Exploring hierarchical refinement techniques for induced fit docking with protein and ligand flexibility

We present a series of molecular‐mechanics‐based protein refinement methods, including two novel ones, applied as part of an induced fit docking procedure. The methods used include minimization; protein and ligand sidechain prediction; a hierarchical ligand placement procedure similar to a‐priori protein loop predictions; and a minimized Monte Carlo approach using normal mode analysis as a move step. The results clearly indicate the importance of a proper opening of the active site backbone, which might not be accomplished when the ligand degrees of freedom are prioritized. The most accurate method consisted of the minimized Monte Carlo procedure designed to open the active site followed by a hierarchical optimization of the sidechain packing around a mobile flexible ligand. The methods have been used on a series of 88 protein‐ligand complexes including both cross‐docking and apo‐docking members resulting in complex conformations determined to within 2.0 Å heavy‐atom RMSD in 75% of cases where the protein backbone rearrangement upon binding is less than 1.0 Å α‐carbon RMSD. We also demonstrate that physics‐based all‐atom potentials can be more accurate than docking‐style potentials when complexes are sufficiently refined. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Blind docking method combining search of low‐resolution binding sites with ligand pose refinement by molecular dynamics‐based global optimization

This study describes the development of a new blind hierarchical docking method, bhDock, its implementation, and accuracy assessment. The bhDock method uses two‐step algorithm. First, a comprehensive set of low‐resolution binding sites is determined by analyzing entire protein surface and ranked by a simple score function. Second, ligand position is determined via a molecular dynamics‐based method of global optimization starting from a small set of high ranked low‐resolution binding sites. The refinement of the ligand binding pose starts from uniformly distributed multiple initial ligand orientations and uses simulated annealing molecular dynamics coupled with guided force‐field deformation of protein–ligand interactions to find the global minimum. Assessment of the bhDock method on the set of 37 protein–ligand complexes has shown the success rate of predictions of 78%, which is better than the rate reported for the most cited docking methods, such as AutoDock, DOCK, GOLD, and FlexX, on the same set of complexes. © 2009 Wiley Periodicals, Inc. J Comput Chem 2010     

Protein farnesyltransferase: Flexible docking studies on inhibitors using computational modeling

Summary Using MacroModel, peptide, peptidomimetic and non-peptidomimetic inhibitors of the zinc metalloenzyme, farnesyltransferase (FTase), were docked into the enzyme binding site. Inhibitor flexibility, farnesyl pyrophosphate substrate flexibility, and partial protein flexibility were taken into account in these docking studies. In addition to CVFM and CVIM, as well as our own inhibitors FTI-276 and FTI-2148, we have docked other farnesyltransferase inhibitors (FTIs) including Zarnestra, which presently is in advanced clinical trials. The AMBER* force field was employed, augmented with parameters that were derived for zinc. A single binding site model that was derived from the crystal structure of CVFM complexed with farnesyltransferase and farnesylpyrophosphate was used for these studies. The docking results using the lowest energy structure from the simulation, or one of the lowest energy structures, were generally in excellent agreement with the X-ray structures. One of the most important findings of this study is that numerous alternative conformations for the methionine side chain can be accommodated by the enzyme suggesting that the methionine pocket can tolerate groups larger than methionine at the C-terminus of the tetrapeptide and suggesting alternative locations for the placement of side chains that may improve potency.     

Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function

A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the free energy change upon binding. Interestingly, this method applies a Lamarckian model of genetics, in which environmental adaptations of an individual's phenotype are reverse transcribed into its genotype and become heritable traits (sic). We consider three search methods, Monte Carlo simulated annealing, a traditional genetic algorithm, and the Lamarckian genetic algorithm, and compare their performance in dockings of seven protein–ligand test systems having known three-dimensional structure. We show that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTO DOCK , and that the Lamarckian genetic algorithm is the most efficient, reliable, and successful of the three. The empirical free energy function was calibrated using a set of 30 structurally known protein–ligand complexes with experimentally determined binding constants. Linear regression analysis of the observed binding constants in terms of a wide variety of structure-derived molecular properties was performed. The final model had a residual standard error of 9.11 kJ mol⁻¹ (2.177 kcal mol⁻¹) and was chosen as the new energy function. The new search methods and empirical free energy function are available in AUTO DOCK , version 3.0. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1639–1662, 1998     

FIPSDock: A new molecular docking technique driven by fully informed swarm optimization algorithm

The accurate prediction of protein–ligand binding is of great importance for rational drug design. We present herein a novel docking algorithm called as FIPSDock, which implements a variant of the Fully Informed Particle Swarm (FIPS) optimization method and adopts the newly developed energy function of AutoDock 4.20 suite for solving flexible protein–ligand docking problems. The search ability and docking accuracy of FIPSDock were first evaluated by multiple cognate docking experiments. In a benchmarking test for 77 protein/ligand complex structures derived from GOLD benchmark set, FIPSDock has obtained a successful predicting rate of 93.5% and outperformed a few docking programs including particle swarm optimization (PSO)@AutoDock, SODOCK, AutoDock, DOCK, Glide, GOLD, FlexX, Surflex, and MolDock. More importantly, FIPSDock was evaluated against PSO@AutoDock, SODOCK, and AutoDock 4.20 suite by cross‐docking experiments of 74 protein–ligand complexes among eight protein targets (CDK2, ESR1, F2, MAPK14, MMP8, MMP13, PDE4B, and PDE5A) derived from Sutherland‐crossdock‐set. Remarkably, FIPSDock is superior to PSO@AutoDock, SODOCK, and AutoDock in seven out of eight cross‐docking experiments. The results reveal that FIPS algorithm might be more suitable than the conventional genetic algorithm‐based algorithms in dealing with highly flexible docking problems. © 2012 Wiley Periodicals, Inc.     

Q-Dock: Low-resolution flexible ligand docking with pocket-specific threading restraints

The rapidly growing number of theoretically predicted protein structures requires robust methods that can utilize low-quality receptor structures as targets for ligand docking. Typically, docking accuracy falls off dramatically when apo or modeled receptors are used in docking experiments. Low-resolution ligand docking techniques have been developed to deal with structural inaccuracies in predicted receptor models. In this spirit, we describe the development and optimization of a knowledge-based potential implemented in Q-Dock, a low-resolution flexible ligand docking approach. Self-docking experiments using crystal structures reveals satisfactory accuracy, comparable with all-atom docking. All-atom models reconstructed from Q-Dock's low-resolution models can be further refined by even a simple all-atom energy minimization. In decoy-docking against distorted receptor models with a root-mean-square deviation, RMSD, from native of approximately 3 A, Q-Dock recovers on average 15-20% more specific contacts and 25-35% more binding residues than all-atom methods. To further improve docking accuracy against low-quality protein models, we propose a pocket-specific protein-ligand interaction potential derived from weakly homologous threading holo-templates. The success rate of Q-Dock employing a pocket-specific potential is 6.3 times higher than that previously reported for the Dolores method, another low-resolution docking approach.     

Theory of docking scores and its application to a customizable scoring function

In general, the docking scoring tends to have a size dependence related to the ranking of compounds. In this paper, we describe a novel method of parameter optimization for docking scores which reduce the size dependence and can efficiently discriminate active compounds from chemical databases. This method is based on a simplified theoretical model of docking scores which enables us to utilize large amounts of data of known active and inactive compounds for a particular target without requiring large computational resources or a complicated procedure. This method is useful for making scoring functions for the identification of novel scaffolds using the knowledge of active compounds for a particular target or a customized scoring function for an interesting family of drug targets.     

Protein‐protein docking by shape‐complementarity and property matching

We present a computational approach to protein‐protein docking based on surface shape complementarity (“ProBinder”). Within this docking approach, we implemented a new surface decomposition method that considers local shape features on the protein surface. This new surface shape decomposition results in a deterministic representation of curvature features on the protein surface, such as “knobs,” “holes,” and “flats” together with their point normals. For the actual docking procedure, we used geometric hashing, which allows for the rapid, translation‐, and rotation‐free comparison of point coordinates. Candidate solutions were scored based on knowledge‐based potentials and steric criteria. The potentials included electrostatic complementarity, desolvation energy, amino acid contact preferences, and a van‐der‐Waals potential. We applied ProBinder to a diverse test set of 68 bound and 30 unbound test cases compiled from the Dockground database. Sixty‐four percent of the protein‐protein test complexes were ranked with an root mean square deviation (RMSD) < 5 Å to the target solution among the top 10 predictions for the bound data set. In 82% of the unbound samples, docking poses were ranked within the top ten solutions with an RMSD < 10 Å to the target solution. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

Ligand-protein docking using a quantum stochastic tunneling optimization method

A novel hybrid optimization method called quantum stochastic tunneling has been recently introduced. Here, we report its implementation within a new docking program called EasyDock and a validation with the CCDC/Astex data set of ligand-protein complexes using the PLP score to represent the ligand-protein potential energy surface and ScreenScore to score the ligand-protein binding energies. When taking the top energy-ranked ligand binding mode pose, we were able to predict the correct crystallographic ligand binding mode in up to 75% of the cases. By using this novel optimization method run times for typical docking simulations are significantly shortened.     

Optimization and dynamics of protein-protein complexes using B-splines

A moving-grid approach for optimization and dynamics of protein-protein complexes is introduced, which utilizes cubic B-spline interpolation for rapid energy and force evaluation. The method allows for the efficient use of full electrostatic potentials joined smoothly to multipoles at long distance so that multiprotein simulation is possible. Using a recently published benchmark of 58 protein complexes, we examine the performance and quality of the grid approximation, refining cocrystallized complexes to within 0.68 A RMSD of interface atoms, close to the optimum 0.63 A produced by the underlying MMFF94 force field. We quantify the theoretical statistical advantage of using minimization in a stochastic search in the case of two rigid bodies, and contrast it with the underlying cost of conjugate gradient minimization using B-splines. The volumes of conjugate gradient minimization basins of attraction in cocrystallized systems are generally orders of magnitude larger than well volumes based on energy thresholds needed to discriminate native from nonnative states; nonetheless, computational cost is significant. Molecular dynamics using B-splines is doubly efficient due to the combined advantages of rapid force evaluation and large simulation step sizes. Large basins localized around the native state and other possible binding sites are identifiable during simulations of protein-protein motion. In addition to providing increased modeling detail, B-splines offer new algorithmic possibilities that should be valuable in refining docking candidates and studying global complex behavior.     

A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6

Successive parameterizations of the GROMOS force field have been used successfully to simulate biomolecular systems over a long period of time. The continuing expansion of computational power with time makes it possible to compute ever more properties for an increasing variety of molecular systems with greater precision. This has led to recurrent parameterizations of the GROMOS force field all aimed at achieving better agreement with experimental data. Here we report the results of the latest, extensive reparameterization of the GROMOS force field. In contrast to the parameterization of other biomolecular force fields, this parameterization of the GROMOS force field is based primarily on reproducing the free enthalpies of hydration and apolar solvation for a range of compounds. This approach was chosen because the relative free enthalpy of solvation between polar and apolar environments is a key property in many biomolecular processes of interest, such as protein folding, biomolecular association, membrane formation, and transport over membranes. The newest parameter sets, 53A5 and 53A6, were optimized by first fitting to reproduce the thermodynamic properties of pure liquids of a range of small polar molecules and the solvation free enthalpies of amino acid analogs in cyclohexane (53A5). The partial charges were then adjusted to reproduce the hydration free enthalpies in water (53A6). Both parameter sets are fully documented, and the differences between these and previous parameter sets are discussed.     

FPDock: Protein–protein docking using flower pollination algorithm

Proteins play their vital role in biological systems through interaction and complex formation with other biological molecules. Indeed, abnormalities in the interaction patterns affect the proteins’ structure and have detrimental effects on living organisms. Research in structure prediction gains its gravity as the functions of proteins depend on their structures. Protein–protein docking is one of the computational methods devised to understand the interaction between proteins. Metaheuristic algorithms are promising to use owing to the hardness of the structure prediction problem. In this paper, a variant of the Flower Pollination Algorithm (FPA) is applied to get an accurate protein–protein complex structure. The algorithm begins execution from a randomly generated initial population, which gets flourished in different isolated islands, trying to find their local optimum. The abiotic and biotic pollination applied in different generations brings diversity and intensity to the solutions. Each round of pollination applies an energy-based scoring function whose value influences the choice to accept a new solution. Analysis of final predictions based on CAPRI quality criteria shows that the proposed method has a success rate of 58% in top10 ranks, which in comparison with other methods like SwarmDock, pyDock, ZDOCK is better. Source code of the work is available at: https://github.com/Sharon1989Sunny/_FPDock_.     

GalaxyDock2: Protein–ligand docking using beta‐complex and global optimization

In this article, an enhanced version of GalaxyDock protein–ligand docking program is introduced. GalaxyDock performs conformational space annealing (CSA) global optimization to find the optimal binding pose of a ligand both in the rigid‐receptor mode and the flexible‐receptor mode. Binding pose prediction has been improved compared to the earlier version by the efficient generation of high‐quality initial conformations for CSA using a predocking method based on a beta‐complex derived from the Voronoi diagram of receptor atoms. Binding affinity prediction has also been enhanced by using the optimal combination of energy components, while taking into consideration the energy of the unbound ligand state. The new version has been tested in terms of binding mode prediction, binding affinity prediction, and virtual screening on several benchmark sets, showing improved performance over the previous version and AutoDock, on which the GalaxyDock energy function is based. GalaxyDock2 also performs better than or comparable to other state‐of‐the‐art docking programs. GalaxyDock2 is freely available at http://galaxy.seoklab.org/softwares/galaxydock.html . © 2013 Wiley Periodicals, Inc.     

Flexible ligand docking using a robust evolutionary algorithm

A flexible ligand docking protocol based on evolutionary algorithms is investigated. The proposed approach incorporates family competition and adaptive rules to integrate decreasing‐based mutations and self‐adaptive mutations to act as global and local search strategies, respectively. The method is applied to a dihydrofolate reductase enzyme with the anticancer drug methotrexate and two analogues of antibacterial drug trimethoprim. Conformations and orientations closed to the crystallographically determined structures are obtained, as well as alternative structures with low energy. Numerical results indicate that the new approach is very robust. The docked lowest‐energy structures have root‐mean‐square derivations ranging from 0.67 to 1.96 Å with respect to the corresponding crystal structures. © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 988–998, 2000     

Adaptation of a fast Fourier transform-based docking algorithm for protein design

Designing proteins with novel protein/protein binding properties can be achieved by combining the tools that have been developed independently for protein docking and protein design. We describe here the sequence-independent generation of protein dimer orientations by protein docking for use as scaffolds in protein sequence design algorithms. To dock monomers into sequence-independent dimer conformations, we use a reduced representation in which the side chains are approximated by spheres with atomic radii derived from known C2 symmetry-related homodimers. The interfaces of C2-related homodimers are usually more hydrophobic and protein core-like than the interfaces of heterodimers; we parameterize the radii for docking against this feature to capture and recreate the spatial characteristics of a hydrophobic interface. A fast Fourier transform-based geometric recognition algorithm is used for docking the reduced representation protein models. The resulting docking algorithm successfully predicted the wild-type homodimer orientations in 65 out of 121 dimer test cases. The success rate increases to approximately 70% for the subset of molecules with large surface area burial in the interface relative to their chain length. Forty-five of the predictions exhibited less than 1 A C(alpha) RMSD compared to the native X-ray structures. The reduced protein representation therefore appears to be a reasonable approximation and can be used to position protein backbones in plausible orientations for homodimer design.     

A new peptide docking strategy using a mean field technique with mutually orthogonal Latin square sampling

The theoretical prediction of the association of a flexible ligand with a protein receptor requires efficient sampling of the conformational space of the ligand. Several docking methodologies are currently available. We propose a new docking technique that performs well at low computational cost. The method uses mutually orthogonal Latin squares to efficiently sample the docking space. A variant of the mean field technique is used to analyze this sample to arrive at the optimum. The method has been previously applied to explore the conformational space of peptides and identify structures with low values for the potential energy. Here we extend this method to simultaneously identify both the low energy conformation as well as a ‘high-scoring’ docking mode. Application of the method to 56 protein–peptide complexes, in which the length of the peptide ligand ranges from three to seven residues, and comparisons with Autodock 3.05, showed that the method works well. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Update of the ATTRACT force field for the prediction of protein–protein binding affinity

Determining the protein–protein interactions is still a major challenge for molecular biology. Docking protocols has come of age in predicting the structure of macromolecular complexes. However, they still lack accuracy to estimate the binding affinities, the thermodynamic quantity that drives the formation of a complex. Here, an updated version of the protein–protein ATTRACT force field aiming at predicting experimental binding affinities is reported. It has been designed on a dataset of 218 protein–protein complexes. The correlation between the experimental and predicted affinities reaches 0.6, outperforming most of the available protocols. Focusing on a subset of rigid and flexible complexes, the performance raises to 0.76 and 0.69, respectively. © 2017 Wiley Periodicals, Inc.     

MCDOCK: A Monte Carlo simulation approach to the molecular docking problem

Prediction of the binding mode of a ligand (a drug molecule) to its macromolecular receptor, or molecular docking, is an important problem in rational drug design. We have developed a new docking method in which a non-conventional Monte Carlo (MC) simulation technique is employed. A computer program, MCDOCK, was developed to carry out the molecular docking operation automatically. The current version of the MCDOCK program (version 1.0) allows for the full flexibility of ligands in the docking calculations. The scoring function used in MCDOCK is the sum of the interaction energy between the ligand and its receptor, and the conformational energy of the ligand. To validate the MCDOCK method, 19 small ligands, the binding modes of which had been determined experimentally using X-ray diffraction, were docked into their receptor binding sites. To produce statistically significant results, 20 MCDOCK runs were performed for each protein–ligand complex. It was found that a significant percentage of these MCDOCK runs converge to the experimentally observed binding mode. The root-mean-square (rms) of all non-hydrogen atoms of the ligand between the predicted and experimental binding modes ranges from 0.25 to 1.84 Å for these 19 cases. The computational time for each run on an SGI Indigo2/R10000 varies from less than 1 min to 15 min, depending upon the size and the flexibility of the ligands. Thus MCDOCK may be used to predict the precise binding mode of ligands in lead optimization and to discover novel lead compounds through structure-based database searching.