Accurate prediction of protonation state as a prerequisite for reliable MM-PB(GB)SA binding free energy calculations of HIV-1 protease inhibitors

Multiple Functional Protein-Protein Interaction Interfaces Allosterically Regulate ATP-Binding in Cyclin-Dependent Kinase-1

The ATP-dependent phosphorylation activity of cyclin-dependent kinase 1 (CDK1), an essential enzyme for cell cycle progression, is regulated by interactions with Cyclin-B, substrate, and Cks proteins. We have recently shown that active site acetylation in CDK1 abrogated binding to Cyclin-B which posits an intriguing long-range communication between the catalytic site and the protein-protein interaction (PPI) interface. Now, we demonstrate a general allosteric link between the CDK1 active site and all three of its PPI interfaces through atomistic molecular dynamics (MD) simulations. Specifically, we examined ATP binding free energies to CDK1 in native nonacetylated (K33wt) and acetylated (K33Ac) forms as well as the acetyl-mimic K33Q and the acetyl-null K33R mutant forms, which are accessible in vitro. In agreement with experiments, ATP binding is stronger in K33wt relative to the other three perturbed states. Free energy decomposition reveals, in addition to expected local changes, significant and selective nonlocal entropic responses to ATP binding/perturbation of K33 from theαC -helix, activation loop (A-loop), andαG -α H segments in CDK1 which interface with Cyclin-B, substrate, and Cks proteins, respectively. Statistical analysis reveals that while entropic responses of protein segments to active site perturbations are on average correlated with their dynamical changes, such correlations are lost in about 9%-48% of the dataset depending on the segment. Besides proving the bi-directional communication between the active site and the CDK1:Cyclin-B interface, our study uncovers a hitherto unknown mode of ATP binding regulation by multiple PPI interfaces in CDK1.

Implicit Solvent with Explicit Ions Generalized Born Model in Molecular Dynamics: Application to DNA

The ion atmosphere surrounding highly charged biomolecules, such as nucleic acids, is crucial for their dynamics, structure, and interactions. Here, we develop an approach for the explicit treatment of ions within an implicit solvent framework suitable for atomistic simulations of biomolecules. The proposed implicit solvent/explicit ions model, GBION, is based on a modified generalized Born (GB) model; it includes separate, modified GB terms for solute-ion and ion-ion interactions. The model is implemented in the AMBER package (version 24), and its performance is thoroughly investigated in atomistic molecular dynamics (MD) simulations of double-stranded DNA on a microsecond time scale. The aggregate characteristics of monovalent (Na+ and K+) and trivalent (Cobalt Hexammine, CoHex3+) counterion distributions around double-stranded DNA predicted by the model are in reasonable agreement with the experiment (where available), all-atom explicit water MD simulations, and the expectation from the Manning condensation theory. The radial distributions of monovalent cations around DNA are reasonably close to the ones obtained using the explicit water model: expressed in units of energy, the maximum deviations of local ion concentrations from the explicit solvent reference are within 1 kBT, comparable to the corresponding deviations expected between different established explicit water models. The proposed GBION model is able to simulate DNA fragments in a large volume of solvent with explicit ions with little additional computational overhead compared with the fully implicit GB treatment of ions. Ions simulated using the developed model explore conformational space at least 2 orders of magnitude faster than in the explicit solvent. These advantages allowed us to observe and explore an unexpected "stacking" mode of DNA condensation in the presence of trivalent counterions (CoHex3+) that was revealed by recent experiments.

Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains

The current practical "workhorses" of the atomistic implicit solvation─the Poisson-Boltzmann (PB) and generalized Born (GB) models─face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE ∼ 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.

Hypericin as a potential drug for treating Alzheimer's disease and type 2 diabetes with a view to drug repositioning

AIMS

Alzheimer's disease (AD) and type 2 diabetes (T2D) are two of the most common diseases in elderly population and they have a high rate of comorbidity. Study has revealed that T2D is a major risk factor of AD, and thus exploring therapeutic approaches that can target both diseases has drawn much interest in recent years. In this study, we tried to explore drugs that could be potentially used to prevent or treat both AD and T2D via a drug repositioning approach.

METHODS

We first searched the known drugs that may be effective to T2D treatment based on the network distance between the T2D-associated genes and drugs deposited in the DrugBank database. Then, via molecular docking, we further screened these drugs by examining their interaction with islet amyloid polypeptide (IAPP) and Aβ42 peptide, the key components involved in the pathogenesis of T2D or AD. Finally, the binding between the selected drug candidates and the target proteins was verified by molecular dynamics (MD) simulation; and the potential function of the drug candidates and the corresponding targets were analyzed.

RESULTS

From multiple resources, 734 T2D-associated genes were collected, and a list of 1109 drug candidates for T2D was obtained. We found that hypericin had the lowest binding energy and the most stable interaction with either IAPP or Aβ42 peptide. In addition, we also found that the target genes regulated by hypericin were differentially expressed in the tissues related to the two diseases.

CONCLUSION

Our results show that hypericin may be able to bind with IAPP and Aβ42 stably and prevent their accumulation, and thus could be a promising drug candidate for treating the comorbidity of AD and T2D.

Streptomyces sp. Strain PBR11, a Forest-Derived Soil Actinomycetia with Antimicrobial Potential

The Actinomycetia isolate PBR11 was isolated from the forest rhizosphere soil of Pobitora Wildlife Sanctuary (PWS), Assam, India. The isolate was identified as Streptomyces sp. with 92.91% sequence similarity to their closest type strain, Streptomyces atrovirens NRRL B-16357 DQ026672. The strain demonstrated significant antimicrobial activity against 19 test pathogens, including multidrug-resistant (MDR) clinical isolates and dermatophytes. Phenol, 2,5-bis(1,1-dimethylethyl), is the major chemical compound detected by gas chromatography-mass spectrometry in the ethyl acetate extract of PBR11 (EtAc-PBR11). The presence of the PKS type II gene (type II polyketide synthases) and chitinase gene suggested that it has been involved in the production of antimicrobial compounds. Metabolic profiling of the EtAc-PBR11 was performed by thin-layer chromatography and flash chromatography resulted in the extraction of two bioactive fractions, namely, PBR11Fr-1 and PBR11Fr-2. Liquid chromatography-tandem mass spectrometry analysis of both the fractions demonstrated the presence of significant antimicrobial compounds, including ethambutol. This is the first report on the detection of antituberculosis drug in the bioactive fractions of Streptomyces sp. PBR11. EtAc-PBR11 and PBR11Fr-1 showed the lowest MIC values (>0.097 and >0.048 μg/mL, respectively) against Candida albicans MTCC 227, whereas they showed the highest MIC values (>0.390 and >0.195 μg/mL, respectively) against Escherichia coli ATCC BAA-2469. The effects of PBR11Fr-1 were investigated on the pathogens by using a scanning electron microscope. The results indicated major morphological alterations in the cytoplasmic membrane. PBR11Fr-1 exhibited low cytotoxicity on normal hepatocyte cell line (CC-1) and the percent cell viability started to decline as the concentration increased from 50 μg/mL (87.07% ± 3.22%) to 100 μg/mL (81.26% ± 2.99%). IMPORTANCE Novel antibiotic breakthroughs are urgently required to combat antimicrobial resistance. Actinomycetia are the principal producers of antibiotics. The present study demonstrated the broad-spectrum antimicrobial potential of an Actinomycetia strain Streptomyces sp. strain PBR11 isolated from the PWS of Assam, India, which represents diverse, poorly screened habitats for novel microorganisms. The strain displayed 92.4% sequence similarity with genes of the closest type strain, indicating that the strain may represent a novel taxon within the phylum Actinomycetota. The metabolomics studies of EtAc-PBR11 revealed structurally diverse antimicrobial agents, including the detection of the antituberculosis drug ethambutol, in the bioactive fraction of Streptomyces sp. PBR11 for the first time. The PBR11 strain also yielded positive results for the antibiotic synthesis gene and the chitinase gene, both of which are responsible for broad-spectrum antimicrobial activity. This suggests that the untouched forest ecosystems have a tremendous potential to harbor potent actinomycetia for future drug discovery.

Protein-Nucleic Acid Interactions for RNA Polymerase II Elongation Factors by Molecular Dynamics Simulations

RNA polymerase II (Pol II) forms a complex with elongation factors to proceed to the elongation stage of the transcription process. In this work, we studied the elongation factor SPT5 and explored the protein-nucleic acid interactions for the isolated systems of KOW1 and KOW4 domains of SPT5 with DNA and RNA, respectively. We performed molecular dynamics (MD) simulations using three commonly used force fields that are CHARMM c36m, AMBER ff14sb, and ff19sb. Simulations showed strong protein-nucleic acid interactions and low electrostatic binding free energies for all force fields used. RNA was found to be highly dynamic with all force fields, while DNA had relatively more stable conformations with the AMBER force fields compared to that with CHARMM. Furthermore, we performed MD simulations of the complete elongation complex using CHARMM c36m and AMBER ff19sb force fields to compare the dynamics and interactions with the isolated systems. Similarly, strong KOW1 and DNA interactions were observed in the complete elongation complex simulations and DNA was further stabilized by a network of interactions involving SPT5-KOW1, SPT4, and rpb2 of Pol II. Overall, our study showed that the differences between CHARMM and AMBER force fields strongly affect the dynamics of the nucleic acids. CHARMM provides highly flexible DNA, while AMBER largely stabilizes the DNA structure. Although the presence of the entire interaction network stabilized the DNA and decreased the differences in the results from the two force fields, the discrepancies of the force fields for smaller systems may reflect their problems in generating accurate dynamics of nucleic acids.

Hybrid QM/MM Free-Energy Evaluation of Drug-Resistant Mutational Effect on the Binding of an Inhibitor Indinavir to HIV-1 Protease

A human immunodeficiency virus-1 (HIV-1) protease is a homodimeric aspartic protease essential for the replication of HIV. The HIV-1 protease is a target protein in drug discovery for antiretroviral therapy, and various inhibitor molecules of transition state analogues have been developed. However, serious drug-resistant mutants have emerged. For understanding the molecular mechanism of the drug resistance, an accurate examination of the impacts of the mutations on ligand binding and enzymatic activity is necessary. Here, we present a molecular simulation study on the ligand binding of indinavir, a potent transition state analogue inhibitor, to the wild-type protein and a V82T/I84V drug-resistant mutant of the HIV-1 protease. We employed a hybrid ab initio quantum mechanical/molecular mechanical (QM/MM) free-energy optimization technique which combines a highly accurate QM description of the ligand molecule and its interaction with statistically ample conformational sampling of the MM protein environment by long-time molecular dynamics simulations. Through the free-energy calculations of protonation states of catalytic groups at the binding pocket and of the ligand-binding affinity changes upon the mutations, we successfully reproduced the experimentally observed significant reduction of the binding affinity upon the drug-resistant mutations and elucidated the underlying molecular mechanism. The present study opens the way for understanding the molecular mechanism of drug resistance through the direct quantitative comparison of ligand binding and enzymatic reaction with the same accuracy.

Prediction and molecular field view of drug resistance in HIV-1 protease mutants

Conquering the mutational drug resistance is a great challenge in anti-HIV drug development and therapy. Quantitatively predicting the mutational drug resistance in molecular level and elucidating the three dimensional structure-resistance relationships for anti-HIV drug targets will help to improve the understanding of the drug resistance mechanism and aid the design of resistance evading inhibitors. Here the MB-QSAR (Mutation-dependent Biomacromolecular Quantitative Structure Activity Relationship) method was employed to predict the molecular drug resistance of HIV-1 protease mutants towards six drugs, and to depict the structure resistance relationships in HIV-1 protease mutants. MB-QSAR models were constructed based on a published data set of K_i values for HIV-1 protease mutants against drugs. Reliable MB-QSAR models were achieved and these models display both well internal and external prediction abilities. Interpreting the MB-QSAR models supplied structural information related to the drug resistance as well as the guidance for the design of resistance evading drugs. This work showed that MB-QSAR method can be employed to predict the resistance of HIV-1 protease caused by polymorphic mutations, which offer a fast and accurate method for the prediction of other drug target within the context of 3D structures.

Characterization and substrate-accelerated thermal inactivation kinetics of a new serine-type arylsulfatase

Arylsulfatase is useful in industrial agar processing by removing sulfate groups. A full-length arylsulfatase gene, designated ArySMA1, was obtained from marine bacteria Serratia sp. SM1. The ArySMA1 gene encoded a novel serine-type arylsulfatase and the enzymatic properties were characterized. The enzyme presented notable capacity of removing sulfate groups from natural algae substrates. Kinetic study suggested that the microscopic thermal inactivation rate of ArySMA1 in free form was smaller than that of the enzyme-substrate complex. The presence of substrate could unexpectedly accelerate ArySMA1 to deactivate at high temperature. Such phenomenon was opposite to many findings that substrate could stabilize enzymes against heat. Molecular dynamics simulation and ANS fluorescent assay indicated the substrate led the hydrophobic regions of the active site more flexible and the sulfate group of the substrate could retard the processivity of ArySMA1 catalysis. This study provides guidance for agar desulfation and down-stream processing industry.

Multiscale Approach for Computing Gated Ligand Binding from Molecular Dynamics and Brownian Dynamics Simulations

We develop an approach to characterize the effects of gating by a multiconformation protein consisting of macrostate conformations that are either accessible or inaccessible to ligand binding. We first construct a Markov state model of the apo-protein from atomistic molecular dynamics simulations from which we identify macrostates and their conformations, compute their relative macrostate populations and interchange kinetics, and structurally characterize them in terms of ligand accessibility. We insert the calculated first-order rate constants for conformational transitions into a multistate gating theory from which we derive a gating factor γ that quantifies the degree of conformational gating. Applied to HIV-1 protease, our approach yields a kinetic network of three accessible (semi-open, open, and wide-open) and two inaccessible (closed and a newly identified, "parted") macrostate conformations. The parted conformation sterically partitions the active site, suggesting a possible role in product release. We find that the binding kinetics of drugs and drug-like inhibitors to HIV-1 protease falls in the slow gating regime. However, because γ = 0.75, conformational gating only modestly slows ligand binding. Brownian dynamics simulations of the diffusional association of eight inhibitors to the protease─having a wide range of experimental association constants (∼10⁴-10¹⁰ M^-1 s^-1)─yields gated rate constants in the range of ∼0.5-5.7 × 10⁸ M^-1 s^-1. This indicates that, whereas the association rate of some inhibitors could be described by the model, for many inhibitors either subsequent conformational transitions or alternate binding mechanisms may be rate-limiting. For systems known to be modulated by conformational gating, the approach could be scaled computationally efficiently to screen association kinetics for a large number of ligands.

The Expanding Role of Pyridine and Dihydropyridine Scaffolds in Drug Design

Pyridine-based ring systems are one of the most extensively used heterocycles in the field of drug design, primarily due to their profound effect on pharmacological activity, which has led to the discovery of numerous broad-spectrum therapeutic agents. In the US FDA database, there are 95 approved pharmaceuticals that stem from pyridine or dihydropyridine, including isoniazid and ethionamide (tuberculosis), delavirdine (HIV/AIDS), abiraterone acetate (prostate cancer), tacrine (Alzheimer's), ciclopirox (ringworm and athlete's foot), crizotinib (cancer), nifedipine (Raynaud's syndrome and premature birth), piroxicam (NSAID for arthritis), nilvadipine (hypertension), roflumilast (COPD), pyridostigmine (myasthenia gravis), and many more. Their remarkable therapeutic applications have encouraged researchers to prepare a larger number of biologically active compounds decorated with pyridine or dihydropyridine, expandeing the scope of finding a cure for other ailments. It is thus anticipated that myriad new pharmaceuticals containing the two heterocycles will be available in the forthcoming decade. This review examines the prospects of highly potent bioactive molecules to emphasize the advantages of using pyridine and dihydropyridine in drug design. We cover the most recent developments from 2010 to date, highlighting the ever-expanding role of both scaffolds in the field of medicinal chemistry and drug development.

Drug Re-positioning Studies for Novel HIV-1 Inhibitors Using Binary QSAR Models and Multi-target-driven In Silico Studies

Current antiretroviral therapies against HIV involve the usage of at least two drugs that target different stages of HIV life cycle. However, potential drug interactions and side effects pose a problem. A promising concept for complex disease treatment is 'one molecule-multiple target' approach to overcome undesired effects of multiple drugs. Additionally, it is beneficial to consider drug re-purposing due to the cost of taking a drug into the market. Taking these into account, here potential anti-HIV compounds are suggested by virtually screening small approved drug molecules and clinical candidates. Initially, binary QSAR models are used to predict the therapeutic activity of around 7900 compounds against HIV and to predict the toxicity of molecules with high therapeutic activities. Selected compounds are considered for molecular docking studies against two targets, HIV-1 protease enzyme, and chemokine co-receptor CCR5. The top docking poses for all 549 molecules are then subjected to short (1 ns) individual molecular dynamics (MD) simulations and they are ranked based on their calculated relative binding free energies. Finally, 25 molecules are selected for long (200 ns) MD simulations, and 5 molecules are suggested as promising multi-target HIV agents. The results of this study may open new avenues for the designing of new dual HIV-1 inhibitor scaffolds.

Fine-Tuning of Sequence Specificity by Near Attack Conformations in Enzyme-Catalyzed Peptide Hydrolysis

The catalytic role of near attack conformations (NACs), molecular states that lie on the pathway between the ground state (GS) and transition state (TS) of a chemical reaction, is not understood completely. Using a computational approach that combines Bürgi–Dunitz theory with all-atom molecular dynamics simulations, the role of NACs in catalyzing the first stages of HIV-1 protease peptide hydrolysis was previously investigated using a substrate that represents the recognized SP1-NC cleavage site of the HIV-1 Gag polyprotein. NACs were found to confer no catalytic effect over the uncatalyzed reaction there ( Δ Δ G N ‡ ∼ 0 kcal/mol). Here, using the same approach, the role of NACs across multiple substrates that each represent a further recognized cleavage site is investigated. Overall rate enhancement varies by | Δ Δ G ‡ | ∼ 12–15 kcal/mol across this set, and although NACs contribute a small and approximately constant barrier to the uncatalyzed reaction (< Δ G N ‡ u > = 4.3 ± 0.3 kcal/mol), they are found to contribute little significant catalytic effect ( | Δ Δ G N ‡ | ∼ 0–2 kcal/mol). Furthermore, no correlation is exhibited between NAC contributions and the overall energy barrier ( R 2 = 0.01). However, these small differences in catalyzed NAC contributions enable rates to match those required for the kinetic order of processing. Therefore, NACs may offer an alternative and subtle mode compared to non-NAC contributions for fine-tuning reaction rates during complex evolutionary sequence selection processes—in this case across cleavable polyproteins whose constituents exhibit multiple functions during the virus life-cycle.

Interactive molecular dynamics in virtual reality for accurate flexible protein-ligand docking

Simulating drug binding and unbinding is a challenge, as the rugged energy landscapes that separate bound and unbound states require extensive sampling that consumes significant computational resources. Here, we describe the use of interactive molecular dynamics in virtual reality (iMD-VR) as an accurate low-cost strategy for flexible protein-ligand docking. We outline an experimental protocol which enables expert iMD-VR users to guide ligands into and out of the binding pockets of trypsin, neuraminidase, and HIV-1 protease, and recreate their respective crystallographic protein-ligand binding poses within 5-10 minutes. Following a brief training phase, our studies shown that iMD-VR novices were able to generate unbinding and rebinding pathways on similar timescales as iMD-VR experts, with the majority able to recover binding poses within 2.15 Å RMSD of the crystallographic binding pose. These results indicate that iMD-VR affords sufficient control for users to carry out the detailed atomic manipulations required to dock flexible ligands into dynamic enzyme active sites and recover crystallographic poses, offering an interesting new approach for simulating drug docking and generating binding hypotheses.

Blockade of Human α7 Nicotinic Acetylcholine Receptor by α-Conotoxin ImI Dendrimer: Insight from Computational Simulations

Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels that are involved in fast synaptic transmission and mediated physiological activities in the nervous system. α-Conotoxin ImI exhibits subtype-specific blockade towards homomeric α7 and α9 receptors. In this study, we established a method to build a 2×ImI-dendrimer/h (human) α7 nAChR model, and based on this model, we systematically investigated the molecular interactions between the 2×ImI-dendrimer and hα7 nAChR. Our results suggest that the 2×ImI-dendrimer possessed much stronger potency towards hα7 nAChR than the α-ImI monomer and demonstrated that the linker between α-ImI contributed to the potency of the 2×ImI-dendrimer by forming a stable hydrogen-bond network with hα7 nAChR. Overall, this study provides novel insights into the binding mechanism of α-ImI dendrimer to hα7 nAChR, and the methodology reported here opens an avenue for the design of more selective dendrimers with potential usage as drug/gene carriers, macromolecular drugs, and molecular probes.

Role of the disulfide bond on the structure and activity of μ-conotoxin PIIIA in the inhibition of NaV1.4

μ-Conotoxin PIIIA, a peptide toxin isolated from Conus purpurascens, blocks the skeletal muscle voltage-gated sodium channel NaV1.4 with significant potency. PIIIA has three disulfide bonds, which contribute largely to its highly constrained and stable structure. In this study, a combination of experimental studies and computational modeling were performed to assess the effects of deletion of the disulfide bonds on the structure and activity of PIIIA. The final results indicate that the three disulfide bonds of PIIIA are required to produce the effective inhibition of NaV1.4, and the removal of any one of the disulfide bonds significantly reduces its binding affinity owing to secondary structure variation, among which the Cys11-Cys22 is the most important for sustaining the structure and activity of PIIIA.

Consistent Prediction of Mutation Effect on Drug Binding in HIV-1 Protease Using Alchemical Calculations

Despite a large number of antiretroviral drugs targeting HIV-1 protease for inhibition, mutations in this protein during the course of patient treatment can render them inefficient. This emerging resistance inspired numerous computational studies of the HIV-1 protease aimed at predicting the effect of mutations on drug binding in terms of free binding energy Δ G, as well as in mechanistic terms. In this study, we analyze ten different protease-inhibitor complexes carrying major resistance-associated mutations (RAMs) G48V, I50V, and L90M using molecular dynamics simulations. We demonstrate that alchemical free energy calculations can consistently predict the effect of mutations on drug binding. By explicitly probing different protonation states of the catalytic aspartic dyad, we reveal the importance of the correct choice of protonation state for the accuracy of the result. We also provide insight into how different mutations affect drug binding in their specific ways, with the unifying theme of how all of them affect the crucial drug binding regions of the protease.

Determination of the μ-Conotoxin PIIIA Specificity Against Voltage-Gated Sodium Channels from Binding Energy Calculations

Voltage-gated sodium (NaV) channels generate and propagate action potentials in excitable cells, and several NaV subtypes have become important targets for pain management. The μ-conotoxins inhibit subtypes of the NaV with varied specificity but often lack of specificity to interested subtypes. Engineering the selectivity of the μ-conotoxins presents considerable complexity and challenge, as it involves the optimization of their binding affinities to multiple highly conserved NaV subtypes. In this study, a model of NaV1.4 bound with μ-conotoxin PIIIA complex was constructed using homology modeling, docking, molecular dynamic simulations and binding energy calculations. The accuracy of this model was confirmed based on the experimental mutagenesis data. The complex models of PIIIA bound with varied subtypes of NaV1.x (x = 1, 2, 3, 5, 6, 7, 8, or 9) were built using NaV1.4/PIIIA complex as a template, and refined using molecular dynamic simulations. The binding affinities of PIIIA to varied subtypes of NaV1.x (x = 1 to 9) were calculated using the Molecular Mechanics Generalized Born/Surface Area (MMGB/SA) and umbrella sampling, and were compared with the experimental values. The binding affinities calculated using MMGB/SA and umbrella sampling are correlated with the experimental values, with the former and the latter giving correlation coefficient of 0.41 (R²) and 0.68 (R²), respectively. Binding energy decomposition suggests that conserved and nonconserved residues among varied NaV subtypes have a synergistic effect on the selectivity of PIIIA.

Accuracy Comparison of Generalized Born Models in the Calculation of Electrostatic Binding Free Energies

The need for accurate yet efficient representation of the aqueous environment in biomolecular modeling has led to the development of a variety of generalized Born (GB) implicit solvent models. While many studies have focused on the accuracy of available GB models in predicting solvation free energies, a systematic assessment of the quality of these models in binding free energy calculations, crucial for rational drug design, has not been undertaken. Here, we evaluate the accuracies of eight common GB flavors (GB-HCT, GB-OBC, GB-neck2, GBNSR6, GBSW, GBMV1, GBMV2, and GBMV3), available in major molecular dynamics packages, in predicting the electrostatic binding free energies ( ΔΔ G_el) for a diverse set of 60 biomolecular complexes belonging to four main classes: protein-protein, protein-drug, RNA-peptide, and small complexes. The GB flavors are examined in terms of their ability to reproduce the results from the Poisson-Boltzmann (PB) model, commonly used as accuracy reference in this context. We show that the agreement with the PB of ΔΔ G_el estimates varies widely between different GB models and also across different types of biomolecular complexes, with R² correlations ranging from 0.3772 to 0.9986. A surface-based "R6" GB model recently implemented in AMBER shows the closest overall agreement with reference PB ( R² = 0.9949, RMSD = 8.75 kcal/mol). The RNA-peptide and protein-drug complex sets appear to be most challenging for all but one model, as indicated by the large deviations from the PB in ΔΔ G_el. Small neutral complexes present the least challenge for most of the GB models tested. The quantitative demonstration of the strengths and weaknesses of the GB models across the diverse complex types provided here can be used as a guide for practical computations and future development efforts.

Andrographolide: A potent antituberculosis compound that targets Aminoglycoside 2'-N-acetyltransferase in Mycobacterium tuberculosis

Tuberculosis (TB) still remains a major challenging infectious disease. The increased rate of emergence of multi-drug resistant and extensively-drug resistant strains of the organism has further complicated the situation, resulting in an urgent need for new anti-TB drugs. Antimycobacterial activity of Andrographis paniculata was evaluated using a rapid LRP assay and the probable targets were identified by docking analysis. The methanolic extract of A. paniculata showed maximum antimycobacterial activity at 250μg/ml against all the tested strains of M. tuberculosis (H37Rv, MDR, and drug sensitive). Based on bioassay guided fractionation, andrographolide was identified as the potent molecule. With the docking analysis, both ICDH (Isocitrate Dehydrogenase) and AAC (Aminoglycoside 2'-N-acetyltransferase) were predicted as targets of andrographolide in M. tuberculosis. Molecular simulation revealed that, ICDH showed low binding affinity to andrographolide. However, for AAC, the andrographolide was observed to be well within the active site after 10ns of molecular simulation. This suggests that ACC (PDB ID 1M4I) could be the probable target for andrographolide.

Computational analyses of curcuminoid analogs against kinase domain of HER2

BACKGROUND

Human epidermal growth factor receptor 2 (HER2) has an important role in cancer aggressiveness and poor prognosis. HER2 has been used as a drug target for cancers. In particular, to effectively treat HER2-positive cancer, small molecule inhibitors were developed to target HER2 kinase. Knowing that curcumin has been used as food to inhibit cancer activity, this study evaluated the efficacy of natural curcumins and curcumin analogs as HER2 inhibitors using in vitro and in silico studies. The curcumin analogs considered in this study composed of 4 groups classified by their core structure, β-diketone, monoketone, pyrazole, and isoxazole.

RESULTS

In the present study, both computational and experimental studies were performed. The specificity of curcumin analogs selected from the docked results was examined against human breast cancer cell lines. The screened curcumin compounds were then subjected to molecular dynamics simulation study. By modifying curcumin analogs, we found that protein-ligand affinity increases. The benzene ring with a hydroxyl group could enhance affinity by forming hydrophobic interactions and the hydrogen bond with the hydrophobic pocket. Hydroxyl, carbonyl or methoxy group also formed hydrogen bonds with residues in the adenine pocket and sugar pocket of HER2-TK. These modifications could suggest the new drug design for potentially effective HER2-TK inhibitors. Two outstanding compounds, bisdemethylcurcumin (AS-KTC006) and 3,5-bis((E)-3,4-dimethoxystyryl)isoxazole (AS-KTC021 ),were well oriented in the binding pocket almost in the simulation time, 30 ns. This evidence confirmed the results of cell-based assays and the docking studies. They possessed more distinguished interactions than known HER2-TK inhibitors, considering them as a promising drug in the near future.

CONCLUSIONS

The series of curcumin compounds were screened using a computational molecular docking and followed by human breast cancer cell lines assay. Both AS-KTC006 and AS-KTC021 could inhibit breast cancer cell lines though inhibiting of HER2-TK. The intermolecular interactions were confirmed by molecular dynamics simulation studies. This information would explore more understanding of curcuminoid structures and HER2-TK.

Molecular dynamics of interactions between rigid and flexible antifolates and dihydrofolate reductase from pyrimethamine-sensitive and pyrimethamine-resistant Plasmodium falciparum

Currently, the usefulness of antimalarials such as pyrimethamine (PYR) is drastically reduced due to the emergence of resistant Plasmodium falciparum (Pf) caused by its dihydrofolate reductase (PfDHFR) mutations, especially the quadruple N51I/C59R/S108N/I164L mutations. The resistance was due to the steric conflict of PYR with S108N. WR99210 (WR), a dihydrotriazine antifolate with a flexible side chain that can avoid such conflict, can overcome this resistance through tight binding with the mutant. To understand factors contributing to different binding affinities of PYR/WR to the wild type (WT) and quadruple mutant (QM), we performed simulations on WR-WT, WR-QM, PYR-WT, and PYR-QM complexes and found that Ile14 and Asp54 were crucial for PYR/WR binding to PfDHFR due to strong hydrogen bonds. The quadruple mutations cause PYR to form, on average, fewer hydrogen bonds with Ile14 and Leu164, and to be displaced from its optimal orientation for Asp54 interaction. The predicted binding affinity ranking (WR-QM ≈ WR-WT ≈ PYR-WT >> PYR-QM) reasonably agrees with the inhibition constant (K(i)) ranking. Our results reveal important residues for tight binding of PYR/WR to WT/QM, which may be used to evaluate the inhibition effectiveness of antimalarials and to provide fundamental information for designing new drugs effective against drug-resistant P. falciparum.

Biological applications of classical electrostatics methods

Continuum electrostatics modeling of solvation based on the Poisson–Boltzmann (PB) equation has gained wide acceptance in biomolecular applications such as energetic analysis and structural visualization. Successful application of the PB solvent models requires careful calibration of the solvation parameters. Extensive testing and validation is also important to ensure accuracy in their applications. Limitation in the continuum modeling of solvation is also a known issue in certain biomolecular applications. Growing interest in membrane systems has further spurred developmental efforts to allow inclusion of membrane in the PB solvent models. Despite their past successes due to careful parameterization, algorithm development and parallel implementation, there is still much to be done to improve their transferability from the small molecular systems upon which they were developed and validated to complex macromolecular systems as advances in technology continue to push forward, providing ever greater computational resources to researchers to study more interesting biological systems of higher complexity.

Computing Clinically Relevant Binding Free Energies of HIV-1 Protease Inhibitors

The use of molecular simulation to estimate the strength of macromolecular binding free energies is becoming increasingly widespread, with goals ranging from lead optimization and enrichment in drug discovery to personalizing or stratifying treatment regimes. In order to realize the potential of such approaches to predict new results, not merely to explain previous experimental findings, it is necessary that the methods used are reliable and accurate, and that their limitations are thoroughly understood. However, the computational cost of atomistic simulation techniques such as molecular dynamics (MD) has meant that until recently little work has focused on validating and verifying the available free energy methodologies, with the consequence that many of the results published in the literature are not reproducible. Here, we present a detailed analysis of two of the most popular approximate methods for calculating binding free energies from molecular simulations, molecular mechanics Poisson-Boltzmann surface area (MMPBSA) and molecular mechanics generalized Born surface area (MMGBSA), applied to the nine FDA-approved HIV-1 protease inhibitors. Our results show that the values obtained from replica simulations of the same protease-drug complex, differing only in initially assigned atom velocities, can vary by as much as 10 kcal mol-1, which is greater than the difference between the best and worst binding inhibitors under investigation. Despite this, analysis of ensembles of simulations producing 50 trajectories of 4 ns duration leads to well converged free energy estimates. For seven inhibitors, we find that with correctly converged normal mode estimates of the configurational entropy, we can correctly distinguish inhibitors in agreement with experimental data for both the MMPBSA and MMGBSA methods and thus have the ability to rank the efficacy of binding of this selection of drugs to the protease (no account is made for free energy penalties associated with protein distortion leading to the over estimation of the binding strength of the two largest inhibitors ritonavir and atazanavir). We obtain improved rankings and estimates of the relative binding strengths of the drugs by using a novel combination of MMPBSA/MMGBSA with normal mode entropy estimates and the free energy of association calculated directly from simulation trajectories. Our work provides a thorough assessment of what is required to produce converged and hence reliable free energies for protein-ligand binding.

Ion specific effects of alkali cations on the catalytic activity of HIV-1 protease

Human immunodeficiency virus 1 protease (HIV-1 PR), an important therapeutic target for the treatment of AIDS, is one of the most well-studied enzymes. However, there is still much to learn about the regulation of the activity and inhibition of this key viral enzyme. Specifically, the mechanism of activation of HIV-1 PR from the viral polyprotein upon HIV maturation is still not understood. It has been suggested that external factors like pH or salt concentration might contribute to regulation of this crucial step in the viral life cycle. Recently, we analyzed the activity of HIV-1 PR in aqueous solutions of sodium and potassium chloride by experimental determination of enzyme kinetics and molecular dynamics simulations. We showed that the effect of salt concentration is cation-specific [Heyda et al., Phys. Chem. Chem. Phys., 2009 (11), 7599]. In this study, we extended this analysis for other alkali cations and found that the dependence of the initial velocity of peptide substrate hydrolysis on the nature of the cation follows the Hofmeister series, with the exception of caesium. Significantly higher catalytic efficiencies both in terms of substrate binding (K(M)) and turnover number (kcat) are observed in the presence of K+ compared to Na+ or Li+ at corresponding salt concentrations. Molecular dynamics simulations suggest that both lithium and sodium are attracted more strongly than potassium and caesium to the protein surface, mostly due to stronger interactions with carboxylate side chain groups of aspartates and glutamates. Furthermore, we observed a surprising decrease in the K(M) value for a specific substrate at very low salt concentration. The molecular mechanism of this phenomenon will be further analyzed.

Protonation and pK changes in protein-ligand binding

Formation of protein-ligand complexes causes various changes in both the receptor and the ligand. This review focuses on changes in pK and protonation states of ionizable groups that accompany protein-ligand binding. Physical origins of these effects are outlined, followed by a brief overview of the computational methods to predict them and the associated corrections to receptor-ligand binding affinities. Statistical prevalence, magnitude and spatial distribution of the pK and protonation state changes in protein-ligand binding are discussed in detail, based on both experimental and theoretical studies. While there is no doubt that these changes occur, they do not occur all the time; the estimated prevalence varies, both between individual complexes and by method. The changes occur not only in the immediate vicinity of the interface but also sometimes far away. When receptor-ligand binding is associated with protonation state change at particular pH, the binding becomes pH dependent: we review the interplay between sub-cellular characteristic pH and optimum pH of receptor-ligand binding. It is pointed out that there is a tendency for protonation state changes upon binding to be minimal at physiologically relevant pH for each complex (no net proton uptake/release), suggesting that native receptor-ligand interactions have evolved to reduce the energy cost associated with ionization changes. As a result, previously reported statistical prevalence of these changes - typically computed at the same pH for all complexes - may be higher than what may be expected at optimum pH specific to each complex. We also discuss whether proper account of protonation state changes appears to improve practical docking and scoring outcomes relevant to structure-based drug design. An overview of some of the existing challenges in the field is provided in conclusion.

Designing a ligand for pharmaceutical purposes

BACKGROUND

Drug approval applications to the FDA have shown a remarkably small increment compared with what was expected. In the last few years several efforts have been made to improve the results of rational drug design approaches and in particular to predict inhibitor-target structure and to evaluate the free energy of binding. Virtual database screening, combined with other computational methods, is one of the most promising methods to overcome this key issue.

OBJECTIVE

It is possible to understand how computational medicinal chemistry is changing, improving from its errors and moving towards becoming a more important tool for drug development.

METHODS

Some of the most recent modeling techniques have been presented and in particular the benefits of combining these techniques are highlighted.

RESULTS/CONCLUSION

At present computational chemists can understand the peculiar problems associated with the study of biological systems and on this basis they can choose the right collection of complementary in silico approaches to solve the medicinal chemistry problem in a better manner.

Identification of HIV inhibitors guided by free energy perturbation calculations

Free energy perturbation (FEP) theory coupled to molecular dynamics (MD) or Monte Carlo (MC) statistical mechanics offers a theoretically precise method for determining the free energy differences of related biological inhibitors. Traditionally requiring extensive computational resources and expertise, it is only recently that its impact is being felt in drug discovery. A review of computer-aided anti-HIV efforts employing FEP calculations is provided here that describes early and recent successes in the design of human immunodeficiency virus type 1 (HIV-1) protease and non-nucleoside reverse transcriptase inhibitors. In addition, our ongoing work developing and optimizing leads for small molecule inhibitors of cyclophilin A (CypA) is highlighted as an update on the current capabilities of the field. CypA has been shown to aid HIV-1 replication by catalyzing the cis/trans isomerization of a conserved Gly-Pro motif in the Nterminal domain of HIV-1 capsid (CA) protein. In the absence of a functional CypA, e.g., by the addition of an inhibitor such as cyclosporine A (CsA), HIV-1 has reduced infectivity. Our simulations of acylurea-based and 1-indanylketone-based CypA inhibitors have determined that their nanomolar and micromolar binding affinities, respectively, are tied to their ability to stabilize Arg55 and Asn102. A structurally novel 1-(2,6-dichlorobenzamido) indole core was proposed to maximize these interactions. FEP-guided optimization, experimental synthesis, and biological testing of lead compounds for toxicity and inhibition of wild-type HIV-1 and CA mutants have demonstrated a dose-dependent inhibition of HIV-1 infection in two cell lines. While the inhibition is modest compared to CsA, the results are encouraging.

Prediction of HIV-1 protease/inhibitor affinity using RosettaLigand

Predicting HIV-1 protease/inhibitor binding affinity as the difference between the free energy of the inhibitor bound and unbound state remains difficult as the unbound state exists as an ensemble of conformations with various degrees of flap opening. We improve computational prediction of protease/inhibitor affinity by invoking the hypothesis that the free energy of the unbound state while difficult to predict is less sensitive to mutation. Thereby the HIV-1 protease/inhibitor binding affinity can be approximated with the free energy of the bound state alone. Bound state free energy can be predicted from comparative models of HIV-1 protease mutant/inhibitor complexes. Absolute binding energies are predicted with R = 0.71 and SE = 5.91 kJ/mol. Changes in binding free energy upon mutation can be predicted with R = 0.85 and SE = 4.49 kJ/mol. Resistance mutations that lower inhibitor binding affinity can thereby be recognized early in HIV-1 protease inhibitor development.

Molecular dynamics trajectory compression with a coarse-grained model

Molecular dynamics trajectories are very data-intensive thereby limiting sharing and archival of such data. One possible solution is compression of trajectory data. Here, trajectory compression based on conversion to the coarse-grained model PRIMO is proposed. The compressed data is about one third of the original data and fast decompression is possible with an analytical reconstruction procedure from PRIMO to all-atom representations. This protocol largely preserves structural features and to a more limited extent also energetic features of the original trajectory.

Resolution of discordant HIV-1 protease resistance rankings using molecular dynamics simulations

The emergence of drug resistance is a major challenge for the effective treatment of HIV. In this article, we explore the application of atomistic molecular dynamics simulations to quantify the level of resistance of a patient-derived HIV-1 protease sequence to the inhibitor lopinavir. A comparative drug ranking methodology was developed to compare drug resistance rankings produced by the Stanford HIVdb, ANRS, and RegaDB clinical decision support systems. The methodology was used to identify a patient sequence for which the three rival online tools produced differing resistance rankings. Mutations at only three positions ( L10I , A71IV, and L90M ) influenced the resistance level assigned to the sequence. We use ensemble molecular dynamics simulations to elucidate the origin of these discrepancies and the mechanism of resistance. By simulating not only the full patient sequences but also systems containing the constituent mutations, we gain insight into why resistance estimates vary and the interactions between the various mutations. In the same way, we also gain valuable knowledge of the mechanistic causes of resistance. In particular, we identify changes in the relative conformation of the two beta sheets that form the protease dimer interface which suggest an explanation of the relative frequency of different amino acids observed in patients at residue 71.

Elucidating the energetics of entropically driven protein-ligand association: calculations of absolute binding free energy and entropy

The binding of proteins and ligands is generally associated with the loss of translational, rotational, and conformational entropy. In many cases, however, the net entropy change due to binding is positive. To develop a deeper understanding of the energetics of entropically driven protein-ligand binding, we calculated the absolute binding free energies and binding entropies for two HIV-1 protease inhibitors Nelfinavir and Amprenavir using the double-decoupling method with molecular dynamics simulations in explicit solvent. For both ligands, the calculated absolute binding free energies are in general agreement with experiments. The statistical error in the computed ΔG(bind) due to convergence problem is estimated to be ≥2 kcal/mol. The decomposition of free energies indicates that, although the binding of Nelfinavir is driven by nonpolar interaction, Amprenavir binding benefits from both nonpolar and electrostatic interactions. The calculated absolute binding entropies show that (1) Nelfinavir binding is driven by large entropy change and (2) the entropy of Amprenavir binding is much less favorable compared with that of Nelfinavir. Both results are consistent with experiments. To obtain qualitative insights into the entropic effects, we decomposed the absolute binding entropy into different contributions based on the temperature dependence of free energies along different legs of the thermodynamic pathway. The results suggest that the favorable entropic contribution to binding is dominated by the ligand desolvation entropy. The entropy gain due to solvent release from binding site appears to be more than offset by the reduction of rotational and vibrational entropies upon binding.

Optimized Potential of Mean Force Calculations for Standard Binding Free Energies

The prediction of protein-ligand binding free energies is an important goal of computational biochemistry, yet accuracy, reproducibility, and cost remain a problem. Nevertheless, these are essential requirements for computational methods to become standard binding prediction tools in discovery pipelines. Here, we present the results of an extensive search for an optimal method based on an ensemble of umbrella sampling all-atom molecular simulations tested on the phosphorylated tetrapeptide, pYEEI, binding to the SH2 domain, resulting in an accurate and converged binding free energy of -9.0 ± 0.5 kcal/mol (compared to an experimental value of -8.0 ± 0.1 kcal/mol). We find that a minimum of 300 ns of sampling is required for every prediction, a target easily achievable using new generation accelerated MD codes. Convergence is obtained by using an ensemble of simulations per window, each starting from different initial conformations, and by optimizing window-width, orthogonal restraints, reaction coordinate harmonic potentials, and window-sample time. The use of uncorrelated initial conformations in neighboring windows is important for correctly sampling conformational transitions from the unbound to bound states that affect significantly the precision of the calculations. This methodology thus provides a general recipe for reproducible and practical computations of binding free energies for a class of semirigid protein-ligand systems, within the limit of the accuracy of the force field used.

Strength of hydrogen bond network takes crucial roles in the dissociation process of inhibitors from the HIV-1 protease binding pocket

To understand the underlying mechanisms of significant differences in dissociation rate constant among different inhibitors for HIV-1 protease, we performed steered molecular dynamics (SMD) simulations to analyze the entire dissociation processes of inhibitors from the binding pocket of protease at atomistic details. We found that the strength of hydrogen bond network between inhibitor and the protease takes crucial roles in the dissociation process. We showed that the hydrogen bond network in the cyclic urea inhibitors AHA001/XK263 is less stable than that of the approved inhibitor ABT538 because of their large differences in the structures of the networks. In the cyclic urea inhibitor bound complex, the hydrogen bonds often distribute at the flap tips and the active site. In contrast, there are additional accessorial hydrogen bonds formed at the lateral sides of the flaps and the active site in the ABT538 bound complex, which take crucial roles in stabilizing the hydrogen bond network. In addition, the water molecule W301 also plays important roles in stabilizing the hydrogen bond network through its flexible movement by acting as a collision buffer and helping the rebinding of hydrogen bonds at the flap tips. Because of its high stability, the hydrogen bond network of ABT538 complex can work together with the hydrophobic clusters to resist the dissociation, resulting in much lower dissociation rate constant than those of cyclic urea inhibitor complexes. This study may provide useful guidelines for design of novel potent inhibitors with optimized interactions.

Molecular mechanism by which palmitate inhibits PKR autophosphorylation

PKR (double-stranded RNA-activated protein kinase) is an important component of the innate immunity, antiviral, and apoptotic pathways. Recently, our group found that palmitate, a saturated fatty acid, is involved in apoptosis by reducing the autophosphorylation of PKR at the Thr451 residue; however, the molecular mechanism by which palmitate reduces PKR autophosphorylation is not known. Thus, we investigated how palmitate affects the phosphorylation of the PKR protein at the molecular and biophysical levels. Biochemical and computational studies show that palmitate binds to PKR, near the ATP-binding site, thereby inhibiting its autophosphorylation at Thr451 and Thr446. Mutation studies suggest that Lys296 and Asp432 in the ATP-binding site on the PKR protein are important for palmitate binding. We further confirmed that palmitate also interacts with other kinases, due to the conserved ATP-binding site. A better understanding of how palmitate interacts with the PKR protein, as well as other kinases, could shed light onto possible mechanisms by which palmitate mediates kinase signaling pathways that could have implications on the efficacy of current drug therapies that target kinases.

Explicit solvent dynamics and energetics of HIV-1 protease flap opening and closing

An accurate description of the conformational dynamics of the β-hairpin flaps of HIV-1 protease is of central importance in elucidating the functional recognition of the enzyme by ligands. Using all-atom molecular dynamics simulations in explicit solvent, with a total of 461 trajectories of ∼50 ns each, we report the closed, semiopen, open, and wide-open flap conformation of the free wild-type protease. The free energy of flap opening and closing from the semiopen state is 0.9 ± 0.2 and 2.4 ± 0.4 kcal/mol, respectively. The mean relaxation time of opening is ∼8 ns, in good agreement with NMR data. The explicit solvent simulations quantitatively confirm the hypothesis that the semiopen state is the dominant population in the free protease whilst fast flap tip fluctuations lead frequently to an open state. More pronounced flap rearrangements lead to a rare wide-open state with the catalytic site completely exposed to the solvent. The structures of the different flap conformations provided herein are of general interest for improved drug design of HIV-1 protease, in particular, the wide-open conformation could be favored by the large Gag and GagPol polyprotein chains. Strategies that take into account multiple flap-gating mechanisms may lead to more effective inhibitors.

Computational mutation scanning and drug resistance mechanisms of HIV-1 protease inhibitors

The drug resistance of various clinically available HIV-1 protease inhibitors has been studied using a new computational protocol, that is, computational mutation scanning (CMS), leading to valuable insights into the resistance mechanisms and structure-resistance correction of the HIV-1 protease inhibitors associated with a variety of active site and nonactive site mutations. By using the CMS method, the calculated mutation-caused shifts of the binding free energies linearly correlate very well with those derived from the corresponding experimental data, suggesting that the CMS protocol may be used as a generalized approach to predict drug resistance associated with amino acid mutations. Because it is essentially important for understanding the structure-resistance correlation and for structure-based drug design to develop an effective computational protocol for drug resistance prediction, the reasonable and computationally efficient CMS protocol for drug resistance prediction should be valuable for future structure-based design and discovery of antiresistance drugs in various therapeutic areas.

Ranking ligand affinity for the DNA minor groove by experiment and simulation

The structural and thermodynamic basis for the strength and selectivity of the interactions of minor groove binders (MGBs) with DNA is not fully understood. In 2003, we reported the first example of a thiazole-containing MGB that bound in a phase-shifted pattern that spanned six base pairs rather than the usual four (for tricyclic distamycin-like compounds). Since then, using DNA footprinting, NMR spectroscopy, isothermal titration calorimetry, and molecular dynamics, we have established that the flanking bases around the central four being read by the ligand have subtle effects on recognition. We have investigated the effect of these flanking sequences on binding and the reasons for the differences and established a computational method to rank ligand affinity against varying DNA sequences.

Accurate ensemble molecular dynamics binding free energy ranking of multidrug-resistant HIV-1 proteases

Accurate calculation of important thermodynamic properties, such as macromolecular binding free energies, is one of the principal goals of molecular dynamics simulations. However, single long simulation frequently produces incorrectly converged quantitative results due to inadequate sampling of conformational space in a feasible wall-clock time. Multiple short (ensemble) simulations have been shown to explore conformational space more effectively than single long simulations, but the two methods have not yet been thermodynamically compared. Here we show that, for end-state binding free energy determination methods, ensemble simulations exhibit significantly enhanced thermodynamic sampling over single long simulations and result in accurate and converged relative binding free energies that are reproducible to within 0.5 kcal/mol. Completely correct ranking is obtained for six HIV-1 protease variants bound to lopinavir with a correlation coefficient of 0.89 and a mean relative deviation from experiment of 0.9 kcal/mol. Multidrug resistance to lopinavir is enthalpically driven and increases through a decrease in the protein-ligand van der Waals interaction, principally due to the V82A/I84V mutation, and an increase in net electrostatic repulsion due to water-mediated disruption of protein-ligand interactions in the catalytic region. Furthermore, we correctly rank, to within 1 kcal/mol of experiment, the substantially increased chemical potency of lopinavir binding to the wild-type protease compared to saquinavir and show that lopinavir takes advantage of a decreased net electrostatic repulsion to confer enhanced binding. Our approach is dependent on the combined use of petascale computing resources and on an automated simulation workflow to attain the required level of sampling and turn around time to obtain the results, which can be as little as three days. This level of performance promotes integration of such methodology with clinical decision support systems for the optimization of patient-specific therapy.

Structure-based design of carbon nanotubes as HIV-1 protease inhibitors: atomistic and coarse-grained simulations

Nanoparticles such as fullerenes and carbon nanotubes have been extensively studied for biomedical applications. In this paper, we report the design of carbon nanotubes as HIV-1 protease inhibitors. Docking and molecular dynamics calculations are performed using an atomistic model to explore the optimal interaction structure and free energy between the nanotube and HIV-1 protease. A coarse-grained model is then developed based on the atomistic model, allowing us to investigate the dynamic behaviors of the protease in the bound and unbound states. The dynamic process reveals that the carbon nanotube is able to bind to the active site of the protease and prevent the active flaps from opening up, thus blocking the function of the protease. This process is strongly influenced by the size of the nanotube. The binding of carbon nanotubes to an alternative binding site other than the active site is also explored. Therefore, carbon nanotube-based inhibitors have great potential for application as HIV-1 protease inhibitors.