Rapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturation.

Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The site identification by ligand competitive saturation (SILCS) methodology is based on functional group affinity patterns in the form of free energy maps that may be used to compute protein-ligand binding poses and affinities. Presented are results obtained from the SILCS methodology for a set of eight target proteins as reported originally in Wang et al. (J. Am. Chem. Soc., 2015, 137, 2695-2703) using free energy perturbation (FEP) methods in conjunction with enhanced sampling and cycle closure corrections. These eight targets have been subsequently studied by many other authors to compare the efficacy of their method while comparing with the outcomes of Wang et al. In this work, we present results for a total of 407 ligands on the eight targets and include specific analysis on the subset of 199 ligands considered previously. Using the SILCS methodology we can achieve an average accuracy of up to 77% and 74% when considering the eight targets with their 199 and 407 ligands, respectively, for rank-ordering ligand affinities as calculated by the percent correct metric. This accuracy increases to 82% and 80%, respectively, when the SILCS atomic free energy contributions are optimized using a Bayesian Markov-chain Monte Carlo approach. We also report other metrics including Pearson's correlation coefficient, Pearlman's predictive index, mean unsigned error, and root mean square error for both sets of ligands. The results obtained for the 199 ligands are compared with the outcomes of Wang et al. and other published works. Overall, the SILCS methodology yields similar or better-quality predictions without a priori need for known ligand orientations in terms of the different metrics when compared to current FEP approaches with significant computational savings while additionally offering quantitative estimates of individual atomic contributions to binding free energies. These results further validate the SILCS methodology as an accurate, computationally efficient tool to support lead optimization and drug discovery.

Impact of electronic polarizability on protein-functional group interactions.

Interactions of proteins with functional groups are key to their biological functions, making it essential that they be accurately modeled. To investigate the impact of the inclusion of explicit treatment of electronic polarizability in force fields on protein-functional group interactions, the additive CHARMM and Drude polarizable force field are compared in the context of the Site-Identification by Ligand Competitive Saturation (SILCS) simulation methodology from which functional group interaction patterns with five proteins for which experimental binding affinities of multiple ligands are available, were obtained. The explicit treatment of polarizability produces significant differences in the functional group interactions in the ligand binding sites including overall enhanced binding of functional groups to the proteins. This is associated with variations of the dipole moments of solutes representative of functional groups in the binding sites relative to aqueous solution with higher dipole moments systematically occurring in the latter, though exceptions occur with positively charged methylammonium. Such variation indicates the complex, heterogeneous nature of the electronic environments of ligand binding sites and emphasizes the inherent limitation of fixed charged, additive force fields for modeling ligand-protein interactions. These effects yield more defined orientation of the functional groups in the binding pockets and a small, but systematic improvement in the ability of the SILCS method to predict the binding orientation and relative affinities of ligands to their target proteins. Overall, these results indicate that the physical model associated with the explicit treatment of polarizability along with the presence of lone pairs in a force field leads to changes in the nature of the interactions of functional groups with proteins versus that occurring with additive force fields, suggesting the utility of polarizable force fields in obtaining a more realistic understanding of protein-ligand interactions.

Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization.

Chemical fragment cosolvent sampling techniques have become a versatile tool in ligand-protein binding prediction. Site-identification by ligand competitive saturation (SILCS) is one such method that maps the distribution of chemical fragments on a protein as free energy fields called FragMaps. Ligands are then simulated via Monte Carlo techniques in the field of the FragMaps (SILCS-MC) to predict their binding conformations and relative affinities for the target protein. Application of SILCS-MC using a number of different scoring schemes and MC sampling protocols against multiple protein targets was undertaken to evaluate and optimize the predictive capability of the method. Seven protein targets and 551 ligands with broad chemical variability were used to evaluate and optimize the model to maximize Pearson's correlation coefficient, Pearlman's predictive index, correct relative binding affinity, and root-mean-square error versus the absolute experimental binding affinities. Across the protein-ligand sets, the relative affinities of the ligands were predicted correctly an average of 69% of the time for the highest overall SILCS protocol. Training the FragMap weighting factors using a Bayesian machine learning (ML) algorithm led to an increase to an average 75% relative correct affinity predictions. Furthermore, once the optimal protocol is identified for a specific protein-ligand system average predictabilities of 76% are achieved. The ML algorithm is successful with small training sets of data (30 or more compounds) due to the use of physically correct FragMap weights as priors. Notably, the 76% correct relative prediction rate is similar to or better than free energy perturbation methods that are significantly computationally more expensive than SILCS. The results further support the utility of SILCS as a powerful and computationally accessible tool to support lead optimization and development in drug discovery.

Development and Application of a Coarse-Grained Model for PNIPAM by Iterative Boltzmann Inversion and Its Combination with Lattice Boltzmann Hydrodynamics.

The polymer poly(N-isopropylacrylamide) (PNIPAM) is studied using a novel combination of multiscale modeling methodologies. We develop an iterative Boltzmann inversion potential of concentrated PNIPAM solutions and combine it with lattice Boltzmann as a Navier-Stokes equation solver for the solvent. We study in detail the influence of the methodology on statics and dynamics of the system. The combination is successful and significantly simpler and faster than other mapping techniques for polymer solution while keeping the correct hydrodynamics. The model can semiquantitatively describe the correct phase behavior and polymer dynamics.

Tunable Permeability of Cross-Linked Microcapsules from pH-Responsive Amphiphilic Diblock Copolymers: A Dissipative Particle Dynamics Study.

Using dissipative particle dynamics simulation, we probe the tunable permeability of cross-linked microcapsules made from pH-sensitive diblock copolymers poly(ethylene oxide)-b-poly(N,N-diethylamino-2-ethyl methacrylate) (PEO-b-PDEAEMA). We first examine the self-assembly of non-cross-linked microcapsules and their pH-responsive collapse and then explore the effects of cross-linking and block interaction on the swelling or deswelling of cross-linked microcapsules. Our results reveal a preferential loading of hydrophobic dicyclopentadiene (DCPD) molecules in PEO-b-PDEAEMA copolymers. Upon reduction of pH, non-cross-linked microcapsules fully decompose into small wormlike clusters as a result of large self-repulsions of protonated copolymers. With increasing degree of cross-linking, the morphology of the microcapsule becomes more stable to pH change. The highly cross-linked microcapsule shell undergoes significant local polymer rearrangement in acidic solution, which eliminates the amphiphilicility and therefore enlarges the permeability of the shell. The responsive cross-linked shell experiences a disperse-to-buckle configurational transition upon reduction of pH, which is effective for the steady or pulsatile regulation of shell permeability. The swelling rate of the cross-linked shell is dependent on both electrostatic and nonelectrostatic interactions between the pH-sensitive groups as well as the other groups. Our study highlights the combination of cross-linking structure and block interactions in stabilizing microcapsules and tuning their selective permeability.

Direct Phase Equilibrium Simulations of NIPAM Oligomers in Water.

NIPAM (N-isopropylacrylamide)-based polymers in water show many interesting properties in experiments, including a lower critical solution temperature (LCST) at 305 K and a conformational transition of single chains at the same temperature. The results of many simulation studies suggest that standard force fields are able to describe the conformational transition and the phase equilibrium well. We show by performing long molecular dynamics simulations of the direct liquid-liquid phase equilibrium of NIPAM trimers in water that there is no LCST in the expected temperature range for any of the force fields under study. The results show further that the relaxation times of single-chain simulations are considerably longer than anticipated. Conformational transitions of single polymers can therefore not necessarily be used as surrogates for a real phase transition.