Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method.

Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.

Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference.

Predicting accurate protein-ligand binding affinities is an important task in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and state-of-the-art deep learning approaches. Despite the recent advances in the application of deep convolutional and graph neural network-based approaches, it remains unclear what the relative advantages of each approach are and how they compare with physics-based methodologies that have found more mainstream success in virtual screening pipelines. We present fusion models that combine features and inference from complementary representations to improve binding affinity prediction. This, to our knowledge, is the first comprehensive study that uses a common series of evaluations to directly compare the performance of three-dimensional (3D)-convolutional neural networks (3D-CNNs), spatial graph neural networks (SG-CNNs), and their fusion. We use temporal and structure-based splits to assess performance on novel protein targets. To test the practical applicability of our models, we examine their performance in cases that assume that the crystal structure is not available. In these cases, binding free energies are predicted using docking pose coordinates as the inputs to each model. In addition, we compare these deep learning approaches to predictions based on docking scores and molecular mechanic/generalized Born surface area (MM/GBSA) calculations. Our results show that the fusion models make more accurate predictions than their constituent neural network models as well as docking scoring and MM/GBSA rescoring, with the benefit of greater computational efficiency than the MM/GBSA method. Finally, we provide the code to reproduce our results and the parameter files of the trained models used in this work. The software is available as open source at https://github.com/llnl/fast. Model parameter files are available at ftp://gdo-bioinformatics.ucllnl.org/fast/pdbbind2016_model_checkpoints/.

System-level analysis of metabolic trade-offs during anaerobic photoheterotrophic growth in Rhodopseudomonas palustris.

BACKGROUND: Living organisms need to allocate their limited resources in a manner that optimizes their overall fitness by simultaneously achieving several different biological objectives. Examination of these biological trade-offs can provide invaluable information regarding the biophysical and biochemical bases behind observed cellular phenotypes. A quantitative knowledge of a cell system's critical objectives is also needed for engineering of cellular metabolism, where there is interest in mitigating the fitness costs that may result from human manipulation. RESULTS: To study metabolism in photoheterotrophs, we developed and validated a genome-scale model of metabolism in Rhodopseudomonas palustris, a metabolically versatile gram-negative purple non-sulfur bacterium capable of growing phototrophically on various carbon sources, including inorganic carbon and aromatic compounds. To quantitatively assess trade-offs among a set of important biological objectives during different metabolic growth modes, we used our new model to conduct an 8-dimensional multi-objective flux analysis of metabolism in R. palustris. Our results revealed that phototrophic metabolism in R. palustris is light-limited under anaerobic conditions, regardless of the available carbon source. Under photoheterotrophic conditions, R. palustris prioritizes the optimization of carbon efficiency, followed by ATP production and biomass production rate, in a Pareto-optimal manner. To achieve maximum carbon fixation, cells appear to divert limited energy resources away from growth and toward CO2 fixation, even in the presence of excess reduced carbon. We also found that to achieve the theoretical maximum rate of biomass production, anaerobic metabolism requires import of additional compounds (such as protons) to serve as electron acceptors. Finally, we found that production of hydrogen gas, of potential interest as a candidate biofuel, lowers the cellular growth rates under all circumstances. CONCLUSIONS: Photoheterotrophic metabolism of R. palustris is primarily regulated by the amount of light it can absorb and not the availability of carbon. However, despite carbon's secondary role as a regulating factor, R. palustris' metabolism strives for maximum carbon efficiency, even when this increased efficiency leads to slightly lower growth rates.

A method to predict blood-brain barrier permeability of drug-like compounds using molecular dynamics simulations.

The blood-brain barrier (BBB) is formed by specialized tight junctions between endothelial cells that line brain capillaries to create a highly selective barrier between the brain and the rest of the body. A major problem to overcome in drug design is the ability of the compound in question to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body ideally should not cross the BBB to avoid possible psychotropic side effects. Thus, the task of predicting the BBB permeability of new compounds is of great importance. Two gold-standard experimental measures of BBB permeability are logBB (the concentration of drug in the brain divided by concentration in the blood) and logPS (permeability surface-area product). Both methods are time-consuming and expensive, and although logPS is considered the more informative measure, it is lower throughput and more resource intensive. With continual increases in computer power and improvements in molecular simulations, in silico methods may provide viable alternatives. Computational predictions of these two parameters for a sample of 12 small molecule compounds were performed. The potential of mean force for each compound through a 1,2-dioleoyl-sn-glycero-3-phosphocholine bilayer is determined by molecular dynamics simulations. This system setup is often used as a simple BBB mimetic. Additionally, one-dimensional position-dependent diffusion coefficients are calculated from the molecular dynamics trajectories. The diffusion coefficient is combined with the free energy landscape to calculate the effective permeability (Peff) for each sample compound. The relative values of these permeabilities are compared to experimentally determined logBB and logPS values. Our computational predictions correlate remarkably well with both logBB (R(2) = 0.94) and logPS (R(2) = 0.90). Thus, we have demonstrated that this approach may have the potential to provide reliable, quantitatively predictive BBB permeability, using a relatively quick, inexpensive method.

Toward fully automated high performance computing drug discovery: a massively parallel virtual screening pipeline for docking and molecular mechanics/generalized Born surface area rescoring to improve enrichment.

In this work we announce and evaluate a high throughput virtual screening pipeline for in-silico screening of virtual compound databases using high performance computing (HPC). Notable features of this pipeline are an automated receptor preparation scheme with unsupervised binding site identification. The pipeline includes receptor/target preparation, ligand preparation, VinaLC docking calculation, and molecular mechanics/generalized Born surface area (MM/GBSA) rescoring using the GB model by Onufriev and co-workers [J. Chem. Theory Comput. 2007, 3, 156-169]. Furthermore, we leverage HPC resources to perform an unprecedented, comprehensive evaluation of MM/GBSA rescoring when applied to the DUD-E data set (Directory of Useful Decoys: Enhanced), in which we selected 38 protein targets and a total of ∼0.7 million actives and decoys. The computer wall time for virtual screening has been reduced drastically on HPC machines, which increases the feasibility of extremely large ligand database screening with more accurate methods. HPC resources allowed us to rescore 20 poses per compound and evaluate the optimal number of poses to rescore. We find that keeping 5-10 poses is a good compromise between accuracy and computational expense. Overall the results demonstrate that MM/GBSA rescoring has higher average receiver operating characteristic (ROC) area under curve (AUC) values and consistently better early recovery of actives than Vina docking alone. Specifically, the enrichment performance is target-dependent. MM/GBSA rescoring significantly out performs Vina docking for the folate enzymes, kinases, and several other enzymes. The more accurate energy function and solvation terms of the MM/GBSA method allow MM/GBSA to achieve better enrichment, but the rescoring is still limited by the docking method to generate the poses with the correct binding modes.

Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines.

A mixed parallel scheme that combines message passing interface (MPI) and multithreading was implemented in the AutoDock Vina molecular docking program. The resulting program, named VinaLC, was tested on the petascale high performance computing (HPC) machines at Lawrence Livermore National Laboratory. To exploit the typical cluster-type supercomputers, thousands of docking calculations were dispatched by the master process to run simultaneously on thousands of slave processes, where each docking calculation takes one slave process on one node, and within the node each docking calculation runs via multithreading on multiple CPU cores and shared memory. Input and output of the program and the data handling within the program were carefully designed to deal with large databases and ultimately achieve HPC on a large number of CPU cores. Parallel performance analysis of the VinaLC program shows that the code scales up to more than 15K CPUs with a very low overhead cost of 3.94%. One million flexible compound docking calculations took only 1.4 h to finish on about 15K CPUs. The docking accuracy of VinaLC has been validated against the DUD data set by the re-docking of X-ray ligands and an enrichment study, 64.4% of the top scoring poses have RMSD values under 2.0 Å. The program has been demonstrated to have good enrichment performance on 70% of the targets in the DUD data set. An analysis of the enrichment factors calculated at various percentages of the screening database indicates VinaLC has very good early recovery of actives.

Pyrrolopyrimidine inhibitors of DNA gyrase B (GyrB) and topoisomerase IV (ParE). Part I: Structure guided discovery and optimization of dual targeting agents with potent, broad-spectrum enzymatic activity.

The bacterial topoisomerases DNA gyrase (GyrB) and topoisomerase IV (ParE) are essential enzymes that control the topological state of DNA during replication. The high degree of conservation in the ATP-binding pockets of these enzymes make them appealing targets for broad-spectrum inhibitor development. A pyrrolopyrimidine scaffold was identified from a pharmacophore-based fragment screen with optimization potential. Structural characterization of inhibitor complexes conducted using selected GyrB/ParE orthologs aided in the identification of important steric, dynamic and compositional differences in the ATP-binding pockets of the targets, enabling the design of highly potent pyrrolopyrimidine inhibitors with broad enzymatic spectrum and dual targeting activity.

Pyrrolopyrimidine inhibitors of DNA gyrase B (GyrB) and topoisomerase IV (ParE), Part II: development of inhibitors with broad spectrum, Gram-negative antibacterial activity.

The structurally related bacterial topoisomerases DNA gyrase (GyrB) and topoisomerase IV (ParE) have long been recognized as prime candidates for the development of broad spectrum antibacterial agents. However, GyrB/ParE targeting antibacterials with spectrum that encompasses robust Gram-negative pathogens have not yet been reported. Using structure-based inhibitor design, we optimized a novel pyrrolopyrimidine inhibitor series with potent, dual targeting activity against GyrB and ParE. Compounds were discovered with broad antibacterial spectrum, including activity against Pseudomonas aeruginosa, Acinetobacter baumannii and Escherichia coli. Herein we describe the SAR of the pyrrolopyrimidine series as it relates to key structural and electronic features necessary for Gram-negative antibacterial activity.

Molecular recognition of aromatic rings by flavin: electrostatics and dispersion determine ring positioning above isoalloxazine.

Aromatic stacking interactions between isoalloxazine (ISA) of flavin and three prototypical aromatics (benzene, pyridine, chlorobenzene) were investigated using electronic structure calculations with Monte Carlo simulated annealing. The Effective Fragment Potential (EFP) method was used to locate the low-energy equilibrium configurations for the three dimer systems. These structures were further characterized through DFT (M06-2X) and MP2 calculations. One equilibrium configuration exists for ISA-benzene; characterizing the stacked dimer surface revealed a steep, single-welled potential that funnels benzene directly between rings II and III, positioning a substituent hydrogen adjacent to the redox-active N5. ISA-pyridine and ISA-chlorobenzene minimum-energy structures contain the aromatic ring in very similar position to that in ISA-benzene. However, the added rotational degree of freedom leads to two distinct binding motifs, having approximately antiparallel or parallel dipole moment alignment with ISA. The existence of the latter binding configuration was unexpected but is explained by the shape of the ISA electrostatic potential. Dispersion is the primary noncovalent interaction driving the positioning of aromatic rings above ISA, while electrostatics determine the orientation in dipole-containing substituted benzenes. The interplay of these interactions can be used to tune molecular recognition properties of synthetic redox cofactors, including positioning desired functional groups adjacent to the redox-active N5.

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure.

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 µm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions.

Toward a small molecule, biomimetic carbonic anhydrase model: theoretical and experimental investigations of a panel of zinc(II) aza-macrocyclic catalysts.

A panel of five zinc-chelated aza-macrocycle ligands and their ability to catalyze the hydration of carbon dioxide to bicarbonate, H(2)O + CO(2) → H(+) + HCO(3)(­), was investigated using quantum-mechanical methods and stopped-flow experiments. The key intermediates in the reaction coordinate were optimized using the M06-2X density functional with aug-cc-pVTZ basis set. Activation energies for the first step in the catalytic cycle, nucleophilic CO(2) addition, were calculated from gas-phase optimized transition-state geometries. The computationally derived trend in activation energies was found to not correspond with the experimentally observed rates. However, activation energies for the second, bicarbonate release step, which were estimated using calculated bond dissociation energies, provided good agreement with the observed trend in rate constants. Thus, the joint theoretical and experimental results provide evidence that bicarbonate release, not CO(2) addition, may be the rate-limiting step in CO(2) hydration by zinc complexes of aza-macrocyclic ligands. pH-independent rate constants were found to increase with decreasing Lewis acidity of the ligand-Zn complex, and the trend in rate constants was correlated with molecular properties of the ligands. It is suggested that tuning catalytic efficiency through the first coordination shell of Zn(2+) ligands is predominantly a balance between increasing charge-donating character of the ligand and maintaining the catalytically relevant pK(a) below the operating pH.

Effects of somatic mutations on CDR loop flexibility during affinity maturation.

Prior studies suggest that antibody affinity maturation is achieved, in part, via prearranging the CDRs for binding. The implication is that the entropy cost of binding is reduced and that this rigidification occurs as a consequence of somatic mutations during maturation. However, how these mutations modulate CDR flexibility is unclear. Here, molecular dynamics simulations captured CDR flexibility differences between four mature antibodies (7G12, AZ28, 28B4, and 48G7) and their germline predecessors. Analysis of their trajectories: (1) rationalized how mutations during affinity maturation restrict CDR motility, (2) captured the equilibrium between bound and unbound conformations for the H3 loop of unliganded 7G12, and (3) predicted a set of new mutations that, according to our simulations, should diminish binding by increasing flexibility.

Discovery of Small-Molecule Inhibitors of SARS-CoV-2 Proteins Using a Computational and Experimental Pipeline.

A rapid response is necessary to contain emergent biological outbreaks before they can become pandemics. The novel coronavirus (SARS-CoV-2) that causes COVID-19 was first reported in December of 2019 in Wuhan, China and reached most corners of the globe in less than two months. In just over a year since the initial infections, COVID-19 infected almost 100 million people worldwide. Although similar to SARS-CoV and MERS-CoV, SARS-CoV-2 has resisted treatments that are effective against other coronaviruses. Crystal structures of two SARS-CoV-2 proteins, spike protein and main protease, have been reported and can serve as targets for studies in neutralizing this threat. We have employed molecular docking, molecular dynamics simulations, and machine learning to identify from a library of 26 million molecules possible candidate compounds that may attenuate or neutralize the effects of this virus. The viability of selected candidate compounds against SARS-CoV-2 was determined experimentally by biolayer interferometry and FRET-based activity protein assays along with virus-based assays. In the pseudovirus assay, imatinib and lapatinib had IC50 values below 10 µM, while candesartan cilexetil had an IC50 value of approximately 67 µM against Mpro in a FRET-based activity assay. Comparatively, candesartan cilexetil had the highest selectivity index of all compounds tested as its half-maximal cytotoxicity concentration 50 (CC50) value was the only one greater than the limit of the assay (>100 µM).

Phosphorylation-induced conformational changes in Rap1b: allosteric effects on switch domains and effector loop.

Rap1b has been implicated in the transduction of the cAMP mitogenic response. Agonists that increase intracellular cAMP rapidly activate (i.e. GTP binding) and phosphorylate Rap1b on Ser(179) at its C terminus. cAMP-dependent protein kinase (PKA)-mediated phosphorylation of Rap1b is required for cAMP-dependent mitogenesis, tumorigenesis, and inhibition of AKT activity. However, the role of phosphorylation still remains unknown. In this study, we utilized amide hydrogen/deuterium exchange mass spectroscopy (DXMS) to assess potential conformational changes and/or mobility induced by phosphorylation. We report here DXMS data comparing exchange rates for PKA-phosphorylated (Rap1-P) and S179D phosphomimetic (Rap1-D) Rap1b proteins. Rap1-P and Rap1-D behaved exactly the same, revealing an increased exchange rate in discrete regions along the protein; these regions include a domain around the phosphorylation site and unexpectedly the two switch loops. Thus, local effects induced by Ser(179) phosphorylation communicate allosterically with distal domains involved in effector interaction. These results provide a mechanistic explanation for the differential effects of Rap1 phosphorylation by PKA on effector protein interaction.

Conformational selection in silico: loop latching motions and ligand binding in enzymes.

Ligand binding frequently induces significant conformational changes in a protein receptor. Understanding and predicting such conformational changes represent an important challenge for computational biology, including applications to structure-based drug design. We describe an approach to this problem based on the assumption that the holo state is at least transiently populated in the absence of a ligand; this hypothesis has been referred to as "conformational selection." Here, we apply a method that tests this hypothesis on a challenging class of ligand-induced conformational changes, which we refer to as loop latching: the closing of a loop around an active site that sequesters the ligand from solvent. The method uses a combination of replica exchange molecular dynamics and a loop prediction algorithm to generate low-energy loop structures, and docking to select the conformation appropriate for binding a particular ligand. On a test set of six proteins, it yields loop structures including hololike conformations, generally below 2 A RMSD from the liganded structure, for loops that span up to 15 residues. Docking serves as a stringent test of the predictions. In five of the six cases, the predicted loop conformations improve the ranks of cognate ligands relative to using the apo structure, although the results remain, in most cases, significantly worse than using a holo structure. The poses of the cognate ligands are correct in four of the six test cases, while they are correct for five of the six using a holo structure.

E9-Im9 colicin DNase-immunity protein biomolecular association in water: a multiple-copy and accelerated molecular dynamics simulation study.

Protein-protein transient and dynamic interactions underlie all biological processes. The molecular dynamics (MD) of the E9 colicin DNase protein, its Im9 inhibitor protein, and their E9-Im9 recognition complex are investigated by combining multiple-copy (MC) MD and accelerated MD (aMD) explicit-solvent simulation approaches, after validation with crystalline-phase and solution experiments. Im9 shows higher flexibility than its E9 counterpart. Im9 displays a significant reduction of backbone flexibility and a remarkable increase in motional correlation upon E9 association. Im9 loops 23-31 and 54-64 open with respect to the E9-Im9 X-ray structure and show high conformational diversity. Upon association a large fraction (approximately 20 nm2) of E9 and Im9 protein surfaces become inaccessible to water. Numerous salt bridges transiently occurring throughout our six 50 ns long MC-MD simulations are not present in the X-ray model. Among these Im9 Glu31-E9 Arg96 and Im9 Glu41-Lys89 involve interface interactions. Through the use of 10 ns of Im9 aMD simulation, we reconcile the largest thermodynamic impact measured for Asp51Ala mutation with Im9 structure and dynamics. Lys57 acts as an essential molecular switch to shift Im9 surface loop towards an ideal configuration for E9 inhibition. This is achieved by switching Asp60-Lys57 and Asp62-Lys57 hydrogen bonds to Asp51-Lys57 salt bridge. E9-Im9 recognition involves shifts of conformational distributions, reorganization of intramolecular hydrogen bond patterns, and formation of new inter- and intramolecular interactions. The description of key transient biological interactions can be significantly enriched by the dynamic and atomic-level information provided by computer simulations.

Efficient Computational Modeling of Human Ventricular Activation and Its Electrocardiographic Representation: A Sensitivity Study.

Patient-specific models of the ventricular myocardium, combined with the computational power to run rapid simulations, are approaching the level where they could be used for personalized cardiovascular medicine. A major remaining challenge is determining model parameters from available patient data, especially for models of the Purkinje-myocardial junctions (PMJs): the sites of initial ventricular electrical activation. There are no non-invasive methods for localizing PMJs in patients, and the relationship between the standard clinical ECG and PMJ model parameters is underexplored. Thus, this study aimed to determine the sensitivity of the QRS complex of the ECG to the anatomical location and regional number of PMJs. The QRS complex was simulated using an image-based human torso and biventricular model, and cardiac electrophysiology was simulated using Cardioid. The PMJs were modeled as discrete current injection stimuli, and the location and number of stimuli were varied within initial activation regions based on published experiments. Results indicate that the QRS complex features were most sensitive to the presence or absence of four "seed" stimuli, and adjusting locations of nearby "regional" stimuli provided finer tuning. Decreasing number of regional stimuli by an order of magnitude resulted in virtually no change in the QRS complex. Thus, a minimal 12-stimuli configuration was identified that resulted in physiological excitation, defined by QRS complex feature metrics and ventricular excitation pattern. Overall, the sensitivity results suggest that parameterizing PMJ location, rather than number, be given significantly higher priority in future studies creating personalized ventricular models from patient-derived ECGs.

MET Exon 14 Mutation Encodes an Actionable Therapeutic Target in Lung Adenocarcinoma.

Targeting somatically activated oncogenes has revolutionized the treatment of non-small cell lung cancer (NSCLC). Mutations in the gene mesenchymal-epithelial transition (MET) near the exon 14 splice sites are recurrent in lung adenocarcinoma and cause exon skipping (METΔ14). Here, we analyzed 4,422 samples from 12 different malignancies to estimate the rate of said exon skipping. METΔ14 mutation and transcript were most common in lung adenocarcinoma. Endogenously expressed levels of METΔ14 transformed human epithelial lung cells in a hepatocyte growth factor-dependent manner. In addition, overexpression of the orthologous mouse allele induced lung adenocarcinoma in a novel, immunocompetent mouse model. Met inhibition showed clinical benefit in this model. In addition, we observed a clinical response to crizotinib in a patient with METΔ14-driven NSCLC, only to observe new missense mutations in the MET activation loop, critical for binding to crizotinib, upon clinical progression. These findings support genomically selected clinical trials directed toward METΔ14 in a fraction of NSCLC patients, confirm second-site mutations for further therapeutic targeting prior to and beyond acquired resistance, and provide an in vivo system for the study of METΔ14 in an immunocompetent host. Cancer Res; 77(16); 4498-505. ©2017 AACR.

Competition between intramolecular hydrogen bonds and solvation in phosphorylated peptides: simulations with explicit and implicit solvent.

The atomic-level mechanisms of protein regulation by post-translational phosphorylation remain poorly understood, except in a few well-studied systems. Molecular mechanics simulations can in principle be used to help understand and predict the effects of protein phosphorylation, but the accuracy of the results will of course depend on the quality of the force field parameters for the phosphorylated residues as well as the quality of the solvent model. The phosphorylated residues typically carry a -2 charge at physiological pH; however, the effects of phosphorylation can sometimes be mimicked by substituting Asp or Glu for the phosphorylated residue. Here we examine the suitability of explicit and implicit solvent models for simulating phospho-serine in both the -1 and -2 charge states. Specifically, we simulate a capped phosphorylated peptide, Ace-Gly-Ser-pSer-Ser-Nme, and compare the results to each other and to experimental observables from an NMR experiment. The first major conclusion is that explicit water models (TIP3P, TIP4P and SPC/E) and a Generalized Born implicit solvent model provide reasonable agreement with the experimental observables, given appropriate partial charges for the phosphate group. The Generalized Born results, however, show greater hydrogen bonding propensity than the explicit solvent results. Distance dependent dielectric treatments perform poorly. The second major conclusion is that many ensemble-averaged properties obtained for the phosphopeptide in the -1 and -2 charge states are strikingly similar; the -1 species has a slightly higher propensity to form internal hydrogen bonds. All of the results can be rationalized by quantifying the strength of the P-O/H-N hydrogen bond, which depends on a sensitive balance between strongly favorable charge/dipole and dipole/dipole interactions and strongly unfavorable desolvation.

Understanding a substrate's product regioselectivity in a family of enzymes: a case study of acetaminophen binding in cytochrome P450s.

Product regioselectivity as influenced by molecular recognition is a key aspect of enzyme catalysis. We applied large-scale two-dimensional (2D) umbrella sampling (USP) simulations to characterize acetaminophen (APAP) binding in the active sites of the family of Cytochrome P450 (CYP) enzymes as a case study to show the different regioselectivity exhibited by a single substrate in comparative enzymes. Our results successfully explain the experimentally observed product regioselectivity for all five human CYPs included in this study, demonstrating that binding events play an important role in determining regioselectivity. In CYP2C9 and CYP3A4, weak interactions in an overall large active site cavity result in a fairly small binding free energy difference between APAP reactive binding states, consistent with experimental results that show little preference for resulting metabolites. In contrast, in CYP1A2 and CYP2E1, APAP is strongly restrained by a compact binding pocket, leading to a preferred binding conformation. The calculated binding equilibrium of APAP within the compact active site of CYP2A6 is able to predict the experimentally documented product ratios and is also applied to explain APAP regioselectivity in CYP1A2 and CYP2C9. APAP regioselectivity seems to be related to the selectivity for one binding conformation over another binding conformation as dictated by the size and shape of the active site. Additionally, unlike docking and molecular dynamics (MD), our free energy calculations successfully reproduced a unique APAP pose in CYP3A4 that had been reported experimentally, suggesting this approach is well suited to find the realistic binding pose and the lowest-energy starting structure for studying the chemical reaction step in the future.