Generalized Born Implicit Solvent Model Research Articles

Tuning Potential Functions to Host-Guest Binding Data.

Software to more rapidly and accurately predict protein-ligand binding affinities is of high interest for early-stage drug discovery, and physics-based methods are among the most widely used technologies for this purpose. The accuracy of these methods depends critically on the accuracy of the potential functions that they use. Potential functions are typically trained against a combination of quantum chemical and experimental data. However, although binding affinities are among the most important quantities to predict, experimental binding affinities have not to date been integrated into the experimental data set used to train potential functions. In recent years, the use of host-guest complexes as simple and tractable models of binding thermodynamics has gained popularity due to their small size and simplicity, relative to protein-ligand systems. Host-guest complexes can also avoid ambiguities that arise in protein-ligand systems such as uncertain protonation states. Thus, experimental host-guest binding data are an appealing additional data type to integrate into the experimental data set used to optimize potential functions. Here, we report the extension of the Open Force Field Evaluator framework to enable the systematic calculation of host-guest binding free energies and their gradients with respect to force field parameters, coupled with the curation of 126 host-guest complexes with available experimental binding free energies. As an initial application of this novel infrastructure, we optimized generalized Born (GB) cavity radii for the OBC2 GB implicit solvent model against experimental data for 36 host-guest systems. This refitting led to a dramatic improvement in accuracy for both the training set and a separate test set with 90 additional host-guest systems. The optimized radii also showed encouraging transferability from host-guest systems to 59 protein-ligand systems. However, the new radii are significantly smaller than the baseline radii and lead to excessively favorable hydration free energies (HFEs). Thus, users of the OBC2 GB model currently may choose between GB cavity radii that yield more accurate binding affinities and GB cavity radii that yield more accurate HFEs. We suspect that achieving good accuracy on both will require more far-reaching adjustments to the GB model. We note that binding free-energy calculations using the OBC2 model in OpenMM gain about a 10× speedup relative to corresponding explicit solvent calculations, suggesting a future role for implicit solvent absolute binding free-energy (ABFE) calculations in virtual compound screening. This study proves the principle of using host-guest systems to train potential functions that are transferrable to protein-ligand systems and provides an infrastructure that enables a range of applications.

A method for protein structure sampling under three-dimensional reference interaction site model solvation based on hybrid Monte Carlo framework

An efficient sampling method for protein structure using molecular dynamics (MD) simulation and a three-dimensional reference interaction-site model (3D-RISM) theory has been developed based on the hybrid Monte Carlo framework. In this method, new structures are generated by MD simulation with the generalized Born implicit solvent model and the Metropolis-like criterion to accept or reject the structure candidate is applied based on the free energy corresponding to the 3D-RISM Hamiltonian at each specified time propagation interval. The resulting ensemble of structures satisfies the 3D-RISM Hamiltonian-based equilibrium distribution. The method was applied to alanine dipeptide and methionine-enkephalin polypeptides in aqueous solution as test cases. The results demonstrated the efficiency of the method.

Replica exchange molecular dynamics simulations reveal self-association sites in M-crystallin caused by mutations provide insights of cataract

Crystallins are ubiquitous, however, prevalence is seen in eye lens. Eye lens crystallins are long-lived and structural intactness is required for maintaining lens transparency and protein solubility. Mutations in crystallins often lead to cataract. In this study, we performed mutations at specific sites of M-crystallin, a close homologue of eye lens crystallin and studied by using replica exchange molecular dynamics simulation with generalized Born implicit solvent model. Mutations were made on the Ca2+ binding residues (K34D and S77D) and in the hydrophobic core (W45R) which is known to cause congenital cataract in homologous γD-crystallin. The chosen mutations caused large motion of the N-terminal Greek key, concomitantly broke the interlocking Greek keys interactions and perturbed the compact core resulting in several folded and partially unfolded states. Partially unfolded states exposed large hydrophobic patches that could act as precursors for self-aggregation. Accumulation of such aggregates is the potential cause of cataract in homologous eye lens crystallins.

Multidimensional Global Optimization and Robustness Analysis in the Context of Protein-Ligand Binding.

Accuracy of protein-ligand binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a global multidimensional optimization pipeline is developed to find optimal atomic radii specifically for protein-ligand binding calculations in implicit solvent. The computational pipeline has these three key components: (1) a massively parallel implementation of a deterministic global optimization algorithm (VTDIRECT95), (2) an accurate yet reasonably fast generalized Born implicit solvent model (GBNSR6), and (3) a novel robustness metric that helps distinguish between nearly degenerate local minima via a postprocessing step of the optimization. A graph-based "kT-connectivity" approach to explore and visualize the multidimensional energy landscape is proposed: local minima that can be reached from the global minimum without exceeding a given energy threshold (kT) are considered to be connected. As an illustration of the capabilities of the optimization pipeline, we apply it to find a global optimum in the space of just five radii: four atomic (O, H, N, and C) radii and water probe radius. The optimized radii, ρW = 1.37 Å, ρC = 1.40 Å, ρH = 1.55 Å, ρN = 2.35 Å, and ρO = 1.28 Å, lead to a closer agreement of electrostatic binding free energies with the explicit solvent reference than two commonly used sets of radii previously optimized for small molecules. At the same time, the ability of the optimizer to find the global optimum reveals fundamental limits of the common two-dielectric implicit solvation model: the computed electrostatic binding free energies are still almost 4 kcal/mol away from the explicit solvent reference. The proposed computational approach opens the possibility to further improve the accuracy of practical computational protocols for binding free energy calculations.

Mechanistic Insights from Replica Exchange Molecular Dynamics Simulations into Mutation Induced Disordered-to-Ordered Transition in Hahellin, a βγ-Crystallin.

Intrinsically disordered proteins (IDPs) form a special category because they lack a unique well-folded 3D structure under physiological conditions. They play crucial role in cell signaling and regulatory functions and are responsible for several diseases. Although they are abundant in nature, only a small fraction of them have been characterized until date. Such proteins adopt a range of conformations and can undergo transformation from disordered-to-ordered state or vice versa upon binding to ligand. Insights of such conformational transition is perplexing in several cases. In the present study, we characterized disordered as well as ordered states and the interactions contributing the transitions through a mutational study by employing replica exchange molecular dynamics simulation with generalized Born implicit solvent model on a protein from the βγ-crystallin superfamily. Most of the proteins within this superfamily are inherently ordered and highly stable. However, Hahellin, although a member of the βγ-crystallin family, is intrinsically disordered in its apo-form which takes a well-ordered βγ-crystallin fold upon binding to Ca2+. It is intriguing that the mutation at the fifth position of the canonical motif to Arg increases the domain stability in several ordered microbial βγ-crystallins with concomitant loss in Ca2+ binding affinity. We carried out similar Ser to Arg mutations at fifth position of the canonical motif for the first time in an intrinsically disordered protein to understand the mechanistic insights of conformational transition. Our study revealed that newly formed ionic and hydrogen bonding interactions at the canonical Ca2+ binding sites play a crucial role in transforming the disordered conformation into ordered βγ-crystallin.

Generalized Born implicit solvent models for small molecule hydration free energies.

Hydration free energy estimation of small molecules from all-atom simulations was widely investigated in recent years, as it provides an essential test of molecular force fields and our understanding of solvation effects. While explicit solvent representations result in highly accurate models, they also require extensive sampling due to the high number of solvent degrees of freedom. Implicit solvent models, such as those based on the generalized Born model for electrostatic solvation effects and a solvent accessible surface area term for nonpolar contributions (GBSA), significantly reduce the number of degrees of freedom and the computational cost to estimate hydration free energies. However, a recent survey revealed a gap in the accuracy between explicit TIP3P solvent estimates and those computed with many common GBSA models. Here we address this shortcoming by providing a thorough comparison of the performance of three implicit solvent models with different nonpolar contributions and a generalized Born term to estimate experimental hydration free energies. Starting with a minimal set of only ten atom types, we demonstrate that a nonpolar term with atom type dependent surface tension coefficients in combination with an accurate generalized Born term and fully optimized parameters performs best in estimating hydration free energies, even yielding comparable results to the explicit TIP3P water model. Analysis of our results provides evidence that the asymmetric behavior of water around oppositely charged atoms is one of the main sources of error for two of the three implicit solvent models. Explicitly accounting for this effect in the parameterization reduces the corresponding errors, suggesting this as a general strategy for improving implicit solvent models. The findings presented here will help to improve the existing generalized Born based implicit solvent models implemented in state-of-the-art molecular simulation packages.

Protein-Ligand Electrostatic Binding Free Energies from Explicit and Implicit Solvation.

Accurate yet efficient computational models of solvent environment are central for most calculations that rely on atomistic modeling, such as prediction of protein-ligand binding affinities. In this study, we evaluate the accuracy of a recently developed generalized Born implicit solvent model, GBNSR6 (Aguilar et al. J. Chem. Theory Comput. 2010, 6, 3613-3639), in estimating the electrostatic solvation free energies (ΔG(pol)) and binding free energies (ΔΔG(pol)) for small protein-ligand complexes. We also compare estimates based on three different explicit solvent models (TIP3P, TIP4PEw, and OPC). The two main findings are as follows. First, the deviation (RMSD = 7.04 kcal/mol) of GBNSR6 binding affinities from commonly used TIP3P reference values is comparable to the deviations between explicit models themselves, e.g. TIP4PEw vs TIP3P (RMSD = 5.30 kcal/mol). A simple uniform adjustment of the atomic radii by a single scaling factor reduces the RMS deviation of GBNSR6 from TIP3P to within the above "error margin" - differences between ΔΔG(pol) estimated by different common explicit solvent models. The simple radii scaling virtually eliminates the systematic deviation (ΔΔG(pol)) between GBNSR6 and two out of the three explicit water models and significantly reduces the deviation from the third explicit model. Second, the differences between electrostatic binding energy estimates from different explicit models is disturbingly large; for example, the deviation between TIP4PEw and TIP3P estimates of ΔΔG(pol) values can be up to ∼50% or ∼9 kcal/mol, which is significantly larger than the "chemical accuracy" goal of ∼1 kcal/mol. The absolute ΔG(pol) calculated with different explicit models could differ by tens of kcal/mol. These discrepancies point to unacceptably high sensitivity of binding affinity estimates to the choice of common explicit water models. The absence of a clear "gold standard" among these models strengthens the case for the use of accurate implicit solvation models for binding energetics, which may be orders of magnitude faster.

Comparison of NMR structures refined under implicit and explicit solvents

Refinements with atomistic molecular dynamics (MD) simulation have contributed to improving the qualities of NMR structures. In most cases, the calculations with atomistic MD simulation for NMR structures employ generalized-Born implicit solvent model (GBIS) to take into accounts solvation effects. Developments in algorithms and computational capacities have ameliorated GBIS to approximate solvation effects that explicit solvents bring about. However, the quantitative comparison of NMR structures in the latest GBIS and explicit solvents is lacking. In this study, we report the direct comparison of NMR structures that atomistic MD simulation coupled with GBIS and water molecules refined. Two model proteins, GB1 and ubiquitin, were recalculated with experimental distance and torsion angle restraints, under a series of simulated annealing time steps. Whereas the root mean square deviations of the resulting structures were apparently similar, AMBER energies, the most favored regions in Ramachandran plot, and MolProbity clash scores witnessed that GBIS-refined structures had the better geometries. The outperformance by GBIS was distinct in the structure calculations with sparse experimental restraints. We show that the superiority stemmed, at least in parts, from the inclusion of all the pairs of non-bonded interactions. The shorter computational times with GBIS than those for explicit solvents makes GBIS a powerful method for improving structural qualities particularly under the conditions that experimental restraints are insufficient. We also propose a method to separate the native-like folds from non-violating diverged structures.

Rapid Alchemical Free Energy Calculation Employing a Generalized Born Implicit Solvent Model

Generalized Born (GB) implicit solvent models are typically used in postprocessing of molecular dynamics trajectories obtained from explicit solvent simulations to estimate binding free energies or effects of mutations in proteins. The possibility to employ a GB implicit solvent model for the calculation of rigorous free energy changes associated with alchemical transformations has been explored. During free energy perturbation (FEP) simulations, Lennard-Jones, Coulomb, and Born radii parameters are transformed gradually in a single-topology series. The FEP calculations are embedded in a replica exchange scheme allowing rapid convergence. The method was tested on the calculation of relative hydration free energies, relative binding free energies of a ligand-receptor system, and in silico alanine scanning of a peptide-protein complex. In all cases, good agreement with available experimental data was obtained. On medium sized protein-ligand systems and using a cluster of graphical processing units, the approach allows the calculation of relative free energy changes associated with a chemical modification of a binding partner within a few minutes of computer time and opens the possibility for systematic in silico studies.

Systematic Assessment of the Effects of an All-Atom Force Field and the Implicit Solvent Model on the Refinement of NMR Structures with Subsets of Distance Restraints

Jun-Goo JeeResearch Institute of Pharmaceutical Sciences, College of Pharmacy, Kyungpook National University, Daegu 702-701, Korea. E-mail: jjee@knu.ac.krReceived February 22, 2014, Accepted March 5, 2014Employment of a time consuming, sophisticated calculation using the all-atom force field and generalized-Born implicit solvent model (GBIS) for refinement of NMR structures has become practical through advancesin computational methods and capacities. GBIS refinement improves the qualities of the resulting NMRstructures with reduced computational times. However, the contribution of GBIS to NMR structures has notbeen sufficiently studied in a quantitative way. In this paper, we report the effects of GBIS on the refined NMRstructures of ubiquitin (UBQ) and GB1 with subsets of distance restraints derived from experimental data.Random omission prepared a series of distance restraints 0.05, 0.1, 0.3, 0.5, and 0.7 times smaller. For eachnumber, we produced five different restraints for statistical analysis. We then recalculated the NMR structuresusing CYANA software, followed by GBIS refinements using the AMBER package. GBIS improved both theprecision and accuracy of all the structures, but to varied levels. The degrees of improvement were significantwhen the input restraints were insufficient. In particular, GBIS enabled GB1 to form an accurate structure evenwith distance restraints of 5%, revealing that the root-mean-square deviation was less than 1 A from the X-raybackbone structure. We also showed that the efficiency of searching the conformational space was moreimportant for finding accurate structures with the calculation of UBQ with 5% distance restraints than thenumber of conformations generated. Our data will provide a meaningful guideline to judge and compare thestructural improvements by GBIS. Key Words : NMR, Generalized-Born implicit solvent model, Ubiquitin, GB1IntroductionSteady advances in algorithms for both NOE assignmentsand structure calculations have made automatic calculationsof protein 3D structures with raw NOE data, provided thechemical shifts are assigned for most atoms and sufficientpeaks exist.

Effects of force fields for refining protein NMR structures with atomistic force fields and generalized-Born implicit solvent model

Atomistic molecular dynamics (MD) simulation has become mature enabling close approximation of the real behaviors of biomolecules. In biomolecular NMR field, atomistic MD simulation coupled with generalized implicit solvent model (GBIS) has contributed to improving the qualities of NMR structures in the refinement stage with experimental restraints. Here all-atom force fields play important roles in defining the optimal positions between atoms and angles, resulting in more precise and accurate structures. Despite successful applications in refining NMR structure, however, the research that has studied the influence of force fields in GBIS is limited. In this study, we compared the qualities of NMR structures of two model proteins, ubiquitin and GB1, under a series of AMBER force fields-ff99SB, ff99SB-ILDN, ff99SB-NMR, ff12SB, and ff13-with experimental restraints. The root mean square deviations of backbone atoms and packing scores that reflect the apparent structural qualities were almost indistinguishable except ff13. Qualitative comparison of parameters, however, indicates that ff99SB-ILDN is more recommendable, at least in the cases of ubiquitin and GB1.

Effects of generalized-Born implicit solvent models in NMR structure refinement

Rapid advances of computational power and method have made it practical to apply the time-consuming calculations with all-atom force fields and sophisticated potential energies into refining NMR structure. Added to the all-atom force field, generalized-Born implicit solvent model (GBIS) contributes substantially to improving the qualities of the resulting NMR structures. GBIS approximates the effects that explicit solvents bring about even with fairly reduced computational times. Although GBIS is employed in the final stage of NMR structure calculation with experimental restraints, the effects by GBIS on structures have been reported notable. However, the detailed effect is little studied in a quantitative way. In this study, we report GBIS refinements of ubiquitin and GB1 structures by six GBIS models of AMBER package with experimental distance and backbone torsion angle restraints. Of GBIS models tested, the calculations with igb

Structure refinement of protein model decoys requires accurate side-chain placement

In this study, the application of temperature-based replica-exchange (T-ReX) simulations for structure refinement of decoys taken from the I-TASSER dataset was examined. A set of eight nonredundant proteins was investigated using self-guided Langevin dynamics (SGLD) with a generalized Born implicit solvent model to sample conformational space. For two of the protein test cases, a comparison of the SGLD/T-ReX method with that of a hybrid explicit/implicit solvent molecular dynamics T-ReX simulation model is provided. Additionally, the effect of side-chain placement among the starting decoy structures, using alternative rotamer conformations taken from the SCWRL4 modeling program, was investigated. The simulation results showed that, despite having near-native backbone conformations among the starting decoys, the determinant of their refinement is side-chain packing to a level that satisfies a minimum threshold of native contacts to allow efficient excursions toward the downhill refinement regime on the energy landscape. By repacking using SCWRL4 and by applying the RWplus statistical potential for structure identification, the SGLD/T-ReX simulations achieved refinement to an average of 38% increase in the number of native contacts relative to the original I-TASSER decoy sets and a 25% reduction in values of C(α) root-mean-square deviation. The hybrid model succeeded in obtaining a sharper funnel to low-energy states for a modeled target than the implicit solvent SGLD model; yet, structure identification remained roughly the same. Without meeting a threshold of near-native packing of side chains, the T-ReX simulations degrade the accuracy of the decoys, and subsequently, refinement becomes tantamount to the protein folding problem.

Comparison between self‐guided Langevin dynamics and molecular dynamics simulations for structure refinement of protein loop conformations

This article presents a comparative analysis of two replica-exchange simulation methods for the structure refinement of protein loop conformations, starting from low-resolution predictions. The methods are self-guided Langevin dynamics (SGLD) and molecular dynamics (MD) with a Nosé-Hoover thermostat. We investigated a small dataset of 8- and 12-residue loops, with the shorter loops placed initially from a coarse-grained lattice model and the longer loops from an enumeration assembly method (the Loopy program). The CHARMM22 + CMAP force field with a generalized Born implicit solvent model (molecular-surface parameterized GBSW2) was used to explore conformational space. We also assessed two empirical scoring methods to detect nativelike conformations from decoys: the all-atom distance-scaled ideal-gas reference state (DFIRE-AA) statistical potential and the Rosetta energy function. Among the eight-residue loop targets, SGLD out performed MD in all cases, with a median of 0.48 Å reduction in global root-mean-square deviation (RMSD) of the loop backbone coordinates from the native structure. Among the more challenging 12-residue loop targets, SGLD improved the prediction accuracy over MD by a median of 1.31 Å, representing a substantial improvement. The overall median RMSD for SGLD simulations of 12-residue loops was 0.91 Å, yielding refinement of a median 2.70 Å from initial loop placement. Results from DFIRE-AA and the Rosetta model applied to rescoring conformations failed to improve the overall detection calculated from the CHARMM force field. We illustrate the advantage of SGLD over the MD simulation model by presenting potential-energy landscapes for several loop predictions. Our results demonstrate that SGLD significantly outperforms traditional MD in the generation and populating of nativelike loop conformations and that the CHARMM force field performs comparably to other empirical force fields in identifying these conformations from the resulting ensembles.

The Adipophilin C Terminus Is a Self-folding Membrane-binding Domain That Is Important for Milk Lipid Secretion

Cytoplasmic lipid droplets (CLD) in mammary epithelial cells undergo secretion by a unique membrane envelopment process to produce milk lipids. Adipophilin (ADPH/Plin2), a member of the perilipin/PAT family of lipid droplet-associated proteins, is hypothesized to mediate CLD secretion through interactions with apical plasma membrane elements. We found that the secretion of CLD coated by truncated ADPH lacking the C-terminal region encoding a putative four-helix bundle structure was impaired relative to that of CLD coated by full-length ADPH. We used homology modeling and analyses of the solution and membrane binding properties of purified recombinant ADPH C terminus to understand how this region possibly mediates CLD secretion. Homology modeling supports the concept that the ADPH C terminus forms a four-helix bundle motif and suggests that this structure can form stable membrane bilayer interactions. Circular dichroism and protease mapping studies confirmed that the ADPH C terminus is an independently folding α-helical structure that is relatively resistant to urea denaturation. Liposome binding studies showed that the purified C terminus binds to phospholipid membranes through electrostatic dependent interactions, and cell culture studies documented that it localizes to the plasma membrane. Collectively, these data provide direct evidence that the ADPH C terminus forms a stable membrane binding helical structure that is important for CLD secretion. We speculate that interactions between the four-helix bundle of ADPH and membrane phospholipids may be an initial step in milk lipid secretion.

Solution structure of the C-terminal DUF1000 domain of the human thioredoxin-like 1 protein

Solution structure of the C-terminal DUF1000 domain of the human thioredoxin-like 1 protein

Accounting for Ligand Conformational Restriction in Calculations of Protein-Ligand Binding Affinities

The conformation adopted by a ligand on binding to a receptor may differ from its lowest-energy conformation in solution. In addition, the bound ligand is more conformationally restricted, which is associated with a configurational entropy loss. The free energy change due to these effects is often neglected or treated crudely in current models for predicting binding affinity. We present a method for estimating this contribution, based on perturbation theory using the quasi-harmonic model of Karplus and Kushick as a reference system. The consistency of the method is checked for small model systems. Subsequently we use the method, along with an estimate for the enthalpic contribution due to ligand-receptor interactions, to calculate relative binding affinities. The AMBER force field and generalized Born implicit solvent model is used. Binding affinities were estimated for a test set of 233 protein-ligand complexes for which crystal structures and measured binding affinities are available. In most cases, the ligand conformation in the bound state was significantly different from the most favorable conformation in solution. In general, the correlation between measured and calculated ligand binding affinities including the free energy change due to ligand conformational change is comparable to or slightly better than that obtained by using an empirically-trained docking score. Both entropic and enthalpic contributions to this free energy change are significant.

Establishing effective simulation protocols for β‐ and α/β‐peptides. III. Molecular mechanical model for acyclic β‐amino acids

All-atom molecular mechanics (MM) force field parameters are developed for the backbone of acyclic beta-amino acid using an improved version of the multiobjective evolutionary algorithm (MOEA). The MM model is benchmarked using beta(3)-homo-Alanine (beta(3)-hAla) diamide in water with SCC-DFTB/MM simulations as the reference. Satisfactory agreements are found between the MM and SCC-DFTB/MM results regarding the distribution of key dihedral angles for the beta(3)-hAla diamide in water. The MM model is further applied to a beta-hepta-peptide in methanol solution. The calculated NOE values and (3)J coupling constants averaged over different trajectories are consistent with experimental data. By contrast, simulations using parameters directly transferred from the CHARMM22 force field for proteins lead to much worse agreement, which highlights the importance of careful parameterization for non-natural peptides, for which the improved MOEA is particularly useful. Finally, as an initial application of the new force field parameters, the behaviors of a short random copolymer consisting of beta amino acids in bulk solution and membrane/water interface are studied using a generalized Born implicit solvent model (GBSW). Results for four selected sequences show that segregation of hydrophobic and cationic groups occur easily at the membrane/solution interface for all sequences. The sequence that features alternating short blocks exhibits signs of lower stability at the interface compared to other sequences. These results confirm the hypothesis in recent experimental studies that beta-amino-acid based random copolymers can develop a high degree of amphiphilicity without regular three-dimensional structure.

Simulations of Surfactant and Lipid Assemblies with Generalized Born Implicit Solvent Models

In recent years, all-atom and coarse-grained models have been developed for theoretical studies of surfactant and lipid assemblies. Here we describe the development of hybrid models incorporating atomic-level representation of surfactant molecules and continuum description for solvent. Specifically, we show that atomistic simulations of neutral, anionic and cationic surfactants can be performed using the generalized Born implicit solvent model by careful parameterization of atomic input radii. We applied the new models to constant pH molecular dynamics simulations of acid-base titration of fatty acid solubilized in dodecyl triehyleneglycol either (DE3), dodecylsulfaphate (SDS) and dodecyl tetramethylammonium (DTA) micelles. We show that simulation results are able to reproduce the experimental data and offer atomically detailed explanation for the abnormal titration behavior of fatty acid in cationic micelles. The combined atomistic solute and continuum solvent models significantly reduce computational and represent an attractive alternative approach for theoretical studies of interfacial phenomena involving surfactant and lipid assemblies.

Automated site preparation in physics‐based rescoring of receptor ligand complexes

Hydrogen atoms are not typically observable in X-ray crystal structures, but inferring their locations is often important in structure-based drug design. In addition, protonation states of the protein can change in response to ligand binding, as can the orientations of OH groups, a subtle form of "induced fit." We implement and evaluate an automated procedure for optimizing polar hydrogens in protein-binding sites in complex with ligands. Specifically, we apply the previously described Independent Cluster Decomposition Algorithm (ICDA) algorithm (Li et al., Proteins 2007;66:824-837), which assigns the ionization states of titratable residues, the amide orientations of Asn/Gln side chains, the imidazole ring orientation in His, and the orientations of OH/SH groups, in a unified algorithm. We test the utility of this method for identifying nativelike ligand poses using 247 protein-ligand complexes from an established database of docked decoys. Pose selection is performed with a physics-based scoring function based on a molecular mechanics energy function and a Generalized Born implicit solvent model. The use of the ICDA receptor preparation protocol, implemented with no knowledge of the native ligand pose, increases the accuracy of pose selection significantly, with the average RMSD over all complexes decreasing from 2.7 to 1.5 A when applying ICDA. Large improvements are seen for specific classes of binding sites with titratable groups, such as aspartyl proteases.