Improved side-chain modeling for protein-protein docking

Success in high-resolution protein-protein docking requires accurate modeling of side-chain conformations at the interface. Most current methods either leave side chains fixed in the conformations observed in the unbound protein structures or allow the side chains to sample a set of discrete rotamer conformations. Here we describe a rapid and efficient method for sampling off-rotamer side-chain conformations by torsion space minimization during protein-protein docking starting from discrete rotamer libraries supplemented with side-chain conformations taken from the unbound structures, and show that the new method improves side-chain modeling and increases the energetic discrimination between good and bad models. Analysis of the distribution of side-chain interaction energies within and between the two protein partners shows that the new method leads to more native-like distributions of interaction energies and that the neglect of side-chain entropy produces a small but measurable increase in the number of residues whose interaction energy cannot compensate for the entropic cost of side-chain freezing at the interface. The power of the method is highlighted by a number of predictions of unprecedented accuracy in the recent CAPRI (Critical Assessment of PRedicted Interactions) blind test of protein-protein docking methods.     

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.     

Docking and scoring with alternative side-chain conformations

We describe a scoring and modeling procedure for docking ligands into protein models that have either modeled or flexible side-chain conformations. Our methodical contribution comprises a procedure for generating new potentials of mean force for the ROTA scoring function which we have introduced previously for optimizing side-chain conformations with the tool IRECS. The ROTA potentials are specially trained to tolerate small-scale positional errors of atoms that are characteristic of (i) side-chain conformations that are modeled using a sparse rotamer library and (ii) ligand conformations that are generated using a docking program. We generated both rigid and flexible protein models with our side-chain prediction tool IRECS and docked ligands to proteins using the scoring function ROTA and the docking programs FlexX (for rigid side chains) and FlexE (for flexible side chains). We validated our approach on the forty screening targets of the DUD database. The validation shows that the ROTA potentials are especially well suited for estimating the binding affinity of ligands to proteins. The results also show that our procedure can compensate for the performance decrease in screening that occurs when using protein models with side chains modeled with a rotamer library instead of using X-ray structures. The average runtime per ligand of our method is 168 seconds on an Opteron V20z, which is fast enough to allow virtual screening of compound libraries for drug candidates.     

Automated docking of monosaccharide substrates and analogues and methyl α-Acarviosinide in the glucoamylase active site

Glucoamylase is an important industrial glucohydrolase with a large specificity range. To investigate its interaction with the monosaccharides D-glucose, D-mannose, and D-galactose and with the substrate analogues 1-deoxynojirimycin, D-glucono-1,5-lactone, and methyl αacarviosinide, MM3(92)-optimized structures were docked into its active site using AutoDock 2.1. The results were compared to structures of glucoamylase complexes obtained by protein crystallography. Charged forms of some substrate analogues were also docked to assess the degree of protonation possessed by glucoamylase inhibitors. Many forms of methyl αa-carviosinide were conformationally mapped by using MM3(92), characterizing the conformational pH dependence found for the acarbose family of glucosidase inhibitors. Their significant conformers, representing the most common states of the inhibitor, were used as initial structures for docking. This constitutes a new approach for the exploration of binding modes of carbohydrate chains. Docking results differ slightly from x-ray crystallographic data, the difference being of the order of the crystallographic error. The estimated energetic interactions, even though agreeing in some cases with experimental binding kinetics, are only qualitative due to the large approximations made by AudoDock force field. © 1997 Wiley-Liss, Inc.     

Fragment-Based flexible ligand docking by evolutionary optimization.

A new computational approach for the efficient docking of flexible ligands in a rigid protein is presented. It exploits the binding modes of functional groups determined by an exhaustive search with solvation. The search in ligand conformational space is performed by a genetic algorithm whose scoring function approximates steric effects and intermolecular hydrogen bonds. Ligand conformations generated by the genetic algorithm are docked in the protein binding site by optimizing the fit of their fragments to optimal positions of chemically related functional groups. We show that the use of optimal binding modes of molecular fragments allows to dock known inhibitors with about ten rotatable bonds in the active site of the uncomplexed and complexed conformations of thrombin and HIV-1 protease.     

Applications of the ArgusLab4/AScore protocol in the structure-based binding affinity prediction of various inhibitors of group-1 and group-2 influenza virus neuraminidases (NAs)

Using the crystal structures of inhibitors bound to either group-2 or group-1 neuraminidases (NAs), AScore/ShapeDock (GaDock) scoring was shown to identify the binding modes in agreement with the experiment for all inhibitors docked in their own NA/inhibitor crystal structures. To investigate the effect of small changes in protein structure on predicted binding modes, in a set of 132 docking experiments (11 inhibitors docked in 12 group-2 NA structures), AScore/ShapeDock (GaDock) identified the correct binding modes of 116 complexes. In a total of 88 docking experiments (8 inhibitors docked in 11 group-1 NA structures), AScore/ShapeDock predicted 80 binding modes correctly. Flexible AScore/ShapeDock docking, as quite reproducible, is suggested to be convenient for designing novel H5N1 inhibitors.     

Computational sampling of a cryptic drug binding site in a protein receptor: explicit solvent molecular dynamics and inhibitor docking to p38 MAP kinase

An increasing number of structural studies reveal alternative binding sites in protein receptors that become apparent only when an inhibitor binds, and correct prediction of these situations presents a significant challenge to computer-aided drug design efforts. A striking example is provided by recent crystal structures of the p38 MAP kinase, where a 10A movement of the Phe169 side-chain creates a new binding site adjacent to the ATP binding site that is exploited by the diaryl urea inhibitor BIRB796. Here, we show that this binding site can be successfully and repeatedly identified in explicit-solvent molecular dynamics (MD) simulations of the protein that begin from an unliganded p38 crystal structure. Ligand-docking calculations performed on 5000 different structural snapshots generated during MD indicate that the conformations sampled are often surprisingly competent to bind the inhibitor BIRB796 in the crystallographically correct position and with docked energies that are generally more favorable than those of other positions. Similar docking studies with an ATP-binding site-directed inhibitor suggest that it may be possible to develop hybrid inhibitors that target both the ATP and cryptic binding sites simultaneously. Intriguingly, both inhibitors are occasionally found to dock correctly even with p38's "DFG" motif in the "wrong" conformation and BIRB796 can successfully dock, albeit infrequently, without significant displacement of the Phe169 side-chain; this suggests that the inhibitor might facilitate the latter's conformational change. Finally, two quite different conformations of p38's DFG motif are also sampled for extended periods of time during the simulations; these may provide new opportunities for inhibitor development. The MD simulations reported here, which total 390 ns in length, therefore demonstrate that existing computational methods may be of surprising utility in predicting cryptic binding sites in protein receptors prior to their experimental discovery.     

Improved modeling of side-chains in proteins with rotamer-based methods: a flexible rotamer model

Side-chain modeling has a widespread application in many current methods for protein tertiary structure determination, prediction, and design. Of the existing side-chain modeling methods, rotamer-based methods are the fastest and most efficient. Classically, a rotamer is conceived as a single, rigid conformation of an amino acid sidechain. Here, we present a flexible rotamer model in which a rotamer is a continuous ensemble of conformations that cluster around the classic rigid rotamer. We have developed a thermodynamically based method for calculating effective energies for the flexible rotamer. These energies have a one-to-one correspondence with the potential energies of the rigid rotamer. Therefore, the flexible rotamer model is completely general and may be used with any rotamer-based method in substitution of the rigid rotamer model. We have compared the performance of the flexible and rigid rotamer models with one side-chain modeling method in particular (the self-consistent mean field theory method) on a set of 20 high quality crystallographic protein structures. For the flexible rotamer model, we obtained average predictions of 85.8% for chi1, 76.5% for chi1+2 and 1.34 A for root-mean-square deviation (RMSD); the corresponding values for core residues were 93.0%, 87.7% and 0.70 A, respectively. These values represent improvements of 7.3% for chi1, 8.1% for chi1+2 and 0.23 A for RMSD over the predictions obtained with the rigid rotamer model under otherwise identical conditions; the corresponding improvements for core residues were 6.9%, 10.5% and 0.43 A, respectively. We found that the predictions obtained with the flexible rotamer model were also significantly better than those obtained for the same set of proteins with another state-of-the-art side-chain placement method in the literature, especially for core residues. The flexible rotamer model represents a considerable improvement over the classic rigid rotamer model. It can, therefore, be used with considerable advantage in all rotamer-based methods commonly applied to protein tertiary structure determination, prediction, and design and also in predictions of free energies in mutational studies.     

Identification of selective MMP-9 inhibitors through multiple e-pharmacophore,ligand-based pharmacophore,molecular docking,and density functional theory approaches

Matrix metalloproteinase-9 (MMP-9) is a significant target for the development of drugs for the treatment of arthritis, CNS disorders, and cancer metastasis. The structure-based and ligand-based methods were used for the virtual screening (VS) of database compounds to obtain potent and selective MMP-9 inhibitors. Experimentally known MMP-9 inhibitors were used to grow up ligand-based three pharmacophore models utilizing Schrodinger suite. The X-ray crystallographic structures of MMP-9 with different inhibitors were used to develop five energy-optimized structure-based (e-pharmacophore) models. All developed pharmacophores were validated and applied to screen the Zinc database. Pharmacophore matched compounds were subjected to molecular docking to retrieve hits with novel scaffolds. The molecules with diverse structures, high docking scores and low binding energies for various crystal structures of MMP-9, were selected as final hits. The Induced fit docking (IFD) analysis provided significant information about the driving of inhibitor to approve a suitable bioactive conformational position in the active site of protein. Since charge transfer reaction occurs during receptor–ligand interaction, therefore, electronic features of hits (ligands) are interesting parameters to explain the binding interactions. Density functional theory (DFT) at B3LYP/6-31G* level was utilized to explore electronic features of hits. The docking study of hits using AutoDock was helpful to establish the binding interactions. The study illustrates that the combined pharmacophore approach is advantageous to identify diverse hits which have better binding affinity to the active site of the enzyme for all possible bioactive conformations. The approach used in the study is worthy to design drugs for other targets.     

Glucose-based spiro-isoxazolines: A new family of potent glycogen phosphorylase inhibitors

A series of glucopyranosylidene-spiro-isoxazolines was prepared through regio- and stereoselective [3+2]-cycloaddition between the methylene acetylated exo-glucal and aromatic nitrile oxides. The deprotected cycloadducts were evaluated as inhibitors of muscle glycogen phosphorylase b. The carbohydrate-based family of five inhibitors displays K_i values ranging from 0.63 to 92.5 μM. The X-ray structures of the enzyme–ligand complexes show that the inhibitors bind preferentially at the catalytic site of the enzyme retaining the less active T-state conformation. Docking calculations with GLIDE in extra-precision (XP) mode yielded excellent agreement with experiment, as judged by comparison of the predicted binding modes of the five ligands with the crystallographic conformations and the good correlation between the docking scores and the experimental free binding energies. Use of docking constraints on the well-defined positions of the glucopyranose moiety in the catalytic site and redocking of GLIDE-XP poses using electrostatic potential fit-determined ligand partial charges in quantum polarized ligand docking (QPLD) produced the best results in this regard.     

Rotamer Dynamics: Analysis of Rotamers in Molecular Dynamics Simulations of Proteins

Given by χ torsional angles, rotamers describe the side-chain conformations of amino acid residues in a protein based on the rotational isomers (hence the word rotamer). Constructed rotamer libraries, based on either protein crystal structures or dynamics studies, are the tools for classifying rotamers (torsional angles) in a way that reflect their frequency in nature. Rotamer libraries are routinely used in structure modeling and evaluation. In this perspective article, we would like to encourage researchers to apply rotamer analyses beyond their traditional use. Molecular dynamics (MD) of proteins highlight the in silico behavior of molecules in solution and thus can identify favorable side-chain conformations. In this article, we used simple computational tools to study rotamer dynamics (RD) in MD simulations. First, we isolated each frame in the MD trajectories in separate Protein Data Bank files via the cpptraj module in AMBER. Then, we extracted torsional angles via the Bio3D module in R language. The classification of torsional angles was also done in R according to the penultimate rotamer library. RD analysis is useful for various applications such as protein folding, study of rotamer-rotamer relationship in protein-protein interaction, real-time correlation between secondary structures and rotamers, study of flexibility of side chains in binding site for molecular docking preparations, use of RD as guide in functional analysis and study of structural changes caused by mutations, providing parameters for improving coarse-grained MD accuracy and speed, and many others. Major challenges facing RD to emerge as a new scientific field involve the validation of results via easy, inexpensive wet-lab methods. This realm is yet to be explored.     

Site-directed fragment-based generation of virtual sialic acid databases against influenza A hemagglutinin

In this study fragment-based drug design is combined with molecular docking simulation technique, to design databases of virtual sialic acid (SA) analogues with new substitutions at C2, C5 and C6 positions of SA scaffold. Using spaces occupied by C2, C5 and C6 natural moieties of SA when bound to hemagglutinin (HA) crystallographic structure, new fragments that are commercially available were docked independently in all the pockets. The oriented fragments were then connected to the SA scaffold with or without incorporation of linker molecules. The completed analogues were docked to the whole SA binding site to estimate their binding conformations and affinities, generating three databases of HA-bound SA analogues. Selected new analogues showed higher estimated affinities than the natural SA when tested against H3N2, H5N1 and H1N1 subtypes of influenza A. An improvement in the binding energies indicates that fragment-based drug design when combined with molecular docking simulation is capable to produce virtual analogues that can become lead compound candidates for anti-flu drug discovery program.     

Docking of sialic acid analogues against influenza A hemagglutinin: a correlational study between experimentally measured and computationally estimated affinities

In this study fragment-based drug design is combined with molecular docking simulation technique, to design databases of virtual sialic acid (SA) analogues with new substitutions at C2, C5 and C6 positions of SA scaffold. Using spaces occupied by C2, C5 and C6 natural moieties of SA when bound to hemagglutinin (HA) crystallographic structure, new fragments that are commercially available were docked independently in all the pockets. The oriented fragments were then connected to the SA scaffold with or without incorporation of linker molecules. The completed analogues were docked to the whole SA binding site to estimate their binding conformations and affinities, generating three databases of HA-bound SA analogues. Selected new analogues showed higher estimated affinities than the natural SA when tested against H3N2, H5N1 and H1N1 subtypes of influenza A. An improvement in the binding energies indicates that fragment-based drug design when combined with molecular docking simulation is capable to produce virtual analogues that can become lead compound candidates for anti-flu drug discovery program.     

ClusPro: an automated docking and discrimination method for the prediction of protein complexes

MOTIVATION: Predicting protein interactions is one of the most challenging problems in functional genomics. Given two proteins known to interact, current docking methods evaluate billions of docked conformations by simple scoring functions, and in addition to near-native structures yield many false positives, i.e. structures with good surface complementarity but far from the native. RESULTS: We have developed a fast algorithm for filtering docked conformations with good surface complementarity, and ranking them based on their clustering properties. The free energy filters select complexes with lowest desolvation and electrostatic energies. Clustering is then used to smooth the local minima and to select the ones with the broadest energy wells-a property associated with the free energy at the binding site. The robustness of the method was tested on sets of 2000 docked conformations generated for 48 pairs of interacting proteins. In 31 of these cases, the top 10 predictions include at least one near-native complex, with an average RMSD of 5 A from the native structure. The docking and discrimination method also provides good results for a number of complexes that were used as targets in the Critical Assessment of PRedictions of Interactions experiment. AVAILABILITY: The fully automated docking and discrimination server ClusPro can be found at http://structure.bu.edu     

Calix[4]arene methylenebisphosphonic acids as inhibitors of protein tyrosine phosphatase 1B

Сalix[4]arenes bearing methylenebisphosphonic or hydroxymethylenebisphosphonic acid fragments at the wide rim of the macrocycle were studied as inhibitors of PTP1B. Some of the inhibitors showed IC₅₀ values in the micromolar range and good selectivity in comparison with other protein tyrosine phosphatases such as TC-PTP, PTPβ, LAR, and CD45. Kinetic studies indicated that the calix[4]arene derivatives influence PTP1B activity as slow-binding inhibitors. Based on molecular docking results, the binding modes of the macrocyclic bisphosphonates in the active centre of PTP1B are discussed.     

Computational analyses of the catalytic and heparin-binding sites and their interactions with glycosaminoglycans in glycoside hydrolase family 79 endo-β-D-glucuronidase (heparanase)

Mammalian heparanase is an endo-β-glucuronidase associated with cell invasion in cancer metastasis, angiogenesis and inflammation. Heparanase cleaves heparan sulfate proteoglycans in the extracellular matrix and basement membrane, releasing heparin/heparan sulfate oligosaccharides of appreciable size. This in turn causes the release of growth factors, which accelerate tumor growth and metastasis. Heparanase has two glycosaminoglycan-binding domains; however, no three-dimensional structure information is available for human heparanase that can provide insights into how the two domains interact to degrade heparin fragments. We have constructed a new homology model of heparanase that takes into account the most recent structural and bioinformatics data available. Heparin analogs and glycosaminoglycan mimetics were computationally docked into the active site with energetically stable ring conformations and their interaction energies were compared. The resulting docked structures were used to propose a model for substrates and conformer selectivity based on the dimensions of the active site. The docking of substrates and inhibitors indicates the existence of a large binding site extending at least two saccharide units beyond the cleavage site (toward the nonreducing end) and at least three saccharides toward the reducing end (toward heparin-binding site 2). The docking of substrates suggests that heparanase recognizes the N-sulfated and O-sulfated glucosamines at subsite +1 and glucuronic acid at the cleavage site, whereas in the absence of 6-O-sulfation in glucosamine, glucuronic acid is docked at subsite +2. These findings will help us to focus on the rational design of heparanase-inhibiting molecules for anticancer drug development by targeting the two heparin/heparan sulfate recognition domains.     

Flexible ligand docking using conformational ensembles.

Molecular docking algorithms suggest possible structures for molecular complexes. They are used to model biological function and to discover potential ligands. A present challenge for docking algorithms is the treatment of molecular flexibility. Here, the rigid body program, DOCK, is modified to allow it to rapidly fit multiple conformations of ligands. Conformations of a given molecule are pre-calculated in the same frame of reference, so that each conformer shares a common rigid fragment with all other conformations. The ligand conformers are then docked together, as an ensemble, into a receptor binding site. This takes advantage of the redundancy present in differing conformers of the same molecule. The algorithm was tested using three organic ligand protein systems and two protein-protein systems. Both the bound and unbound conformations of the receptors were used. The ligand ensemble method found conformations that resembled those determined in X-ray crystal structures (RMS values typically less than 1.5 A). To test the method's usefulness for inhibitor discovery, multi-compound and multi-conformer databases were screened for compounds known to bind to dihydrofolate reductase and compounds known to bind to thymidylate synthase. In both cases, known inhibitors and substrates were identified in conformations resembling those observed experimentally. The ligand ensemble method was 100-fold faster than docking a single conformation at a time and was able to screen a database of over 34 million conformations from 117,000 molecules in one to four CPU days on a workstation.     

Calixarene-based phosphinic acids as inhibitors of protein tyrosine phosphatases

In the present work, the derivatives of calix[4]arene, thiacalix[4]arene, and sulfonylcalix[4]arene bearing four methylene(phenyl)phosphinic acid groups on the upper rim of the macrocycle were synthesized and studied as inhibitors of human protein tyrosine phosphatases. The inhibitory capacities of the three compounds towards PTP1B were higher than those for protein tyrosine phosphatases TC–PTP, MEG1, MEG2, and SHP2. The most potent sulfonylcalix[4]arene phosphinic acid displayed K_i value of 32?nM. The thiacalix[4]arene phosphinic acid was found to be a low micromolar inhibitor of PTP1B with selectivity over the other PTPs. The kinetic experiments showed that the inhibitors compete with the substrate for the active site of the enzyme. Molecular docking was performed to explain possible binding modes of the calixarene-based phosphinic inhibitors of PTP1B.     

Understanding microscopic binding of macrophage migration inhibitory factor with phenolic hydrazones by molecular docking, molecular dynamics simulations and free energy calculations

Macrophage migration inhibitory factor (MIF), an immunoregulatory protein, is a potential target for a number of inflammatory diseases. In the current work, the interactions between MIF and a series of phenolic hydrazones were studied by molecular docking, molecular dynamics (MD) simulations, binding free energy calculations, and binding energy decomposition analysis to determine the structural requirement for achieving favorable biological activity of phenolic hydrazones. First, molecular docking was used to predict the binding modes of inhibitors in the binding site of MIF. The good correlation between the predicted docking scores and the experimental activities shows that the binding conformations of the inhibitors in the active site of MIF are well predicted. Moreover, our results suggest that the flexibility of MIF is essential in ligand binding process. Then, MD simulations and MM/GBSA free energy calculations were employed to determine the dynamic binding process and compare the binding modes of the inhibitors with different activities. The predicted binding free energies given by MM/GBSA are not well correlated with the experimental activities for the two subsets of the inhibitors; however, for each subset, a good correlation between the predicted binding free energies and the experimental activities is achieved. The MM/GBSA free energy decomposition analysis highlights the importance of hydrophobic residues for the MIF binding of the studied inhibitors. Based on the essential factors for MIF-inhibitor interactions derived from the theoretical predictions, some derivatives were designed and the higher inhibitory activities of several candidates were confirmed by molecular docking studies. The structural insights obtained from our study are useful for designing potent inhibitors of MIF.