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

Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking

Many available methods aimed at incorporating the receptor flexibility in ligand docking are computationally expensive, require a high level of user intervention, and were tested only on benchmarks of limited size and diversity. Here we describe the four-dimensional (4D) docking approach that allows seamless incorporation of receptor conformational ensembles in a single docking simulation and reduces the sampling time while preserving the accuracy of traditional ensemble docking. The approach was tested on a benchmark of 99 therapeutically relevant proteins and 300 diverse ligands (half of them experimental or marketed drugs). The conformational variability of the binding pockets was represented by the available crystallographic data, with the total of 1113 receptor structures. The 4D docking method reproduced the correct ligand binding geometry in 77.3% of the benchmark cases, matching the success rate of the traditional approach but employed on average only one-fourth of the time during the ligand sampling phase.

RosettaLigand docking with full ligand and receptor flexibility

Computational docking of small-molecule ligands into protein receptors is an important tool for modern drug discovery. Although conformational adjustments are frequently observed between the free and ligand-bound states, the conformational flexibility of the protein is typically ignored in protein-small molecule docking programs. We previously described the program RosettaLigand, which leverages the Rosetta energy function and side-chain repacking algorithm to account for flexibility of all side chains in the binding site. Here we present extensions to RosettaLigand that incorporate full ligand flexibility as well as receptor backbone flexibility. Including receptor backbone flexibility is found to produce more correct docked complexes and to lower the average RMSD of the best-scoring docked poses relative to the rigid-backbone results. On a challenging set of retrospective and prospective cross-docking tests, we find that the top-scoring ligand pose is correctly positioned within 2 A RMSD for 64% (54/85) of cases overall.

Exploiting ordered waters in molecular docking

A current weakness in docking is the treatment of water-mediated protein-ligand interactions. We explore switching ordered water molecules "on" and "off" during docking screens of a large library. The method assumes additivity and scales linearly with the number of waters sampled despite the exponential growth in configurations. It is tested for ligand enrichment against 24 targets, exploring up to 256 water configurations. Water inclusion increased enrichment substantially for 12 targets, while most others were largely unaffected.

Active site pressurization: a new tool for structure-guided drug design and other studies of protein flexibility

We present a new molecular dynamics methodology to assist in structure-based drug design and other studies that seek to predict protein deformability. Termed Active Site Pressurization (ASP), the new methodology simply injects a resin into the ligand binding-site of a protein during the course of a molecular dynamics simulation such that novel, energetically reasonable protein conformations are generated in an unbiased way that may be better representations of the ligand binding conformation than are currently available. Here we apply two different versions of the ASP methodology to three proteins, cytochrome P450cam, PcrA helicase, and glycogen synthase kinase 3beta (GSK3beta), and show that the method is capable of inducing significant conformational changes when compared to the X-ray crystal structures. Application of the ASP methodology therefore provides a view of binding site flexibility that is a rich source of data for inclusion in a variety of further investigations, including high-throughput virtual screening, lead hopping, revealing alternative modes of deformation, and revealing hidden exit and entrance tunnels.

Structural glycobiology: a game of snakes and ladders

Oligo- and polysaccharides are infamous for being extremely flexible molecules, populating a series of well-defined rotational isomeric states under physiological conditions. Characterization of this heterogeneous conformational ensemble has been a major obstacle impeding high-resolution structure determination of carbohydrates and acting as a bottleneck in the effort to understand the relationship between the carbohydrate structure and function. This challenge has compelled the field to develop and apply theoretical and experimental methods that can explore conformational ensembles by both capturing and deconvoluting the structural and dynamic properties of carbohydrates. This review focuses on computational approaches that have been successfully used in combination with experiment to detail the three-dimensional structure of carbohydrates in a solution and in a complex with proteins. In addition, emerging experimental techniques for three-dimensional structural characterization of carbohydrate-protein complexes and future challenges in the field of structural glycobiology are discussed. The review is divided into five sections: (1) The complexity and plasticity of carbohydrates, (2) Predicting carbohydrate-protein interactions, (3) Calculating relative and absolute binding free energies for carbohydrate-protein complexes, (4) Emerging and evolving techniques for experimental characterization of carbohydrate-protein structures, and (5) Current challenges in structural glycoscience.

Docking to RNA via root-mean-square-deviation-driven energy minimization with flexible ligands and flexible targets

Structure-based drug design is now well-established for proteins as a key first step in the lengthy process of developing new drugs. In many ways, RNA may be a better target to treat disease than a protein because it is upstream in the translation pathway, so inhibiting a single mRNA molecule could prevent the production of thousands of protein gene products. Virtual screening is often the starting point for structure-based drug design. However, computational docking of a small molecule to RNA seems to be more challenging than that to protein due to the higher intrinsic flexibility and highly charged structure of RNA. Previous attempts at docking to RNA showed the need for a new approach. We present here a novel algorithm using molecular simulation techniques to account for both nucleic acid and ligand flexibility. In this approach, with both the ligand and the receptor permitted some flexibility, they can bind one another via an induced fit, as the flexible ligand probes the surface of the receptor. A possible ligand can explore a low-energy path at the surface of the receptor by carrying out energy minimization with root-mean-square-distance constraints. Our procedure was tested on 57 RNA complexes (33 crystal and 24 NMR structures); this is the largest data set to date to reproduce experimental RNA binding poses. With our procedure, the lowest-energy conformations reproduced the experimental binding poses within an atomic root-mean-square deviation of 2.5 A for 74% of tested complexes.

Computational chemistry approaches to drug discovery in signal transduction

The advent of therapeutic strategies aimed at targeting specific macromolecular components of deregulated signaling pathways associated with particular disease states has given rise to the idea that it should be possible to design ligands as drug candidates to these targets from first principles. This concept has been beckoning for a long time but structure-based ligand design only became feasible once it was possible to determine the 3-D structures of molecular targets at atomic resolution. However, structure-based design turned out to be difficult, chiefly because under physiological conditions both receptors and ligands are not static but they behave dynamically. While it is possible to design ligands with high steric and electronic complementarity to a receptor site, it is always uncertain how biologically relevant the assumed conformations of both ligand and receptor actually are. The fact that it remains beyond our current abilities to predict with sufficient accuracy the affinity between hypothetical ligand and receptor poses is in part connected with this problem and continues to confound the reliable prediction of drug-like ligands for therapeutic targets. Nevertheless, significant progress has been made and so-called virtual screening methods that use computational methods to dock candidate ligands into receptor sites and to score the resulting complexes are now used routinely as one of the components in drug discovery screening campaigns. Here an overview is given of the underlying principles, implementations, and applications of structure-guided computational design technologies. Although the emphasis is on receptor-based strategies, mention will also be made of some of the more established ligand-based approaches, such as similarity analyses and quantitative structure-activity relationship methods.

The effect of different electrostatic potentials on docking accuracy: a case study using DOCK5.4

As a commonly used structure-based approach for virtual screening, molecular design and lead optimization, molecular docking can search the preferred orientation and conformation of a ligand for its optimal binding to a receptor or enzyme active site. In doing so, selecting an appropriate method to calculate the electrostatic potentials is critical. In the current report, nine different semi-empirical and empirical methods, including AM1, AM1-BCC, Del-Re, MMFF, Gasteiger, Hückel, Gasteiger-Hückel, Pullman and formal charges were investigated for their performance on the prediction of docking poses using the DOCK5.4 program. The results demonstrated that the AM1-BCC charges had the highest success rate.

Molecular dynamics simulations of ligand-induced backbone conformational changes in the binding site of the periplasmic lysine-, arginine-, ornithine-binding protein

The periplasmic lysine-, arginine-, ornithine-binding protein (LAOBP) traps its ligands by a large hinge bending movement between two globular domains. The overall geometry of the binding site remains largely unchanged between the open (unliganded) and closed (liganded) forms, with only a small number of residues exhibiting limited movement of their side chains. However, in the case of the ornithine-bound structure, the backbone peptide bond between Asp11 and Thr12 undergoes a large rotation. Molecular dynamics simulations have been used to investigate the origin and mechanism of this backbone movement. Simulations allowing flexibility of a limited region and of the whole binding site, with and without bound ligands, suggest that this conformational change is induced by the binding of ornithine, leading to the stabilisation of an energetically favourable alternative conformation.

Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go

Accelerating the drug discovery process requires predictive computational protocols capable of reducing or simplifying the synthetic and/or combinatorial challenge. Docking-based virtual screening methods have been developed and successfully applied to a number of pharmaceutical targets. In this review, we first present the current status of docking and scoring methods, with exhaustive lists of these. We next discuss reported comparative studies, outlining criteria for their interpretation. In the final section, we describe some of the remaining developments that would potentially lead to a universally applicable docking/scoring method.

Flexible ligand docking to multiple receptor conformations: a practical alternative

State of the art docking algorithms predict an incorrect binding pose for about 50-70% of all ligands when only a single fixed receptor conformation is considered. In many more cases, lack of receptor flexibility results in meaningless ligand binding scores, even when the correct pose is obtained. Incorporating conformational rearrangements of the receptor binding pocket into predictions of both ligand binding pose and binding score is crucial for improving structure-based drug design and virtual ligand screening methodologies. However, direct modeling of protein binding site flexibility remains challenging because of the large conformational space that must be sampled, and difficulties remain in constructing a suitably accurate energy function. Here we show that using multiple fixed receptor conformations, either experimentally determined by crystallography or NMR, or computationally generated, is a practical shortcut that may improve docking calculations. In several cases, such an approach has led to experimentally validated predictions.

A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE)

Protein binding sites undergo ligand specific conformational changes upon ligand binding. However, most docking protocols rely on a fixed conformation of the receptor, or on the prior knowledge of multiple conformations representing the variation of the pocket, or on a known bounding box for the ligand. Here we described a general induced fit docking protocol that requires only one initial pocket conformation and identifies most of the correct ligand positions as the lowest score. We expanded a previously used diverse "cross-docking" benchmark to thirty ligand-protein pairs extracted from different crystal structures. The algorithm systematically scans pairs of neighbouring side chains, replaces them by alanines, and docks the ligand to each 'gapped' version of the pocket. All docked positions are scored, refined with original side chains and flexible backbone and re-scored. In the optimal version of the protocol pairs of residues were replaced by alanines and only one best scoring conformation was selected from each 'gapped' pocket for refinement. The optimal SCARE (SCan Alanines and REfine) protocol identifies a near native conformation (under 2 angstroms RMSD) as the lowest rank for 80% of pairs if the docking bounding box is defined by the predicted pocket envelope, and for as many as 90% of the pairs if the bounding box is derived from the known answer with approximately 5 angstroms margin as used in most previous publications. The presented fully automated algorithm takes about 2 h per pose of a single processor time, requires only one pocket structure and no prior knowledge about the binding site location. Furthermore, the results for conformationally conserved pockets do not deteriorate due to substantial increase of the pocket variability.

Ligand-protein docking with water molecules

The presence of water molecules plays an important role in the accuracy of ligand-protein docking predictions. Comprehensive docking simulations have been performed on a large set of ligand-protein complexes whose crystal structures contain water molecules in their binding sites. Only those water molecules found in the immediate vicinity of both the ligand and the protein were considered. We have investigated whether prior optimization of the orientation of water molecules in either the presence or absence of the bound ligand has any effect on the accuracy of docking predictions. We have observed a statistically significant overall increase in accuracy when water molecules are included during docking simulations and have found this to be independent of the method of optimization of the orientation of water molecules. These results confirm the importance of including water molecules whenever possible in a ligand-protein docking simulation. Our findings also reveal that prior optimization of the orientation of water molecules, in the absence of any bound ligand, does not have a detrimental effect on the improved accuracy of ligand-protein docking. This is important, given the use of docking simulations to predict the binding modes of new ligands or drug molecules.

Energy minimization in low‐frequency normal modes to efficiently allow for global flexibility during systematic protein–protein docking

Protein-protein association can frequently involve significant backbone conformational changes of the protein partners. A computationally rapid method has been developed that allows to approximately account for global conformational changes during systematic protein-protein docking starting from many thousands of start configurations. The approach employs precalculated collective degrees of freedom as additional variables during protein-protein docking minimization. The global collective degrees of freedom are obtained from normal mode analysis using a Gaussian network model for the protein. Systematic docking searches were performed on 10 test systems that differed in the degree of conformational change associated with complex formation and in the degree of overlap between observed conformational changes and precalculated flexible degrees of freedom. The results indicate that in case of docking searches that minimize the influence of local side chain conformational changes inclusion of global flexibility can significantly improve the agreement of the near-native docking solutions with the corresponding experimental structures. For docking of unbound protein partners in several cases an improved ranking of near native docking solutions was observed. This was achieved at a very modest ( approximately 2-fold) increase of computational demands compared to rigid docking. For several test cases the number of docking solutions close to experiment was also significantly enhanced upon inclusion of soft collective degrees of freedom. This result indicates that inclusion of global flexibility can facilitate in silico protein-protein association such that a greater number of different start configurations results in favorable complex formation.

Protein-ligand docking with multiple flexible side chains

In this work, we validate and analyze the results of previously published cross docking experiments and classify failed dockings based on the conformational changes observed in the receptors. We show that a majority of failed experiments (i.e. 25 out of 33, involving four different receptors: cAPK, CDK2, Ricin and HIVp) are due to conformational changes in side chains near the active site. For these cases, we identify the side chains to be made flexible during docking calculation by superimposing receptors and analyzing steric overlap between various ligands and receptor side chains. We demonstrate that allowing these side chains to assume rotameric conformations enables the successful cross docking of 19 complexes (ligand all atom RMSD < 2.0 A) using our docking software FLIPDock. The number of side receptor side chains interacting with a ligand can vary according to the ligand's size and shape. Hence, when starting from a complex with a particular ligand one might have to extend the region of potential interacting side chains beyond the ones interacting with the known ligand. We discuss distance-based methods for selecting additional side chains in the neighborhood of the known active site. We show that while using the molecular surface to grow the neighborhood is more efficient than Euclidian-distance selection, the number of side chains selected by these methods often remains too large and additional methods for reducing their count are needed. Despite these difficulties, using geometric constraints obtained from the network of bonded and non-bonded interactions to rank residues and allowing the top ranked side chains to be flexible during docking makes 22 out of 25 complexes successful.

FLIPDock: docking flexible ligands into flexible receptors

Conformational changes of biological macromolecules when binding with ligands have long been observed and remain a challenge for automated docking methods. Here we present a novel protein-ligand docking software called FLIPDock (Flexible LIgand-Protein Docking) allowing the automated docking of flexible ligand molecules into active sites of flexible receptor molecules. In FLIPDock, conformational spaces of molecules are encoded using a data structure that we have developed recently called the Flexibility Tree (FT). While the FT can represent fully flexible ligands, it was initially designed as a hierarchical and multiresolution data structure for the selective encoding of conformational subspaces of large biological macromolecules. These conformational subspaces can be built to span a range of conformations important for the biological activity of a protein. A variety of motions can be combined, ranging from domains moving as rigid bodies or backbone atoms undergoing normal mode-based deformations, to side chains assuming rotameric conformations. In addition, these conformational subspaces are parameterized by a small number of variables which can be searched during the docking process, thus effectively modeling the conformational changes in a flexible receptor. FLIPDock searches the variables using genetic algorithm-based search techniques and evaluates putative docking complexes with a scoring function based on the AutoDock3.05 force-field. In this paper, we describe the concepts behind FLIPDock and the overall architecture of the program. We demonstrate FLIPDock's ability to solve docking problems in which the assumption of a rigid receptor previously prevented the successful docking of known ligands. In particular, we repeat an earlier cross docking experiment and demonstrate an increased success rate of 93.5%, compared to original 72% success rate achieved by AutoDock over the 400 cross-docking calculations. We also demonstrate FLIPDock's ability to handle conformational changes involving backbone motion by docking balanol to an adenosine-binding pocket of protein kinase A.

Identification of an aminoacridine derivative that binds to RNA tetraloops

RNA folds into diverse structures that form unique targets for small molecules and thus provide significant potential for controlling biological processes involving RNA with small-molecule ligands. We are investigating molecular recognition of tetraloop RNA by small molecules. RNA tetraloops are four-nucleotide stem loops with unusual stability that are involved in biological processes involving RNA by forming binding sites for proteins and other RNAs. We have sequentially used the docking programs DOCK and AutoDock to screen 1990 small molecules in the NCI diversity set to identify molecules selective for RNA tetraloops over double-stranded RNA. The compounds predicted to bind to tetraloop RNA were evaluated for binding RNA tetraloops using 1H NMR spectroscopy and fluorescence techniques. An aminoacridine derivative (AD2) was identified that binds to a GAAA tetraloop in a 2:1 ratio with dissociation constants of 1.0 and 4.0 microM. AD2 binds with approximately 20-fold and 9-fold higher affinity to tetraloop RNA than to double- and single-stranded RNAs, respectively.

Virtual ligand screening: strategies, perspectives and limitations

In contrast to high-throughput screening, in virtual ligand screening (VS), compounds are selected using computer programs to predict their binding to a target receptor. A key prerequisite is knowledge about the spatial and energetic criteria responsible for protein–ligand binding. The concepts and prerequisites to perform VS are summarized here, and explanations are sought for the enduring limitations of the technology. Target selection, analysis and preparation are discussed, as well as considerations about the compilation of candidate ligand libraries. The tools and strategies of a VS campaign, and the accuracy of scoring and ranking of the results, are also considered.

Lennard-Jones potential and dummy atom settings to overcome the AUTODOCK limitation in treating flexible ring systems

Here, we present a setting-up procedure of AutoDock parameters that allows the management of cycle and macrocycle flexibility during the docking process. In particular, the glue dummy atom type is introduced into calculations, and a novel empirical pseudo-Lennard-Jones potential function is applied to describe the intramolecular interactions occurring between two glue dummy atoms. The reliability of such an original protocol is tested by evaluation of 21 cyclic ligands in the corresponding binding site. As a result, the binding mode of 17 ligands is well-reproduced with respect to the X-ray crystallographic structure, with an root-mean-square deviation lower than 2 Angstrom for 15 of them.

Evaluation of docking programs for predicting binding of Golgi α-mannosidase II inhibitors: A comparison with crystallography

Golgi alpha-mannosidase II (GMII), a zinc-dependent glycosyl hydrolase, is a promising target for drug development in anti-tumor therapies. Using X-ray crystallography, we have determined the structure of Drosophila melanogaster GMII (dGMII) complexed with three different inhibitors exhibiting IC50's ranging from 80 to 1000 microM. These structures, along with those of seven other available dGMII/inhibitor complexes, were then used as a basis for the evaluation of seven docking programs (GOLD, Glide, FlexX, AutoDock, eHiTS, LigandFit, and FITTED). We found that small inhibitors could be accurately docked by most of the software, while docking of larger compounds (i.e., those with extended aromatic cycles or long aliphatic chains) was more problematic. Overall, Glide provided the best docking results, with the most accurately predicted binding around the active site zinc atom. Further evaluation of Glide's performance revealed its ability to extract active compounds from a benchmark library of decoys.

Calculation of Protein-Ligand Binding Affinities

Accurate methods of computing the affinity of a small molecule with a protein are needed to speed the discovery of new medications and biological probes. This paper reviews physics-based models of binding, beginning with a summary of the changes in potential energy, solvation energy, and configurational entropy that influence affinity, and a theoretical overview to frame the discussion of specific computational approaches. Important advances are reported in modeling protein-ligand energetics, such as the incorporation of electronic polarization and the use of quantum mechanical methods. Recent calculations suggest that changes in configurational entropy strongly oppose binding and must be included if accurate affinities are to be obtained. The linear interaction energy (LIE) and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) methods are analyzed, as are free energy pathway methods, which show promise and may be ready for more extensive testing. Ultimately, major improvements in modeling accuracy will likely require advances on multiple fronts, as well as continued validation against experiment.

Protein/ligand binding free energies calculated with quantum mechanics/molecular mechanics

The calculation of binding affinities for flexible ligands has hitherto required the availability of reliable molecular mechanics parameters for the ligands, a restriction that can in principle be lifted by using a mixed quantum mechanics/molecular mechanics (QM/MM) representation in which the ligand is treated quantum mechanically. The feasibility of this approach is evaluated here, combining QM/MM with the Poisson-Boltzmann/surface area model of continuum solvation and testing the method on a set of 47 benzamidine derivatives binding to trypsin. The experimental range of the absolute binding energy (DeltaG = -3.9 to -7.6 kcal/mol) is reproduced well, with a root-mean-square (RMS) error of 1.2 kcal/mol. When QM/MM is applied without reoptimization to the very different ligands of FK506 binding protein the RMS error is only 0.7 kcal/mol. The results show that QM/MM is a promising new avenue for automated docking and scoring of flexible ligands. Suggestions are made for further improvements in accuracy.

Insights from modeling the 3D structure of NAD(P)H-dependent D-xylose reductase of Pichia stipitis and its binding interactions with NAD and NADP

NAD(P)H-dependent d-xylose reductase is a homodimeric oxidoreductase that belongs to the aldo-keto reductase superfamily. The enzyme has the special function to catalyze the first step in the assimilation of xylose into yeast metabolic pathways. Performing this function via reducing the open chain xylose to xylitol, the xylose reductase of Pichia stipitis is one of the most important enzymes that can be used to construct recombinant Saccharomyces cerevisiae strain for utilizing xylose and producing alcohol. To investigate into the interaction mechanism of the enzyme with its ligand NAD and NADP, the 3D structure was developed for the NAD(P)H-dependent d-xylose reductase from P. stipitis. With the 3D structure, the molecular docking operations were conducted to find the most stable bindings of the enzyme with NAD and NADP, respectively. Based on these results, the binding pockets of the enzyme for NAD and NADP have been explicitly defined. It has been found that the residues in forming the binding pockets for both NAD and NADP are almost the same and mainly hydrophilic. These findings may be used to guide mutagenesis studies, providing useful clues to modify the enzyme to improve the utilization of xylose for producing alcohol. Also, because human aldose reductases have the function to reduce the open chain form of glucose to sorbitol, a process physiologically significant for diabetic patients at the time that their blood glucose levels are elevated, the information gained through this study may also stimulate the development of new strategies for therapeutic treatment of diabetes.

Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins

Background

Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the results are often not satisfactory. Although different amino acid compositions among the interaction sites of different complexes have been observed, such differences have not been integrated into the prediction process. Furthermore, the evolution information has not been exploited to achieve a more powerful propensity.

Result

In this study, the residue interface propensities of four kinds of complexes (homo-permanent complexes, homo-transient complexes, hetero-permanent complexes and hetero-transient complexes) are investigated. These propensities, combined with sequence profiles and accessible surface areas, are inputted to the support vector machine for the prediction of protein binding sites. Such propensities are further improved by taking evolutional information into consideration, which results in a class of novel propensities at the profile level, i.e. the binary profiles interface propensities. Experiment is performed on the 1139 non-redundant protein chains. Although different residue interface propensities among different complexes are observed, the improvement of the classifier with residue interface propensities can be negligible in comparison with that without propensities. The binary profile interface propensities can significantly improve the performance of binding sites prediction by about ten percent in term of both precision and recall.

Conclusion

Although there are minor differences among the four kinds of complexes, the residue interface propensities cannot provide efficient discrimination for the complicated interfaces of proteins. The binary profile interface propensities can significantly improve the performance of binding sites prediction of protein, which indicates that the propensities at the profile level are more accurate than those at the residue level.

Accuracy of binding mode prediction with a cascadic stochastic tunneling method

We investigate the accuracy of the binding modes predicted for 83 complexes of the high-resolution subset of the ASTEX/CCDC receptor-ligand database using the atomistic FlexScreen approach with a simple forcefield-based scoring function. The median RMS deviation between experimental and predicted binding mode was just 0.83 A. Over 80% of the ligands dock within 2 A of the experimental binding mode, for 60 complexes the docking protocol locates the correct binding mode in all of ten independent simulations. Most docking failures arise because (a) the experimental structure clashed in our forcefield and is thus unattainable in the docking process or (b) because the ligand is stabilized by crystal water.

Towards in silico lead optimization: scores from ensembles of protein/ligand conformations reliably correlate with biological activity

Accurately ranking protein/ligand interactions and distinguishing subtle differences between homologous compounds in a virtual focused library in silico is essential in a structure-based drug discovery program. In order to establish a predictive model to design novel inhibitors of dihydrofolate reductase (DHFR) from the parasitic protozoa, Cryptosporidium hominis, we docked a series of 30 DHFR inhibitors with measured inhibition constants against the crystal structure of the protein. By including protein flexibility and averaging the energies of the 25 lowest protein/ligand conformers we obtained more accurate total nonbonded energies from which we calculated a predicted biological activity. The calculated and measured biological activities showed reliable correlations of 72.9%. Additionally, visual analysis of the ensemble of protein/ligand conformations revealed alternative ligand binding pockets in the active site. Using the same principles we then created a homology model of DHFR from Toxoplasma gondii and docked 11 inhibitors. A correlation of 50.2% between docking score and activity validates both the method and the model. The correlations presented here are particularly compelling considering the high structural similarity of the ligands and the fact that we have used structures derived from crystallographic data and homology modeling. These docking principles may be useful in any lead optimization study where accurate ranking of similar compounds is desired.

Exploring the subtleties of drug-receptor interactions: the case of matrix metalloproteinases

By solving high-resolution crystal structures of a large number (14 in this case) of adducts of matrix metalloproteinase 12 (MMP12) with strong, nanomolar, inhibitors all derived from a single ligand scaffold, it is shown that the energetics of the ligand-protein interactions can be accounted for directly from the structures to a level of detail that allows us to rationalize for the differential binding affinity between pairs of closely related ligands. In each case, variations in binding affinities can be traced back to slight improvements or worsening of specific interactions with the protein of one or more ligand atoms. Isothermal calorimetry measurements show that the binding of this class of MMP inhibitors is largely enthalpy driven, but a favorable entropic contribution is always present. The binding enthalpy of acetohydroxamic acid (AHA), the prototype zinc-binding group in MMP drug discovery, has been also accurately measured. In principle, this research permits the planning of either improved inhibitors, or inhibitors with improved selectivity for one or another MMP. The present analysis is applicable to any drug target for which structural information on adducts with a series of homologous ligands can be obtained, while structural information obtained from in silico docking is probably not accurate enough for this type of study.

ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility

Protein-small molecule docking algorithms provide a means to model the structure of protein-small molecule complexes in structural detail and play an important role in drug development. In recent years the necessity of simulating protein side-chain flexibility for an accurate prediction of the protein-small molecule interfaces has become apparent, and an increasing number of docking algorithms probe different approaches to include protein flexibility. Here we describe a new method for docking small molecules into protein binding sites employing a Monte Carlo minimization procedure in which the rigid body position and orientation of the small molecule and the protein side-chain conformations are optimized simultaneously. The energy function comprises van der Waals (VDW) interactions, an implicit solvation model, an explicit orientation hydrogen bonding potential, and an electrostatics model. In an evaluation of the scoring function the computed energy correlated with experimental small molecule binding energy with a correlation coefficient of 0.63 across a diverse set of 229 protein- small molecule complexes. The docking method produced lowest energy models with a root mean square deviation (RMSD) smaller than 2 A in 71 out of 100 protein-small molecule crystal structure complexes (self-docking). In cross-docking calculations in which both protein side-chain and small molecule internal degrees of freedom were varied the lowest energy predictions had RMSDs less than 2 A in 14 of 20 test cases.

Water at biomolecular binding interfaces

Water molecules are often found at the binding interface of biomolecular complexes mediating the interaction between polar groups via hydrogen bonds, or simply filling space providing van der Waals interactions. Recent studies have demonstrated the importance of taking such water molecules into account in docking and binding affinity prediction. Here, we review the recent experimental and theoretical work aimed at quantifying the influence of interfacial water on the thermodynamic properties of binding. We highlight especially our recent results obtained by inhomogeneous fluid solvation theory in several systems and the prediction of the thermodynamic consequences of displacement of the bound water molecule by ligand modification. Finally, we discuss possible directions for further progress in this field.

Ensemble docking of multiple protein structures: Considering protein structural variations in molecular docking

One approach to incorporate protein flexibility in molecular docking is the use of an ensemble consisting of multiple protein structures. Sequentially docking each ligand into a large number of protein structures is computationally too expensive to allow large-scale database screening. It is challenging to achieve a good balance between docking accuracy and computational efficiency. In this work, we have developed a fast, novel docking algorithm utilizing multiple protein structures, referred to as ensemble docking, to account for protein structural variations. The algorithm can simultaneously dock a ligand into an ensemble of protein structures and automatically select an optimal protein structure that best fits the ligand by optimizing both ligand coordinates and the conformational variable m, where m represents the m-th structure in the protein ensemble. The docking algorithm was validated on 10 protein ensembles containing 105 crystal structures and 87 ligands in terms of binding mode and energy score predictions. A success rate of 93% was obtained with the criterion of root-mean-square deviation <2.5 A if the top five orientations for each ligand were considered, comparable to that of sequential docking in which scores for individual docking are merged into one list by re-ranking, and significantly better than that of single rigid-receptor docking (75% on average). Similar trends were also observed in binding score predictions and enrichment tests of virtual database screening. The ensemble docking algorithm is computationally efficient, with a computational time comparable to that for docking a ligand into a single protein structure. In contrast, the computational time for the sequential docking method increases linearly with the number of protein structures in the ensemble. The algorithm was further evaluated using a more realistic ensemble in which the corresponding bound protein structures of inhibitors were excluded. The results show that ensemble docking successfully predicts the binding modes of the inhibitors, and discriminates the inhibitors from a set of noninhibitors with similar chemical properties. Although multiple experimental structures were used in the present work, our algorithm can be easily applied to multiple protein conformations generated by computational methods, and helps improve the efficiency of other existing multiple protein structure(MPS)-based methods to accommodate protein flexibility.

Protein-ligand docking: current status and future challenges

Understanding the ruling principles whereby protein receptors recognize, interact, and associate with molecular substrates and inhibitors is of paramount importance in drug discovery efforts. Protein-ligand docking aims to predict and rank the structure(s) arising from the association between a given ligand and a target protein of known 3D structure. Despite the breathtaking advances in the field over the last decades and the widespread application of docking methods, several downsides still exist. In particular, protein flexibility-a critical aspect for a thorough understanding of the principles that guide ligand binding in proteins-is a major hurdle in current protein-ligand docking efforts that needs to be more efficiently accounted for. In this review the key concepts of protein-ligand docking methods are outlined, with major emphasis being given to the general strengths and weaknesses that presently characterize this methodology. Despite the size of the field, the principal types of search algorithms and scoring functions are reviewed and the most popular docking tools are briefly depicted. Recent advances that aim to address some of the traditional limitations associated with molecular docking are also described. A selection of hand-picked examples is used to illustrate these features.

Normal mode analysis as a prerequisite for drug design: Application to matrix metalloproteinases inhibitors

We demonstrate the utility of normal mode analysis in correctly predicting the binding modes of inhibitors in the active sites of matrix metalloproteinases (MMPs). We show the accuracy in predicting the positions of MMP-3 inhibitors is strongly dependent on which structure is used as the target, especially when it has been energy minimized. This dependency can be overcome by using intermediate structures generated along one of the normal modes previously calculated for a given target. These results may be of prime importance for further in silico drug discovery.

Solvated docking: introducing water into the modelling of biomolecular complexes

MOTIVATION

Interfacial water, which plays an important role in mediating biomolecular interactions, has been neglected in the modelling of biomolecular complexes.

METHODS

We present a solvated docking approach that explicitly accounts for the presence of water in protein-protein complexes. Our solvated docking protocol is based on the concept of the first encounter complex in which a water layer is present in-between the molecules. It mimics the pathway from this initial complex towards the final assembly in which most waters have been expelled from the interface. Docking is performed from solvated biomolecules and waters are removed in a biased Monte Carlo procedure based on water-mediated contact propensities obtained from an analysis of high-resolution crystal structures.

RESULTS

We demonstrate the feasibility of this approach for protein-protein complexes representing both 'wet' and 'dry' interfaces. Solvated docking leads to improvements both in quality and scoring. Water molecules are recovered that closely match the ones in the crystal structures.

AVAILABILTY

Solvated docking will be made available in the future release of HADDOCK version 2.0 (http://www.nmr.chem.uu.nl/haddock).

Effective handling of induced-fit motion in flexible docking

For structure-based drug design, where various ligand structures need to be docked to a target protein structure, a docking method that can handle conformational flexibility of not only the ligand, but also the protein, is indispensable. We have developed a simple and effective approach for dealing with the local induced-fit motion of the target protein, and implemented it in our docking tool, ADAM. Our approach efficiently combines the following two strategies: a vdW-offset grid in which the protein cavity is enlarged uniformly, and structure optimization allowing the motion of ligand and protein atoms. To examine the effectiveness of our approach, we performed docking validation studies, including redocking in 18 test cases and foreign-docking, in which various ligands from foreign crystal structures of complexes are docked into a target protein structure, in 22 cases (on five target proteins). With the original ADAM, the correct docking modes (RMSD < 2.0 A) were not present among the top 20 models in one case of redocking and four cases of foreign-docking. When the handling of induced-fit motion was implemented, the correct solutions were acquired in all 40 test cases. In foreign-docking on thymidine kinase, the correct docking modes were obtained as the top-ranked solutions for all 10 test ligands by our combinatorial approach, and this appears to be the best result ever reported with any docking tool. The results of docking validation have thus confirmed the effectiveness of our approach, which can provide reliable docking models even in the case of foreign-docking, where conformational change of the target protein cannot be ignored. We expect that this approach will contribute substantially to actual drug design, including virtual screening.

Mapping residues in the ligand-binding domain of the 5-HT(3) receptor onto d-tubocurarine structure

The serotonin 5-HT(3) receptor (5-HT(3)R) is a member of the cys-loop ligand-gated ion channel family. We have used the combination of site-directed mutagenesis, homology modeling of the 5-HT(3)R extracellular domain, and ligand docking simulations as a way to map the architecture of the 5-HT(3)R ligand binding domain. Mutation of Phe226 in loop C of the binding site to tyrosine (F226Y) has no effect on the apparent affinity of the competitive antagonist d-tubocurarine (dTC) for the receptor. On the other hand, replacement of Asn128 in loop A of the binding site with alanine (N128A) increases the apparent affinity of dTC by approximately 10-fold. Double-mutant cycle analysis employing a panel of dTC analogs with substitutions at various positions to identify specific points of interactions between the dTC analogs and Asn128 suggests that Asn128 makes a direct interaction with the 2'N of dTC. Molecular modeling of the 5-HT(3)R extracellular domain using the antagonist-bound conformation of the Aplysia californica acetylcholine binding protein as a template followed by ligand docking simulations produces two classes of structures of the 5-HT(3)R/dTC complex; only one of these has the 2'N of dTC positioned at Asn128 and thus is consistent with the data from this study and previously published data. The use of the rigid dTC analogs as "molecular rulers" in conjunction with double-mutant cycle analysis of mutant receptors can allow the spatial mapping of the position of various residues in the ligand-binding site.

BDT: an easy-to-use front-end application for automation of massive docking tasks and complex docking strategies with AutoDock

MOTIVATION

AutoGrid/AutoDock is one of the most popular software packages for docking, but its automation is not trivial for tasks such as (1) the virtual screening of a library of ligands against a set of possible receptors; (2) the use of receptor flexibility and (3) making a blind-docking experiment with the whole receptor surface. This is an obstacle for research teams in the fields of Chemistry and the Life Sciences who are interested in conducting this kind of experiment but do not have enough programming skills. To overcome these limitations, we have designed BDT, an easy-to-use graphic interface for AutoGrid/AutoDock.

AVAILABILITY

BDT is available for free, upon request, for non-commercial research.

Catalytic site prediction and virtual screening of cytochrome P450 2D6 substrates by consideration of water and rescoring in automated docking

Automated docking strategies successfully applied to binding mode predictions of ligands in Cyt P450 crystal structures in an earlier study (de Graaf et al. J. Med. Chem. 2005, 7, 2308-2318) were used for the catalytic site prediction (CSP) of 65 substrates in a CYP2D6 homology model. The consideration of water molecules at predicted positions in the active site and the rescoring of pooled docking poses from four different docking programs (AutoDock, FlexX, GOLD-Goldscore, and GOLD-Chemscore) with the SCORE scoring function enabled the successful prediction of experimentally reported sites of catalysis of more than 80% of the substrates. Three docking algorithms (FlexX, GOLD-Goldscore, and GOLD-Chemscore) were subsequently used in combination with six scoring functions (Chemscore, DOCK, FlexX, GOLD, PMF, and SCORE) to assess the ability of docking-based virtual screening methods to prioritize known CYP2D6 substrates seeded into a drug-like chemical database (in the absence and presence of active-site water molecules). Finally, the optimal docking strategy in terms of virtual screening accuracy, GOLD-Chemscore with the consideration of active-site water (60% of known substrates recovered in the top 5% of the ranked drug-like database), was verified experimentally; it was successfully used to identify high-affinity CYP2D6 ligands among a larger proprietary database.

Exploring structurally conserved solvent sites in protein families

Protein-bound water molecules are important components of protein structure, and therefore, protein function and energetics. Although structural conservation of solvent has been studied in a few protein families, a lack of suitable computational tools has hindered more comprehensive analyses. Herein we present a semiautomated computational approach for identifying solvent sites that are conserved among proteins sharing a common three-dimensional structure. This method is tested on six protein families: (1) monodomain cytochrome c, (2) fatty-acid binding protein, (3) lactate/malate dehydrogenase, (4) parvalbumin, (5) phospholipase A2, and (6) serine protease. For each family, the method successfully identified previously known conserved solvent sites. Moreover, the method discovered 22 novel conserved solvent sites, some of which have higher degrees of conservation than the previously known sites. All six families studied had solvent sites with more than 90% conservation and these sites were invariably located in regions of the protein with very high sequence conservation. These results suggest that highly conserved solvent sites, by virtue of their proximity to conserved residues, should be considered as one of the defining three-dimensional structural characteristics of protein families and folds.

Tryptophan rotamers as evidenced by X-ray, fluorescence lifetimes, and molecular dynamics modeling

We investigated the native-state dynamics of the Bacillus caldolyticus cold-shock protein mutant Bc-Csp L66E, using fluorescence and appropriate molecular dynamics methods. Two fluorescence lifetimes were found, the amplitudes of which agree very well with tryptophan rotamer populations, obtained from parallel tempering calculations. Rotamer lifetimes were predicted by transition-state theory from high-temperature simulations. Transition pathways were extracted from the transition rates between individual rotameric states. The molecular dynamics also reveal the loop fluctuations in the native state.

Discovering New Classes of Brugia malayi Asparaginyl-tRNA Synthetase Inhibitors and Relating Specificity to Conformational Change

SLIDE software, which models the flexibility of protein and ligand side chains while docking, was used to screen several large databases to identify inhibitors of Brugia malayi asparaginyl-tRNA synthetase (AsnRS), a target for anti-parasitic drug design. Seven classes of compounds identified by SLIDE were confirmed as micromolar inhibitors of the enzyme. Analogs of one of these classes of inhibitors, the long side-chain variolins, cannot bind to the adenosyl pocket of the closed conformation of AsnRS due to steric clashes, though the short side-chain variolins identified by SLIDE apparently bind isosterically with adenosine. We hypothesized that an open conformation of the motif 2 loop also permits the long side-chain variolins to bind in the adenosine pocket and that their selectivity for Brugia relative to human AsnRS can be explained by differences in the sequence and conformation of this loop. Loop flexibility sampling using Rigidity Optimized Conformational Kinetics (ROCK) confirms this possibility, while scoring of the relative affinities of the different ligands by SLIDE correlates well with the compounds' ranks in inhibition assays. Combining ROCK and SLIDE provides a promising approach for exploiting conformational flexibility in structure-based screening and design of species selective inhibitors.

Probing molecular docking in a charged model binding site

A model binding site was used to investigate charge-charge interactions in molecular docking. This simple site, a small (180A(3)) engineered cavity in cyctochrome c peroxidase (CCP), is negatively charged and completely buried from solvent, allowing us to explore the balance between electrostatic energy and ligand desolvation energy in a system where many of the common approximations in docking do not apply. A database with about 5300 molecules was docked into this cavity. Retrospective testing with known ligands and decoys showed that overall the balance between electrostatic interaction and desolvation energy was captured. More interesting were prospective docking scre"ens that looked for novel ligands, especially those that might reveal problems with the docking and energy methods. Based on screens of the 5300 compound database, both high-scoring and low-scoring molecules were acquired and tested for binding. Out of 16 new, high-scoring compounds tested, 15 were observed to bind. All of these were small heterocyclic cations. Binding constants were measured for a few of these, they ranged between 20microM and 60microM. Crystal structures were determined for ten of these ligands in complex with the protein. The observed ligand geometry corresponded closely to that predicted by docking. Several low-scoring alkyl amino cations were also tested and found to bind. The low docking score of these molecules owed to the relatively high charge density of the charged amino group and the corresponding high desolvation penalty. When the complex structures of those ligands were determined, a bound water molecule was observed interacting with the amino group and a backbone carbonyl group of the cavity. This water molecule mitigates the desolvation penalty and improves the interaction energy relative to that of the "naked" site used in the docking screen. Finally, six low-scoring neutral molecules were also tested, with a view to looking for false negative predictions. Whereas most of these did not bind, two did (phenol and 3-fluorocatechol). Crystal structures for these two ligands in complex with the cavity site suggest reasons for their binding. That these neutral molecules do, in fact bind, contradicts previous results in this site and, along with the alkyl amines, provides instructive false negatives that help identify weaknesses in our scoring functions. Several improvements of these are considered.

De novo ligand design to an ensemble of protein structures

We describe a combinatorial method for de novo ligand design to an ensemble of receptor structures. Receptor conformations, protonation states, and structural water molecules are considered consistently within the framework of de novo ligand design. The method relies on Monte Carlo optimization to search the space of ligand structures, conformations, and rigid-body movements as well as receptor models. The method is applied to an ensemble of HIV protease and human collagenase receptor models. Ligand structures generated de novo exhibit the correct hydrogen-bonding pattern in the core of the active site, with hydrophobic groups extending into the receptor S1 and S1' pocket space. Furthermore, it is shown that known ligands are recovered in the correct binding mode and in the native, most tightly binding receptor model.

Novel procedure for modeling ligand/receptor induced fit effects

We present a novel protein-ligand docking method that accurately accounts for both ligand and receptor flexibility by iteratively combining rigid receptor docking (Glide) with protein structure prediction (Prime) techniques. While traditional rigid-receptor docking methods are useful when the receptor structure does not change substantially upon ligand binding, success is limited when the protein must be "induced" into the correct binding conformation for a given ligand. We provide an in-depth description of our novel methodology and present results for 21 pharmaceutically relevant examples. Traditional rigid-receptor docking for these 21 cases yields an average RMSD of 5.5 A. The average ligand RMSD for docking to a flexible receptor for the 21 pairs is 1.4 A; the RMSD is < or =1.8 A for 18 of the cases. For the three cases with RMSDs greater than 1.8 A, the core of the ligand is properly docked and all key protein/ligand interactions are captured.

COMPARISON OF METHODS FOR THE PREDICTION OF THE METABOLIC SITES FOR CYP3A4-MEDIATED METABOLIC REACTIONS

Predictions of the metabolic sites for new chemical entities, synthesized or only virtual, are important in the early phase of drug discovery to guide chemistry efforts in the synthesis of new compounds with reduced metabolic liability. This information can now be obtained from in silico predictions, and therefore, a thorough and unbiased evaluation of the computational techniques available is needed. Several computational methods to predict the metabolic hot spots are emerging. In this study, metabolite identification using MetaSite and a docking methodology, GLUE, were compared. Moreover, the published CYP3A4 crystal structure and computed CYP3A4 homology models were compared for their usefulness in predicting metabolic sites. A total of 227 known CYP3A4 substrates reported to have one or more metabolites adding up to 325 metabolic pathways were analyzed. Distance-based fingerprints and four-point pharmacophore derived from GRID molecular interaction fields were used to characterize the substrate and protein in MetaSite and the docking methodology, respectively. The CYP3A4 crystal structure and homology model with the reactivity factor enabled achieved a similar prediction success (78%) using the MetaSite method. The docking method had a relatively lower prediction success (approximately 57% for the homology model), although it still may provide useful insights for interactions between ligand and protein, especially for uncommon reactions. The MetaSite methodology is automated, rapid, and has relatively accurate predictions compared with the docking methodology used in this study.

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.