽  
Enhanced Sampling ◽  
Relative Binding ◽  
Stringent Test ◽  
Flexible Protein ◽  
P38α Map Kinase

<div><div><div><p>The quantum mechanical bespoke (QUBE) force field is used to retrospectively calculate the relative binding free energy of a series of 17 flexible inhibitors of p38α MAP kinase. The size and flexibility of the chosen molecules represent a stringent test of the derivation of force field parameters from quantum mechanics, and enhanced sampling is required to reduce the dependence of the results on the starting structure. Competitive accuracy with a widely-used biological force field is achieved, indicating that quantum mechanics derived force fields are approaching the accuracy required to provide guidance in prospective drug discovery campaigns.</p></div></div></div>

Association/dissociation Mechanisms of Intrinsically Disordered Region of Protein Beyond Conformational Selection and Induced Fit

2019 ◽  
Author(s):  
Duy Phuoc Tran ◽  
Akio Kitao
Keyword(s):  
Free Energy ◽  
Transcriptional Activation ◽  
Binding Free Energy ◽  
Energy Structure ◽  
Induced Fit ◽  
Hydrogen Bond Formation ◽  
Conformational Selection ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Region ◽  
Dissociation Mechanisms

<p>We investigate association and dissociation mechanisms of a typical intrinsically disordered region (IDR), transcriptional activation subdomain of tumor repressor protein p53 (TAD-p53) with murine double-minute clone 2 protein (MDM2). Using the combination of cycles of association and dissociation parallel cascade molecular dynamics, multiple standard MD, and Markov state model, we are successful in obtaining the lowest free energy structure of MDM2/TAD-p53 complex as the structure very close to that in crystal without prior knowledge. This method also reproduces the experimentally measured standard binding free energy, and association and dissociation rate constants solely with the accumulated MD simulation cost of 11.675 μs, in spite of the fact that actual dissociation occurs in the order of a second. Although there exist a few complex intermediates with similar free energies, TAD-p53 first binds MDM2 as the second lowest free energy intermediate dominantly (> 90% in flux), taking a form similar to one of the intermediate structures in its monomeric state. The mechanism of this step has a feature of conformational selection. In the second step, dehydration of the interface, formation of π-π stackings of the side-chains, and main-chain relaxation/hydrogen bond formation to complete α-helix take place, showing features of induced fit. In addition, dehydration (dewetting) is a key process for the final relaxation around the complex interface. These results demonstrate a more fine-grained view of the IDR association/dissociation beyond classical views of protein conformational change upon binding.</p>

scholarly journals Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Germano Heinzelmann ◽  
Michael K. Gilson
Keyword(s):  
Free Energy ◽  
Early Stage ◽  
Binding Free Energy ◽  
Low Cost ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Drug Candidates ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies

AbstractAbsolute binding free energy calculations with explicit solvent molecular simulations can provide estimates of protein-ligand affinities, and thus reduce the time and costs needed to find new drug candidates. However, these calculations can be complex to implement and perform. Here, we introduce the software BAT.py, a Python tool that invokes the AMBER simulation package to automate the calculation of binding free energies for a protein with a series of ligands. The software supports the attach-pull-release (APR) and double decoupling (DD) binding free energy methods, as well as the simultaneous decoupling-recoupling (SDR) method, a variant of double decoupling that avoids numerical artifacts associated with charged ligands. We report encouraging initial test applications of this software both to re-rank docked poses and to estimate overall binding free energies. We also show that it is practical to carry out these calculations cheaply by using graphical processing units in common machines that can be built for this purpose. The combination of automation and low cost positions this procedure to be applied in a relatively high-throughput mode and thus stands to enable new applications in early-stage drug discovery.

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