Flexible protein–ligand docking by global energy optimization in internal coordinates

Characterization of Human DHRS6, an Orphan Short Chain Dehydrogenase/Reductase Enzyme

Human DHRS6 is a previously uncharacterized member of the short chain dehydrogenases/reductase family and displays significant homologies to bacterial hydroxybutyrate dehydrogenases. Substrate screening reveals sole NAD(+)-dependent conversion of (R)-hydroxybutyrate to acetoacetate with K(m) values of about 10 mm, consistent with plasma levels of circulating ketone bodies in situations of starvation or ketoacidosis. The structure of human DHRS6 was determined at a resolution of 1.8 A in complex with NAD(H) and reveals a tetrameric organization with a short chain dehydrogenases/reductase-typical folding pattern. A highly conserved triad of Arg residues ("triple R" motif consisting of Arg(144), Arg(188), and Arg(205)) was found to bind a sulfate molecule at the active site. Docking analysis of R-beta-hydroxybutyrate into the active site reveals an experimentally consistent model of substrate carboxylate binding and catalytically competent orientation. GFP reporter gene analysis reveals a cytosolic localization upon transfection into mammalian cells. These data establish DHRS6 as a novel, cytosolic type 2 (R)-hydroxybutyrate dehydrogenase, distinct from its well characterized mitochondrial type 1 counterpart. The properties determined for DHRS6 suggest a possible physiological role in cytosolic ketone body utilization, either as a secondary system for energy supply in starvation or to generate precursors for lipid and sterol synthesis.

PSI-DOCK: towards highly efficient and accurate flexible ligand docking

We have developed a new docking method, Pose-Sensitive Inclined (PSI)-DOCK, for flexible ligand docking. An improved SCORE function has been developed and used in PSI-DOCK for binding free energy evaluation. The improved SCORE function was able to reproduce the absolute binding free energies of a training set of 200 protein-ligand complexes with a correlation coefficient of 0.788 and a standard error of 8.13 kJ/mol. For ligand binding pose exploration, a unique searching strategy was designed in PSI-DOCK. In the first step, a tabu-enhanced genetic algorithm with a rapid shape-complementary scoring function is used to roughly explore and store potential binding poses of the ligand. Then, these predicted binding poses are optimized and compete against each other by using a genetic algorithm with the accurate SCORE function to determine the binding pose with the lowest docking energy. The PSI-DOCK 1.0 program is highly efficient in identifying the experimental binding pose. For a test dataset of 194 complexes, PSI-DOCK 1.0 achieved a 67% success rate (RMSD < 2.0 A) for only one run and a 74% success rate for 10 runs. PSI-DOCK can also predict the docking binding free energy with high accuracy. For a test set of 64 complexes, the correlation between the experimentally observed binding free energies and the docking binding free energies for 64 complexes is r = 0.777 with a standard deviation of 7.96 kJ/mol. Moreover, compared with other docking methods, PSI-DOCK 1.0 is extremely easy to use and requires minimum docking preparations. There is no requirement for the users to add hydrogen atoms to proteins because all protein hydrogen atoms and the flexibility of the terminal protein atoms are intrinsically taken into account in PSI-DOCK. There is also no requirement for the users to calculate partial atomic charges because PSI-DOCK does not calculate an electrostatic energy term. These features are not only convenient for the users but also help to avoid the influence of different preparation methods.

Virtual screening to enrich a compound collection with CDK2 inhibitors using docking, scoring, and composite scoring models

Docking programs can generate subsets of a compound collection with an increased percentage of actives against a target (enrichment) by predicting their binding mode (pose) and affinity (score), and retrieving those with the highest scores. Using the QXP and GOLD programs, we compared the ability of six single scoring functions (PLP, Ligscore, Ludi, Jain, ChemScore, PMF) and four composite scoring models (Mean Rank: MR, Rank-by-Vote: Vt, Bayesian Statistics: BS and PLS Discriminant Analysis: DA) to separate compounds that are active against CDK2 from inactives. We determined the enrichment for the entire set of actives (IC50 < 10 microM) and for three activity subsets. In all cases, the enrichment for each subset was lower than for the entire set of actives. QXP outperformed GOLD at pose prediction, but yielded only moderately better enrichments. Five to six scoring functions yielded good enrichments with GOLD poses, while typically only two worked well with QXP poses. For each program, two scoring functions generally performed better than the others (Ligscore2 and Ludi for GOLD; QXP and Jain for QXP). Composite scoring functions yielded better results than single scoring functions. The consensus approaches MR and Vt worked best when separating micromolar inhibitors from inactives. The statistical approaches BS and DA, which require training data, performed best when distinguishing between low and high nanomolar inhibitors. The key observation that all hit rate profiles for all four activity intervals for all scoring schemes for both programs are significantly better than random, is evidence that docking can be successfully applied to enrich compound collections.

The Kaposi's sarcoma-associated herpesvirus complement control protein (KCP) binds to heparin and cell surfaces via positively charged amino acids in CCP1-2

The Kaposi's sarcoma-associated herpesvirus (KSHV) complement control protein (KCP) inhibits the human complement system, and is similar in structure and function to endogenous complement inhibitors. Other inhibitors such as C4b-binding protein and factor H, as well as the viral homologue vaccinia virus complement control protein are known to bind heparin and, for the two latter, also to glycosaminoglycans at the surface of cells. We report here that KCP also binds to heparin at physiological ionic strength. With help of site directed mutagenesis, positively charged amino acids in the two N-terminal complement control protein (CCP) domains 1-2 were found to be necessary for heparin binding. In silico molecular docking of heparin to KCP confirmed the experimental data, and further explored the heparin binding site, enabling us to present a model of the KCP-heparin interaction. Furthermore, the docking analysis also yielded insights of the KCP structure, by indicating that the angle between CCP domains 1-2 during the initial binding of heparin is more extended than in the model we have previously presented. We also found that KCP binds to heparan sulfate and weakly to glycosaminoglycans at the surface of cells. This might indicate that KCP at the surface of viral particles aids in the primary attachment to the target cells, which is known to involve binding to heparan sulfate. Therefore, the present study contributes to the knowledge of heparin-protein interactions in general as well as to the understanding of the biology of KSHV.

Identification of 12Cysbeta on tubulin as the binding site of tubulyzine

We have undertaken quantitative binding site studies in order to identify the binding site of the known microtubule destabilizing agents, the tubulyzines, in the tubulin dimer. Two different approaches were employed that utilized the tubulyzines and their derivatives. The first approach was based on a chemical affinity labeling method using tubulyzine affinity derivatives, and the second approach employed the mass spectrometric measurement of the differential reactivity of cysteines using the tubulyzines and monobromobimane. Based on overlapping data from these two approaches, we propose that the tubulyzines bind at the guanosine-5'-triphosphate binding site of beta-tubulin. Interestingly, we also show that the tubulyzines' binding to tubulin induces a conformational change in tubulin that prevents further interaction of the 239Cysbeta with other reagents.

A Flexible Docking Procedure for the Exploration of Peptide Binding Selectivity to Known Structures and Homology Models of PDZ Domains

PDZ domains are important scaffolding modules that typically bind to the C-termini of their interaction partners. Several structures of such complexes have been solved, revealing a conserved binding site in the PDZ domain and an extended conformation of the bound peptide. A compendium of information regarding PDZ complexes demonstrates that dissimilar C-terminal peptides bind to the same PDZ domain, and different PDZ domains can bind the same peptides. A detailed understanding of the PDZ-peptide recognition is needed to elucidate this complexity. To this end, we have designed a family of docking protocols for PDZ domains (termed PDZ-DocScheme) that is based on simulated annealing molecular dynamics and rotamer optimization, and is applicable to the docking of long peptides (20-40 rotatable bonds) to both known PDZ structures and to the more complicated problem of homology models of these domains. The resulting protocol reproduces the structures of PDZ complexes with peptides 4-8 amino acids long within 1-2 A from the experimental structure when the docking is performed to the original structure. If the structure of the target PDZ domain is an apo structure or a homology model, the docking protocol yields structures within 3 A in 9 out of 12 test cases. The automated docking procedure PDZ-DocScheme can serve in the generation of a structural context for validation of PDZ domain specificity from mutagenesis and ligand binding data.

Structure based approach to the design of bicyclic-1H-isoindole-1,3(2H)-dione based androgen receptor antagonists

A novel series of isoindoledione based compounds were identified as potent antagonists of the androgen receptor (AR). Co-crystallization of members of this family of inhibitors was successfully accomplished with the T877A AR LBD. A working model of how this class of compounds functions to antagonize the AR was created. Based on this model, it was proposed that expanding the bicyclic portion of the molecule should result in analogs which function as effective antagonists against a variety of AR isoforms. In contrast to what was predicted by the model, SAR around this new series was dictated by the aniline portion rather than the bicyclic portion of the molecule.

Comparative evaluation of eight docking tools for docking and virtual screening accuracy

Eight docking programs (DOCK, FLEXX, FRED, GLIDE, GOLD, SLIDE, SURFLEX, and QXP) that can be used for either single-ligand docking or database screening have been compared for their propensity to recover the X-ray pose of 100 small-molecular-weight ligands, and for their capacity to discriminate known inhibitors of an enzyme (thymidine kinase) from randomly chosen "drug-like" molecules. Interestingly, both properties are found to be correlated, since the tools showing the best docking accuracy (GLIDE, GOLD, and SURFLEX) are also the most successful in ranking known inhibitors in a virtual screening experiment. Moreover, the current study pinpoints some physicochemical descriptors of either the ligand or its cognate protein-binding site that generally lead to docking/scoring inaccuracies.

The synthesis and evaluation of [2.2.1]-bicycloazahydantoins as androgen receptor antagonists

A novel series of [2.2.1]-azahydantoins has been designed and synthesized in an enantiospecific manner. The ability of these compounds to act as antagonists to the androgen receptor was investigated and several were found to have potent activity in vitro.

Pocketome via comprehensive identification and classification of ligand binding envelopes

We developed a new computational algorithm for the accurate identification of ligand binding envelopes rather than surface binding sites. We performed a large scale classification of the identified envelopes according to their shape and physicochemical properties. The predicting algorithm, called PocketFinder, uses a transformation of the Lennard-Jones potential calculated from a three-dimensional protein structure and does not require any knowledge about a potential ligand molecule. We validated this algorithm using two systematically collected data sets of ligand binding pockets from complexed (bound) and uncomplexed (apo) structures from the Protein Data Bank, 5616 and 11,510, respectively. As many as 96.8% of experimental binding sites were predicted at better than 50% overlap level. Furthermore 95.0% of the asserted sites from the apo receptors were predicted at the same level. We demonstrate that conformational differences between the apo and bound pockets do not dramatically affect the prediction results. The algorithm can be used to predict ligand binding pockets of uncharacterized protein structures, suggest new allosteric pockets, evaluate feasibility of protein-protein interaction inhibition, and prioritize molecular targets. Finally the data base of the known and predicted binding pockets for the human proteome structures, the human pocketome, was collected and classified. The pocketome can be used for rapid evaluation of possible binding partners of a given chemical compound.

Integrating virtual screening in lead discovery

Target- and ligand-based virtual screening have emerged as resource-saving techniques that have been successfully applied to identify novel chemotypes in biologically active molecules. Eight confirmed virtual screening hits have recently been described and are discussed in this review, with focus on the workflow. These are then evaluated in the light of pharmacokinetics prediction (e.g. Caco-2 permeability, cytochrome P450 inhibition and hERG binding). We anticipate problems for five of these hits (e.g. cardiac toxicity), which warrant further experiments. Future challenges include dynamic tautomer/protonation treatment for both ligands and targets and improved pre- and post- virtual screening filters.

A hybrid method of molecular dynamics and harmonic dynamics for docking of flexible ligand to flexible receptor

We have developed a new docking method to consider receptor flexibility, a hybrid method of molecular dynamics and harmonic dynamics. The global motions of the whole receptor were approximately introduced into those of the receptor in the docking simulation as harmonic dynamics. On the other hand, the local flexibility of the side chains was also considered by conventional molecular dynamics. We confirmed that this new method can reproduce the fluctuations of the whole receptor by making a comparison of the directions and amplitudes of the global fluctuations. Then this method was applied to the docking of HIV-1 protease and its ligand. As a result, we observed a docking process where the ligand enters into the binding pocket well, which implies that this method is effective enough to reproduce a molecular complex formation.

Assessment of Docking Poses: Interactions-Based Accuracy Classification (IBAC) versus Crystal Structure Deviations

Six docking programs (FlexX, GOLD, ICM, LigandFit, the Northwestern University version of DOCK, and QXP) were evaluated in terms of their ability to reproduce experimentally observed binding modes (poses) of small-molecule ligands to macromolecular targets. The accuracy of a pose was assessed in two ways: First, the RMS deviation of the predicted pose from the crystal structure was calculated. Second, the predicted pose was compared to the experimentally observed one regarding the presence of key interactions with the protein. The latter assessment is referred to as interactions-based accuracy classification (IBAC). In a number of cases significant discrepancies were found between IBAC and RMSD-based classifications. Despite being more subjective, the IBAC proved to be a more meaningful measure of docking accuracy in all these cases.

A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance

A thorough evaluation of some of the most advanced docking and scoring methods currently available is described, and guidelines for the choice of an appropriate protocol for docking and virtual screening are defined. The generation of a large and highly curated test set of pharmaceutically relevant protein-ligand complexes with known binding affinities is described, and three highly regarded docking programs (Glide, GOLD, and ICM) are evaluated on the same set with respect to their ability to reproduce crystallographic binding orientations. Glide correctly identified the crystallographic pose within 2.0 A in 61% of the cases, versus 48% for GOLD and 45% for ICM. In general Glide appears to perform most consistently with respect to diversity of binding sites and ligand flexibility, while the performance of ICM and GOLD is more binding site-dependent and it is significantly poorer when binding is predominantly driven by hydrophobic interactions. The results also show that energy minimization and reranking of the top N poses can be an effective means to overcome some of the limitations of a given docking function. The same docking programs are evaluated in conjunction with three different scoring functions for their ability to discriminate actives from inactives in virtual screening. The evaluation, performed on three different systems (HIV-1 protease, IMPDH, and p38 MAP kinase), confirms that the relative performance of different docking and scoring methods is to some extent binding site-dependent. GlideScore appears to be an effective scoring function for database screening, with consistent performance across several types of binding sites, while ChemScore appears to be most useful in sterically demanding sites since it is more forgiving of repulsive interactions. Energy minimization of docked poses can significantly improve the enrichments in systems with sterically demanding binding sites. Overall Glide appears to be a safe general choice for docking, while the choice of the best scoring tool remains to a larger extent system-dependent and should be evaluated on a case-by-case basis.

Testing a Flexible-receptor Docking Algorithm in a Model Binding Site

Sampling receptor flexibility is challenging for database docking. We consider a method that treats multiple flexible regions of the binding site independently, recombining them to generate different discrete conformations. This algorithm scales linearly rather than exponentially with the receptor's degrees of freedom. The method was first evaluated for its ability to identify known ligands of a hydrophobic cavity mutant of T4 lysozyme (L99A). Some 200000 molecules of the Available Chemical Directory (ACD) were docked against an ensemble of cavity conformations. Surprisingly, the enrichment of known ligands from among a much larger number of decoys in the ACD was worse than simply docking to the apo conformation alone. Large decoys, accommodated in the larger cavity conformations sampled in the ensemble, were ranked better than known small ligands. The calculation was redone with an energy correction term that considered the cost of forming the larger cavity conformations. Enrichment improved, as did the balance between high-ranking large and small ligands. In a second retrospective test, the ACD was docked against a conformational ensemble of thymidylate synthase. Compared to docking against individual enzyme conformations, the flexible receptor docking approach improved enrichment of known ligands. Including a receptor conformational energy weighting term improved enrichment further. To test the method prospectively, the ACD database was docked against another cavity mutant of lysozyme (L99A/M102Q). A total of 18 new compounds predicted to bind this polar cavity and to change its conformation were tested experimentally; 14 were found to bind. The bound structures for seven ligands were determined by X-ray crystallography. The predicted geometries of these ligands all corresponded to the observed geometries to within 0.7A RMSD or better. Significant conformational changes of the cavity were observed in all seven complexes. In five structures, part of the observed accommodations were correctly predicted; in two structures, the receptor conformational changes were unanticipated and thus never sampled. These results suggest that although sampling receptor flexibility can lead to novel ligands that would have been missed when docking a rigid structure, it is also important to consider receptor conformational energy.

Identification of Protein–Protein Interaction Sites from Docking Energy Landscapes

Protein recognition is one of the most challenging and intriguing problems in structural biology. Despite all the available structural, sequence and biophysical information about protein-protein complexes, the physico-chemical patterns, if any, that make a protein surface likely to be involved in protein-protein interactions, remain elusive. Here, we apply protein docking simulations and analysis of the interaction energy landscapes to identify protein-protein interaction sites. The new protocol for global docking based on multi-start global energy optimization of an all-atom model of the ligand, with detailed receptor potentials and atomic solvation parameters optimized in a training set of 24 complexes, explores the conformational space around the whole receptor without restrictions. The ensembles of the rigid-body docking solutions generated by the simulations were subsequently used to project the docking energy landscapes onto the protein surfaces. We found that highly populated low-energy regions consistently corresponded to actual binding sites. The procedure was validated on a test set of 21 known protein-protein complexes not used in the training set. As much as 81% of the predicted high-propensity patch residues were located correctly in the native interfaces. This approach can guide the design of mutations on the surfaces of proteins, provide geometrical details of a possible interaction, and help to annotate protein surfaces in structural proteomics.

Molecular recognition and docking algorithms

Molecular docking is an invaluable tool in modern drug discovery. This review focuses on methodological developments relevant to the field of molecular docking. The forces important in molecular recognition are reviewed and followed by a discussion of how different scoring functions account for these forces. More recent applications of computational chemistry tools involve library design and database screening. Last, we summarize several critical methodological issues that must be addressed in future developments.

ICM-DISCO docking by global energy optimization with fully flexible side-chains

The ICM-DISCO (Docking and Interface Side-Chain Optimization) protein-protein-docking method is a direct stochastic global energy optimization from multiple starting positions of the ligand. The first step is performed by docking of a rigid all-atom ligand molecule to a set of soft receptor potentials precalculated on a 0.5 A grid from realistic solvent-corrected force-field energies. This step finds the correct solution as the lowest energy conformation in almost 100% of the cases in which interfaces do not change on binding. The second step is needed to deal with the induced changes and includes the global optimization of the interface side-chains of up to 400 best solutions. The CAPRI predictions were performed fully automatically with this method. Available experimental information was included as a filtering step to favor expected docking surfaces. In three of the seven proposed targets, the ICM-DISCO method found a good solution (>50% of correct contacts) within the five submitted models. The procedure is global and fully automated. We demonstrate that the algorithm handles the induced changes of surface side-chains but is less successful if the backbone undergoes large-scale rearrangements.

Discovery of diverse thyroid hormone receptor antagonists by high-throughput docking

Treatment of hyperthyroidism, a common clinical condition that can have serious manifestations in the elderly, has remained essentially unchanged for >30 years. Directly antagonizing the effect of the thyroid hormone at the receptor level may be a significant improvement for the treatment of hyperthyroid patients. We built a computer model of the thyroid hormone receptor (TR) ligand-binding domain in its predicted antagonist-bound conformation and used a virtual screening algorithm to select 100 TR antagonist candidates out of a library of >250,000 compounds. We were able to obtain 75 of the compounds selected in silico and studied their ability to act as antagonists by using cultured cells that express TR. Fourteen of these compounds were found to antagonize the effect of T3 on TR with IC50s ranging from 1.5 to 30 microM. A small virtual library of compounds, derived from the highest affinity antagonist (1-850) that could be rapidly synthesized, was generated. A second round of virtual screening identified new compounds with predicted increased antagonist activity. These second generation compounds were synthesized, and their ability to act as TR antagonists was confirmed by transfection and receptor binding experiments. The extreme structural diversity of the antagonist compounds shows how receptor-based virtual screening can identify diverse chemistries that comply with the structural rules of TR antagonism.

ICFF: a new method to incorporate implicit flexibility into an internal coordinate force field

We introduce a new method to accurately "project" a Cartesian force field onto an internal coordinate molecular model with fixed-bond geometry. The algorithm automatically generates the Internal Coordinate Force Field (ICFF), which is a close approximation of the "source" Cartesian force field. The ICFF method reduces the number of free variables in a model by at least 10-fold and facilitates the fast convergence of geometry optimizations, an advantage that is critical for many applications such as the docking of flexible ligands or conformational modeling of macromolecules. Although covalent geometry is fixed in an ICFF model, implicit flexibility is incorporated into the force field parameters in the following two ways. First, we formulate an empirical torsion energy term in ICFF as a sixfold Fourier series and develop a procedure to calculate the Fourier coefficients from the conformational energy profiles of the fully flexible Cartesian model. The ICFF torsion parameters thus represent not only torsion component of the source force field, but also bond bending, bond stretching, and "1-4" van der Waals interactions. Second, we use a soft polynomial repulsion function for "1-5" and "1-6" interactions to mimic the flexibility of bonds, connecting these atoms. Also, we suggest a way to use a local part of the Cartesian force field to automatically generate fixed covalent geometries, compatible with the ICFF energy function. Here, we present an implementation of the ICFF algorithm, which employs the MMFF94s Cartesian force field as a "source." Extensive benchmarking of ICFF with a representative set of organic molecules demonstrates that the implicit flexibility model accurately reproduces MMFF94s equilibrium conformational energy differences (RMSD approximately 0.64 kcal) and, most importantly, detailed torsion energy profiles (RMSD approximately 0.37 kcal). This accuracy is characteristic of the method, because all the ICFF parameters (except one scaling factor in the "1-5,1-6" repulsion term) are derived directly from the source Cartesian force field and do not depend on any particular molecular set. In contrast, the rigid geometry model with the MMFF94s energy function yields highly biased estimations in this test with the RMSD exceeding 1.2 kcal for the equilibrium energy comparisons and approximately 3.4 kcal for the torsion energy profiles.

Principles of docking: An overview of search algorithms and a guide to scoring functions

The docking field has come of age. The time is ripe to present the principles of docking, reviewing the current state of the field. Two reasons are largely responsible for the maturity of the computational docking area. First, the early optimism that the very presence of the "correct" native conformation within the list of predicted docked conformations signals a near solution to the docking problem, has been replaced by the stark realization of the extreme difficulty of the next scoring/ranking step. Second, in the last couple of years more realistic approaches to handling molecular flexibility in docking schemes have emerged. As in folding, these derive from concepts abstracted from statistical mechanics, namely, populations. Docking and folding are interrelated. From the purely physical standpoint, binding and folding are analogous processes, with similar underlying principles. Computationally, the tools developed for docking will be tremendously useful for folding. For large, multidomain proteins, domain docking is probably the only rational way, mimicking the hierarchical nature of protein folding. The complexity of the problem is huge. Here we divide the computational docking problem into its two separate components. As in folding, solving the docking problem involves efficient search (and matching) algorithms, which cover the relevant conformational space, and selective scoring functions, which are both efficient and effectively discriminate between native and non-native solutions. It is universally recognized that docking of drugs is immensely important. However, protein-protein docking is equally so, relating to recognition, cellular pathways, and macromolecular assemblies. Proteins function when they are bound to other molecules. Consequently, we present the review from both the computational and the biological points of view. Although large, it covers only partially the extensive body of literature, relating to small (drug) and to large protein-protein molecule docking, to rigid and to flexible. Unfortunately, when reviewing these, a major difficulty in assessing the results is the non-uniformity in the formats in which they are presented in the literature. Consequently, we further propose a way to rectify it here.

ConsDock: A new program for the consensus analysis of protein-ligand interactions

Protein-based virtual screening of chemical libraries is a powerful technique for identifying new molecules that may interact with a macromolecular target of interest. Because of docking and scoring limitations, it is more difficult to apply as a lead optimization method because it requires that the docking/scoring tool is able to propose as few solutions as possible and all of them with a very good accuracy for both the protein-bound orientation and the conformation of the ligand. In the present study, we present a consensus docking approach (ConsDock) that takes advantage of three widely used docking tools (Dock, FlexX, and Gold). The consensus analysis of all possible poses generated by several docking tools is performed sequentially in four steps: (i) hierarchical clustering of all poses generated by a docking tool into families represented by a leader; (ii) definition of all consensus pairs from leaders generated by different docking programs; (iii) clustering of consensus pairs into classes, represented by a mean structure; and (iv) ranking the different means starting from the most populated class of consensus pairs. When applied to a test set of 100 protein-ligand complexes from the Protein Data Bank, ConsDock significantly outperforms single docking with respect to the docking accuracy of the top-ranked pose. In 60% of the cases investigated here, ConsDock was able to rank as top solution a pose within 2 A RMSD of the X-ray structure. It can be applied as a postprocessing filter to either single- or multiple-docking programs to prioritize three-dimensional guided lead optimization from the most likely docking solution.

Identification and mapping of small-molecule binding sites in proteins: computational tools for structure-based drug design

The number of protein structures is currently increasing at an impressive rate. The growing wealth of data calls for methods to efficiently exploit structural information for medicinal and pharmaceutical purposes. Given the three-dimensional (3D) structure of a validated protein target, the identification of functionally relevant binding sites and the analysis ('mapping') of these sites with respect to molecular recognition properties are important initial tasks in structure-based drug design. To address these tasks, a variety of computational tools have been developed. Approaches to identify binding pockets include geometric analyses of protein surfaces, comparisons of protein structures, similarity searches in databases of protein cavities, and docking scans to reveal areas of high ligand complementarity. In the context of binding-site analysis, powerful data mining tools help to retrieve experimental information about related protein-ligand complexes. To identify interaction hot spots, various potential functions and knowledge-based approaches are available for mapping binding regions. The results may subsequently be used to guide virtual screenings for new ligands via pharmacophore searches or docking simulations.

Human type 10 17 beta-hydroxysteroid dehydrogenase: molecular modelling and substrate docking

17 beta-hydroxysteroid dehydrogenases catalyze the oxidoreduction of hydroxy/oxo groups at position C17 of steroid hormones, thereby constituting a prereceptor control mechanism of hormone action. At present, 11 different mammalian 17 beta-hydroxysteroid dehydrogenases have been identified, catalyzing the cell- and steroid-specific activation and inactivation of estrogens and androgens. The human type 10 17 beta-hydroxysteroid dehydrogenase (17 beta-HSD-10) is a multifunctional mitochondrial enzyme that efficiently catalyzes the oxidative inactivation at C17 of androgens and estrogens. However, it also mediates oxidation of 3 alpha-hydroxy groups of androgens, thereby reactivating androgen metabolites. Finally, it is involved in beta-oxidation of fatty acids by catalyzing the L-hydroxyacyl CoA dehydrogenase reaction of the beta-oxidation cycle. These features and expression profiles suggest a critical role of 17 beta-HSD-10 in neurodegenerative and steroid-dependent cancer forms. Since no three-dimensional structure of 17 beta-HSD-10 is available, homology modelling was carried out to understand the molecular basis of these substrate specificities. The structure obtained displays the properties of a one-domain, alpha/beta fold enzyme of the SDR family. The active site is located within a large, hydrophobic cleft, which forms optimal contacts with the different steroid surfaces. The data provide explanations for the substrate specificities toward the various classes of sex steroid hormones. The model is suitable to explore substrate and inhibitor characteristics that may be used in the development of novel strategies in the treatment of degenerative or malignant diseases.

Soft protein-protein docking in internal coordinates

The association of two biological macromolecules is a fundamental biological phenomenon and an unsolved theoretical problem. Docking methods for ab initio prediction of association of two independently determined protein structures usually fail when they are applied to a large set of complexes, mostly because of inaccuracies in the scoring function and/or difficulties on simulating the rearrangement of the interface residues on binding. In this work we present an efficient pseudo-Brownian rigid-body docking procedure followed by Biased Probability Monte Carlo Minimization of the ligand interacting side-chains. The use of a soft interaction energy function precalculated on a grid, instead of the explicit energy, drastically increased the speed of the procedure. The method was tested on a benchmark of 24 protein-protein complexes in which the three-dimensional structures of their subunits (bound and free) were available. The rank of the near-native conformation in a list of candidate docking solutions was <20 in 85% of complexes with no major backbone motion on binding. Among them, as many as 7 out of 11 (64%) protease-inhibitor complexes can be successfully predicted as the highest rank conformations. The presented method can be further refined to include the binding site predictions and applied to the structures generated by the structural proteomics projects. All scripts are available on the Web.

Protein-protein association kinetics and protein docking

Rigid body protein docking methods frequently yield false positive structures that have good surface complementarity, but are far from the native complex. The main reason for this is the uncertainty of the protein structures to be docked, including the positions of solvent-exposed sidechains. Substantial efforts have been devoted to finding near-native structures by rescoring the docked conformations and employing various filters. An alternative approach emulates the process of protein-protein association, that is, first finding the region in which binding is likely to occur and then refining the complex while allowing for flexibility.

Aminoglycoside binding in the major groove of duplex RNA: the thermodynamic and electrostatic forces that govern recognition

We use a combination of spectroscopic, calorimetric, viscometric and computer modeling techniques to characterize the binding of the aminoglycoside antibiotic, tobramycin, to the polymeric RNA duplex, poly(rI).poly(rC), which exhibits the characteristic A-type conformation that is conserved among natural and synthetic double-helical RNA sequences. Our results reveal the following significant features: (i) CD-detected binding of tobramycin to poly(rI).poly(rC) reveals an apparent site size of four base-pairs per bound drug molecule; (ii) tobramycin binding enhances the thermal stability of the host poly(rI).poly(rC) duplex, the extent of which decreases upon increasing in Na(+) concentration and/or pH conditions; (iii) the enthalpy of tobramycin- poly(rI).poly(rC) complexation increases with increasing pH conditions, an observation consistent with binding-induced protonation of one or more drug amino groups; (iv) the affinity of tobramycin for poly(rI).poly(rC) is sensitive to both pH and Na(+) concentration, with increases in pH and/or Na(+) concentration resulting in a concomitant reduction in binding affinity. The salt dependence of the tobramycin binding affinity reveals that the drug binds to the host RNA duplex as trication. (v) The thermodynamic driving force for tobramycin- poly(rI).poly(rC) complexation depends on pH conditions. Specifically, at pH< or =6.0, tobramycin binding is entropy driven, but is enthalpy driven at pH > 6.0. (vi) Viscometric data reveal non-intercalative binding properties when tobramycin complexes with poly(rI).poly(rC), consistent with a major groove-directed mode of binding. These data also are consistent with a binding-induced reduction in the apparent molecular length of the host RNA duplex. (vii) Computer modeling studies reveal a tobramycin-poly(rI). poly(rC) complex in which the drug fits snugly at the base of the RNA major groove and is stabilized, at least in part, by an array of hydrogen bonding interactions with both base and backbone atoms of the host RNA. These studies also demonstrate an inability of tobramycin to form a stable low-energy complex with the minor groove of the poly(rI).poly(rC) duplex. In the aggregate, our results suggest that tobramycin-RNA recognition is dictated and controlled by a broad range of factors that include electrostatic interactions, hydrogen bonding interactions, drug protonation reactions, and binding-induced alterations in the structure of the host RNA. These modulatory effects on tobramycin-RNA complexation are discussed in terms of their potential importance for the selective recognition of specific RNA structural motifs, such as asymmetric internal loops or hairpin loop-stem junctions, by aminoglycoside antibiotics and their derivatives.

Soft docking an L and a D peptide to an anticholera toxin antibody using internal coordinate mechanics

BACKGROUND

The tremendous increase in sequential and structural information is a challenge for computer-assisted modelling to predict the binding modes of interacting biomolecules. One important area is the structural understanding of protein-peptide interactions, information that is increasingly important for the design of biologically active compounds.

RESULTS

We predicted the three-dimensional structure of a complex between the monoclonal antibody TE33 and its cholera-toxin-derived peptide epitope VPGSQHID. Using the internal coordinate mechanics (ICM) method of flexible docking, the bound conformation of the initially extended peptide epitope to the antibody crystal or modelled structure reproduced the known binding conformation to a root mean square deviation of between 1.9 A and 3.1 A. The predicted complexes are in good agreement with binding data obtained from substitutional analyses in which each epitope residue is replaced by all other amino acids. Furthermore, a de novo prediction of the recently discovered TE33-binding D peptide dwGsqhydp (single-letter amino acid code where D amino acids are represented by lower-case letters) explains results obtained from binding studies with 172 peptide analogues.

CONCLUSIONS

Despite the difficulties arising from the huge conformational space of a peptide, this approach allowed the prediction of the correct binding orientation and the majority of essential binding features of a peptide-antibody complex.

How and why phosphotyrosine-containing peptides bind to the SH2 and PTB domains

BACKGROUND

Specific recognition of phosphotyrosine-containing protein segments by Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains plays an important role in intracellular signal transduction. Although many SH2/PTB-domain-containing receptor-peptide complex structures have been solved, little has been done to study the problem computationally. Prediction of the binding geometry and the binding constant of any peptide-protein pair is an extremely important problem.

RESULTS

A procedure to predict binding energies of phosphotyrosine-containing peptides with SH2/PTB domains was developed. The average deviation between experimentally measured binding energies and theoretical evaluations was 1.8 kcal/mol. Binding states of unphosphorylated peptides were also predicted reasonably well. Ab initio predictions of binding geometry of fully flexible peptides correctly identified conformations of two pentapeptides and a hexapeptide complexed with a v-Src SH2 domain receptor with root mean square deviations (rmsds) of 0.3 A, 1.2 A and 1.5 A, respectively.

CONCLUSIONS

The binding energies of phosphotyrosine-containing complexes can be effectively predicted using the procedure developed here. It was also possible to predict the bound conformations of flexible short peptides correctly from random starting conformations.

Predicting structural effects in HIV-1 protease mutant complexes with flexible ligand docking and protein side-chain optimization

We present a computational approach for predicting structures of ligand-protein complexes and analyzing binding energy landscapes that combines Monte Carlo simulated annealing technique to determine the ligand bound conformation with the dead-end elimination algorithm for side-chain optimization of the protein active site residues. Flexible ligand docking and optimization of mobile protein side-chains have been performed to predict structural effects in the V32I/I47V/V82I HIV-1 protease mutant bound with the SB203386 ligand and in the V82A HIV-1 protease mutant bound with the A77003 ligand. The computational structure predictions are consistent with the crystal structures of these ligand-protein complexes. The emerging relationships between ligand docking and side-chain optimization of the active site residues are rationalized based on the analysis of the ligand-protein binding energy landscape.

Development of filter functions for protein-ligand docking

Current docking methods can generate bound conformations of a ligand close to the experimentally observed structure of a protein-ligand complex. However, the scoring functions used to evaluate the potential solutions are not yet reliable enough at giving the highest ranks to the best structure predictions. One approach to this problem is the use of filter functions that are applied to all docked conformations to remove structures with certain energetically unfavorable properties. We present a computationally efficient scheme for such a postprocessing of docking results. For each of the conformations generated for a given protein-ligand complex, four properties are calculated: the fraction of the ligand volume buried inside the binding pocket, the size of lipophilic cavities along the protein-ligand interface, the solvent-accessible surface (SAS) of nonpolar parts of the ligand, and the number of close contacts between nonhydrogen-bonded polar atoms of the ligand and the protein. These four terms were used to filter out the majority of the calculated solutions and to rescore the remaining ones. On a test set of 32 protein-ligand complexes, this protocol significantly improves the accuracy of the structure predictions.