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.     

Theoretical study on the structure and stability of [1,2,5] oxadiazolo [3,4-e] [1,2,3,4]-tetrazine-4,6-Di-N-dioxide (FTDO)

In protein–ligand docking, an optimization algorithm is used to find the best binding pose of a ligand against a protein target. This algorithm plays a vital role in determining the docking accuracy. To evaluate the relative performance of different optimization algorithms and provide guidance for real applications, we performed a comparative study on six efficient optimization algorithms, containing two evolutionary algorithm (EA)-based optimizers (LGA, DockDE) and four particle swarm optimization (PSO)-based optimizers (SODock, varCPSO, varCPSO-ls, FIPSDock), which were implemented into the protein–ligand docking program AutoDock. We unified the objective functions by applying the same scoring function, and built a new fitness accuracy as the evaluation criterion that incorporates optimization accuracy, robustness, and efficiency. The varCPSO and varCPSO-ls algorithms show high efficiency with fast convergence speed. However, their accuracy is not optimal, as they cannot reach very low energies. SODock has the highest accuracy and robustness. In addition, SODock shows good performance in efficiency when optimizing drug-like ligands with less than ten rotatable bonds. FIPSDock shows excellent robustness and is close to SODock in accuracy and efficiency. In general, the four PSO-based algorithms show superior performance than the two EA-based algorithms, especially for highly flexible ligands. Our method can be regarded as a reference for the validation of new optimization algorithms in protein–ligand docking.     

Pre-docking filter for protein and ligand 3D structures

Virtual drug screening using protein-ligand docking techniques is a time-consuming process, which requires high computational power for binding affinity calculation. There are millions of chemical compounds available for docking. Eliminating compounds that are unlikely to exhibit high binding affinity from the screening set should speed-up the virtual drug screening procedure. We performed docking of 6353 ligands against twenty-one protein X-ray crystal structures. The docked ligands were ranked according to their calculated binding affinities, from which the top five hundred and the bottom five hundred were selected. We found that the volume and number of rotatable bonds of the top five hundred docked ligands are similar to those found in the crystal structures and corresponded with the volume of the binding sites. In contrast, the bottom five hundred set contains ligands that are either too large to enter the binding site, or too small to bind with high specificity and affinity to the binding site. A pre-docking filter that takes into account shapes and volumes of the binding sites as well as ligand volumes and flexibilities can filter out low binding affinity ligands from the screening sets. Thus, the virtual drug screening procedure speed is increased.     

In silico multi-filter screening approaches for developing novel beta-secretase inhibitors

A large database of chemical structures was screened for potential inhibitors of β-secretase was carried out using in silico multi-filter techniques. Substructure screening, computer-aided ligand docking, binding free energy calculations, and partial interaction energy analyses were performed successively to identify chemical compounds which could serve as different scaffolds from known β-secretase inhibitors for future drug design. We showed that our in silico multi-filter screening retrieved all known inhibitors from the compound database investigated, which suggests that the other compounds identified as inhibitors by this computerized screening process are potential β-secretase inhibitors.     

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.     

De novo design of potential RecA inhibitors using multi objective optimization

De novo ligand design involves optimization of several ligand properties such as binding affinity, ligand volume, drug likeness, etc. Therefore, optimization of these properties independently and simultaneously seems appropriate. In this paper, the ligand design problem is modeled in a multiobjective using Archived MultiObjective Simulated Annealing (AMOSA) as the underlying search algorithm. The multiple objectives considered are the energy components similarity to a known inhibitor and a novel drug likeliness measure based on Lipinski's rule of five. RecA protein of Mycobacterium tuberculosis, causative agent of tuberculosis, is taken as the target for the drug design. To gauge the goodness of the results, they are compared to the outputs of LigBuilder, NEWLEAD, and Variable genetic algorithm (VGA). The same problem has also been modeled using a well-established genetic algorithm-based multiobjective optimization technique, Nondominated Sorting Genetic Algorithm-II (NSGA-II), to find the efficacy of AMOSA through comparative analysis. Results demonstrate that while some small molecules designed by the proposed approach are remarkably similar to the known inhibitors of RecA, some new ones are discovered that may be potential candidates for novel lead molecules against tuberculosis.     

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.     

Hydrogen bonding penalty upon ligand binding

Ligand binding involves breakage of hydrogen bonds with water molecules and formation of new hydrogen bonds between protein and ligand. In this work, the change of hydrogen bonding energy in the binding process, namely hydrogen bonding penalty, is evaluated with a new method. The hydrogen bonding penalty can not only be used to filter unrealistic poses in docking, but also improve the accuracy of binding energy calculation. A new model integrated with hydrogen bonding penalty for free energy calculation gives a root mean square error of 0.7 kcal/mol on 74 inhibitors in the training set and of 1.1 kcal/mol on 64 inhibitors in the test set. Moreover, an application of hydrogen bonding penalty into a high throughput docking campaign for EphB4 inhibitors is presented, and remarkably, three novel scaffolds are discovered out of seven tested. The binding affinity and ligand efficiency of the most potent compound is about 300 nM and 0.35 kcal/mol per non-hydrogen atom, respectively.     

Interactions of ketoamide inhibitors on HCV NS3/4A protease target: molecular docking studies

HCV infection in more than 200 million individuals worldwide is a principal health problem. Prior to the development of HCV protease inhibitor combination therapy, HCV infected patients were treated with pegylated interferon-α and ribavirin. The adverse side effects associated with this type of treatment may lead to the discontinuation of treatment in certain number of patients. Currently, the inhibitors of NS3/4A Protease were found promising candidates for the treatment of HCV infection. There are several inhibitors of HCV NS3/4A protease that are passing through clinical improvement showing good potency against HCV infections in a number of patients. To further recognize binding interactions and activity trend, the molecular docking studies were performed on a number of HCV NS3/4A protease ketoamide inhibitors via MOE docking protocol. The docking analysis resulted in the detection of important ligand interactions with respect to binding site of target proteinand produced good correlation coefficient (r² = 0.690) between docking score and biological activities. These molecular docking results should, in our view, contribute for further optimization of ketoamide derivatives as NS3/4A protease inhibitors.     

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.     

Efficient docking of peptides to proteins without prior knowledge of the binding site

Reliability in docking of ligand molecules to proteins or other targets is an important challenge for molecular modeling. Applications of the docking technique include not only prediction of the binding mode of novel drugs, but also other problems like the study of protein-protein interactions. Here we present a study on the reliability of the results obtained with the popular AutoDock program. We have performed systematical studies to test the ability of AutoDock to reproduce eight different protein/ligand complexes for which the structure was known, without prior knowledge of the binding site. More specifically, we look at factors influencing the accuracy of the final structure, such as the number of torsional degrees of freedom in the ligand. We conclude that the Autodock program package is able to select the correct complexes based on the energy without prior knowledge of the binding site. We named this application blind docking, as the docking algorithm is not able to "see" the binding site but can still find it. The success of blind docking represents an important finding in the era of structural genomics.     

Developing β-secretase inhibitors for treatment of Alzheimer's disease

β-Secretase (memapsin 2; BACE-1) is the first protease in the processing of amyloid precursor protein leading to the production of amyloid-β (Aβ) in the brain. It is believed that high levels of brain Aβ are responsible for the pathogenesis of Alzheimer's disease (AD). Therefore, β-secretase is a major therapeutic target for the development of inhibitor drugs. During the past decade, steady progress has been made in the evolution of β-secretase inhibitors toward better drug properties. Recent inhibitors are potent, selective and have been shown to penetrate the blood-brain barrier to inhibit Aβ levels in the brains of experimental animals. Moreover, continuous administration of a β-secretase inhibitor was shown to rescue age-related cognitive decline in transgenic AD mice. A small number of β-secretase inhibitors have also entered early phase clinical trials. These developments offer some optimism for the clinical development of a disease-modifying drug for AD.     

BetaDock: shape-priority docking method based on beta-complex

This paper presents an approach and a software, BetaDock, to the docking problem by putting the priority on shape complementarity between a receptor and a ligand. The approach is based on the theory of the β-complex. Given the Voronoi diagram of the receptor whose topology is stored in the quasi-triangulation, the β-complex corresponding to water molecule is computed. Then, the boundary of the β-complex defines the β-shape which has the complete proximity information among all atoms on the receptor boundary. From the β-shape, we first compute pockets where the ligand may bind. Then, we quickly place the ligand within each pocket by solving the singular value decomposition problem and the assignment problem. Using the conformations of the ligands within the pockets as the initial solutions, we run the genetic algorithm to find the optimal solution for the docking problem. The performance of the proposed algorithm was verified through a benchmark test and showed that BetaDock is superior to a popular docking software AutoDock 4.     

BetaDock: Shape-Priority Docking Method Based on Beta-Complex

Abstract

This paper presents an approach and a software, BetaDock, to the docking problem by putting the priority on shape complementarity between a receptor and a ligand. The approach is based on the theory of the β-complex. Given the Voronoi diagram of the receptor whose topology is stored in the quasi-triangulation, the β-complex corresponding to water molecule is computed. Then, the boundary of the β-complex defines the β-shape which has the complete proximity information among all atoms on the receptor boundary. From the β-shape, we first compute pockets where the ligand may bind. Then, we quickly place the ligand within each pocket by solving the singular value decomposition problem and the assignment problem. Using the conformations of the ligands within the pockets as the initial solutions, we run the genetic algorithm to find the optimal solution for the docking problem. The performance of the proposed algorithm was verified through a benchmark test and showed that BetaDock is superior to a popular docking software AutoDock 4.     

Pharmacophore modeling and structure-based virtual screening to identify potent inhibitors targeting LuxP of Vibrio harveyi

The main aim of the study is to identify molecules that can disrupt quorum sensing (QS) system of Vibrio harveyi and therefore perhaps the production of toxins. Recently, a novel class of dioxazaborocane derivatives has been found to block AI-2 QS by targeting LuxPQ, but the mechanism of protein inhibition is still unclear. In order to investigate the possible binding modes, all the derivatives were docked into the binding site of LuxP using induced fit docking (IFD). The computed binding affinity is in good agreement with the experimental data. Resultant protein–ligand complexes were simulated using Desmond module and the result revealed better binding of ligands in the binding site of LuxP. Both pharmacophore- and structure-based virtual screening was performed to identify novel hits against LuxP. A filtering protocol, including lipinski filters, number of rotatable bonds and three levels of docking precisions were used for the selection of hits with specific properties. The virtual screening results were then combined and analyzed, which retrieved six hits with significant Glide score, binding affinity toward LuxP. The pharmacokinetic properties of the retrieved hits are in the acceptable range. Enrichment calculation was performed to validate the final hits, to discriminate the active compounds from the inactive compounds. The identified hits could serve as a base for further drug development against LuxP of Vibrio harveyi.     

MADAMM: a multistaged docking with an automated molecular modeling protocol

Dealing with receptor flexibility in docking methodology is still a problem. The main reason behind this difficulty is the large number of degrees of freedom that have to be considered in this kind of calculations. In this paper, we present an automated procedure, called MADAMM, that allows flexibilization of both the receptor and the ligand during a multistaged docking with an automated molecular modeling protocol. We show that the orientation of particular residues at the interface between the protein and the ligand have a crucial influence on the way they interact during the docking process, and the standard docking methodologies failed to predict their correct mode of binding. We present some examples that demonstrate the capabilities of this approach when compared with traditional docking methodologies.     

Flavonols and flavones as BACE-1 inhibitors: Structure–activity relationship in cell-free,cell-based and in silico studies reveal novel pharmacophore features

Generation and accumulation of the amyloid β peptide (Aβ) following proteolytic processing of the amyloid precursor protein (APP) by BACE-1 (Beta-site APP Cleaving Enzyme-1, β-secretase) and γ-secretase is a main causal factor of Alzheimer's disease (AD). Consequently, inhibition of BACE-1, a rate-limiting enzyme in the production of Aβ, is an attractive therapeutic approach for the treatment of AD. In this study, we discovered that natural flavonoids act as non-peptidic BACE-1 inhibitors and potently inhibit BACE-1 activity and reduce the level of secreted Aβ in primary cortical neurons. In addition, we demonstrated the calculated docking poses of flavonoids to BACE-1 and revealed the interactions of flavonoids with the BACE-1 catalytic center. We firstly revealed novel pharmacophore features of flavonoids by using cell-free, cell-based and in silico docking studies. These results contribute to the development of new BACE-1 inhibitors for the treatment of AD.