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Generative molecular design (GMD) is an increasingly popular strategy for drug discovery, using machine learning models to propose, evaluate and optimize chemical structures against a set of target design criteria. We present the ATOM-GMD platform, a scalable multiprocessing framework to optimize many parameters simultaneously over large populations of proposed molecules. ATOM-GMD uses a junction tree variational autoencoder mapping structures to latent vectors, along with a genetic algorithm operating on latent vector elements, to search a diverse molecular space for compounds that meet the design criteria. We used the ATOM-GMD framework in a lead optimization case study to develop potent and selective histamine H1 receptor antagonists. We synthesized 103 of the top scoring compounds and measured their properties experimentally. Six of the tested compounds bind H1 withKi’s between 10 and 100 nM and are at least 100-fold selective relative to muscarinic M2 receptors, validating the effectiveness of our GMD approach.

Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods. As part of a multi-institutional COVID-19 pandemic response, over 500 million small molecules were computationally screened against four protein structures from the novel coronavirus (SARS-CoV-2), which causes COVID-19. Three enhancements to Deep Fusion were made in order to evaluate more than 5 billion docked poses on SARS-CoV-2 protein targets. First, the Deep Fusion concept was refined by formulating the architecture as one, coherently backpropagated model (Coherent Fusion) to improve binding-affinity prediction accuracy. Secondly, the model was trained using a distributed, genetic hyper-parameter optimization. Finally, a scalable, high-throughput screening capability was developed to maximize the number of ligands evaluated and expedite the path to experimental evaluation. In this work, we present both the methods developed for machine learning-based high-throughput screening and results from using our computational pipeline to find SARS-CoV-2 inhibitors.

Nerve agents have experienced a resurgence in recent times with their use against civilian targets during the attacks in Syria (2012), the poisoning of Sergei and Yulia Skripal in the United Kingdom (2018) and Alexei Navalny in Russia (2020), strongly renewing the importance of antidote development against these lethal substances. The current standard treatment against their effects relies on the use of small molecule-based oximes that can efficiently restore acetylcholinesterase (AChE) activity. Despite their efficacy in reactivating AChE, the action of drugs like 2-pralidoxime (2-PAM) is primarily limited to the peripheral nervous system (PNS) and, thus, provides no significant protection to the central nervous system (CNS). This lack of action in the CNS stems from their ionic nature that, on one end makes them very powerful reactivators and on the other renders them ineffective at crossing the Blood Brain Barrier (BBB) to reach the CNS. In this report, we describe the use of an iterative approach composed of parallel chemical and in silico syntheses, computational modeling, and a battery of detailed in vitro and in vivo assays that resulted in the identification of a promising, novel CNS-permeable oxime reactivator. Additional experiments to determine acute and chronic toxicity are ongoing.

Drug-induced liver injury (DILI) is the most common cause of acute liver failure and a frequent reason for withdrawal of candidate drugs during preclinical and clinical testing. An important type of DILI is cholestatic liver injury, caused by buildup of bile salts within hepatocytes; it is frequently associated with inhibition of bile salt transporters, such as the bile salt export pump (BSEP). Reliable in silico models to predict BSEP inhibition directly from chemical structures would significantly reduce costs during drug discovery and could help avoid injury to patients. Unfortunately, models published to date have been insufficiently accurate to encourage wide adoption. We report our development of classification and regression models for BSEP inhibition with substantially improved performance over previously published models. Our model development leveraged the ATOM Modeling PipeLine (AMPL) developed by the ATOM Consortium, which enabled us to train and evaluate thousands of candidate models. In the course of model development, we assessed a variety of schemes for chemical featurization, dataset partitioning and class labeling, and identified those producing models that generalized best to novel chemical entities. Our best performing classification model was a neural network with ROC AUC = 0.88 on our internal test dataset and 0.89 on an independent external compound set. Our best regression model, the first ever reported for predicting BSEP IC50s, yielded a test set $R^2 = 0.56$ and mean absolute error 0.37, corresponding to a mean 2.3-fold error in predicted IC50s, comparable to experimental variation. These models will thus be useful as inputs to mechanistic predictions of DILI and as part of computational pipelines for drug discovery.

Organophosphorus-based (OP) nerve agents represent some of the most toxic substances known to mankind. The current standard of care for exposure has changed very little in the past decades, and relies on a combination of atropine to block receptor activity and oxime-type acetylcholinesterase (AChE) reactivators to reverse the OP binding to AChE. Although these oximes can block the effects of nerve agents, their overall efficacy is reduced by their limited capacity to cross the blood-brain barrier (BBB). RS194B, a new oxime developed by Radic et al. (J. Biol. Chem., 2012) has shown promise for enhanced ability to cross the BBB. To fully assess the potential of this compound as an effective treatment for nerve agent poisoning, a comprehensive evaluation of its pharmacokinetic (PK) and biodistribution profiles was performed using both intravenous and intramuscular exposure routes. The ultra-sensitive technique of accelerator mass spectrometry was used to quantify the compound's PK profile, tissue distribution, and brain/plasma ratio at four dose concentrations in guinea pigs. PK analysis revealed a rapid distribution of the oxime with a plasma t1/2 of ∼1 h. Kidney and liver had the highest concentrations per gram of tissue followed by lung, spleen, heart and brain for all dose concentrations tested. The Cmax in the brain ranged between 0.03 and 0.18% of the administered dose, and the brain-to-plasma ratio ranged from 0.04 at the 10 mg/kg dose to 0.18 at the 200 mg/kg dose demonstrating dose dependent differences in brain and plasma concentrations. In vitro studies show that both passive diffusion and active transport contribute little to RS194B traversal of the BBB. These results indicate that biodistribution is widespread, but very low quantities accumulate in the guinea pig brain, indicating this compound may not be suitable as a centrally active reactivator.

Membrane permeability is a key property to consider during the drug design process, and particularly vital when dealing with small molecules that have intracellular targets as their efficacy highly depends on their ability to cross the membrane. In this work, we describe the use of umbrella sampling molecular dynamics (MD) computational modeling to comprehensively assess the passive permeability profile of a range of compounds through a lipid bilayer. The model was initially calibrated through in vitro validation studies employing a parallel artificial membrane permeability assay (PAMPA). The model was subsequently evaluated for its quantitative prediction of permeability profiles for a series of custom synthesized and closely related compounds. The results exhibited substantially improved agreement with the PAMPA data, relative to alternative existing methods. Our work introduces a computational model that underwent progressive molding and fine-tuning as a result of its synergistic collaboration with numerous in vitro PAMPA permeability assays. The presented computational model introduces itself as a useful, predictive tool for permeability prediction.

Summary Traditional chemical weapon agents (CWAs) are known to bind acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE). Their lethality is known to be different for different mammalian species. We have modeled the binding affinity of CWAs to AChE and BuChE in human, rabbit, rat and mouse using molecular docking and free energy calculations. Through molecular docking we are able to correctly bind the CWAs at the active site. Using molecular mechanics generalized Born surface area (MMGBSA) calculations, we determined the binding free energy in the active site. Through these calculations, we observe that correct orientation at the active site is critical to binding.

Acetylcholinesterase (AChE) reactivators were developed for the treatment of organophosphate intoxication. Standard care involves the use of anticonvulsants (e.g., diazepam), parasympatolytics (e.g., atropine) and oximes that restore AChE activity. However, oximes also bind to the active site of AChE, simultaneously acting as reversible inhibitors. The goal of the present study is to determine how oxime structure influences the inhibition of human recombinant AChE (hrAChE). Therefore, 24 structurally different oximes were tested and the results compared to the previous eel AChE (EeAChE) experiments. Structural factors that were tested included the number of pyridinium rings, the length and structural features of the linker, and the number and position of the oxime group on the pyridinium ring.

Summary Acetylcholinesterase (AChE) reactivators (oximes) are generally used as antidotes in case of nerve agent poisoning. Because of their affinity to AChE, they may also act as weak inhibitors of AChE. Their inhibition potency against AChE was determined by an in vitro method based on the interaction between AChE and oxime reactivator in the concentration range 10 -1 to 10 -8 M. We used eel AChE for these assays. We found that AChE inhibition strongly depends on the oxime structure. The aim of the present study is to describe the structure-activity relationship (SAR) between oxime structure and inhibition of AChE. AChE reactivators tested include both monoquaternary and bisquaternary structures with the oxime group in different positions on the pyridine ring and with changes in the connecting linker in the case of the bisquaternary compounds. We found AChE inhibition to be highest in bisquaternary oximes that have a longer linker length and have the oxime group in the ortho position. Increased AChE inhibition in monoquaternary oximes was highest when the meta position was occupied by the oxime nucleophile. In addition, different substituents in the connecting chain (in case of bisquaternary oximes) modulated their inhibition potency.

We evaluate the accuracy of density functional theory quantum calculations of biomolecular subsystems using a simple electrostatic embedding scheme. Our scheme is based on dividing the system of interest into a primary and secondary subsystem. A finite difference discretization of the Kohn-Sham equations is used for the primary subsystem, while its electrostatic environment is modeled with a simple one-electron potential. Force-field atomic partial charges are used to generate smeared Gaussian charge densities and to model the secondary subsystem. We illustrate the utility of this approach with calculations of truncated dipeptide chains. We analyze quantitatively the accuracy of this approach by calculating atomic forces and comparing results with full QM calculations. The impact of the choice made in terminating dangling bonds at the frontier of the QM region is also investigated.

Enzymes are complicated solvated systems that typically require many atoms to simulate their function with any degree of accuracy. We have recently developed numerical techniques for large scale first-principles molecular dynamics simulations and applied them to the study of the enzymatic reaction catalyzed by acetylcholinesterase. We carried out density functional theory calculations for a quantum-mechanical (QM) subsystem consisting of 612 atoms with an O(N) complexity finite-difference approach. The QM subsystem is embedded inside an external potential field representing the electrostatic effect due to the environment. We obtained finite-temperature sampling by first-principles molecular dynamics for the acylation reaction of acetylcholine catalyzed by acetylcholinesterase. Our calculations show two energy barriers along the reaction coordinate for the enzyme-catalyzed acylation of acetylcholine. The second barrier (8.5 kcal/mol) is rate-limiting for the acylation reaction and in good agreement with experiment.

The re-emergence of chemical weapons as a global threat in hands of terrorist groups, together with an increasing number of pesticides intoxications and environmental contaminations worldwide, has called the attention of the scientific community for the need of improvement in the technologies for detoxification of organophosphorus (OP) compounds. A compelling strategy is the use of bioremediation by enzymes that are able to hydrolyze these molecules to harmless chemical species. Several enzymes have been studied and engineered for this purpose. However, their mechanisms of action are not well understood. Theoretical investigations may help elucidate important aspects of these mechanisms and help in the development of more efficient bio-remediators. In this review, we point out the major contributions of computational methodologies applied to enzyme based detoxification of OPs. Furthermore, we highlight the use of PTE, PON, DFP, and BuChE as enzymes used in OP detoxification process and how computational tools such as molecular docking, molecular dynamics simulations and combined quantum mechanical/molecular mechanics have and will continue to contribute to this very important area of research.

Transmissible spongiform encephalopathies are a class of fatal neurodegenerative diseases linked to the prion protein. The prion protein normally exists in a soluble, globular state (PrP(C)) that appears to participate in copper metabolism in the central nervous system and/or signal transduction. Infection or disease occurs when an alternatively folded form of the prion protein (PrP(Sc)) converts soluble and predominantly alpha-helical PrP(C) into aggregates rich in beta-structure. The structurally disordered N-terminus adopts beta-structure upon conversion to PrP(Sc) at low pH. Chemical chaperones, such as trimethylamine N-oxide (TMAO), can prevent formation of PrP(Sc) in scrapie-infected mouse neuroblastoma cells [Tatzelt, J., et al. (1996) EMBO J. 15, 6363-6373]. To explore the mechanism of TMAO protection of PrP(C) at the atomic level, molecular dynamics simulations were performed under conditions normally leading to conversion (low pH) with and without 1 M TMAO. In PrP(C) simulations at low pH, the helix content drops and the N-terminus is brought into the small native beta-sheet, yielding a PrP(Sc)-like state. Addition of 1 M TMAO leads to a decreased radius of gyration, a greater number of protein-protein hydrogen bonds, and a greater number of tertiary contacts due to the N-terminus forming an Omega-loop and packing against the structured core of the protein, not due to an increase in the level of extended structure as with the PrP(C) to PrP(Sc) simulation. In simulations beginning with the "PrP(Sc)-like" structure (derived from PrP(C) simulated at low pH in pure water) in 1 M TMAO, similar structural reorganization at the N-terminus occurred, disrupting the extended sheet. The mechanism of protection by TMAO appears to be exclusionary in nature, consistent with previous theoretical and experimental studies. The TMAO-induced N-terminal conformational change prevents residues that are important in the conversion of PrP(C) to PrP(Sc) from assuming extended sheet structure at low pH.

Proteins are very sensitive to their solvent environments. Urea is a common chemical denaturant of proteins, yet some animals contain high concentrations of urea. These animals have evolved an interesting mechanism to counteract the effects of urea by using trimethylamine N -oxide (TMAO). The molecular basis for the ability of TMAO to act as a chemical chaperone remains unknown. Here, we describe molecular dynamics simulations of a small globular protein, chymotrypsin inhibitor 2, in 8 M urea and 4 M TMAO/8 M urea solutions, in addition to other control simulations, to investigate this effect at the atomic level. In 8 M urea, the protein unfolds, and urea acts in both a direct and indirect manner to achieve this effect. In contrast, introduction of 4 M TMAO counteracts the effect of urea and the protein remains well structured. TMAO makes few direct interactions with the protein. Instead, it prevents unfolding of the protein by structuring the solvent. In particular, TMAO orders the solvent and discourages it from competing with intraprotein H bonds and breaking up the hydrophobic core of the protein.

Molecular dynamics simulations of the protein chymotrypsin inhibitor 2 in 8 M urea at 60°C were undertaken to investigate the molecular basis of chemical denaturation. The protein unfolded rapidly under these conditions, but it retained its native structure in a control simulation in water at the same temperature. The overall process of unfolding in urea was similar to that observed in thermal denaturation simulations above the protein's T m of 75°C. The first step in unfolding was expansion of the hydrophobic core. Then, the core was solvated by water and later by urea. The denatured structures in both urea and at high temperature contained residual native helical structure, whereas the β-structure was completely disrupted. The average residence time for urea around hydrophilic groups was six times greater than around hydrophobic residues and in all cases greater than the corresponding water residence times. Water self-diffusion was reduced 40% in 8 M urea. Urea altered water structure and dynamics, thereby diminishing the hydrophobic effect and encouraging solvation of hydrophobic groups. In addition, through urea's weakening of water structure, water became free to compete with intraprotein interactions. Urea also interacted directly with polar residues and the peptide backbone, thereby stabilizing nonnative conformations. These simulations suggest that urea denatures proteins via both direct and indirect mechanisms.

Irreversible inactivation of human acetylcholinesterase (hAChE) by organophosphorous pesticides (OPs) and chemical weapon agents (CWA) has severe morbidity and mortality consequences. We present data from quantum mechanics/molecular mechanics (QM/MM) and 80 classical molecular dynamics (MD) simulations of the apo and soman-adducted forms of hAChE to investigate the effects on the dynamics and protein structure when the catalytic Serine 203 is phosphonylated. We find that the soman phosphonylation of the active site Ser203 follows a water assisted addition-elimination mechanism with the elimination of the fluoride ion being the highest energy barrier at 6.5 kcal/mole. We observe soman-dependent changes in backbone and sidechain motions compared to the apo form of the protein. These alterations restrict the soman-adducted hAChE to a structural state that is primed for the soman adduct to be cleaved and removed from the active site. The altered motions and resulting structures provide alternative pathways into and out of the hAChE active site. In the soman-adducted protein both side and back door pathways are viable for soman adduct access. Correlation analysis of the apo and soman adducted MD trajectories shows that the correlation of gorge entrance and back door motion is disrupted when hAChE is adducted. This supports the hypothesis that substrate and product can use two different pathways as entry and exit sites in the apo form of the protein. These alternative pathways have important implications for the rational design of medical countermeasures.

We have traditionally relied on extremely elevated temperatures (498K, 225 degrees C) to investigate the unfolding process of proteins within the timescale available to molecular dynamics simulations with explicit solvent. However, recent advances in computer hardware have allowed us to extend our thermal denaturation studies to much lower temperatures. Here we describe the results of simulations of chymotrypsin inhibitor 2 at seven temperatures, ranging from 298K to 498K. The simulation lengths vary from 94ns to 20ns, for a total simulation time of 344ns, or 0.34 micros. At 298K, the protein is very stable over the full 50ns simulation. At 348K, corresponding to the experimentally observed melting temperature of CI2, the protein unfolds over the first 25ns, explores partially unfolded conformations for 20ns, and then refolds over the last 35ns. Above its melting temperature, complete thermal denaturation occurs in an activated process. Early unfolding is characterized by sliding or breathing motions in the protein core, leading to an unfolding transition state with a weakened core and some loss of secondary structure. After the unfolding transition, the core contacts are rapidly lost as the protein passes on to the fully denatured ensemble. While the overall character and order of events in the unfolding process are well conserved across temperatures, there are substantial differences in the timescales over which these events take place. We conclude that 498K simulations are suitable for elucidating the details of protein unfolding at a minimum of computational expense.

Molecular dynamics simulations with explicit waters have been employed to investigate the dominant source of elastin's elasticity. An elastin-like peptide, (VPGVG)(18), was pulled and released in molecular dynamics simulations, at 10 and 42 degrees C, lasting several nanoseconds, which is consistent with the experimentally determined dielectric and NMR relaxation time scales. At elastin's physiological temperature and degree of extension, the simulations indicate that the orientational entropy of waters hydrating hydrophobic groups decreases during pulling of the molecule, but it increases upon release. In contrast, the main-chain fluctuations and other measures of mobility suggest that elastin's backbone is more dynamic in the extended than released state. These results and the agreement between the simulations with various experimental observations suggest that hydrophobic hydration is an important source of the entropy-based elasticity of elastin. Moreover, elastin tends to reorder itself to form a hydrophobic globule when it was held in its extended state, indicating that the hydrophobic effect also contributes in the holding process. On the whole, our simulations support the hydrophobic mechanism of elasticity and provide a framework for description of the molecular basis of this phenomenon.

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.

Membrane permeability is a key property to consider in drug design, especially when the drugs in question need to cross the blood-brain barrier (BBB). A comprehensive in vivo assessment of the BBB permeability of a drug takes considerable time and financial resources. A current, simplified in vitro model to investigate drug permeability is a Parallel Artificial Membrane Permeability Assay (PAMPA) that generally provides higher throughput and initial quantification of a drug's passive permeability. Computational methods can also be used to predict drug permeability. These methods are highly advantageous as they do not require the synthesis of the desired drug, and can be implemented rapidly using high-performance computing. In this study, we have used umbrella sampling Molecular Dynamics (MD) methods to assess the passive permeability of a range of compounds through a lipid bilayer. Furthermore, the permeability of these compounds was comprehensively quantified using the PAMPA assay to calibrate and validate the MD methodology. After demonstrating a firm correlation between the two approaches, we then implemented our MD method to quantitatively predict the most permeable potential drug from a series of potential scaffolds. This permeability was then confirmed by the in vitro PAMPA methodology. Thus, in this work we have illustrated the potential that these computational methods hold as useful tools to help predict a drug's permeability in a faster and more cost-effective manner. Release number: LLNL-ABS-677757.

Rarely is any solution simply solute and water. In vivo, solutes, such as proteins and nucleic acids, swim in a sea of water, salts, ions, small molecules, and lipids, not to mention other macromolecules. In vitro, virtually all solutions contain a mixture of aqueous solvents, or "cosolvents" [i.e., solvent(s) in addition to water], that can alter the dynamics, behavior, solubility, and stability of proteins and nucleic acids. We have developed models for a number of cosolvents, including the denaturant urea and the small chemical chaperone trimethylamine N-oxide (TMAO). This chapter examines the models for these two cosolvents in the context of experimental data. The direct and indirect effects of these molecules on water and protein are studied with molecular dynamics simulations. These observations and conclusions are drawn from simulations of these molecules in pure water and as a cosolvent for the protein chymotrypsin inhibitor 2. Urea-induced denaturation occurs initially through attack of the protein by water and hydration of hydrophobic protein moieties as a result of disruption of the hydrogen bonding network of water by urea. This indirect denaturing effect of urea is followed by more direct action as urea replaces some waters involved in the initial hydration of the hydrophobic core and subsequently binds to polar residues and the protein main chain to compete with the intraprotein hydrogen bonds. In the case of TMAO, we find that it encourages water-water interactions, thereby stabilizing the protein as a result of the increased penalty for the hydration of hydrophobic residues.

Rarely is any solution simply solute and water. In vivo , solutes, such as proteins and nucleic acids, swim in a sea of water, salts, ions, small molecules, and lipids, not to mention other macromolecules. In vitro , virtually all solutions contain a mixture of aqueous solvents, or “cosolvents” [i.e., solvent(s) in addition to water], that can alter the dynamics, behavior, solubility, and stability of proteins and nucleic acids. We have developed models for a number of cosolvents, including the denaturant urea and the small chemical chaperone trimethylamine N ‐oxide (TMAO). This chapter examines the models for these two cosolvents in the context of experimental data. The direct and indirect effects of these molecules on water and protein are studied with molecular dynamics simulations. These observations and conclusions are drawn from simulations of these molecules in pure water and as a cosolvent for the protein chymotrypsin inhibitor 2. Urea‐induced denaturation occurs initially through attack of the protein by water and hydration of hydrophobic protein moieties as a result of disruption of the hydrogen bonding network of water by urea. This indirect denaturing effect of urea is followed by more direct action as urea replaces some waters involved in the initial hydration of the hydrophobic core and subsequently binds to polar residues and the protein main chain to compete with the intraprotein hydrogen bonds. In the case of TMAO, we find that it encourages water–water interactions, thereby stabilizing the protein as a result of the increased penalty for the hydration of hydrophobic residues.

Many carcinogens have been shown to cause tissue specific tumors in animal models. The mechanism for this specificity has not been fully elucidated and is usually attributed to differences in organ metabolism. For heterocyclic amines, potent carcinogens that are formed in well-done meat, the ability to either bind to the estrogen receptor and activate or inhibit an estrogenic response will have a major impact on carcinogenicity. Here, we describe our work with the human estrogen receptor alpha (ERalpha), the mutagenic/carcinogenic heterocyclic amines PhIP, MeIQx, and IFP, and the hydroxylated metabolite of PhIP, N2-hydroxy-PhIP. We demonstrate both by computational docking and NMR analysis that PhIP binds with the ligand binding domain (LBD). This binding competes with estradiol (E2) in the native E2 binding cavity of the receptor. In vitro assays show that PhIP, in contrast to the other heterocyclic amines, increases cell proliferation in MCF-7 human breast cancer cells and activates the ERalpha receptor. We also find that other heterocyclic amines and N2-hydroxy-PhIP inhibit ERalpha activation. We propose that the mechanism for the tissue-specific carcinogenicity seen in the rat breast tumors and the presumptive human breast cancer associated with the consumption of well-done meat maybe mediated by this receptor activation.

The human gene for catechol O-methyltransferase (COMT) contains a common polymorphism that results in substitution of methionine (M) for valine (V) at residue 108 of the soluble form of the protein. While the two proteins have similar kinetic properties, 108M COMT loses activity more rapidly than 108V COMT at 37 degrees C. The cosubstrate S-adenosylmethionine (SAM) stabilizes the activity of 108M COMT at 40 degrees C. The 108M allele has been associated with increased risk for breast cancer, obsessive-compulsive disorder, and aggressive and highly antisocial manifestations of schizophrenia. In the current work, we have constructed homology models for both human COMT polymorphs and performed molecular dynamics simulations of these models at 25, 37, and 50 degrees C to explore the structural consequences of the 108V/M polymorphism. The simulations indicated that replacing valine with the larger methionine residue led to greater solvent exposure of residue 108 and heightened packing interactions between M108 and helices alpha2, alpha4 (especially with R78), and alpha5. These altered packing interactions propagated subtle changes between the polymorphic site and the active site 16 A away, leading to a loosening of the active site. At physiological temperature, 108M COMT sampled a larger distribution of conformations than 108V. 108M COMT was more prone to active-site distortion and had greater overall, and SAM binding site, solvent accessibility than 108V COMT at 37 degrees C. Similar structural perturbations were observed in the 108V protein only at 50 degrees C. Addition of SAM tightened up the cosubstrate pocket in both proteins and prevented the altered packing at the polymorphic site in 108M COMT.

The processes of evaporation and condensation of water at small scales is not well understood. Understanding these processes and their contributions to water droplet dynamics is important across a wide range of disciplines from climate studies to mass spectrometry and biomolecular imaging. Here we describe the creation of an analytical model of evaporation/condensation that accurately predicts water droplet size and temperature over long time scales. We use this analytical model in concert with molecular dynamics simulations of water nano-droplets to understand the dynamics of these small water droplets at the atomic level. We show that the models and assumptions made in the molecular dynamics simulations are robust and in agreement with experiments and our analytical model. The synergism of these two models highlight and capture the important factors involved in evaporation/condensation of water nano-droplets such as curvature. This ability to predict size and temperature and characterize the droplets at the atomic level will allow for the precise manipulation of water droplets as vehicles for biomolecule payloads in x-ray imaging experiments.

Many carcinogens have been shown to cause tissue specific tumors in animal models. The mechanism for this specificity has not been fully elucidated and is usually attributed to differences in organ metabolism. For heterocyclic amines, potent carcinogens that are formed in well-done meat, the ability to either bind to the estrogen receptor and activate or inhibit an estrogenic response will have a major impact on carcinogenicity. Here we describe our work with the human estrogen receptor alpha (hERa) and the mutagenic/carcinogenic heterocyclic amines PhIP, MeIQx, IFP, and the hydroxylated metabolite of PhIP, N2-hydroxy-PhIP. We found that PhIP, in contrast to the other heterocyclic amines, increased cell-proliferation in MCF-7 human breast cancer cells and activated the hERa receptor. We show mechanistic data supporting this activation both computationally by homology modeling and docking, and by NMR confirmation that PhIP binds with the ligand binding domain (LBD). This binding competes with estradiol (E2) in the native E2 binding cavity of the receptor. We also find that other heterocyclic amines and N2-hydroxy-PhIP inhibit ER activation presumably by binding into another cavity on the LBD. Moreover, molecular dynamics simulations of inhibitory heterocyclic amines reveal a disruption of the surface of the receptor protein involved withmore » protein-protein signaling. We therefore propose that the mechanism for the tissue specific carcinogenicity seen in the rat breast tumors and the presumptive human breast cancer associated with the consumption of well-done meat maybe mediated by this receptor activation.« less

Many clinical diagnostic tests depend on the accurate detection and quantification of proteins and peptides and their functions. Alterations of protein structure, and the resulting consequences on dynamics, can affect the outcome of laboratory tests. These changes can be a result of mutations, other in vivo factors, or even the experimental conditions of the diagnostic test.The relationship between protein structure and dynamics and experimentally observable properties used in diagnostic assays are discussed in light of transmissible spongiform encephalopathies, or prion diseases.This review describes current efforts and possible future directions of prion diagnostic development. Recent advances in therapeutic development are also addressed.The intent of the review is to highlight the role of protein dynamics and conformational change in protein-based diagnostics and treatments for prion disease.

Trimethylamine n-oxide (TMAO) is a naturally occurring osmolyte that stabilizes proteins and offsets the destabilizing effects of urea. To investigate the molecular mechanism of these effects, we have studied the thermodynamics of interaction between TMAO and protein functional groups. The solubilities of a homologous series of cyclic dipeptides were measured by differential refractive index and the dissolution heats were determined calorimetrically as a function of TMAO concentration at 25 degrees C. The transfer free energy of the amide unit (-CONH-) from water to 1 M TMAO is large and positive, indicating an unfavorable interaction between the TMAO solution and the amide unit. This unfavorable interaction is enthalpic in origin. The interaction between TMAO and apolar groups is slightly favorable. The transfer free energy of apolar groups from water to TMAO consists of favorable enthalpic and unfavorable entropic contributions. This is in contrast to the contributions for the interaction between urea and apolar groups. Molecular dynamics simulations were performed to provide a structural framework for the interpretation of these results. The simulations show enhancement of water structure by TMAO in the form of a slight increase in the number of hydrogen bonds per water molecule, stronger water hydrogen bonds, and long-range spatial ordering of the solvent. These findings suggest that TMAO stabilizes proteins via enhancement of water structure, such that interactions with the amide unit are discouraged.