Response surface methodology in docking study of small molecule BACE-1 inhibitors

Computational evaluation of ligand-receptor binding via docking strategy is a well established approach in structure-based drug design. This technique has been applied frequently in developing molecules of biological interest. However, any procedure would require an optimization set up to be more efficient, economic and time-saving. Advantages of modern statistical optimization methods over conventional one-factor-at-a-time studies have been well revealed. The optimization by experimental design provides a combination of factor levels simultaneously satisfying the requirements considered for each of the responses and factors. In this study, response surface method was applied to optimize the prominent factors (number of genetic algorithm runs, population size, maximum number of evaluations, torsion degrees for ligand and number of rotatable bonds in ligand) in AutoDock4.2-based binding study of small molecule β-secretase inhibitors as anti-alzheimer agents. Results revealed that a number of rotatable bonds in ligand and maximum number of docking evaluations were determinant variables affecting docking outputs. The interference between torsion degrees for ligand and number of genetic algorithm runs for docking procedure was found to be the significant interaction term in our model. Optimized docking outputs exhibited a high correlation with experimental fluorescence resonance energy transfer-based IC(50)s for β-secretase inhibitors (R(2)?=?0.9133).     

A hierarchical method for molecular docking using cloud computing

Discovering small molecules that interact with protein targets will be a key part of future drug discovery efforts. Molecular docking of drug-like molecules is likely to be valuable in this field; however, the great number of such molecules makes the potential size of this task enormous. In this paper, a method to screen small molecular databases using cloud computing is proposed. This method is called the hierarchical method for molecular docking and can be completed in a relatively short period of time. In this method, the optimization of molecular docking is divided into two subproblems based on the different effects on the protein–ligand interaction energy. An adaptive genetic algorithm is developed to solve the optimization problem and a new docking program (FlexGAsDock) based on the hierarchical docking method has been developed. The implementation of docking on a cloud computing platform is then discussed. The docking results show that this method can be conveniently used for the efficient molecular design of drugs.     

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

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

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

We present a computational approach for predicting structures of ligand-protein complexes and analyzing binding energy landscapes that combines Monte Carlo simulated annealing technique to determine the ligand bound conformation with the dead-end elimination algorithm for side-chain optimization of the protein active site residues. Flexible ligand docking and optimization of mobile protein side-chains have been performed to predict structural effects in the V32I/I47V/V82I HIV-1 protease mutant bound with the SB203386 ligand and in the V82A HIV-1 protease mutant bound with the A77003 ligand. The computational structure predictions are consistent with the crystal structures of these ligand-protein complexes. The emerging relationships between ligand docking and side-chain optimization of the active site residues are rationalized based on the analysis of the ligand-protein binding energy landscape. Proteins 33:295–310, 1998. © 1998 Wiley-Liss, Inc.     

New general approach for determining the solution structure of a ligand bound weakly to a receptor: structure of a fibrinogen Aalpha-like peptide bound to thrombin (S195A) obtained using NOE distance constraints and an ECEPP/3 flexible docking program.

A new approach incorporating flexible docking simulations and NMR data is presented for calculating the bound conformation of a ligand that interacts weakly with an enzyme. This approach consists of sampling directly the conformation of a flexible ligand inside a receptor active site containing surrounding flexible loops. To make this sampling efficient, a ligand-growing procedure has been adopted. Optimization of the ECEPP/3-plus-NOE constraint function is carried out by using a collective variable Monte Carlo minimization technique. Numerous energy minimizations are made possible for such a large system by using a Bezier splines energy grid technique. This new flexible docking approach was applied to determine the structure of a fibrinogen Aalpha-like peptide (7DFLAEGGGVRGPRV20) bound to an active site mutant of thrombin [thrombin(S195A)]. Structure calculations of the bound ligand, using 2D-transferred NOESY distance constraints in the DIANA program, showed that the N-terminal portion of the peptide (D7-R16) involves a chain reversal, whereas the C-terminal portion (G17-V20) adopts a fold that exists in several different orientations. In addition, the ECEPP/3 flexible docking package was used to assess the conformational variability of the ligand and surrounding 60D-insertion loop of thrombin. Amino acid residues (17-20) of the peptide interact with a region of the enzyme that exhibits broad specificity, with a preferred direction between the 60D-insertion loop and Pro37 of thrombin.     

Automatic identification and representation of protein binding sites for molecular docking.

Molecular docking is a popular way to screen for novel drug compounds. The method involves aligning small molecules to a protein structure and estimating their binding affinity. To do this rapidly for tens of thousands of molecules requires an effective representation of the binding region of the target protein. This paper presents an algorithm for representing a protein's binding site in a way that is specifically suited to molecular docking applications. Initially the protein's surface is coated with a collection of molecular fragments that could potentially interact with the protein. Each fragment, or probe, serves as a potential alignment point for atoms in a ligand, and is scored to represent that probe's affinity for the protein. Probes are then clustered by accumulating their affinities, where high affinity clusters are identified as being the "stickiest" portions of the protein surface. The stickiest cluster is used as a computational binding "pocket" for docking. This method of site identification was tested on a number of ligand-protein complexes; in each case the pocket constructed by the algorithm coincided with the known ligand binding site. Successful docking experiments demonstrated the effectiveness of the probe representation.     

Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP

Most current docking methods to identify possible ligands and putative binding sites on a receptor molecule assume a rigid receptor structure to allow virtual screening of large ligand databases. However, binding of a ligand can lead to changes in the receptor protein conformation that are sterically necessary to accommodate a bound ligand. An approach is presented that allows relaxation of the protein conformation in precalculated soft flexible degrees of freedom during ligand-receptor docking. For the immunosuppressant FK506-binding protein FKBP, the soft flexible modes are extracted as principal components of motion from a molecular dynamics simulation. A simple penalty function for deformations in the soft flexible mode is used to limit receptor protein deformations during docking that avoids a costly recalculation of the receptor energy by summing over all receptor atom pairs at each step. Rigid docking of the FK506 ligand binding to an unbound FKBP conformation failed to identify a geometry close to experiment as favorable binding site. In contrast, inclusion of the flexible soft modes during systematic docking runs selected a binding geometry close to experiment as lowest energy conformation. This has been achieved at a modest increase of computational cost compared to rigid docking. The approach could provide a computationally efficient way to approximately account for receptor flexibility during docking of large numbers of putative ligands and putative docking geometries.     

Receptor–ligand molecular docking

Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein–ligand applications. We summarise the main topics and recent computational and methodological advances in protein–ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.     

Molecular docking study and development of an empirical binding free energy model for phosphodiesterase 4 inhibitors

In the present work, several computational methodologies were combined to develop a model for the prediction of PDE4B inhibitors' activity. The adequacy of applying the ligand docking approach, keeping the enzyme rigid, to the study of a series of PDE4 inhibitors was confirmed by a previous molecular dynamics analysis of the complete enzyme. An exhaustive docking procedure was performed to identify the most probable binding modes of the ligands to the enzyme, including the active site metal ions and the surrounding structural water molecules. The enzyme-inhibitor interaction enthalpies, refined by using the semiempirical molecular orbital approach, were combined with calculated solvation free energies and entropy considerations in an empirical free energy model that enabled the calculation of binding free energies that correlated very well with experimentally derived binding free energies. Our results indicate that both the inclusion of the structural water molecules close to the ions in the binding site and the use of a free energy model with a quadratic dependency on the ligand free energy of solvation are important aspects to be considered for molecular docking investigations involving the PDE4 enzyme family.     

Challenges and advances in computational docking: 2009 in review

Docking is a computational technique that places a small molecule (ligand) in the binding site of its macromolecular target (receptor) and estimates its binding affinity. This review addresses methodological developments that have occurred in the docking field in 2009, with a particular focus on the more difficult, and sometimes controversial, aspects of this promising computational discipline. These developments aim to address the main challenges of docking: receptor representation (such aspects as structural waters, side chain protonation, and, most of all, flexibility (from side chain rotation to domain movement)), ligand representation (protonation, tautomerism and stereoisomerism, and the effect of input conformation), as well as accounting for solvation and entropy of binding. This review is strongly focused on docking advances in the context of drug design, specifically in virtual screening and fragment-based drug design.     

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.     

The particle concept: placing discrete water molecules during protein-ligand docking predictions.

Water is known to play a significant role in the formation of protein-ligand complexes. In this paper, we focus on the influence of water molecules on the structure of protein-ligand complexes. We present an algorithmic approach, called the particle concept, for integrating the placement of single water molecules in the docking algorithm of FLEXX. FLEXX is an incremental construction approach to ligand docking consisting of three phases: the selection of base fragments, the placement of the base fragments, and the incremental reconstruction of the ligand inside the active site of a protein. The goal of the extension is to find water molecules at favorable places in the protein-ligand interface which may guide the placement of the ligand. In a preprocessing phase, favorable positions of water molecules inside the active site are calculated and stored in a list of possible water positions. During the incremental construction phase, water molecules are placed at the precomputed positions if they can form additional hydrogen bonds to the ligand. Steric constraints resulting from the water molecules as well as the geometry of the hydrogen bonds are used to optimize the ligand orientation in the active site during the reconstruction process. We have tested the particle concept on a series of 200 protein-ligand complexes. Although the average improvement of the prediction results is minor, we were able to predict water molecules between the protein and the ligand correctly in several cases. For instance in the case of HIV-1 protease, where a single water molecule between the protein and the ligand is known to be of importance in complex formation, significant improvements can be achieved.     

Docking by least-squares fitting of molecular surface patterns.

Molecular surfaces are fitted to each other by a new solution to the problem of docking a ligand into the active site of a protein molecule. The procedure constructs patterns of points on the surfaces and superimposes them upon each other using a least-squares best-fit algorithm. This brings the surfaces into contact and provides a direct measure of their local complementarity. The search over the ligand surface produces a large number of dockings, of which a small fraction having the best complementarity and the least steric hindrance are evaluated for electrostatic interaction energy. When applied to molecules taken from crystallographically observed complexes, this procedure consistently assigns the lowest electrostatic energies to correct dockings. On independently determined structures, the ability of the method to discern correct dockings depends on how much conformational difference there is between the free and complexed forms of the molecules. The procedure is found to be fast enough on contemporary workstation computers to permit many conformations to be considered, and tolerant enough to make rather coarse bond dihedral sampling a practicable way to overcome the problem of structural flexibility.     

Exploring docking methods for virtual screening: application to the identification of neuraminidase and Ftsz potential inhibitors

Virtual screenings based on molecular docking play a major role in medicinal chemistry for the identification of new bioactive molecules. For this purpose, several docking methods can be used. Here, using Arguslab as software and a Gold Platinum subset library of commercially available compounds from Asinex, two docking methods associated to the scoring function Ascore were employed to investigate virtual screenings. One method is based on a genetic algorithm and the other based on a shape-based method. As case studies, both docking techniques were explored by targeting the PC190723 binding site of FtsZ protein from Staphylococcus aureus and the active site of N8 neuraminidase from Influenza virus. Following four docking sequences for each docking engine, the genetic algorithm led to multiple docking results, whereas the shape-based method gave reproducible results. The present study shows that the stochastic nature of the genetic algorithm will require the biological evaluation of more compounds than the shape-based method. This study showed that both methods are complementary and also led to the identification of neuraminidase and FtsZ potential inhibitors.     

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.     

Methodology and problems of protein-ligand docking: case study of dihydroorotate dehydrogenase,thymidine kinase,and phosphodiesterase 4

The docking methodology was applied to three different therapeutically interesting enzymes: human dihydroorotate dehydrogenase (DHODH), Herpes simplex virus type I thymidine kinase (HSV1 TK) and human phosphodiesterase 4 (PDE4). Programs FlexX, AutoDock and DOCK where used. The three targets represent three distinct cases. For DHODH and HSV1 TK, the binding modes of substrate and inhibitors within the active site are known, while the binding orientation of cAMP within PDE4 has been solely hypothesized. Active site of DHODH is mainly hydrophobic and the binding mode of the inhibitor brequinar was used as a template for evaluating the docking strategies. The presence of cofactors revealed to be crucial for the definition of the docking site. The HSV1 TK active site is small and polar and contains crystal water molecules and ATP. Docking of thymidine and aciclovir (ACV) within the active site was analyzed by keeping or removing water molecules. It showed the crucial role of water in predicting the binding of pyrimidines and purines. The crystal structure of PDE4 contains magnesium and zinc cations as well as catalytic water molecule but no ligand. Several docking experiments of cAMP and rolipram were performed and the results showed clear-cut dependence between the ligand orientation and the presence of metals in the active site. All three cases show specific problems of the docking methodology, depending on the character of the active site.     

3D structure generation,virtual screening and docking of human Ras-associated binding (Rab3A) protein involved in tumourigenesis

Rab3A is expressed predominantly in brain and synaptic vesicles. Rab3A is involved specifically in tethering and docking of synaptic vesicles prior to fusion which is a critical step in regulated release of neurotransmitters. The precise function of Rab3A is still not known. However, up-regulation of Rab3A has been reported in malignant neuroendocrine and breast cancer cells. In the present study, the structure of Rab3A protein was generated using MODELLER 9v8 software. The modeled protein structure was validated and subjected to molecular docking analyses. Docking with GTP was carried out on the binding site of Rab3A using GOLD software. The Rab3A-GTP complex has best GOLD fitness value of 77.73. Ligplot shows hydrogen bondings (S16, S17, V18, G19, K20, T21, S22, S31, T33, A35, S38, T39 and G65) and hydrophobic interacting residues (F25, F32, P34, F36, V37, D62 and A64) with the GTP ligands in the binding site of Rab3A protein. Here, the ligand molecules of NCI diversity set II from the ZINC database against the active site of the Rab3A protein were screened. For this purpose, the incremental construction algorithm of GLIDE and the genetic algorithm of GOLD were used. Docking results were analyzed for top ranking compounds using a consensus scoring function of X-Score to calculate the binding affinity and Ligplot was used to measure protein–ligand interactions. Five compounds which possess good inhibitory activity and may act as potential high affinity inhibitors against Rab3A active site were identified. The top ranking molecule (ZINC13152284) has a Glide score of ?6.65 kcal/mol, X-Score of ?3.02 kcal/mol and GOLD score of 64.54 with 03 hydrogen bonds and 09 hydrophobic contacts. This compound is thus a good starting point for further development of strong inhibitors.     

Methodology and Problems of Protein‐Ligand Docking: Case Study of Dihydroorotate Dehydrogenase,Thymidine Kinase,and Phosphodiesterase 4

Abstract

The docking methodology was applied to three different therapeutically interesting enzymes: human dihydroorotate dehydrogenase (DHODH), Herpes simplex virus type I thymidine kinase (HSV1 TK) and human phosphodiesterase 4 (PDE4). Programs FlexX, AutoDock and DOCK where used. The three targets represent three distinct cases. For DHODH and HSV1 TK, the binding modes of substrate and inhibitors within the active site are known, while the binding orientation of cAMP within PDE4 has been solely hypothesized. Active site of DHODH is mainly hydrophobic and the binding mode of the inhibitor brequinar was used as a template for evaluating the docking strategies. The presence of cofactors revealed to be crucial for the definition of the docking site. The HSV1 TK active site is small and polar and contains crystal water molecules and ATP. Docking of thymidine and aciclovir (ACV) within the active site was analyzed by keeping or removing water molecules. It showed the crucial role of water in predicting the binding of pyrimidines and purines. The crystal structure of PDE4 contains magnesium and zinc cations as well as catalytic water molecule but no ligand. Several docking experiments of cAMP and rolipram were performed and the results showed clear‐cut dependence between the ligand orientation and the presence of metals in the active site. All three cases show specific problems of the docking methodology, depending on the character of the active site.     

A hierarchical method for generating low-energy conformers of a protein-ligand complex

A novel dynamical protocol for finding the low-energy conformations of a protein-ligand complex is described. The energy functions examined consist of an empirical force field with four different dielectric screening models; the generalized Born/surface area model also is examined. Application of the method to three complexes of known crystal structure provides insights into the energy functions used for selecting low-energy docked conformations and into the structure of the binding-energy surface. Evidence is presented that the local energy minima of a ligand in a binding site are arranged in a hierarchical fashion. This observation motivates the construction of a hierarchical docking algorithm that substantially enriches the population of ligand conformations close to the crystal conformation. The algorithm is also adapted to permit docking into a flexible binding site and preliminary tests of this method are presented. Proteins 33:475–495, 1998. © 1998 Wiley-Liss, Inc.