Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock

Homology modeling of cannabinoid receptors: discovery of cannabinoid analogues for therapeutic use

Cannabinoids represent a promising class of compounds for developing novel therapeutic agents. Since the isolation and identification of the major psychoactive component Δ(9)-THC in Cannabis sativa in the 1960s, numerous analogues of the classical plant cannabinoids have been synthesized and tested for their biological activity. These compounds primarily target the cannabinoid receptors 1 (CB1) and Cannabinoid receptors 2 (CB2). This chapter focuses on CB1. Despite the lack of crystal structures for CB1, protein-based homology modeling approaches and molecular docking methods can be used in the design and discovery of cannabinoid analogues. Efficient synthetic approaches for therapeutically interesting cannabinoid analogues have been developed to further facilitate the drug discovery process.

AMMOS software: method and application

Recent advances in computational sciences enabled extensive use of in silico methods in projects at the interface between chemistry and biology. Among them virtual ligand screening, a modern set of approaches, facilitates hit identification and lead optimization in drug discovery programs. Most of these approaches require the preparation of the libraries containing small organic molecules to be screened or a refinement of the virtual screening results. Here we present an overview of the open source AMMOS software, which is a platform performing an automatic procedure that allows for a structural generation and optimization of drug-like molecules in compound collections, as well as a structural refinement of protein-ligand complexes to assist in silico screening exercises.

Characterization of the inhibitor-resistant SHV β-lactamase SHV-107 in a clinical Klebsiella pneumoniae strain coproducing GES-7 enzyme

The clinical Klebsiella pneumoniae INSRA6884 strain exhibited nonsusceptibility to all penicillins tested (MICs of 64 to >2,048 μg/ml). The MICs of penicillins were weakly reduced by clavulanate (from 2,048 to 512 μg/ml), and tazobactam restored piperacillin susceptibility. Molecular characterization identified the genes bla(GES-7) and a new β-lactamase gene, bla(SHV-107), which encoded an enzyme that differed from SHV-1 by the amino acid substitutions Leu35Gln and Thr235Ala. The SHV-107-producing Escherichia coli strain exhibited only a β-lactam resistance phenotype with respect to amoxicillin, ticarcillin, and amoxicillin-clavulanate combination. The kinetic parameters of the purified SHV-107 enzyme revealed a high affinity for penicillins. However, catalytic efficiency for these antibiotics was lower for SHV-107 than for SHV-1. No hydrolysis was detected against oxyimino-β-lactams. The 50% inhibitory concentration (IC(50)) for clavulanic acid was 9-fold higher for SHV-107 than for SHV-1, but the inhibitory effects of tazobactam were unchanged. Molecular dynamics simulation suggested that the Thr235Ala substitution affects the accommodation of clavulanate in the binding site and therefore its inhibitory activity.

Systematic placement of structural water molecules for improved scoring of protein-ligand interactions

Structural water molecules are found in many protein-ligand complexes. They are known to be vital in mediating hydrogen-bonding interactions and, in some cases, key for facilitating tight binding. It is thus very important to consider water molecules when attempting to model protein-ligand interactions for cognate ligand identification, virtual screening and drug design. While the rigid treatment of water molecules present in structures is feasible, the more relevant task of treating all possible positions and orientations of water molecules with each possible ligand pose is computationally daunting. Current methods in molecular docking provide partial treatment for such water molecules, with modest success. Here we describe a new method employing dead-end elimination to place water molecules within a binding site, bridging interactions between protein and ligand. Dead-end elimination permits a thorough, though still incomplete, treatment of water placement. The results show that this method is able to place water molecules correctly within known complexes and to create physically reasonable hydrogen bonds. The approach has also been incorporated within an inverse molecular design approach, to model a variety of compounds in the process of de novo ligand design. The inclusion of structural water molecules, combined with ranking based on the electrostatic contribution to binding affinity, improves a number of otherwise poor energetic predictions.

AADS--an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors

We report here a robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures. The active site finder identifies all cavities in a protein and scores them based on the physicochemical properties of functional groups lining the cavities in the protein. The accuracy realized on 620 proteins with sizes ranging from 100 to 600 amino acids with known drug active sites is 100% when the top ten cavity points are considered. These top ten cavity points identified are then submitted for an automated docking of an input ligand/candidate molecule. The docking protocol uses an all atom energy based Monte Carlo method. Eight low energy docked structures corresponding to different locations and orientations of the candidate molecule are stored at each cavity point giving 80 docked structures overall which are then ranked using an effective free energy function and top five structures are selected. The predicted structure and energetics of the complexes agree quite well with experiment when tested on a data set of 170 protein-ligand complexes with known structures and binding affinities. The AADS methodology is implemented on an 80 processor cluster and presented as a freely accessible, easy to use tool at http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp .

Toward a robust search method for the protein-drug docking problem

Predicting the binding mode(s) of a drug molecule to a target receptor is pivotal in structure-based rational drug design. In contrast to most approaches to solve this problem, the idea in this paper is to analyze the search problem from a computational perspective. By building on top of an existing docking tool, new methods are proposed and relevant computational results are proven. These methods and results are applicable for other place-and-join frameworks as well. A fast approximation scheme for the docking of rigid fragments is described that guarantees certain geometric approximation factors. It is also demonstrated that this can be translated into an energy approximation for simple scoring functions. A polynomial time algorithm is developed for the matching phase of the docked rigid fragments. It is demonstrated that the generic matching problem is NP-hard. At the same time, the optimality of the proposed algorithm is proven under certain scoring function conditions. The matching results are also applicable for some of the fragment-based de novo design methods. On the practical side, the proposed method is tested on 829 complexes from the PDB. The results show that the closest predicted pose to the native structure has the average RMS deviation of 1.06 A.

Monoamine neurotransmitters as substrates for novel tick sulfotransferases, homology modeling, molecular docking, and enzyme kinetics

Blacklegged ticks (Ixodes scapularis) transmit the causative agent of Lyme disease in the Northeastern United States. Current research focuses on elucidating biochemical pathways that may be disrupted to prevent pathogen transmission, thereby preventing disease. Genome screening reported transcripts coding for two putative sulfotransferases in whole tick extracts of the nymphal and larval stages. Sulfotransferases are known to sulfonate phenolic and alcoholic receptor agonists such as 17β-estradiol, thereby inactivating the receptor ligands. We used bioinformatic approaches to predict substrates for Ixosc Sult 1 and Ixosc Sult 2 and tested the predictions with biochemical assays. Homology models of 3D protein structure were prepared, and visualization of the electrostatic surface of the ligand binding cavities showed regions of negative electrostatic charge. Molecular docking identified potential substrates including dopamine, R-octopamine and S-octopamine, which docked into Ixosc Sult 1 with favorable binding affinity and correct conformation for sulfonation. Dopamine, but not R- or S-octopamine, also docked into Ixosc Sult 2 in catalytic binding mode. The predictions were confirmed using cytosolic fractions of whole tick extracts. Dopamine was a good substrate (K(m) = 0.1-0.4 μM) for the native Ixodes scapularis sulfotransferases from larval and nymphal stages regardless of their fed/unfed status. Octopamine sulfonation was detected only after feeding when gene expression data suggests that Ixosc Sult 1 is present. Because dopamine is known to stimulate salivation in ticks through receptor stimulation, these results imply that the function(s) of Ixosc Sult 1 or 2 may include inactivation of the salivation signal via sulfonation of dopamine and/or octopamine.

Flexibility and inhibitor binding in cdc25 phosphatases

Cdc25 phosphatases involved in cell cycle checkpoints are now active targets for the development of anti-cancer therapies. Rational drug design would certainly benefit from detailed structural information for Cdc25s. However, only apo- or sulfate-bound crystal structures of the Cdc25 catalytic domain have been described so far. Together with previously available crystalographic data, results from molecular dynamics simulations, bioinformatic analysis, and computer-generated conformational ensembles shown here indicate that the last 30-40 residues in the C-terminus of Cdc25B are partially unfolded or disordered in solution. The effect of C-terminal flexibility upon binding of two potent small molecule inhibitors to Cdc25B is then analyzed by using three structural models with variable levels of flexibility, including an equilibrium distributed ensemble of Cdc25B backbone conformations. The three Cdc25B structural models are used in combination with flexible docking, clustering, and calculation of binding free energies by the linear interaction energy approximation to construct and validate Cdc25B-inhibitor complexes. Two binding sites are identified on top and beside the Cdc25B active site. The diversity of interaction modes found increases with receptor flexibility. Backbone flexibility allows the formation of transient cavities or compact hydrophobic units on the surface of the stable, folded protein core that are unexposed or unavailable for ligand binding in rigid and densely packed crystal structures. The present results may help to speculate on the mechanisms of small molecule complexation to partially unfolded or locally disordered proteins.

Preparation and refinement of model protein-ligand complexes

The formation of ligand-protein complexes are critical for the correct functioning of a cell. The prediction of these interactions is important for our understanding of how the cell works and for the development of new drug molecules. Homology modeling is a method for predicting the structure of a protein based on a crystal structure template. Once a model of the protein is complete, a ligand-docking algorithm predicts the ligand-protein model interaction by searching for the best steric and energetically favorable fit. A refinement of the ligand-binding pocket improves the predicted interactions by considering the flexible nature of the ligand-binding pocket. In this chapter, we describe, from first principles, methods to identify and prepare the ligand-binding pocket in a protein model, to dock the ligand, and refine the resulting complex.

Mining flexible-receptor docking experiments to select promising protein receptor snapshots

Background

Molecular docking simulation is the Rational Drug Design (RDD) step that investigates the affinity between protein receptors and ligands. Typically, molecular docking algorithms consider receptors as rigid bodies. Receptors are, however, intrinsically flexible in the cellular environment. The use of a time series of receptor conformations is an approach to explore its flexibility in molecular docking computer simulations, but it is extensively time-consuming. Hence, selection of the most promising conformations can accelerate docking experiments and, consequently, the RDD efforts.

Results

We previously docked four ligands (NADH, TCL, PIF and ETH) to 3,100 conformations of the InhA receptor from M. tuberculosis. Based on the receptor residues-ligand distances we preprocessed all docking results to generate appropriate input to mine data. Data preprocessing was done by calculating the shortest interatomic distances between the ligand and the receptor’s residues for each docking result. They were the predictive attributes. The target attribute was the estimated free-energy of binding (FEB) value calculated by the AutodDock3.0.5 software. The mining inputs were submitted to the M5P model tree algorithm. It resulted in short and understandable trees. On the basis of the correlation values, for NADH, TCL and PIF we obtained more than 95% correlation while for ETH, only about 60%. Post processing the generated model trees for each of its linear models (LMs), we calculated the average FEB for their associated instances. From these values we considered a LM as representative if its average FEB was smaller than or equal the average FEB of the test set. The instances in the selected LMs were considered the most promising snapshots. It totalized 1,521, 1,780, 2,085 and 902 snapshots, for NADH, TCL, PIF and ETH respectively.

Conclusions

By post processing the generated model trees we were able to propose a criterion of selection of linear models which, in turn, is capable of selecting a set of promising receptor conformations. As future work we intend to go further and use these results to elaborate a strategy to preprocess the receptors 3-D spatial conformation in order to predict FEB values. Besides, we intend to select other compounds, among the million catalogued, that may be promising as new drug candidates for our particular protein receptor target.

Accounting for water molecules in drug design

IMPORTANCE OF THE FIELD

Water molecules often appear around ligands in protein crystal structures. Reliable prediction of the effects of water on ligand binding remains a challenge. Solvation effects are crucial for lead optimization where a 100-fold difference in binding affinity is significant but correspond to only ∼3 kcal/mol in binding free energy. Well-known examples, such as nonpeptidic urea inhibitors of HIV protease, prove that careful examination of water molecules and their energetics can contribute significantly to a successful drug design campaign.

AREAS COVERED IN THIS REVIEW

In this review, we examine methods to account for the effect of water in ligand binding at two stages of drug discovery: lead identification via docking calculations and lead optimization. We provide a survey of the models and techniques available to account for water in drug design.

WHAT THE READER WILL GAIN

The reader will become aware of common practices and pitfalls in dealing with water molecules in structure-based drug design.

TAKE HOME MESSAGE

Although solvation effects are not fully understood, some pragmatic recommendations at the end of the article provide guidance for modelers in this area as well as new practitioners.

Computer-aided drug-discovery techniques that account for receptor flexibility

Protein flexibility plays a critical role in ligand binding to both orthosteric and allosteric sites. We here review some of the computer-aided drug-design techniques currently used to account for protein flexibility, ranging from methods that probe local receptor flexibility in the region of the protein immediately adjacent to the binding site, to those that account for general flexibility in all protein regions.

A comparative analysis of the role of water in the binding pockets of ionotropic glutamate receptors

The binding pockets within proteins often contain water molecules. The ligand-binding core of ionotropic glutamate receptors represents an example where the binding pocket has many crystallographically reported waters, but the precise role remains unclear. It is also unclear to what extent the dynamic properties of these waters are conserved across the different receptor subtypes. In order to shed some light on these aspects we have performed multiple molecular dynamics simulations of the ligand binding core of four glutamate bound iGluR structures (GluA2, GluK1, GluK2, and GluN2A) and one apo structure (GluA2). We find that the water positions are reproduced from the simulations, but they also reveal that all but one water molecule in the binding site can be rearranged or replaced with water molecules from the bulk that enter the binding site through transient water channels. This one exception is not reported in the apo crystal structure but within 15 ns of simulation, a water molecule enters the site from the bulk suggesting that it is a favoured position regardless of the state of the protein. Further calculations demonstrate that whilst it is not needed in order to be able to predict the correct binding pose, it does contribute a large favourable interaction energy. We also find that one conserved water has a much stronger interaction with the protein in GluA2, GluK1 and GluK2 compared to the GluN2A receptor. The position of this water molecule is such that it can influence the dynamics of the proposed switch in the GluA2 and GluK1/2 receptors.

Emerging methods for ensemble-based virtual screening

Ensemble based virtual screening refers to the use of conformational ensembles from crystal structures, NMR studies or molecular dynamics simulations. It has gained greater acceptance as advances in the theoretical framework, computational algorithms, and software packages enable simulations at longer time scales. Here we focus on the use of computationally generated conformational ensembles and emerging methods that use these ensembles for discovery, such as the Relaxed Complex Scheme or Dynamic Pharmacophore Model. We also discuss the more rigorous physics-based computational techniques such as accelerated molecular dynamics and thermodynamic integration and their applications in improving conformational sampling or the ranking of virtual screening hits. Finally, technological advances that will help make virtual screening tools more accessible to a wider audience in computer aided drug design are discussed.

Advances and challenges in protein-ligand docking

Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion.

Mapping spatial relationships between residues in the ligand-binding domain of the 5-HT3 receptor using a molecular ruler

The serotonin 5-HT(3) receptor (5-HT(3)R) is a member of the Cys-loop ligand-gated ion channel family. We used a combination of site-directed mutagenesis, homology modeling, and ligand-docking simulations to analyze antagonist-receptor interactions. Mutation of E236, which is near loop C of the binding site, to aspartate prevents expression of the receptor on the cell surface, and no specific ligand binding can be detected. On the other hand, mutation to glutamine, asparagine, or alanine produces receptors that are expressed on the cell surface, but decreases receptor affinity for the competitive antagonist d-tubocurarine (dTC) 5-35-fold. The results of a double-mutant cycle analysis employing a panel of dTC analogs to identify specific points of interactions between the dTC analogs and E236 are consistent with E236 making a direct physical interaction with the 12 -OH of dTC. dTC is a rigid molecule of known three-dimensional structure. Together with previous studies linking other regions of dTC to specific residues in the binding site, these data allow us to define the relative spatial arrangement of three different residues in the ligand-binding site: R92 (loop D), N128 (loop A), and E236 (near loop C). Molecular modeling employing these distance constraints followed by molecular-dynamics simulations produced a dTC/receptor complex consistent with the experimental data. The use of the rigid ligands as molecular rulers in conjunction with double-mutant cycle analysis provides a means of mapping the relative positions of various residues in the ligand-binding site of any ligand-receptor complex, and thus is a useful tool for delineating the architecture of the binding site.

Inhibitory effect of quercetin on matrix metalloproteinase 9 activity molecular mechanism and structure-activity relationship of the flavonoid-enzyme interaction

Epidemiological studies have demonstrated an inverse association between the consumption of flavonoid-rich diets and the risk of atherosclerosis. In addition, an increased activity of the matrix metalloproteinase 9 (MMP-9) has been implicated in the development and progression of atherosclerotic lesions. Even though the relationship between flavonoid chemical structure and the inhibitory property on MMP activity has been established, the molecular mechanisms of this inhibition are still unknown. Herein, we first evaluated the inhibitory effect of quercetin on MMP-9 activity by zymography and a fluorescent gelatin dequenching assay, secondly we determined the most probable sites and modes of quercetin interaction with the MMP-9 catalytic domain by using molecular modelling techniques, and finally, we investigated the structure-activity relationship of the inhibitory effect of flavonoids on MMP-9 activity. We show that quercetin inhibited MMP-9 activity with an IC(50) value of 22 microM. By using docking and molecular dynamics simulations, it was shown that quercetin interacted in the S1' subsite of the MMP-9 active site. Moreover, the structure-activity relationship analysis demonstrated that flavonoid R(3)(')-OH and R(4)(')-OH substitutions were relevant to the inhibitory property against MMP-9 activity. In conclusion, our data constitute the first evidence about the quercetin and MMP-9 interaction, suggesting a mechanism to explain the inhibitory effect of the flavonoid on the enzymatic activity of MMP-9, which provides an additional molecular target for the cardioprotective activity of quercetin.

Ligand-protein cross-docking with water molecules

The accuracy of ligand-protein docking may be affected by the presence of water molecules on the surface of the protein. Cross-docking simulations have been performed on a number of ligand-protein complexes for various proteins whose crystal structures contain water molecules in their binding sites. Only common sets of water molecules found in the binding site of the proteins were considered. A statistically significant overall increase in accuracy was observed when water molecules were included in cross-docking simulations. These results confirm the importance of including water molecules whenever possible in ligand-protein docking simulations.

Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein-ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.

DynaDock: A new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility

Molecular docking programs play an important role in drug development and many well-established methods exist. However, there are two situations for which the performance of most approaches is still not satisfactory, namely inclusion of receptor flexibility and docking of large, flexible ligands like peptides. In this publication a new approach is presented for docking peptides into flexible receptors. For this purpose a two step procedure was developed: first, the protein-peptide conformational space is scanned and approximate ligand poses are identified and second, the identified ligand poses are refined by a new molecular dynamics-based method, optimized potential molecular dynamics (OPMD). The OPMD approach uses soft-core potentials for the protein-peptide interactions and applies a new optimization scheme to the soft-core potential. Comparison with refinement results obtained by conventional molecular dynamics and a soft-core scaling approach shows significant improvements in the sampling capability for the OPMD method. Thus, the number of starting poses needed for successful refinement is much lower than for the other methods. The algorithm was evaluated on 15 protein-peptide complexes with 2-16mer peptides. Docking poses with peptide RMSD values <2.10 A from the equilibrated experimental structures were obtained in all cases. For four systems docking into the unbound receptor structures was performed, leading to peptide RMSD values <2.12 A. Using a specifically fitted scoring function in 11 of 15 cases the best scoring poses featured a peptide RMSD < or = 2.10 A.

Virtual Screening with AutoDock: Theory and Practice

IMPORTANCE TO THE FIELD: Virtual screening is a computer-based technique for identifying promising compounds to bind to a target molecule of known structure. Given the rapidly increasing number of protein and nucleic acid structures, virtual screening continues to grow as an effective method for the discovery of new inhibitors and drug molecules. AREAS COVERED IN THIS REVIEW: We describe virtual screening methods that are available in the AutoDock suite of programs, and several of our successes in using AutoDock virtual screening in pharmaceutical lead discovery. WHAT THE READER WILL GAIN: A general overview of the challenges of virtual screening is presented, along with the tools available in the AutoDock suite of programs for addressing these challenges. TAKE HOME MESSAGE: Virtual screening is an effective tool for the discovery of compounds for use as leads in drug discovery, and the free, open source program AutoDock is an effective tool for virtual screening.

AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique.

In Pursuit of Fully Flexible Protein-Ligand Docking: Modeling the Bilateral Mechanism of Binding

Modern structure-based drug design aims at accounting for the intrinsic flexibility of therapeutic relevant targets. Over the last few years a considerable amount of docking approaches that encounter this challenging problem has emerged. Here we provide the readership with an overview of established methods for fully flexible protein-ligand docking and current developments in the field. All methods are based on one of two fundamental models which describe the dynamic behavior of proteins upon ligand binding. Methods for ensemble docking (ED) model the protein conformational change before the ligand is placed, whereas induced-fit docking (IFD) optimizes the protein structure afterwards. A third category of docking approaches is formed by recent approaches that follow both concepts. This categorization allows to comprehensively discover strengths and weaknesses of the individual processes and to extract information for their applicability in real world docking scenarios.

Molecular docking screens using comparative models of proteins

Two orders of magnitude more protein sequences can be modeled by comparative modeling than have been determined by X-ray crystallography and NMR spectroscopy. Investigators have nevertheless been cautious about using comparative models for ligand discovery because of concerns about model errors. We suggest how to exploit comparative models for molecular screens, based on docking against a wide range of crystallographic structures and comparative models with known ligands. To account for the variation in the ligand-binding pocket as it binds different ligands, we calculate "consensus" enrichment by ranking each library compound by its best docking score against all available comparative models and/or modeling templates. For the majority of the targets, the consensus enrichment for multiple models was better than or comparable to that of the holo and apo X-ray structures. Even for single models, the models are significantly more enriching than the template structure if the template is paralogous and shares more than 25% sequence identity with the target.

Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock

BACKGROUND

Molecular docking methods are commonly used for predicting binding modes and energies of ligands to proteins. For accurate complex geometry and binding energy estimation, an appropriate method for calculating partial charges is essential. AutoDockTools software, the interface for preparing input files for one of the most widely used docking programs AutoDock 4, utilizes the Gasteiger partial charge calculation method for both protein and ligand charge calculation. However, it has already been shown that more accurate partial charge calculation - and as a consequence, more accurate docking- can be achieved by using quantum chemical methods. For docking calculations quantum chemical partial charge calculation as a routine was only used for ligands so far. The newly developed Mozyme function of MOPAC2009 allows fast partial charge calculation of proteins by quantum mechanical semi-empirical methods. Thus, in the current study, the effect of semi-empirical quantum-mechanical partial charge calculation on docking accuracy could be investigated.

RESULTS

The docking accuracy of AutoDock 4 using the original AutoDock scoring function was investigated on a set of 53 protein ligand complexes using Gasteiger and PM6 partial charge calculation methods. This has enabled us to compare the effect of the partial charge calculation method on docking accuracy utilizing AutoDock 4 software. Our results showed that the docking accuracy in regard to complex geometry (docking result defined as accurate when the RMSD of the first rank docking result complex is within 2 A of the experimentally determined X-ray structure) significantly increased when partial charges of the ligands and proteins were calculated with the semi-empirical PM6 method. Out of the 53 complexes analyzed in the course of our study, the geometry of 42 complexes were accurately calculated using PM6 partial charges, while the use of Gasteiger charges resulted in only 28 accurate geometries. The binding affinity estimation was not influenced by the partial charge calculation method - for more accurate binding affinity prediction development of a new scoring function for AutoDock is needed.

CONCLUSION

Our results demonstrate that the accuracy of determination of complex geometry using AutoDock 4 for docking calculation greatly increases with the use of quantum chemical partial charge calculation on both the ligands and proteins.

Computational multiscale modeling in protein--ligand docking

In biological systems, the binding of small molecule ligands to proteins is a crucial process for almost every aspect of biochemistry and molecular biology. Enzymes are proteins that function by catalyzing specific biochemical reactions that convert reactants into products. Complex organisms are typically composed of cells in which thousands of enzymes participate in complex and interconnected biochemical pathways. Some enzymes serve as sequential steps in specific pathways (such as energy metabolism), while others function to regulate entire pathways and cellular functions [1]. Small molecule ligands can be designed to bind to a specific enzyme and inhibit the biochemical reaction. Inhibiting the activity of key enzymes may result in the entire biochemical pathways being turned on or off [2], [3]. Many small molecule drugs marketed today function in this generic way as enzyme inhibitors. If research identifies a specific enzyme as being crucial to the progress of disease, then this enzyme may be targeted with an inhibitor, which may slow down or reverse the progress of disease. In this way, enzymes are targeted from specific pathogens (e.g., virus, bacteria, fungi) for infectious diseases [4], [5], and human enzymes are targeted for noninfectious diseases such as cardiovascular disease, cancer, diabetes, and neurodegenerative diseases [6].

Nucleotide's bilinear indices: novel bio-macromolecular descriptors for bioinformatics studies of nucleic acids. I. Prediction of paromomycin's affinity constant with HIV-1 Psi-RNA packaging region

A new set of nucleotide-based bio-macromolecular descriptors are presented. This novel approach to bio-macromolecular design from a linear algebra point of view is relevant to nucleic acids quantitative structure-activity relationship (QSAR) studies. These bio-macromolecular indices are based on the calculus of bilinear maps on Re(n)[b(mk)(x (m),y (m)):Re(n) x Re(n)-->Re] in canonical basis. Nucleic acid's bilinear indices are calculated from kth power of non-stochastic and stochastic nucleotide's graph-theoretic electronic-contact matrices, M(m)(k) and (s)M(m)(k), respectively. That is to say, the kth non-stochastic and stochastic nucleic acid's bilinear indices are calculated using M(m)(k) and (s)M(m)(k) as matrix operators of bilinear transformations. Moreover, biochemical information is codified by using different pair combinations of nucleotide-base properties as weightings (experimental molar absorption coefficient epsilon(260) at 260 nm and pH=7.0, first (Delta E(1)) and second (Delta E(2)) single excitation energies in eV, and first (f(1)) and second (f(2)) oscillator strength values (of the first singlet excitation energies) of the nucleotide DNA-RNA bases. As example of this approach, an interaction study of the antibiotic paromomycin with the packaging region of the HIV-1 Psi-RNA have been performed and it have been obtained several linear models in order to predict the interaction strength. The best linear model obtained by using non-stochastic bilinear indices explains about 91% of the variance of the experimental Log K (R=0.95 and s=0.08 x 10(-4)M(-1)) as long as the best stochastic bilinear indices-based equation account for 93% of the Log K variance (R=0.97 and s=0.07 x 10(-4)M(-1)). The leave-one-out (LOO) press statistics, evidenced high predictive ability of both models (q(2)=0.86 and s(cv)=0.09 x 10(-4)M(-1) for non-stochastic and q(2)=0.91 and s(cv)=0.08 x 10(-4)M(-1) for stochastic bilinear indices). The nucleic acid's bilinear indices-based models compared favorably with other nucleic acid's indices-based approaches reported nowadays. These models also permit the interpretation of the driving forces of the interaction process. In this sense, developed equations involve short-reaching (k<or=3), middle-reaching (4<k<9), and far-reaching (k=10 or greater) nucleotide's bilinear indices. This situation points to electronic and topologic nucleotide's backbone interactions control of the stability profile of paromomycin-RNA complexes. Consequently, the present approach represents a novel and rather promising way to theoretical-biology studies.

Structural features of mammalian histidine decarboxylase reveal the basis for specific inhibition

For a long time the structural and molecular features of mammalian histidine decarboxylase (EC 4.1.1.22), the enzyme that produces histamine, have evaded characterization. We overcome the experimental problems for the study of this enzyme by using a computer-based modelling and simulation approach, and have now the conditions to use histidine decarboxylase as a target in histamine pharmacology. In this review, we present the recent (last 5 years) advances in the structure-function relationship of histidine decarboxylase and the strategy for the discovery of new drugs.

Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation

Computational methods for docking ligands have been shown to be remarkably dependent on precise protein conformation, where acceptable results in pose prediction have been generally possible only in the artificial case of re-docking a ligand into a protein binding site whose conformation was determined in the presence of the same ligand (the "cognate" docking problem). In such cases, on well curated protein/ligand complexes, accurate dockings can be returned as top-scoring over 75% of the time using tools such as Surflex-Dock. A critical application of docking in modeling for lead optimization requires accurate pose prediction for novel ligands, ranging from simple synthetic analogs to very different molecular scaffolds. Typical results for widely used programs in the "cross-docking case" (making use of a single fixed protein conformation) have rates closer to 20% success. By making use of protein conformations from multiple complexes, Surflex-Dock yields an average success rate of 61% across eight pharmaceutically relevant targets. Following docking, protein pocket adaptation and rescoring identifies single pose families that are correct an average of 67% of the time. Consideration of the best of two pose families (from alternate scoring regimes) yields a 75% mean success rate.

Identification of xenoestrogens in food additives by an integrated in silico and in vitro approach

In the search for xenoestrogens within food additives, we have analyzed the Joint FAO-WHO expert committee database, containing 1500 compounds, using an integrated in silico and in vitro approach. This analysis identified 31 potential estrogen receptor alpha ligands that were reduced to 13 upon applying a stringent filter based on ligand volume and binding mode. Among the 13 potential xenoestrogens, four were already known to exhibit an estrogenic activity, and the other nine were assayed in vitro, determining the binding affinity to the receptor and biological effects. Propyl gallate was found to act as an antagonist, and 4-hexylresorcinol was found to act as a potent transactivator; both ligands were active at nanomolar concentrations, as predicted by the in silico analysis. Some caution should be issued for the use of propyl gallate and 4-hexylresorcinol as food additives.