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. 2011 Jun;79(6):1940-51.
doi: 10.1002/prot.23018. Epub 2011 Apr 12.

Virtual screening using molecular simulations

Tianyi Yang  1 , Johnny C Wu, Chunli Yan, Yuanfeng Wang, Ray Luo, Michael B Gonzales, Kevin N Dalby, Pengyu Ren
Affiliations

Virtual screening using molecular simulations

Tianyi Yang et al. Proteins. 2011 Jun.

Abstract

Effective virtual screening relies on our ability to make accurate prediction of protein-ligand binding, which remains a great challenge. In this work, utilizing the molecular-mechanics Poisson-Boltzmann (or Generalized Born) surface area approach, we have evaluated the binding affinity of a set of 156 ligands to seven families of proteins, trypsin β, thrombin α, cyclin-dependent kinase (CDK), cAMP-dependent kinase (PKA), urokinase-type plasminogen activator, β-glucosidase A, and coagulation factor Xa. The effect of protein dielectric constant in the implicit-solvent model on the binding free energy calculation is shown to be important. The statistical correlations between the binding energy calculated from the implicit-solvent approach and experimental free energy are in the range of 0.56-0.79 across all the families. This performance is better than that of typical docking programs especially given that the latter is directly trained using known binding data whereas the molecular mechanics is based on general physical parameters. Estimation of entropic contribution remains the barrier to accurate free energy calculation. We show that the traditional rigid rotor harmonic oscillator approximation is unable to improve the binding free energy prediction. Inclusion of conformational restriction seems to be promising but requires further investigation. On the other hand, our preliminary study suggests that implicit-solvent based alchemical perturbation, which offers explicit sampling of configuration entropy, can be a viable approach to significantly improve the prediction of binding free energy. Overall, the molecular mechanics approach has the potential for medium to high-throughput computational drug discovery.

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Figures

Figure 1
Figure 1
Correlation between MM-GBSA predicted and experimental binding free energy. The R values shown in the figures are the Pearson product-moment correlation coefficients. The protein dielectric constant in MM-GBSA calculation was set to 4.0. A through F refer to the following protein targets, respectively: trypsin β, thrombin α, CDK+PKA, urokinase-type plasminogen activator, β-glucosidase A and coagulation factor Xa. The average standard deviations for MM-GBSA(MM-PBSA) calculations are 3.7(9.0), 2.0(5.6), 1.5(2.3), 1.4(2.0), 1.0(1.8) and 1.2(1.5) kcal/mol for trypsin β, thrombin α, CDK+PKA, urokinase-type plasminogen activator, β-glucosidase A and coagulation factor Xa, respectively.
Figure 2
Figure 2
Correlation between experimental binding free energies and MM-GBSA calculations using different dielectric constants for the families of trypsin β, thrombin α, CDK+PKA, urokinase-type plasminogen activator, β-glucosidase A and coagulation factor Xa.
Figure 3
Figure 3
Binding Pocket of PDB ID 1oif. The ligand is shown as lines. The two GLU residues close to each other are represented in sticks. According to PropKa , GLU166 has a pKa value 9.72 and GLU351 5.13.
Figure 4
Figure 4
Comparison of experimental – and calculated binding free energies from BAR/GK and PM-PB/SA calculations for trypsin-benzamidine analogs.
Figure 5
Figure 5
The effect of MD simulation lengths on the calculated binding affinity of the trypsin family. A) correlation coefficients between MM-GBSA calculation and experimental values. One configuration snapshot was recorded every picosecond. The forward direction (diamond markers) starts with the first snapshot recorded and is along the trajectory of the simulation. The backward (square markers) starts from the last snapshot and is along the time-reversed direction. B) Comparison of MM-GBSA (diamonds) and MM-PBSA predictions. Both use trajectory segments in forward direction starting from the beginning of simulations.
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