Development and evaluation of a generic evolutionary method for protein-ligand docking

We have developed a generic evolutionary method with an empirical scoring function for the protein-ligand docking, which is a problem of paramount importance in structure-based drug design. This approach, referred to as the GEMDOCK (Generic Evolutionary Method for molecular DOCKing), combines both continuous and discrete search mechanisms. We tested our approach on seven protein-ligand complexes, and the docked lowest energy structures have root-mean-square derivations ranging from 0.32 to 0.99 A with respect to the corresponding crystal ligand structures. In addition, we evaluated GEMDOCK on crossdocking experiments, in which some complexes with an identical protein used for docking all crystallized ligands of these complexes. GEMDOCK yielded 98% docked structures with RMSD below 2.0 A when the ligands were docked into foreign protein structures. We have reported the validation and analysis of our approach on various search spaces and scoring functions. Experimental results show that our approach is robust, and the empirical scoring function is simple and fast to recognize compounds. We found that if GEMDOCK used the RMSD scoring function, then the prediction accuracy was 100% and the docked structures had RMSD below 0.1 A for each test system. These results suggest that GEMDOCK is a useful tool, and may systematically improve the forms and parameters of a scoring function, which is one of major bottlenecks for molecular recognition.     

QXP: Powerful, rapid computer algorithms for structure-based drug design

New methods for docking, template fitting and building pseudo-receptors are described. Full conformational searches are carried out for flexible cyclic and acyclic molecules. QXP (quick explore) search algorithms are derived from the method of Monte Carlo perturbation with energy minimization in Cartesian space. An additional fast search step is introduced between the initial perturbation and energy minimization. The fast search produces approximate low-energy structures, which are likely to minimize to a low energy. For template fitting, QXP uses a superposition force field which automatically assigns short-range attractive forces to similar atoms in different molecules. The docking algorithms were evaluated using X-ray data for 12 protein–ligand complexes. The ligands had up to 24 rotatable bonds and ranged from highly polar to mostly nonpolar. Docking searches of the randomly disordered ligands gave rms differences between the lowest energy docked structure and the energy-minimized X-ray structure, of less than 0.76 Å for 10 of the ligands. For all the ligands, the rms difference between the energy-minimized X-ray structure and the closest docked structure was less than 0.4 Å, when parts of one of the molecules which are in the solvent were excluded from the rms calculation. Template fitting was tested using four ACE inhibitors. Three ACE templates have been previously published. A single run using QXP generated a series of templates which contained examples of each of the three. A pseudo-receptor, complementary to an ACE template, was built out of small molecules, such as pyrrole, cyclopentanone and propane. When individually energy minimized in the pseudo-receptor, each of the four ACE inhibitors moved with an rms of less than 0.25 Å. After random perturbation, the inhibitors were docked into the pseudo-receptor. Each lowest energy docked structure matched the energy-minimized geometry with an rms of less than 0.08 Å. Thus, the pseudo-receptor shows steric and chemical complementarity to all four molecules. The QXP program is reliable, easy to use and sufficiently rapid for routine application in structure-based drug design.     

Comparison of two implementations of the incremental construction algorithm in flexible docking of thrombin inhibitors

A set of 32 known thrombin inhibitors representing different chemical classes has been used to evaluate the performance of two implementations of incremental construction algorithms for flexible molecular docking: DOCK 4.0 and FlexX 1.5. Both docking tools are able to dock 10–35% of our test set within 2 Å of their known, bound conformations using default sampling and scoring parameters. Although flexible docking with DOCK or FlexX is not able to reconstruct all native complexes, it does offer a significant improvement over rigid body docking of single, rule-based conformations, which is still often used for docking of large databases. Docking of sets of multiple conformers of each inhibitor, obtained with a novel protocol for diverse conformer generation and selection, yielded results comparable to those obtained by flexible docking. Chemical scoring, which is an empirically modified force field scoring method implemented in DOCK 4.0, outperforms both interaction energy scoring by DOCK and the Böhm scoring function used by FlexX in rigid and flexible docking of thrombin inhibitors. Our results indicate that for reliable docking of flexible ligands the selection of anchor fragments, conformational sampling and currently available scoring methods still require improvement.     

A novel scoring function for molecular docking

We present a novel scoring function for docking of small molecules to protein binding sites. The scoring function is based on a combination of two main approaches used in the field, the empirical and knowledge-based approaches. To calibrate the scoring function we used an iterative procedure in which a ligand's position and its score were determined self-consistently at each iteration. The scoring function demonstrated superiority in prediction of ligand positions in docking tests against the commonly used Dock, FlexX and Gold docking programs. It also demonstrated good accuracy of binding affinity prediction for the docked ligands.     

Automated flexible ligand docking method and its application for database search

We have developed a new docking program that explores ligand flexibility. This program can be applied to database searches. The program is similar in concept to earlier efforts, but it has been automated and improved. The algorithm begins by selecting an anchor fragment of a ligand. This fragment is protonated, as needed, and then placed in the receptor by the DOCK algorithm, followed by minimization using a simplex method. Finally, the conformations of the remaining parts of the putative ligands are searched by a limited backtrack method and minimized to get the most stable conformation. To test the efficiency of this method, the program was used to regenerate ten ligand–protein complex structures. In all cases, the docked ligands basically reproduced the crystallographic binding modes. The efficiency of this method was further tested by a database search. Ten percent of molecules from the Available Chemicals Directory (ACD) were docked to a dihydrofolate reductase structure. Most of the top-ranking molecules (7 of the top 13 hits) are dihydrofolate or methotrexate derivatives, which are known to be DHFR inhibitors, demonstrating the suitability of this program for screening molecular databases. © 1997 John Wiley & Sons, Inc. J Comput Chem 18 : 1812–1825, 1997     

Surrogate docking: structure-based virtual screening at high throughput speed

Summary Structure-based screening using fully flexible docking is still too slow for large molecular libraries. High quality docking of a million molecule library can take days even on a cluster with hundreds of CPUs. This performance issue prohibits the use of fully flexible docking in the design of large combinatorial libraries. We have developed a fast structure-based screening method, which utilizes docking of a limited number of compounds to build a 2D QSAR model used to rapidly score the rest of the database. We compare here a model based on radial basis functions and a Bayesian categorization model. The number of compounds that need to be actually docked depends on the number of docking hits found. In our case studies reasonable quality models are built after docking of the number of molecules containing 50 docking hits. The rest of the library is screened by the QSAR model. Optionally a fraction of the QSAR-prioritized library can be docked in order to find the true docking hits. The quality of the model only depends on the training set size – not on the size of the library to be screened. Therefore, for larger libraries the method yields higher gain in speed no change in performance. Prioritizing a large library with these models provides a significant enrichment with docking hits: it attains the values of 13 and 35 at the beginning of the score-sorted libraries in our two case studies: screening of the NCI collection and a combinatorial libraries on CDK2 kinase structure. With such enrichments, only a fraction of the database must actually be docked to find many of the true hits. The throughput of the method allows its use in screening of large compound collections and in the design of large combinatorial libraries. The strategy proposed has an important effect on efficiency but does not affect retrieval of actives, the latter being determined by the quality of the docking method itself. Electronic supplementary material is available at http://dx.doi.org/10.1007/s10822-005-9002-6.     

Multiple automatic base selection: Protein–ligand docking based on incremental construction without manual intervention

A possible way of tackling the molecular docking problem arising in computer- aided drug design is the use of the incremental construction method. This method consists of three steps: the selection of a part of a molecule, a so- called base fragment, the placement of the base fragment into the active site of a protein, and the subsequent reconstruction of the complete drug molecule. Assuming that a part of a drug molecule is known, which is specific enough to be a good base fragment, the method is proven to be successful for a large set of docking examples. In addition, it leads to the fastest algorithms for flexible docking published so far. In most real-world applications of docking, large sets of ligands have to be tested for affinity to a given protein. Thus, manual selection of a base fragment is not practical. On the other hand, the selection of a base fragment is critical in that only few selections lead to a low-energy structure. We overcome this limitation by selecting a representative set of base fragments instead of a single one. In this paper, we present a set of rules and algorithms to automate this selection. In addition, we extend the incremental construction method to deal with multiple fragmentations of the drug molecule. Our results show that with multiple automated base selection, the quality of the docking predictions is almost as good as with one manually preselected base fragment. In addition, the set of solutions is more diverse and alternative binding modes with low scores are found. Although the run time of the overall algorithm increases, the method remains fast enough to search through large ligand data sets.     

Identification of novel PfDHODH inhibitors as antimalarial agents via pharmacophore-based virtual screening followed by molecular docking and in vivo antimalarial activity

Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) catalyses the fourth reaction of de novo pyrimidine biosynthesis in parasites, and represents an important target for the treatment of malaria. In this study, we describe pharmacophore-based virtual screening combined with docking study and biological evaluation as a rational strategy for identification of novel hits as antimalarial agents. Pharmacophore models were established from known PfDHODH inhibitors using the GALAHAD module with IC₅₀ values ranging from 0.033 μM to 142 μM. The best pharmacophore model consisted of three hydrogen bond acceptor, one hydrogen bond donor and one hydrophobic features. The pharmacophore models were validated through receiver operating characteristic and Günere–Henry scoring methods. The best pharmacophore model as a 3D search query was searched against the IBS database. Several compounds with different structures (scaffolds) were retrieved as hit molecules. Among these compounds, those with a Q_FIT value of more than 81 were docked in the PfDHODH enzyme to further explore the binding modes of these compounds. In silico pharmacokinetic and toxicities were predicted for the best docked molecules. Finally, the identified hits were evaluated in vivo for their antimalarial activity in a parasite inhibition assay. The hits reported here showed good potential to become novel antimalarial agents.     

Critical evaluation of search algorithms for automated molecular docking and database screening

The DOCK program explores possible orientations of a molecule within a macromolecular active site by superimposing atoms onto precomputed site points. Here we compare a number of different search methods, including an exhaustive matching algorithm based on a single docking graph. We evaluate the performance of each method by screening a small database of molecules to a variety of macromolecular targets. By varying the amount of sampling, we can monitor the time convergence of scores and rankings. We not only show that the site point–directed search is tenfold faster than a random search, but that the single graph matching algorithm boosts the speed of database screening up to 60-fold. The new algorithm, in fact, outperforms the bipartite graph matching algorithm currently used in DOCK. The results indicate that a critical issue for rapid database screening is the extent to which a search method biases run time toward the highest-ranking molecules. The single docking graph matching algorithm will be incorporated into DOCK version 4.0. © 1997 John Wiley & Sons, Inc. J Comput Chem 18: 1175–1189     

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.     

Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy

Probing protein surfaces to accurately predict the binding site and conformation of a small molecule is a challenge currently addressed through mainly two different approaches: blind docking and cavity detection-guided docking. Although cavity detection-guided blind docking has yielded high success rates, it is less practical when a large number of molecules must be screened against many detected binding sites. On the other hand, blind docking allows for simultaneous search of the whole protein surface, which however entails the loss of accuracy and speed. To bridge this gap, in this study, we developed and tested BLinDPyPr, an automated pipeline which uses FTMap and DOCK6 to perform a hybrid blind docking strategy. Through our algorithm, FTMap docked probe clusters are converted into DOCK6 spheres for determining binding regions. Because these spheres are solely derived from FTMap probes, their locations are contained in and specific to multiple potential binding pockets, which become the regions that are simultaneously probed and chosen by the search algorithm based on the properties of each candidate ligand. This method yields pose prediction results (45.2–54.3% success rates) comparable to those of site-specific docking with the classic DOCK6 workflow (49.7–54.3%) and is half as time-consuming as the conventional blind docking method with DOCK6.     

Multiple ligand simultaneous docking: Orchestrated dancing of ligands in binding sites of protein

Present docking methodologies simulate only one single ligand at a time during docking process. In reality, the molecular recognition process always involves multiple molecular species. Typical protein–ligand interactions are, for example, substrate and cofactor in catalytic cycle; metal ion coordination together with ligand(s); and ligand binding with water molecules. To simulate the real molecular binding processes, we propose a novel multiple ligand simultaneous docking (MLSD) strategy, which can deal with all the above processes, vastly improving docking sampling and binding free energy scoring. The work also compares two search strategies: Lamarckian genetic algorithm and particle swarm optimization, which have respective advantages depending on the specific systems. The methodology proves robust through systematic testing against several diverse model systems: E. coli purine nucleoside phosphorylase (PNP) complex with two substrates, SHP2NSH2 complex with two peptides and Bcl‐xL complex with ABT‐737 fragments. In all cases, the final correct docking poses and relative binding free energies were obtained. In PNP case, the simulations also capture the binding intermediates and reveal the binding dynamics during the recognition processes, which are consistent with the proposed enzymatic mechanism. In the other two cases, conventional single‐ligand docking fails due to energetic and dynamic coupling among ligands, whereas MLSD results in the correct binding modes. These three cases also represent potential applications in the areas of exploring enzymatic mechanism, interpreting noisy X‐ray crystallographic maps, and aiding fragment‐based drug design, respectively. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

Extra precision docking,free energy calculation and molecular dynamics studies on glutamic acid derivatives as MurD inhibitors

The binding modes of well known MurD inhibitors have been studied using molecular docking and molecular dynamics (MD) simulations. The docking results of inhibitors 1-30 revealed similar mode of interaction with Escherichia coli-MurD. Further, residues Thr36, Arg37, His183, Lys319, Lys348, Thr321, Ser415 and Phe422 are found to be important for inhibitors and E. coli-MurD interactions. Our docking procedure precisely predicted crystallographic bound inhibitor 7 as evident from root mean square deviation (0.96 Å). In addition inhibitors 2 and 3 have been successfully cross-docked within the MurD active site, which was pre-organized for the inhibitor 7. Induced fit best docked poses of 2, 3, 7 and 15/2Y1O complexes were subjected to 10 ns MD simulations to determine the stability of the predicted binding conformations. Induce fit derived docked complexes were found to be in a state of near equilibrium as evident by the low root mean square deviations between the starting complex structure and the energy minimized final average MD complex structures. The results of molecular docking and MD simulations described in this study will be useful for the development of new MurD inhibitors with high potency.     

A comparison of heuristic search algorithms for molecular docking

This paper describes the implementation and comparison of four heuristic search algorithms (genetic algorithm, evolutionary programming, simulated annealing and tabu search) and a random search procedure for flexible molecular docking. To our knowledge, this is the first application of the tabu search algorithm in this area. The algorithms are compared using a recently described fast molecular recognition potential function and a diverse set of five protein–ligand systems. Statistical analysis of the results indicates that overall the genetic algorithm performs best in terms of the median energy of the solutions located. However, tabu search shows a better performance in terms of locating solutions close to the crystallographic ligand conformation. These results suggest that a hybrid search algorithm may give superior results to any of the algorithms alone.     

New approach to molecular docking and its application to virtual screening of chemical databases

This paper describes the validation of a molecular docking method and its application to virtual database screening. The code flexibly docks ligand molecules into rigid receptor structures using a tabu search methodology driven by an empirically derived function for estimating the binding affinity of a protein-ligand complex. The docking method has been tested on 70 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. The lowest energy geometry produced by the docking protocol is within 2.0 A root mean square of the experimental binding mode for 79% of the complexes. The method has been applied to the problem of virtual database screening to identify known ligands for thrombin, factor Xa, and the estrogen receptor. A database of 10,000 randomly chosen "druglike" molecules has been docked into the three receptor structures. In each case known receptor ligands were included in the study. The results showed good separation between the predicted binding affinities of the known ligand set and the database subset.     

An anchor-dependent molecular docking process for docking small flexible molecules into rigid protein receptors

A molecular docking method designated as ADDock, anchor- dependent molecular docking process for docking small flexible molecules into rigid protein receptors, is presented in this article. ADDock makes the bond connection lists for atoms based on anchors chosen for building molecular structures for docking small flexible molecules or ligands into rigid active sites of protein receptors. ADDock employs an extended version of piecewise linear potential for scoring the docked structures. Since no translational motion for small molecules is implemented during the docking process, ADDock searches the best docking result by systematically changing the anchors chosen, which are usually the single-edge connected nodes or terminal hydrogen atoms of ligands. ADDock takes intact ligand structures generated during the docking process for computing the docked scores; therefore, no energy minimization is required in the evaluation phase of docking. The docking accuracy by ADDock for 92 receptor-ligand complexes docked is 91.3%. All these complexes have been docked by other groups using other docking methods. The receptor-ligand steric interaction energies computed by ADDock for some sets of active and inactive compounds selected and docked into the same receptor active sites are apparently separated. These results show that based on the steric interaction energies computed between the docked structures and receptor active sites, ADDock is able to separate active from inactive compounds for both being docked into the same receptor.     

Focused grid‐based resampling for protein docking and mapping

The fast Fourier transform (FFT) sampling algorithm has been used with success in application to protein‐protein docking and for protein mapping, the latter docking a variety of small organic molecules for the identification of binding hot spots on the target protein. Here we explore the local rather than global usage of the FFT sampling approach in docking applications. If the global FFT based search yields a near‐native cluster of docked structures for a protein complex, then focused resampling of the cluster generally leads to a substantial increase in the number of conformations close to the native structure. In protein mapping, focused resampling of the selected hot spot regions generally reveals further hot spots that, while not as strong as the primary hot spots, also contribute to ligand binding. The detection of additional ligand binding regions is shown by the improved overlap between hot spots and bound ligands. © 2016 Wiley Periodicals, Inc.     

An improved adaptive genetic algorithm for protein–ligand docking

A new optimization model of molecular docking is proposed, and a fast flexible docking method based on an improved adaptive genetic algorithm is developed in this paper. The algorithm takes some advanced techniques, such as multi-population genetic strategy, entropy-based searching technique with self-adaptation and the quasi-exact penalty. A new iteration scheme in conjunction with above techniques is employed to speed up the optimization process and to ensure very rapid and steady convergence. The docking accuracy and efficiency of the method are evaluated by docking results from GOLD test data set, which contains 134 protein-ligand complexes. In over 66.2% of the complexes, the docked pose was within 2.0 A root-mean-square deviation (RMSD) of the X-ray structure. Docking time is approximately in proportion to the number of the rotatable bonds of ligands.     

Impact of scoring functions on enrichment in docking-based virtual screening: an application study on renin inhibitors

The docking program LigandFit/Cerius(2) has been used to perform shape-based virtual screening of databases against the aspartic protease renin, a target of determined three-dimensional structure. The protein structure was used in the induced fit binding conformation that occurs when renin is bound to the highly active renin inhibitor 1 (IC(50) = 2 nM). The scoring was calculated using several different scoring functions in order to get insight into the predictability of the magnitude of binding interactions. A database of 1000 diverse and druglike compounds, comprised of 990 members of a virtual database generated by using the iLib diverse software and 10 known active renin inhibitors, was docked flexibly and scored to determine appropriate scoring functions. All seven scoring functions used (LigScore1, LigScore2, PLP1, PLP2, JAIN, PMF, LUDI) were able to retrieve at least 50% of the active compounds within the first 20% (200 molecules) of the entire test database. A hit rate of 90% in the top 1.4% resulted using the quadruple consensus scoring of LigScore2, PLP1, PLP2, and JAIN. Additionally, a focused database was created with the iLib diverse software and used for the same procedure as the test database. Docking and scoring of the 990 focused compounds and the 10 known actives were performed. A hit rate of 100% in the top 8.4% resulted with use of the triple consensus scoring of PLP1, PLP2, and PMF. As expected, a ranking of the known active compounds within the focused database compared to the test database was observed. Adequate virtual screening conditions were derived empirically. They can be used for proximate docking and scoring application of compounds with putative renin inhibiting potency.     

分子对接打分修正及在内皮脂肪酶选择性抑制剂筛选中的应用

内皮脂肪酶（EL）是脂代谢调控甘油三酯脂酶家族的新成员,其功能主要为水解富含磷脂的高密度脂蛋白（HDL）,对其进行选择性抑制而不影响其同源蛋白脂蛋白脂肪酶（LPL）能够提高血浆中HDLc的浓度水平,有利于预防及治疗动脉粥样硬化疾病。目前分子对接中的打分函数对大分子及大蛋白口袋具有偏向性,使得基于分子对接的虚拟筛选成功率普遍不高。本文中,我们将EL和LPL分别与Specs小分子库进行了分子对接,分析了对接打分与重原子数及接触面积的关系,发现对接打分与重原子数及接触面积之间有极高的相关性,即存在重原子数的叠加效应（重原子数越大,打分越好的趋势）。我们建立了基于重原子数和接触面积的EL、LPL对接打分标准曲线,利用此标准曲线进行对接打分的修正,并用已知抑制剂和生成的decoy分子作为验证集进行了验证。随后,我们应用此打分修正策略对传统中药库（TCMD）进行了基于分子对接的虚拟筛选,发现经过打分修正后的分子排名与重原子数之间的分布更为均衡,同时我们对EL打分较高、LPL打分较低且类药性较好的分子进行了结合模式的分析,为高活性高选择性EL抑制剂的发现奠定了基础。