Discovery of a Novel Binding Trench in HIV Integrase

Discovery of multiple hidden allosteric sites by combining Markov state models and experiments

The discovery of drug-like molecules that bind pockets in proteins that are not present in crystallographic structures yet exert allosteric control over activity has generated great interest in designing pharmaceuticals that exploit allosteric effects. However, there have only been a small number of successes, so the therapeutic potential of these pockets--called hidden allosteric sites--remains unclear. One challenge for assessing their utility is that rational drug design approaches require foreknowledge of the target site, but most hidden allosteric sites are only discovered when a small molecule is found to stabilize them. We present a means of decoupling the identification of hidden allosteric sites from the discovery of drugs that bind them by drawing on new developments in Markov state modeling that provide unprecedented access to microsecond- to millisecond-timescale fluctuations of a protein's structure. Visualizing these fluctuations allows us to identify potential hidden allosteric sites, which we then test via thiol labeling experiments. Application of these methods reveals multiple hidden allosteric sites in an important antibiotic target--TEM-1 β-lactamase. This result supports the hypothesis that there are many as yet undiscovered hidden allosteric sites and suggests our methodology can identify such sites, providing a starting point for future drug design efforts. More generally, our results demonstrate the power of using Markov state models to guide experiments.

Investigating drug-target association and dissociation mechanisms using metadynamics-based algorithms

CONSPECTUS: This Account highlights recent advances and discusses major challenges in the field of drug-target recognition, binding, and unbinding studied using metadynamics-based approaches, with particular emphasis on their role in structure-based design. Computational chemistry has significantly contributed to drug design and optimization in an extremely broad range of areas, including prediction of target druggability and drug likeness, de novo design, fragment screening, ligand docking, estimation of binding affinity, and modulation of ADMET (absorption, distribution, metabolism, excretion, toxicity) properties. Computationally driven drug discovery must continuously adapt to keep pace with the evolving knowledge of the factors that modulate the pharmacological action of drugs. There is thus an urgent need for novel computational approaches that integrate the vast amount of complex information currently available for small (bio)organic compounds, biologically relevant targets and their complexes, while also accounting accurately for the thermodynamics and kinetics of drug-target association, the intrinsic dynamical behavior of biomolecular systems, and the complexity of protein-protein networks. Understanding the mechanism of drug binding to and unbinding from biological targets is fundamental for optimizing lead compounds and designing novel biologically active ones. One major challenge is the accurate description of the conformational complexity prior to and upon formation of drug-target complexes. Recently, enhanced sampling methods, including metadynamics and related approaches, have been successfully applied to investigate complex mechanisms of drugs binding to flexible targets. Metadynamics is a family of enhanced sampling techniques aimed at enhancing the rare events and reconstructing the underlying free energy landscape as a function of a set of order parameters, usually referred to as collective variables. Studies of drug binding mechanisms have predicted the most probable association and dissociation pathways and the related binding free energy profile. In addition, the availability of an efficient open-source implementation, running on cost-effective GPU (i.e., graphical processor unit) architectures, has considerably decreased the learning curve and the computational costs of the methods, and increased their adoption by the community. Here, we review the recent contributions of metadynamics and other enhanced sampling methods to the field of drug-target recognition and binding. We discuss how metadynamics has been used to search for transition states, to predict binding and unbinding paths, to treat conformational flexibility, and to compute free energy profiles. We highlight the importance of such predictions in drug discovery. Major challenges in the field and possible solutions will finally be discussed.

Molecular dynamics simulations: from structure function relationships to drug discovery

Molecular dynamics (MD) simulation is an emerging in silico technique with potential applications in diverse areas of pharmacology. Over the past three decades MD has evolved as an area of importance for understanding the atomic basis of complex phenomena such as molecular recognition, protein folding, and the transport of ions and small molecules across membranes. The application of MD simulations in isolation and in conjunction with experimental approaches have provided an increased understanding of protein structure-function relationships and demonstrated promise in drug discovery.

Cytochrome P450 BM3 of Bacillus megaterium - a possible endosulfan biotransforming gene

Computing chemistry was applied to understand biotransformation mechanism of an organochlorine pesticide, endosulfan. The stereo specific metabolic activity of human CYP-2B6 (cytochrome P450) on endosulfan has been well demonstrated. Sequence and structural similarity search revealed that the bacterium Bacillus megaterium encodes CYP-BM3, which is similar to CYP-2B6. The functional similarity was studied at organism level by batch-scale studies and it was proved that B. megaterium could metabolize endosulfan to endosulfan sulfate, as CYP-2B6 does in human system. The gene expression analyses also confirmed the possible role of CYP-BM3 in endosulfan metabolism. Thus, our results show that the protein structure based in-silico approach can help us to understand and identify microbes for remediation strategy development. To the best of our knowledge this is the first report which has extrapolated the bacterial gene for endosulfan biotransformation through in silico prediction approach for metabolic gene identification.

Ensemble-based docking using biased molecular dynamics

Proteins are dynamic molecules, and understanding their movements, especially as they relate to molecular recognition and protein-ligand interactions, poses a significant challenge to structure-based drug discovery. In most instances, protein flexibility is underrepresented in computer-aided drug design due to uncertainties on how it should be accurately modeled as well as the computational cost associated with attempting to incorporate flexibility in the calculations. One approach that aims to address these issues is ensemble-based docking. With this technique, ligands are docked to an ensemble of rigid protein conformations. Molecular dynamics (MD) simulations can be used to generate the ensemble of protein conformations for the subsequent docking. Here we present a novel approach that uses biased-MD simulations to generate the docking ensemble. The MD simulations are biased toward an initial protein-ligand X-ray complex structure. The biasing maintains some of the original crystallographic pocket-ligand information and thereby enhances sampling of the more relevant conformational space of the protein. Resulting trajectories are clustered to select a representative set of protein conformations, and ligands are docked to that reduced set of conformations. Cross-docking to this ensemble and then selecting the lowest scoring pose enables reliable identification of the correct binding mode. Various levels of biasing are investigated, and the method is validated for cyclin-dependent kinase 2 and factor Xa.

Discovery of Staphylococcus aureus sortase A inhibitors using virtual screening and the relaxed complex scheme

Staphylococcus aureus is the leading cause of hospital-acquired infections in the United States. The emergence of multidrug-resistant strains of S. aureus has created an urgent need for new antibiotics. Staphylococcus aureus uses the sortase A enzyme to display surface virulence factors suggesting that compounds that inhibit its activity will function as potent anti-infective agents. Here, we report the identification of several inhibitors of sortase A using virtual screening methods that employ the relaxed complex scheme, an advanced computer-docking methodology that accounts for protein receptor flexibility. Experimental testing validates that several compounds identified in the screen inhibit the activity of sortase A. A lead compound based on the 2-phenyl-2,3-dihydro-1H-perimidine scaffold is particularly promising, and its binding mechanism was further investigated using molecular dynamics simulations and conducting preliminary structure-activity relationship studies.

Recent Developments in Molecular Simulation Approaches to Study Spherical Virus Capsids

Viruses are a particularly challenging systems to study via molecular simulation methods. Virus capsids typically consist of over 100 subunit proteins and reach dimensions of over 100 nm; solvated viruses capsid systems can be over 1 million atoms in size. In this review, I will present recent developments which have attempted to overcome the significant computational expense to perform simulations which can inform experimental studies, make useful predictions about biological phenomena and calculate material properties relevant to nanotechnology design efforts.

Current and emerging opportunities for molecular simulations in structure-based drug design

An overview of the current capabilities and limitations of molecular simulation of biomolecular complexes in the context of computer-aided drug design is provided. Steady improvements in computer hardware coupled with more refined representations of energetics are leading to a new appreciation of the driving forces of molecular recognition. Molecular simulations are poised to more frequently guide the interpretation of biophysical measurements of biomolecular complexes. Ligand design strategies emerge from detailed analyses of computed structural ensembles. The feasibility of routine applications to ligand optimization problems hinges upon successful extensive large scale validation studies and the development of protocols to intelligently automate computations.

LIBSA--a method for the determination of ligand-binding preference to allosteric sites on receptor ensembles

Incorporation of receptor flexibility into computational drug discovery through the relaxed complex scheme is well suited for screening against a single binding site. In the absence of a known pocket or if there are multiple potential binding sites, it may be necessary to do docking against the entire surface of the target (global docking). However no suitable and easy-to-use tool is currently available to rank global docking results based on the preference of a ligand for a given binding site. We have developed a protocol, termed LIBSA for LIgand Binding Specificity Analysis, that analyzes multiple docked poses against a single or ensemble of receptor conformations and returns a metric for the relative binding to a specific region of interest. By using novel filtering algorithms and the signal-to-noise ratio (SNR), the relative ligand-binding frequency at different pockets can be calculated and compared quantitatively. Ligands can then be triaged by their tendency to bind to a site instead of ranking by affinity alone. The method thus facilitates screening libraries of ligand cores against a large library of receptor conformations without prior knowledge of specific pockets, which is especially useful to search for hits that selectively target a particular site. We demonstrate the utility of LIBSA by showing that it correctly identifies known ligand binding sites and predicts the relative preference of a set of related ligands for different pockets on the same receptor.

istar: a web platform for large-scale protein-ligand docking

Protein-ligand docking is a key computational method in the design of starting points for the drug discovery process. We are motivated by the desire to automate large-scale docking using our popular docking engine idock and thus have developed a publicly-accessible web platform called istar. Without tedious software installation, users can submit jobs using our website. Our istar website supports 1) filtering ligands by desired molecular properties and previewing the number of ligands to dock, 2) monitoring job progress in real time, and 3) visualizing ligand conformations and outputting free energy and ligand efficiency predicted by idock, binding affinity predicted by RF-Score, putative hydrogen bonds, and supplier information for easy purchase, three useful features commonly lacked on other online docking platforms like DOCK Blaster or iScreen. We have collected 17,224,424 ligands from the All Clean subset of the ZINC database, and revamped our docking engine idock to version 2.0, further improving docking speed and accuracy, and integrating RF-Score as an alternative rescoring function. To compare idock 2.0 with the state-of-the-art AutoDock Vina 1.1.2, we have carried out a rescoring benchmark and a redocking benchmark on the 2,897 and 343 protein-ligand complexes of PDBbind v2012 refined set and CSAR NRC HiQ Set 24Sept2010 respectively, and an execution time benchmark on 12 diverse proteins and 3,000 ligands of different molecular weight. Results show that, under various scenarios, idock achieves comparable success rates while outperforming AutoDock Vina in terms of docking speed by at least 8.69 times and at most 37.51 times. When evaluated on the PDBbind v2012 core set, our istar platform combining with RF-Score manages to reproduce Pearson's correlation coefficient and Spearman's correlation coefficient of as high as 0.855 and 0.859 respectively between the experimental binding affinity and the predicted binding affinity of the docked conformation. istar is freely available at http://istar.cse.cuhk.edu.hk/idock.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways

Simulations can provide tremendous insight into the atomistic details of biological mechanisms, but micro- to millisecond timescales are historically only accessible on dedicated supercomputers. We demonstrate that cloud computing is a viable alternative that brings long-timescale processes within reach of a broader community. We used Google's Exacycle cloud-computing platform to simulate two milliseconds of dynamics of a major drug target, the G-protein-coupled receptor β2AR. Markov state models aggregate independent simulations into a single statistical model that is validated by previous computational and experimental results. Moreover, our models provide an atomistic description of the activation of a G-protein-coupled receptor and reveal multiple activation pathways. Agonists and inverse agonists interact differentially with these pathways, with profound implications for drug design.

Exploring the role of receptor flexibility in structure-based drug discovery

The proper understanding of biomolecular recognition mechanisms that take place in a drug target is of paramount importance to improve the efficiency of drug discovery and development. The intrinsic dynamic character of proteins has a strong influence on biomolecular recognition mechanisms and models such as conformational selection have been widely used to account for this dynamic association process. However, conformational changes occurring in the receptor prior and upon association with other molecules are diverse and not obvious to predict when only a few structures of the receptor are available. In view of the prominent role of protein flexibility in ligand binding and its implications for drug discovery, it is of great interest to identify receptor conformations that play a major role in biomolecular recognition before starting rational drug design efforts. In this review, we discuss a number of recent advances in computer-aided drug discovery techniques that have been proposed to incorporate receptor flexibility into structure-based drug design. The allowance for receptor flexibility provided by computational techniques such as molecular dynamics simulations or enhanced sampling techniques helps to improve the accuracy of methods used to estimate binding affinities and, thus, such methods can contribute to the discovery of novel drug leads.

Computational models for neglected diseases: gaps and opportunities

Neglected diseases, such as Chagas disease, African sleeping sickness, and intestinal worms, affect millions of the world's poor. They disproportionately affect marginalized populations, lack effective treatments or vaccines, or existing products are not accessible to the populations affected. Computational approaches have been used across many of these diseases for various aspects of research or development, and yet data produced by computational approaches are not integrated and widely accessible to others. Here, we identify gaps in which computational approaches have been used for some neglected diseases and not others. We also make recommendations for the broad-spectrum integration of these techniques into a neglected disease drug discovery and development workflow.

iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints

Many crucial functions in life, such as heartbeat, sensory transduction and central nervous system response, are controlled by cell signalings via various ion channels. Therefore, ion channels have become an excellent drug target, and study of ion channel-drug interaction networks is an important topic for drug development. However, it is both time-consuming and costly to determine whether a drug and a protein ion channel are interacting with each other in a cellular network by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (three-dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most protein ion channels are still unknown. With the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop the sequence-based computational method to address this problem. To take up the challenge, we developed a new predictor called iCDI-PseFpt, in which the protein ion-channel sample is formulated by the PseAAC (pseudo amino acid composition) generated with the gray model theory, the drug compound by the 2D molecular fingerprint, and the operation engine is the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iCDI-PseFpt via the jackknife cross-validation was 87.27%, which is remarkably higher than that by any of the existing predictors in this area. As a user-friendly web-server, iCDI-PseFpt is freely accessible to the public at the website http://www.jci-bioinfo.cn/iCDI-PseFpt/. Furthermore, for the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in the paper just for its integrity. It has not escaped our notice that the current approach can also be used to study other drug-target interaction networks.

Molecular simulations of aromatase reveal new insights into the mechanism of ligand binding

CYP19A1, also known as aromatase or estrogen synthetase, is the rate-limiting enzyme in the biosynthesis of estrogens from their corresponding androgens. Several clinically used breast cancer therapies target aromatase. In this work, explicitly solvated all-atom molecular dynamics simulations of aromatase with a model of the lipid bilayer and the transmembrane helix are performed. The dynamics of aromatase and the role of titration of an important amino acid residue involved in aromatization of androgens are investigated via two 250-ns long simulations. One simulation treats the protonated form of the catalytic aspartate 309, which appears more consistent with crystallographic data for the active site, while the simulation of the deprotonated form shows some notable conformational shifts. Ensemble-based computational solvent mapping experiments indicate possible novel druggable binding sites that could be utilized by next-generation inhibitors. In addition, the effects of protonation on the ligand positioning and channel dynamics are investigated using geometrical models that estimate the opening width of critical channels. Significant differences in channel dynamics between the protonated and deprotonated trajectories are exhibited, suggesting that the mechanism for substrate and product entry and the aromatization process may be coupled to a "locking" mechanism and channel opening. Our results may be particularly relevant in the design of novel drugs, which may be useful therapeutic treatments of cancers such as those of the breast and prostate.

Modelling and enhanced molecular dynamics to steer structure-based drug discovery

The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes.

Molecular dynamics study of carbon nanotube as a potential dual-functional inhibitor of HIV-1 integrase

HIV-1 integrase (IN) plays an important role in integrating viral DNA into human genome, which has been considered as the drug target for anti-AIDS therapy. The appearance of drug-resistance mutants urgently requires novel inhibitors that act on non-active site of HIV-1 IN. Nanoparticles have such unique geometrical and chemical properties, which inspires us that nanoparticles like nanotubes may serve as better HIV-1 IN inhibitors than the conventional inhibitors. To test this hypothesis, we performed molecular dynamics (MD) simulation to study the binding of a carbon nanotube (CNT) to a full-length HIV-1 IN. The results showed that the CNT could stably bind to the C-terminal domain (CTD) of HIV-1 IN. The CNT also induced a domain-shift which disrupted the binding channel for viral DNA. Further MD simulation showed that a HIV-1 IN inhibitor, 5ClTEP was successfully sealed inside the uncapped CNT. These results indicate that the CNT may serve as a potential dual-functional HIV-1 IN inhibitor, not only inducing conformation change as an allosteric inhibitor but also carrying small-molecular inhibitors as a drug delivery system.

Extra precision docking, free energy calculation and molecular dynamics simulation studies of CDK2 inhibitors

Molecular docking, free energy calculation and molecular dynamics (MD) simulation studies have been performed, to explore the putative binding modes of 3,5-diaminoindazoles, imidazo(1,2-b)pyridazines and triazolo(1,5-a) pyridazines series of Cyclin-dependent kinase (CDK2) inhibitors. To evaluate the effectiveness of docking protocol in flexible docking, we have selected crystallographic bound compound to validate our docking procedure as evident from root mean square deviations (RMSDs). We found different binding sites namely catalytic, inhibitory phosphorylation, cyclin binding and CKS-binding site of the CDK2 contributing towards the binding of these compounds. Moreover, correlation between free energy of binding and biological activity yielded a statistically significant correlation coefficient. Finally, three representative protein-ligand complexes were subjected to molecular dynamics simulation to determine the stability of the predicted conformations. The low value of the RMSDs between the initial complex structure and the energy minimized final average complex structure suggests that the derived docked complexes are close to equilibrium. We suggest that the phenylacetyl type of substituents and cyclohexyl moiety make the favorable interactions with a number of residues in the active site, and show better inhibitory activity to improve the pharmacokinetic profile of compounds against CDK2. The structure-based drug design strategy described in this study will be highly useful for the development of new inhibitors with high potency and selectivity.

Patient-specific modelling in drug design, development and selection including its role in clinical decision-making

Genomics has made enormous progress in the twelve years since the publication of the first draft human genome sequence, but it has not yet been translated into the clinic. Despite spiralling development costs, the number of new drug registrations is not increasing. One reason for this lies in the genetic complexity of disease. Most diseases involve dysregulation in pathways that involve many genes, and many (including most cancers) are themselves genetically heterogeneous. Systems biology involves the multi-level simulation of physiology, cell biology and biochemistry using complex computational techniques. We show here using case studies in cancer and HIV how such computational models, and particularly models based on individual patient data, can be used for drug design and development, and in the selection of the appropriate treatment for a given patient in the face of resistance mutations. If these techniques are to be adopted in routine clinical practice, clinicians will need better training in modern approaches to the integrated analysis of large-scale heterogeneous data and multi-scale models, while developers will need to provide much more usable tools. Investment in computational infrastructure is needed so that results can be returned on clinically relevant timescales and data warehouses designed with data protection as well as accessibility in mind.

Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design

Protein flexibility plays a major role in biomolecular recognition. In many cases, it is not obvious how molecular structure will change upon association with other molecules. In proteins, these changes can be major, with large deviations in overall backbone structure, or they can be more subtle as in a side-chain rotation. Either way the algorithms that predict the favorability of biomolecular association require relatively accurate predictions of the bound structure to give an accurate assessment of the energy involved in association. Here, we review a number of techniques that have been proposed to accommodate receptor flexibility in the simulation of small molecules binding to protein receptors. We investigate modifications to standard rigid receptor docking algorithms and also explore enhanced sampling techniques, and the combination of free energy calculations and enhanced sampling techniques. The understanding and allowance for receptor flexibility are helping to make computer simulations of ligand protein binding more accurate. These developments may help improve the efficiency of drug discovery and development. Efficiency will be essential as we begin to see personalized medicine tailored to individual patients, which means specific drugs are needed for each patient's genetic makeup.

State-of-the-art and dissemination of computational tools for drug-design purposes: a survey among Italian academics and industrial institutions

During the first edition of the Computationally Driven Drug Discovery meeting, held in November 2011 at Dompé Pharma (L'Aquila, Italy), a questionnaire regarding the diffusion and the use of computational tools for drug-design purposes in both academia and industry was distributed among all participants. This is a follow-up of a previously reported investigation carried out among a few companies in 2007. The new questionnaire implemented five sections dedicated to: research group identification and classification; 18 different computational techniques; software information; hardware data; and economical business considerations. In this article, together with a detailed history of the different computational methods, a statistical analysis of the survey results that enabled the identification of the prevalent computational techniques adopted in drug-design projects is reported and a profile of the computational medicinal chemist currently working in academia and pharmaceutical companies in Italy is highlighted.

Shape and pharmacophore-based virtual screening to identify potential cytochrome P450 sterol 14α-demethylase inhibitors

Sterol 14α-demethylase (CYP51) is a cytochrome P450 heme thiolate containing enzyme involved in biosynthesis of membrane sterols, including sterol in animals, ergosterol in fungi, and a variety of C24-modified sterols in plants and protozoa. Several clinical drugs have been developed to reduce the impact of fungal diseases, but their clinical uses have been limited by the emergence of drug resistance and insufficiencies in their antifungal activity. Therefore, in order to identify potential CYP51 inhibitors, we have implemented a virtual screening (VS) protocol by using both phase shape and pharmacophore model (AHHRR) against Asinex, ChemBridge and Maybridge databases. A filtering protocol, including Lipinski filter, number of rotatable bonds and different precisions of molecular docking was applied in hits selection. The results indicated that both shape-based and pharmacophore-based screening yielded the best result with potential inhibitors. The searched compounds were also evaluated with ADME properties, which show excellent pharmacokinetic properties under the acceptable range. We identified potential CYP51 inhibitors for further investigation, they could also be employed to design ligands with enhanced inhibitory potencies and to predict the potencies of analogs to guide synthesis/or prepare synthetic antifungal analogs against CYP51.

Biomolecular simulation: a computational microscope for molecular biology

Molecular dynamics simulations capture the behavior of biological macromolecules in full atomic detail, but their computational demands, combined with the challenge of appropriately modeling the relevant physics, have historically restricted their length and accuracy. Dramatic recent improvements in achievable simulation speed and the underlying physical models have enabled atomic-level simulations on timescales as long as milliseconds that capture key biochemical processes such as protein folding, drug binding, membrane transport, and the conformational changes critical to protein function. Such simulation may serve as a computational microscope, revealing biomolecular mechanisms at spatial and temporal scales that are difficult to observe experimentally. We describe the rapidly evolving state of the art for atomic-level biomolecular simulation, illustrate the types of biological discoveries that can now be made through simulation, and discuss challenges motivating continued innovation in this field.

Derivation of fixed partial charges for amino acids accommodating a specific water model and implicit polarization

We have developed the IPolQ method for fitting nonpolarizable point charges to implicitly represent the energy of polarization for systems in pure water. The method involves iterative cycles of molecular dynamics simulations to estimate the water charge density around the solute of interest, followed by quantum mechanical calculations at the MP2/cc-pV(T+d)Z level to determine updated solute charges. Lennard-Jones parameters are updated starting from the Amber FF99SB nonbonded parameter set to accommodate the new charge model, guided by the comparisons to experimental hydration free energies (HFEs) of neutral amino acid side chain analogs and assumptions about the computed HFEs for charged side chains. These Lennard-Jones parameter adjustments for side-chain analogs are assumed to be transferable to amino acids generally, and new charges for all standard amino acids are then derived in the presence of water modeled by TIP4P-Ew. Overall, the new charges depict substantially more polarized amino acids, particularly in the backbone moieties, than previous Amber charge sets. Efforts to complete a new force field with appropriate torsion parameters for this charge model are underway. The IPolQ method is general and applicable to arbitrary solutes.

Agonist and antagonist binding to the nuclear vitamin D receptor: dynamics, mutation effects and functional implications

Purpose

The thermodynamically favored complex between the nuclear vitamin D receptor (VDR) and 1α,25(OH)₂-vitamin D₃ (1,25D3) triggers a shift in equilibrium to favor VDR binding to DNA, heterodimerization with the nuclear retinoid x receptor (RXR) and subsequent regulation of gene transcription. The key amino acids and structural requirements governing VDR binding to nuclear coactivators (NCoA) are well defined. Yet very little is understood about the internal changes in amino acid flexibility underpinning the control of ligand affinity, helix 12 conformation and function. Herein, we use molecular dynamics (MD) to study how the backbone and side-chain flexibility of the VDR differs when a) complexed to 1α,25(OH)₂-vitamin D₃ (1,25D3, agonist) and (23S),25-dehydro-1α(OH)-vitamin D₃-26,23-lactone (MK, antagonist); b) residues that form hydrogen bonds with the C25-OH (H305 and H397) of 1,25D3 are mutated to phenylalanine; c) helix 12 conformation is changed and ligand is removed; and d) x-ray water near the C1- and C3-OH groups of 1,25D3 are present or replaced with explicit solvent.

Methods

We performed molecular dynamic simulations on the apo- and holo-VDRs and used T-Analyst to monitor the changes in the backbone and side-chain flexibility of residues that form regions of the VDR ligand binding pocket (LBP), NCoA surface and control helix 12 conformation.

Results

The VDR-1,25D3 and VDR-MK MD simulations demonstrate that 1,25D3 and MK induce highly similar changes in backbone and side-chain flexibility in residues that form the LBP. MK however did increase the backbone and side-chain flexibility of L404 and R274 respectively. MK also induced expansion of the VDR charge clamp (i.e. NCoA surface) and weakened the intramolecular interaction between H305---V418 (helix 12) and TYR401 (helix 11). In VDR_FF, MK induced a generally more rigid LBP and stronger interaction between F397 and F422 than 1,25D3, and reduced the flexibility of the R274 side-chain. Lastly the VDR MD simulations indicate that R274 can sample multiple conformations in the presence of ligand. When the R274 is extended, the β-OH group of 1,25D3 lies proximal to the backbone carbonyl oxygen of R274 and the side-chain forms H-bonds with hinge domain residues. This differs from the x-ray, kinked geometry, where the side-chain forms an H-bond with the 1α-OH group. Furthermore, 1,25D3, but not MK was observed to stabilize the x-ray geometry of R274 during the > 30 ns MD runs.

Conclusions

The MD methodology applied herein provides an in silico foundation to be expanded upon to better understand the intrinsic flexibility of the VDR and better understand key side-chain and backbone movements involved in the bimolecular interaction between the VDR and its’ ligands.

Electronic supplementary material

The online version of this article (doi:10.1186/2193-9616-1-2) contains supplementary material, which is available to authorized users.

Consensus docking: improving the reliability of docking in a virtual screening context

Structure-based virtual screening relies on scoring the predicted binding modes of compounds docked into the target. Because the accuracy of this scoring relies on the accuracy of the docking, methods that increase docking accuracy are valuable. Here, we present a relatively straightforward method for improving the probability of identifying accurately docked poses. The method is similar in concept to consensus scoring schemes, which have been shown to increase ranking power and thus hit rates, but combines information about predicted binding modes rather than predicted binding affinities. The pose prediction success rate of each docking program alone was found in this trial to be 55% for Autodock, 58% for DOCK, and 64% for Vina. By using more than one docking program to predict the binding pose, correct poses were identified in 82% or more of cases, a significant improvement. In a virtual screen, these more reliably posed compounds can be preferentially advanced to subsequent scoring stages to improve hit rates. Consensus docking can be easily introduced into established structure-based virtual screening methodologies.

Exploring the selectivity of a ligand complex with CDK2/CDK1: a molecular dynamics simulation approach

Cyclin-dependent kinases (CDKs) are core components of the cell cycle machinery that govern the transition between phases during cell cycle progression. Abnormalities in CDKs activity and regulation are common features of cancer, making CDK family members attractive targets for the development of anticancer drugs. Their inhibitors have entered in clinical trials to treat cancer. Very recently, Heathcote et al. (J. Med. Chem. 2010, 53:8508-8522) have found a ligand BS194 that has a high affinity with CDK2 (IC(50) = 3 nM) but shows low affinity with CDK1 (IC(50) = 30 nM). To understand the selectivity, we used homology modeling, molecular docking, molecular dynamics, and free-energy calculation to analyze the interactions. A rational three-dimensional model of the CDK1/BS194 complex is built. We found that Leu83 is a key residue that recognizes BS194 more effectively with CDK2 with good binding free energies rather than CDK1. Energetic analysis reveals that van der Waals interaction and non-polar contributions to solvent are favorable in the formation of complexes and amine group of the ligand, which plays a crucial role for binding selectivity between CDK2 and CDK1.

Variation in Active Site Amino Residues of H1N1 Swine Flu Neuraminidase

In this paper, we report the variations of amino acid residues between H5N1 and H1N1 swine flu neuraminidase sequences at protein level. Random search in NCBI Flu database resulted in Canadian viral gene and analysis using blast technique revealed sites that are variant among sequences for which 3-dimensional structures were known. PDB summary database and multiple alignments were employed for validation of the results. Based on the mutations observed within active site region, homology derived model was constructed using swiss-pdb viewer. The residue variation observed was with respect to Tyr347 in H5N1 versus Asn344 in H1N1 neuraminidase sequence, which resulted in geometrical modification of ligand binding domain.

Virtual screening and biological evaluation of inhibitors targeting the XPA-ERCC1 interaction

BACKGROUND

Nucleotide excision repair (NER) removes many types of DNA lesions including those induced by UV radiation and platinum-based therapy. Resistance to platinum-based therapy correlates with high expression of ERCC1, a major element of the NER machinery. The interaction between ERCC1 and XPA is essential for a successful NER function. Therefore, one way to regulate NER is by inhibiting the activity of ERCC1 and XPA.

METHODOLOGY/PRINCIPAL FINDINGS

Here we continued our earlier efforts aimed at the identification and characterization of novel inhibitors of the ERCC1-XPA interaction. We used a refined virtual screening approach combined with a biochemical and biological evaluation of the compounds for their ability to interact with ERCC1 and to sensitize cells to UV radiation. Our findings reveal a new validated ERCC1-XPA inhibitor that significantly sensitized colon cancer cells to UV radiation indicating a strong inhibition of the ERCC1-XPA interaction.

CONCLUSIONS

NER is a major factor in acquiring resistance to platinum-based therapy. Regulating the NER pathway has the potential of improving the efficacy of platinum treatments. One approach that we followed is to inhibit the essential interaction between the two NER elements, ERCC1 and XPA. Here, we performed virtual screening against the ERCC1-XPA interaction and identified novel inhibitors that block the XPA-ERCC1 binding. The identified inhibitors significantly sensitized colon cancer cells to UV radiation indicating a strong inhibition of the ERCC1-XPA interaction.

Dynamics of Plasmodium falciparum enoyl-ACP reductase and implications on drug discovery

Enoyl-acyl carrier protein reductase (ENR) is a crucial enzyme in the type II fatty acid synthesis pathway of many pathogens such as Plasmodium falciparum, the etiological agent of the most severe form of malaria. Because of its essential function of fatty acid double bond reduction and the absence of a human homologue, PfENR is an interesting drug target. Although extensive knowledge of the protein structure has been gathered over the last decade, comparatively little remains known about the dynamics of this crucial enzyme. Here, we perform extensive molecular dynamics simulations of tetrameric PfENR in different states of cofactor and ligand binding, and with a variety of different ligands bound. A pocket-volume analysis is also performed, and virtual screening is used to identify potential druggable hotspots. The implications of the results for future drug-discovery projects are discussed.

Inhibitors of human immunodeficiency virus type I integration

PURPOSE OF REVIEW

The virally encoded enzyme integrase plays a critical role in HIV-1 replication and has long been considered a promising target for the development of agents to treat HIV-1 infection. It is only recently, however, that the efficacy of integrase inhibitors has been demonstrated in experimental animal model systems of retroviral infection, and in HIV-1 infected subjects. Several compounds that have shown potent efficacy in short-term monotherapy studies have initiated phase two and three clinical studies in 2006.

RECENT FINDINGS

Although the first inhibitors in this new class of antiretroviral therapy are in the earliest stages of clinical development, the study of integrase function and inhibitor mechanism, as well as recent insights on resistance to prototypical inhibitors in vitro, have important implications for the discovery and development of these agents.

SUMMARY

This review will summarize the role of integrase in HIV-1 infection, the mechanism of integrase inhibitors, and the results of resistance studies on preclinical compounds which suggest there may be multiple opportunities for developing inhibitors against this essential HIV-1 target.

Implicit ligand theory: rigorous binding free energies and thermodynamic expectations from molecular docking

A rigorous formalism for estimating noncovalent binding free energies and thermodynamic expectations from calculations in which receptor configurations are sampled independently from the ligand is derived. Due to this separation, receptor configurations only need to be sampled once, facilitating the use of binding free energy calculations in virtual screening. Demonstrative calculations on a host-guest system yield good agreement with previous free energy calculations and isothermal titration calorimetry measurements. Implicit ligand theory provides guidance on how to improve existing molecular docking algorithms and insight into the concepts of induced fit and conformational selection in noncovalent macromolecular recognition.

From base pair to bedside: molecular simulation and the translation of genomics to personalized medicine

Despite the promises made that genomic sequencing would transform therapy by introducing a new era of personalized medicine, relatively few tangible breakthroughs have been made. This has led to the recognition that complex interactions at multiple spatial, temporal, and organizational levels may often combine to produce disease. Understanding this complexity requires that existing and future models are used and interpreted within a framework that incorporates knowledge derived from investigations at multiple levels of biological function. It also requires a computational infrastructure capable of dealing with the vast quantities of data generated by genomic approaches. In this review, we discuss the use of molecular modeling to generate quantitative and qualitative insights at the smallest scales of the systems biology hierarchy, how it can play an important role in the development of a systems understanding of disease and in the application of such knowledge to help discover new therapies and target existing ones on a personal level.

Molecular dynamics simulation in virus research

Virus replication in the host proceeds by chains of interactions between viral and host proteins. The interactions are deeply influenced by host immune molecules and anti-viral compounds, as well as by mutations in viral proteins. To understand how these interactions proceed mechanically and how they are influenced by mutations, one needs to know the structures and dynamics of the proteins. Molecular dynamics (MD) simulation is a powerful computational method for delineating motions of proteins at an atomic-scale via theoretical and empirical principles in physical chemistry. Recent advances in the hardware and software for biomolecular simulation have rapidly improved the precision and performance of this technique. Consequently, MD simulation is quickly extending the range of applications in biology, helping to reveal unique features of protein structures that would be hard to obtain by experimental methods alone. In this review, we summarize the recent advances in MD simulations in the study of virus–host interactions and evolution, and present future perspectives on this technique.