scholarly journals Hot-spots and their contribution to the self-assembly of the viral capsid: in-silico prediction and analysis

2019 ◽  
Author(s):  
Armando Díaz-Valle ◽  
José Marcos Falcón-González ◽  
Mauricio Carrillo-Tripp
Keyword(s):  
Free Energy ◽  
Self Assembly ◽  
Hot Spots ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
The Self ◽  
Atomic Scale ◽  
Viral Capsid ◽  
Macromolecular Complex ◽  
Single Mutation

AbstractIn order to rationally design biopolymers that mimic biological functions, first, we need to elucidate the molecular mechanisms followed by nature. For example, the viral capsid is a macromolecular complex formed by self-assembled proteins which, in many cases, are biopolymers with an identical amino acid sequence. Specific protein-protein interactions drive the capsid self-assembly process, leading to several distinct protein interfaces. Following the hot-spot hypothesis, we propose a conservation-based methodology to identify those interface residues that are crucial elements on the self-assembly and thermodynamic stability of the capsid. We validate our predictions by computational free energy calculations using an atomic-scale molecular model of an icosahedral virus. Our results show that a single mutation in any of the hot-spots significantly perturbs the quaternary interaction, decreasing the absolute value of the binding free energy, without altering the tertiary structure. Our methodology can lead to a strategy to rationally modulate the capsid’s thermodynamic properties.

scholarly journals Hot Spots and Their Contribution to the Self-Assembly of the Viral Capsid: In Silico Prediction and Analysis

2019 ◽  
Vol 20 (23) ◽  
pp. 5966 ◽  
Author(s):  
Armando Díaz-Valle ◽  
José Marcos Falcón-González ◽  
Mauricio Carrillo-Tripp
Keyword(s):  
Free Energy ◽  
Self Assembly ◽  
Hot Spots ◽  
Binding Free Energy ◽  
Hot Spot ◽  
Free Energy Calculations ◽  
The Self ◽  
Viral Capsid ◽  
Macromolecular Complex ◽  
Single Mutation

The viral capsid is a macromolecular complex formed by a defined number of self-assembled proteins, which, in many cases, are biopolymers with an identical amino acid sequence. Specific protein–protein interactions (PPI) drive the capsid self-assembly process, leading to several distinct protein interfaces. Following the PPI hot spot hypothesis, we present a conservation-based methodology to identify those interface residues hypothesized to be crucial elements on the self-assembly and thermodynamic stability of the capsid. We validate the predictions through a rigorous physical framework which integrates molecular dynamics simulations and free energy calculations by Umbrella sampling and the potential of mean force using an all-atom molecular representation of the capsid proteins of an icosahedral virus in an explicit solvent. Our results show that a single mutation in any of the structure-conserved hot spots significantly perturbs the quaternary protein–protein interaction, decreasing the absolute value of the binding free energy, without altering the protein’s secondary nor tertiary structure. Our conservation-based hot spot prediction methodology can lead to strategies to rationally modulate the capsid’s thermodynamic properties.

scholarly journals Hot-spots and their contribution to the self-assembly of the viral capsid: in-vitro validation

2019 ◽  
Author(s):  
Alejandra Gabriela Valdez-Lara ◽  
Mariana Andrade-Medina ◽  
Josué Alejandro Alemán-Vilis ◽  
Aldo Adrián Pérez-Montoya ◽  
Nayely Pineda-Aguilar ◽  
...  
Keyword(s):  
Self Assembly ◽  
Hot Spots ◽  
Point Mutations ◽  
The Self ◽  
Viral Capsid ◽  
Single Mutation ◽  
Chlorotic Mottle Virus ◽  
Chlorotic Mottle ◽  
Interface Residues

AbstractThe viral capsid is a macromolecular complex formed by self-assembled proteins (CPs) which, in many cases, are biopolymers with an identical amino acid sequence. Specific CP-CP interactions drive the capsid self-assembly process. However, it is believed that only a small set of protein-protein interface residues significantly contribute to the formation of the capsid; the so-called “hot-spots”. Here, we investigate the effect of in-vitro point-mutations on the Bromoviridae family structure-conserved interface residues of the icosahedral Cowpea Chlorotic Mottle Virus, previously hypothesized as hot-spots. We study the self-assembly of those mutated recombinant CPs for the formation of capsids by Thermal Shift Assay (TSA). We show that the TSA biophysical technique is a reliable way to characterize capsid assembly. Our results show that point-mutations on non-conserved interface residues produce capsids indistinguishable from the wild-type. In contrast, a single mutation on structure-conserved residues E176 or V189 prevents the formation of the capsid while maintaining the tertiary fold of the CP. Our findings provide experimental evidence of the in-silico conservation-based hot-spot prediction accuracy. As a whole, our methodology provides a framework that could aid in the rational development of molecules to inhibit virus formation, or advance capsid bioengineering to design for their stability, function and applications.

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>

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 Molecular modeling study on the drug resistance mechanism of NS5B polymerase to TMC647055

2016 ◽  
Vol 94 (2) ◽  
pp. 147-158 ◽  
Author(s):  
Huiqun Wang ◽  
Wei Cui ◽  
Chenchen Guo ◽  
Bo-Zhen Chen ◽  
Mingjuan Ji
Keyword(s):  
Drug Resistance ◽  
Free Energy ◽  
Rational Design ◽  
Binding Free Energy ◽  
Resistance Mechanism ◽  
Free Energy Calculations ◽  
Side Chain ◽  
Free Energy Decomposition ◽  
Hcv Ns5b ◽  
Ns5b Polymerase

NS5B polymerase plays an important role in viral replication machinery. TMC647055 (TMC) is a novel and potent non-nucleoside inhibitor of the HCV NS5B polymerase. However, mutations that result in drug resistance to TMC have been reported. In this study, we used molecular dynamics (MD) simulations, binding free energy calculations, and free energy decomposition to investigate the drug resistance mechanism of HCV to TMC resulting from L392I, P495T, P495S, and P495L mutations in NS5B polymerase. From the calculated results we determined that the decrease in the binding affinity between TMC and NS5BL392I polymerase is mainly caused by the extra methyl group at the CB atom of Ile. The polarity of the side-chain of residue 495 has no distinct influence on residue 495 binding with TMC, whereas the smaller size of the side-chain of residue 495 causes a substantial decrease in the van der Walls interaction between TMC and residue 495. Moreover, the longer length of the side-chain of residue 495 has a significant effect on the electrostatic interaction between TMC and Arg-503. Finally, we performed the same calculations and detailed analysis on other 3 mutations (L392V, P495V, and P495I). The results further confirmed our conclusions. The computational results not only reveal the drug resistance mechanism between TMC647055 and NS5B polymerase, but also provide valuable information for the rational design of more potent non-nucleoside inhibitors targeting HCV NS5B polymerase.

Assessing the ligand selectivity of sphingosine kinases using molecular dynamics and MM-PBSA binding free energy calculations

2016 ◽  
Vol 12 (4) ◽  
pp. 1174-1182 ◽  
Author(s):  
Liang Fang ◽  
Xiaojian Wang ◽  
Meiyang Xi ◽  
Tianqi Liu ◽  
Dali Yin
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Model Building ◽  
Binding Free Energy ◽  
Homology Model ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Ligand Selectivity ◽  
Sphingosine Kinases ◽  
Binding Free Energy Calculations

Three residues of SK1 were identified important for selective SK1 inhibitory activity via SK2 homology model building, molecular dynamics simulation, and MM-PBSA studies.

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