A Large-Scale Test of Free-Energy Simulation Estimates of Protein-Ligand Binding Affinities.

We have performed a large-scale test of alchemical perturbation calculations with the Bennett acceptance-ratio (BAR) approach to estimate relative affinities for the binding of 107 ligands to 10 different proteins. Employing 20-Å truncated spherical systems and only one intermediate state in the perturbations, we obtain an error of less than 4 kJ/mol for 54% of the studied relative affinities and a precision of 0.5 kJ/mol on average. However, only four of the proteins gave acceptable errors, correlations, and rankings. The results could be improved by using nine intermediate states in the simulations or including the entire protein in the simulations using periodic boundary conditions. However, 27 of the calculated affinities still gave errors of more than 4 kJ/mol, and for three of the proteins the results were not satisfactory. This shows that the performance of BAR calculations depends on the target protein and that several transformations gave poor results owing to limitations in the molecular-mechanics force field or the restricted sampling possible within a reasonable simulation time. Still, the BAR results are better than docking calculations for most of the proteins

The Normal-Mode Entropy in the MM/GBSA Method: Effect of System Truncation, Buffer Region, and Dielectric Constant

We have performed a systematic study of the entropy term in the MM/GBSA (molecular Mechanics combined with generalized Born and surface area solvation) approach to calculate ligand-binding affinities The entropies are calculated by a normal mode analysis of harmonic frequencies from minimized snapshots of molecular dynamics simulations. For computational reasons, these calculations have normally been performed on truncated systems. We have studied the binding of eight inhibitors of blood clotting factor Xa, nine ligands of ferritin, and two ligands of HIV-1 protease and show that removing protein residues with. distances. larger than 8-16 angstrom to the ligand, including a 4 angstrom shell of fixed protein residues and water molecules, change the absolute entropies by 1-5 kJ/mol on average. However, the change is systematic, so relative entropies for different ligands change by only 0.7-1.6 kJ/mol on average. Consequently, entropies from truncated systems give relative binding affinities that are identical to those obtained for the Whole protein within statistical uncertainty (172 kJ/mol). We have also tested to use a distance dependent dielectric constant in the minimization and. frequency calculation (epsilon = 4r), but it typically gives slightly different entropies and poorer binding, affinities. Therefore, we recommend entropies calculated with the smallest truncation radius (8 angstrom) and epsilon =1 Such an approach also gives an improved precision for the calculated binding free energies

Assessment of Computational Methods for Ligand Binding

Most drugs act on biomacromolecules. The Cost of developing new drugs is very high. A method to accurately predict binding affinities would be very useful. We have studied molecular mechanics with generalised Born and surface--area solvation (MM/GBSA) and alchemical free energy perturbation methods (FEP) for use in calculations of ligand binding energies. For the MM/GBSA method we have tested: Calculating the non-polar solvation term with the polarized continuum model, a method based on cavity and dispersion terms, and a method based on a linear relation to the solvent-accessible surface area. Replacing molecular mechanics terms with energies calculated with the semiempirical quantum mechanics AM1, RM1, PM6 Hamiltonians, and adding hydrogen bond and dispersion corrections. Inclusion of explicit water in the binding site of a protein. Effect of system truncation on estimated energies. The results show that for continuum solvation models knowledge of hydration state of binding site is important. The rest of variations of the MM/GBSA method for the tested systems showed only minor improvements. We have done a large systematic study of calculating relative binding free energies for 10 proteins binding 107 ligands with Bennett acceptance ratio (BAR) method. For the most of systems binding affinities could be calculated within 4 kJ/mol of experimental values. We have also participated in the SAMPL3 and SAMPL4 blind binding challenges to see how the MM/GBSA and FEP methods perform. The MM/GBSA failed to predict experimental binding affinities, which might be due to poor precision of the method as experimental data had very narrow range of about 9 kJ/mol. In SAMPL4, the BAR method gave the best predicted binding affinities

Can the protonation state of histidine residues be determined from molecular dynamics simulations?

Histidine (His) residues in proteins can attain three different protonation states at normal pH. This constitutes a prominent problem when adding protons to a protein crystal structure, e.g. in order to perform molecular simulations. Typically, the His protonation is deduced from the hydrogen-bond pattern in crystal structures. Here, we study whether it is possible to detect erroneous His protonation state by analysing short molecular dynamics (MD) trajectories. We systematically vary the His protonation state and measure the root-mean-squared deviation (RMSD) of the His residues and nearby residues relative to the starting structure, as well as the distribution of the dihedral angle that determines the rotation of the His side chain. We study three proteins, hisactophilin with 31 solvent-exposed His residues, galectin-3, for which an experimental assignment is available for two of the His residues, and trypsin, for which the hydrogen-bond analysis is quite conclusive. The results show that improper protonation states have larger RMSD values and larger widths of the dihedral distribution, compared to the correct protonation states. Unfortunately, the variation among different His residues in the same and different proteins is so large that it is hard to define unambiguous thresholds between proper and improper protonation states. Therefore, simulations of all three protonation states are needed for conclusive results. For trypsin, we could obtain a conclusive assignment for all three His residues, which was better than the simple hydrogen-bond analysis. For galectin-3, the MD trajectories confirmed the results of hydrogen-bond analysis and experiments. They also gave additional, more uncertain information for some of the residues. However, for the solvent-exposed His residues in hisactophilin, no unambiguous conclusions regarding the protonation states could be reached. On the other hand, this indicates that protein structures are quite insensitive to the protonation state of the His residues, besides those that involve direct hydrogen bonds to the His side chain. (C) 2012 Elsevier B.V. All rights reserved

Prediction of broad spectrum pathogen attachment to coating materials for biomedical devices data

Bacterial infections in healthcare settings are a frequent accompaniment to both routine procedures such as catheterization and surgical site interventions. Their impact is becoming even more marked as the numbers of medical devices that are used to manage chronic health conditions and improve quality of life increases. The resistance of pathogens to multiple antibiotics is also increasing, adding an additional layer of complexity to the problems of employing safe and effective medical procedures. One approach to reducing the rate of infections associated with implanted and indwelling medical devices is the use of polymers that resist the formation of bacterial biofilms. To significantly accelerate the discovery of such materials, we show how state of the art machine learning methods can generate quantitative predictions for the attachment of multiple pathogens to a large library of polymers in a single model for the first time. Such models facilitate design of polymers with very low pathogen attachment across different bacterial species that will be candidate materials for implantable or indwelling medical devices such as urinary catheters, cochlear implants and pacemakers

Effect of explicit water molecules on ligand-binding affinities calculated with the MM/GBSA approach.

We tested different approaches to including the effect of binding-site water molecules for ligand-binding affinities within the MM/GBSA approach (molecular mechanics combined with generalised Born and surface-area solvation). As a test case, we studied the binding of nine phenol analogues to ferritin. The effect of water molecules mediating the interaction between the receptor and the ligand can be studied by considering a few water molecules as a part of the receptor. We extended previous methods by allowing for a variable number of water molecules in the binding site. The effect of displaced water molecules can also be considered within the MM/GBSA philosophy by calculating the affinities of binding-site water molecules, both before and after binding of the ligand. To obtain proper energies, both the water molecules and the ligand need then to be converted to non-interacting ghost molecules and a single-average approach (i.e., the same structures are used for bound and unbound states) based on the simulations of both the complex and the free receptor can be used to improve the precision. The only problem is to estimate the free energy of an unbound water molecule. With an experimental estimate of this parameter, promising results were obtained for our test case

A Large-Scale Test of Free-Energy Simulation Estimates of Protein-Ligand Binding Affinities. [Elektronisk resurs]

We have performed a large-scale test of alchemical perturbation calculations with the Bennett acceptance-ratio (BAR) approach to estimate relative affinities for the binding of 107 ligands to 10 different proteins. Employing 20-Å truncated spherical systems and only one intermediate state in the perturbations, we obtain an error of less than 4 kJ/mol for 54% of the studied relative affinities and a precision of 0.5 kJ/mol on average. However, only four of the proteins gave acceptable errors, correlations, and rankings. The results could be improved by using nine intermediate states in the simulations or including the entire protein in the simulations using periodic boundary conditions. However, 27 of the calculated affinities still gave errors of more than 4 kJ/mol, and for three of the proteins the results were not satisfactory. This shows that the performance of BAR calculations depends on the target protein and that several transformations gave poor results owing to limitations in the molecular-mechanics force field or the restricted sampling possible within a reasonable simulation time. Still, the BAR results are better than docking calculations for most of the proteins