Prediction of hydration free energies for aliphatic and aromatic chloro derivatives using molecular dynamics simulations with the OPLS-AA force field

International audienceAll-atom molecular dynamics computer simulations were used to blindly predict the hydration free energies of a range of chloro-organic compounds as part of the SAMPL3 challenge. All compounds were parameterized within the framework of the OPLS-AA force field, using an established protocol to compute the absolute hydration free energy via a windowed free energy perturbation approach and thermodynamic integration. Three different approaches to deriving partial charge parameters were pursued: (1) using existing OPLS-AA atom types and charges with minor adjustments of partial charges on equivalent connecting atoms; (2) calculation of quantum mechanical charges via geometry optimization, followed by electrostatic potential (ESP) fitting, using Jaguar at the LMP2/cc-pVTZ(-F) level; and (3) via geometry optimization and CHelpG charges (Gaussian03 at the HF/6-31G* level), followed by two-stage RESP fitting. Protocol 3 generated the most accurate predictions with a root mean square (RMS) error of 1.2 kcal·mol −1 for the entire data set. It was found that the deficiency of the standard OPLS-AA parameters, protocol 1 (RMS error 2.4 kcal·mol −1 overall), was mostly due to compounds with more than three chlorine substituents on an aromatic ring. For this latter subset, the RMS errors were 1.4 kcal·mol −1 (protocol 3) and 4.3 kcal·mol −1 (protocol 1), respectively. We propose new OPLS-AA atom types for aromatic carbon and chlorine atoms in rings with ≥ 4 Cl-substituents that perform better than the best QM-based approach, resulting in an RMS error of 1.2 kcal·mol −1 for these difficult compounds

GAFF/IPolQ-Mod+LJ-Fit: Optimized force field parameters for solvation free energy predictions

Rational drug design featuring explicit solubility considerations can greatly benefit from molecular dynamics simulations, as they allow for the prediction of the Gibbs free energy of solvation and thus relative solubilities. In our previous work (A. Mecklenfeld, G. Raabe. J. Chem. Theory Comput. 13 no. 12 (2017) 6266–6274), we have compared solvation free energy results obtained with the General Amber Force Field (GAFF) and its default restrained electrostatic potential (RESP) partial charges to those obtained by modified implicitly polarized charges (IPolQ-Mod) for an implicit representation of impactful polarization effects. In this work, we have adapted Lennard-Jones parameters for GAFF atom types in combination with IPolQ-Mod to further improve the accuracies of solvation free energy and liquid density predictions. We thereby focus on prominent atom types in common drugs. For the refitting, 357 respectively 384 systems were considered for free energies and densities and validation was performed for 142 free energies and 100 densities of binary mixtures. By the in-depth comparison of simulation results for default GAFF, GAFF with IPolQ-Mod and our new set of parameters, which we label GAFF/IPolQ-Mod+LJ-Fit, we can clearly highlight the improvements of our new model for the description of both relative solubilities and fluid phase behaviour.</p

The future of molecular dynamics simulations in drug discovery

Molecular dynamics simulations can now track rapid processes—those occurring in less than about a millisecond—at atomic resolution for many biologically relevant systems. These simulations appear poised to exert a significant impact on how new drugs are found, perhaps even transforming the very process of drug discovery. We predict here future results we can expect from, and enhancements we need to make in, molecular dynamics simulations over the coming 25 years, and in so doing set out several Grand Challenges for the field. In the context of the problems now facing the pharmaceutical industry, we ask how we can best address drug discovery needs of the next quarter century using molecular dynamics simulations, and we suggest some possible approaches

Solvated interaction energy: from small-molecule to antibody drug design

Scoring functions are ubiquitous in structure-based drug design as an aid to predicting binding modes and estimating binding affinities. Ideally, a scoring function should be broadly applicable, obviating the need to recalibrate and refit its parameters for every new target and class of ligands. Traditionally, drugs have been small molecules, but in recent years biologics, particularly antibodies, have become an increasingly important if not dominant class of therapeutics. This makes the goal of having a transferable scoring function, i.e., one that spans the range of small-molecule to protein ligands, even more challenging. One such broadly applicable scoring function is the Solvated Interaction Energy (SIE), which has been developed and applied in our lab for the last 15 years, leading to several important applications. This physics-based method arose from efforts to understand the physics governing binding events, with particular care given to the role played by solvation. SIE has been used by us and many independent labs worldwide for virtual screening and discovery of novel small-molecule binders or optimization of known drugs. Moreover, without any retraining, it is found to be transferrable to predictions of antibody-antigen relative binding affinities and as accurate as functions trained on protein-protein binding affinities. SIE has been incorporated in conjunction with other scoring functions into ADAPT (Assisted Design of Antibody and Protein Therapeutics), our platform for affinity modulation of antibodies. Application of ADAPT resulted in the optimization of several antibodies with 10-to-100-fold improvements in binding affinity. Further applications included broadening the specificity of a single-domain antibody to be cross-reactive with virus variants of both SARS-CoV-1 and SARS-CoV-2, and the design of safer antibodies by engineering of a pH switch to make them more selective towards acidic tumors while sparing normal tissues at physiological pH

Computational methodology for solubility prediction: Application to sparingly soluble organic/inorganic materials.

The solubility of a crystalline material can be estimated from the absolute free energy of the solid and the excess solvation free energy. In the earlier work, we presented a general-purpose molecular-dynamics-based methodology enabling solubility predictions of crystalline compounds, yielding accurate estimates of the aqueous solubilities of naphthalene at various pressures and temperatures. In the present work, we investigate a number of prototypical complex materials, including phenanthrene, calcite, and aragonite over a range of temperatures and pressures. Our results provide stronger evidence for the power of the methodology for universal solubility predictions.BP International Centre for Advanced Materials (BP-ICAM

Accuracy comparison of several common implicit solvent models and their implementations in the context of protein-ligand binding.

In this study several commonly used implicit solvent models are compared with respect to their accuracy of estimating solvation energies of small molecules and proteins, as well as desolvation penalty in protein-ligand binding. The test set consists of 19 small proteins, 104 small molecules, and 15 protein-ligand complexes. We compared predicted hydration energies of small molecules with their experimental values; the results of the solvation and desolvation energy calculations for small molecules, proteins and protein-ligand complexes in water were also compared with Thermodynamic Integration calculations based on TIP3P water model and Amber12 force field. The following implicit solvent (water) models considered here are: PCM (Polarized Continuum Model implemented in DISOLV and MCBHSOLV programs), GB (Generalized Born method implemented in DISOLV program, S-GB, and GBNSR6 stand-alone version), COSMO (COnductor-like Screening Model implemented in the DISOLV program and the MOPAC package) and the Poisson-Boltzmann model (implemented in the APBS program). Different parameterizations of the molecules were examined: we compared MMFF94 force field, Amber12 force field and the quantum-chemical semi-empirical PM7 method implemented in the MOPAC package. For small molecules, all of the implicit solvent models tested here yield high correlation coefficients (0.87-0.93) between the calculated solvation energies and the experimental values of hydration energies. For small molecules high correlation (0.82-0.97) with the explicit solvent energies is seen as well. On the other hand, estimated protein solvation energies and protein-ligand binding desolvation energies show substantial discrepancy (up to 10kcal/mol) with the explicit solvent reference. The correlation of polar protein solvation energies and protein-ligand desolvation energies with the corresponding explicit solvent results is 0.65-0.99 and 0.76-0.96 respectively, though this difference in correlations is caused more by different parameterization and less by methods and indicates the need for further improvement of implicit solvent models parameterization. Within the same parameterization, various implicit methods give practically the same correlation with results obtained in explicit solvent model for ligands and proteins: e.g. correlation values of polar ligand solvation energies and the corresponding energies in the frame of explicit solvent were 0.953-0.966 for the APBS program, the GBNSR6 program and all models used in the DISOLV program. The DISOLV program proved to be on a par with the other used programs in the case of proteins and ligands solvation energy calculation. However, the solution of the Poisson-Boltzmann equation (APBS program) and Generalized Born method (implemented in the GBNSR6 program) proved to be the most accurate in calculating the desolvation energies of complexes

Computational compound screening of biomolecules and soft materials by molecular simulations

Decades of hardware, methodological, and algorithmic development have propelled molecular dynamics (MD) simulations to the forefront of materials-modeling techniques, bridging the gap between electronic-structure theory and continuum methods. The physics-based approach makes MD appropriate to study emergent phenomena, but simultaneously incurs significant computational investment. This topical review explores the use of MD outside the scope of individual systems, but rather considering many compounds. Such an in silico screening approach makes MD amenable to establishing coveted structure--property relationships. We specifically focus on biomolecules and soft materials, characterized by the significant role of entropic contributions and heterogeneous systems and scales. An account of the state of the art for the implementation of an MD-based screening paradigm is described, including automated force-field parametrization, system preparation, and efficient sampling across both conformation and composition. Emphasis is placed on machine-learning methods to enable MD-based screening. The resulting framework enables the generation of compound--property databases and the use of advanced statistical modeling to gather insight. The review further summarizes a number of relevant applications.Comment: 48 pages, 3 figure