Activation Free Energy, Substrate Binding Free Energy, and Enzyme Efficiency Fall in a Very Narrow Range of Values for Most Enzymes

ACS Catalysis ◽  
2020 ◽  
Vol 10 (15) ◽  
pp. 8444-8453
Author(s):  
Sérgio F. Sousa ◽  
Ana R. Calixto ◽  
Pedro Ferreira ◽  
Maria J. Ramos ◽  
Carmay Lim ◽  
...  
Keyword(s):  
Free Energy ◽  
Narrow Range ◽  
Binding Free Energy ◽  
Substrate Binding ◽  
Energy Substrate ◽  
Activation Free Energy ◽  
Enzyme Efficiency ◽  
Range Of Values

scholarly journals Enzyme catalysis by entropy without Circe effect

2016 ◽  
Vol 113 (9) ◽  
pp. 2406-2411 ◽  
Author(s):  
Masoud Kazemi ◽  
Fahmi Himo ◽  
Johan Åqvist
Keyword(s):  
Free Energy ◽  
Density Functional ◽  
Cytidine Deaminase ◽  
Binding Free Energy ◽  
Substrate Binding ◽  
Enzymatic Reaction ◽  
Valence Bond ◽  
Activation Entropy ◽  
Uncatalyzed Reaction ◽  
Rate Limiting

Entropic effects have often been invoked to explain the extraordinary catalytic power of enzymes. In particular, the hypothesis that enzymes can use part of the substrate-binding free energy to reduce the entropic penalty associated with the subsequent chemical transformation has been very influential. The enzymatic reaction of cytidine deaminase appears to be a distinct example. Here, substrate binding is associated with a significant entropy loss that closely matches the activation entropy penalty for the uncatalyzed reaction in water, whereas the activation entropy for the rate-limiting catalytic step in the enzyme is close to zero. Herein, we report extensive computer simulations of the cytidine deaminase reaction and its temperature dependence. The energetics of the catalytic reaction is first evaluated by density functional theory calculations. These results are then used to parametrize an empirical valence bond description of the reaction, which allows efficient sampling by molecular dynamics simulations and computation of Arrhenius plots. The thermodynamic activation parameters calculated by this approach are in excellent agreement with experimental data and indeed show an activation entropy close to zero for the rate-limiting transition state. However, the origin of this effect is a change of reaction mechanism compared the uncatalyzed reaction. The enzyme operates by hydroxide ion attack, which is intrinsically associated with a favorable activation entropy. Hence, this has little to do with utilization of binding free energy to pay the entropic penalty but rather reflects how a preorganized active site can stabilize a reaction path that is not operational in solution.

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>

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.

scholarly journals Binding free energy predictions in host-guest systems using Autodock4. A retrospective analysis on SAMPL6, SAMPL7 and SAMPL8 challenges

2021 ◽  
Author(s):  
Lorenzo Casbarra ◽  
Piero Procacci
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Drug Discovery ◽  
Binding Free Energy ◽  
Cost Ratio ◽  
Benefit Cost Ratio ◽  
Docking Program ◽  
Benefit Cost ◽  
Overall Reliability ◽  
Absolute Binding Free Energy

AbstractWe systematically tested the Autodock4 docking program for absolute binding free energy predictions using the host-guest systems from the recent SAMPL6, SAMPL7 and SAMPL8 challenges. We found that Autodock4 behaves surprisingly well, outperforming in many instances expensive molecular dynamics or quantum chemistry techniques, with an extremely favorable benefit-cost ratio. Some interesting features of Autodock4 predictions are revealed, yielding valuable hints on the overall reliability of docking screening campaigns in drug discovery projects.

scholarly journals TIES 20: Relative Binding Free Energy with a Flexible Superimposition Algorithm and Partial Ring Morphing

2021 ◽  
Vol 17 (2) ◽  
pp. 1250-1265
Author(s):  
Mateusz K. Bieniek ◽  
Agastya P. Bhati ◽  
Shunzhou Wan ◽  
Peter V. Coveney
Keyword(s):  
Free Energy ◽  
Binding Free Energy ◽  
Relative Binding
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