Critical assessment of the automated AutoDock as a new docking tool for virtual screening

A major problem in virtual screening concerns the accuracy of the binding free energy between a target protein and a putative ligand. Here we report an example supporting the outperformance of the AutoDock scoring function in virtual screening in comparison to the other popular docking programs. The original AutoDock program is in itself inefficient to be used in virtual screening because the grids of interaction energy have to be calculated for each putative ligand in chemical database. However, the automation of the AutoDock program with the potential grids defined in common for all putative ligands leads to more than twofold increase in the speed of virtual database screening. The utility of the automated AutoDock in virtual screening is further demonstrated by identifying the actual inhibitors of various target enzymes in chemical databases with accuracy higher than the other docking tools including DOCK and FlexX. These results exemplify the usefulness of the automated AutoDock as a new promising tool in structure-based virtual screening.     

Virtual Screening for HIV Protease Inhibitors: A Comparison of AutoDock 4 and Vina

Background

The AutoDock family of software has been widely used in protein-ligand docking research. This study compares AutoDock 4 and AutoDock Vina in the context of virtual screening by using these programs to select compounds active against HIV protease.

Methodology/Principal Findings

Both programs were used to rank the members of two chemical libraries, each containing experimentally verified binders to HIV protease. In the case of the NCI Diversity Set II, both AutoDock 4 and Vina were able to select active compounds significantly better than random (AUC = 0.69 and 0.68, respectively; p<0.001). The binding energy predictions were highly correlated in this case, with r = 0.63 and ι = 0.82. For a set of larger, more flexible compounds from the Directory of Universal Decoys, the binding energy predictions were not correlated, and only Vina was able to rank compounds significantly better than random.

Conclusions/Significance

In ranking smaller molecules with few rotatable bonds, AutoDock 4 and Vina were equally capable, though both exhibited a size-related bias in scoring. However, as Vina executes more quickly and is able to more accurately rank larger molecules, researchers should look to it first when undertaking a virtual screen.     

Toward the virtual screening of Cdc25A phosphatase inhibitors with the homology modeled protein structure

Cdc25 phosphatases have been considered as attractive drug targets for anticancer therapy due to the correlation of their overexpression with a wide variety of cancers. As a method for the discovery of novel inhibitors of Cdc25 phosphatases, we have evaluated the computer-aided drug design protocol involving the homology modeling of Cdc25A and virtual screening with the two docking tools: FlexX and the modified AutoDock program implementing the effects of ligand solvation in the scoring function. The homology modeling with the X-ray crystal structure of Cdc25B as a template provides a high-quality structure of Cdc25A that enables the structure-based inhibitor design. Of the two docking programs under consideration, AutoDock is found to be more accurate than FlexX in terms of scoring putative ligands. A detailed binding mode analysis of the known inhibitors shows that they can be stabilized in the active site of Cdc25A through the simultaneous establishment of the multiple hydrogen bonds and the hydrophobic interactions. The present study demonstrates the usefulness of the modified AutoDock program as a docking tool for virtual screening of new Cdc25 phosphatase inhibitors as well as for binding mode analysis to elucidate the activities of known inhibitors. Figure Structures and available IC50 values (in μM) of the twenty Cdc25 phosphatase inhibitors seeded in docking library     

A ClpP protein model as tuberculosis target for screening marine compounds

ATP-dependent Clp protease (ClpP) is a core unit of a major bacterial protease complex employing as a new attractive drug target for that isolates, which are resistant to antibiotics. Mycobacterium tuberculosis, a gram-positive bacterium, is one of the major causes of hospital acquired infections. ClpP in Mycobacterium tuberculosis is usually tightly regulated and strictly requires a member of the family of Clp-ATPase and often further accessory proteins for proteolytic activation. Inhibition of ClpP eliminates these safeguards and start proteolytic degradation. Such uncontrolled proteolysis leads to inhibition of bacterial cell division and eventually cell death. In order to inhibit Clp protease, at first three dimensional structure model of ClpP in Mycobacterium tuberculosis was determined by comparative homology modeling program MODELLER based on crystal structure of the proteolytic component of the caseinolytic Clp protease (ClpP) from E. coli as a template protein and has 55%sequence identity with ClpP protein. The computed model's energy was minimized and validated using PROCHECK to obtain a stable model structure and is submitted in Protein Model Database (PMDB-ID: PM0075741). Stable model was further used for virtual screening against marine derived bioactive compound database through molecular docking studies using AutoDock 3.05. The docked complexes were validated and enumerated based on the AutoDock Scoring function to pick out the best marine inhibitors based on docked Energy. Thus from the entire 186 Marine compounds which were Docked, we got best 5 of them with optimal docked Energy (Ara-A: -14.31 kcal/mol, Dysinosin C: - 14.90kcal/mol, Nagelamide A: -20.49 kcal/mol, Strobilin: -8.02 kcal/mol, Manoalide: -8.81 kcal/mol). Further the five best-docked complexes were analyzed through Python Molecular Viewer software for their interaction studies. Thus from the Complex scoring and binding ability its deciphered that these Marine compounds could be promising inhibitors for ClpP as Drug target yet pharmacological studies have to confirm it.     

Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina

The Mur E enzyme of Mur pathway of Mycobacterium tuberculosis is an attractive drug target as it is unique to bacteria and is absent in mammalian cells. The virtual screening of large libraries of drug like molecules against a protein target is a common strategy used to identify novel inhibitors. However, the method has a large number of pitfalls, with large variations in accuracy caused in part by inaccurate protocols, use of improper standards and libraries, and system dependencies such as the potential for nonspecific docking from large active-site cavities. The screening of drug-like small molecules from diversity sets can, however, be used to short-list potential fragments as building blocks to generate leads with improved specificity. We describe a protocol to implement this strategy, which involves an analysis of the active site and known inhibitors to identify orthospecific determinants, virtual screening of a drug-like diversity library to identify potential drug primitives, and inspection of the potential docked fragments for both binding potential and toxicity. The protocol is implemented on the M.tb Mur E protein which has a large active site with poor enrichment of known positives and a set of drug-like molecules that meets this criteria is presented for further analysis.

Abbreviations

MTB - Mycobacterium tuberculosis, NCI - National Cancer Institute, PDB - Protein Databank.     

BDT: an easy-to-use front-end application for automation of massive docking tasks and complex docking strategies with AutoDock

MOTIVATION: AutoGrid/AutoDock is one of the most popular software packages for docking, but its automation is not trivial for tasks such as (1) the virtual screening of a library of ligands against a set of possible receptors; (2) the use of receptor flexibility and (3) making a blind-docking experiment with the whole receptor surface. This is an obstacle for research teams in the fields of Chemistry and the Life Sciences who are interested in conducting this kind of experiment but do not have enough programming skills. To overcome these limitations, we have designed BDT, an easy-to-use graphic interface for AutoGrid/AutoDock. AVAILABILITY: BDT is available for free, upon request, for non-commercial research.     

DINC: A new AutoDock-based protocol for docking large ligands

Background

Using the popular program AutoDock, computer-aided docking of small ligands with 6 or fewer rotatable bonds, is reasonably fast and accurate. However, docking large ligands using AutoDock's recommended standard docking protocol is less accurate and computationally slow.

Results

In our earlier work, we presented a novel AutoDock-based incremental protocol (DINC) that addresses the limitations of AutoDock's standard protocol by enabling improved docking of large ligands. Instead of docking a large ligand to a target protein in one single step as done in the standard protocol, our protocol docks the large ligand in increments. In this paper, we present three detailed examples of docking using DINC and compare the docking results with those obtained using AutoDock's standard protocol. We summarize the docking results from an extended docking study that was done on 73 protein-ligand complexes comprised of large ligands. We demonstrate not only that DINC is up to 2 orders of magnitude faster than AutoDock's standard protocol, but that it also achieves the speed-up without sacrificing docking accuracy. We also show that positional restraints can be applied to the large ligand using DINC: this is useful when computing a docked conformation of the ligand. Finally, we introduce a webserver for docking large ligands using DINC.

Conclusions

Docking large ligands using DINC is significantly faster than AutoDock's standard protocol without any loss of accuracy. Therefore, DINC could be used as an alternative protocol for docking large ligands. DINC has been implemented as a webserver and is available at http://dinc.kavrakilab.org. Applications such as therapeutic drug design, rational vaccine design, and others involving large ligands could benefit from DINC and its webserver implementation.

     

Modeling of human M1 aminopeptidases for in silico screening of potential Plasmodium falciparum alanine aminopeptidase (PfA-M1) specific inhibitors

Plasmodium falciparum alanine M1-aminopeptidase (PfA-M1) is a validated target for anti-malarial drug development. Presence of significant similarity between PfA-M1 and human M1-aminopeptidases, particularly within regions of enzyme active site leads to problem of non-specificity and off-target binding for known aminopeptidase inhibitors. Molecular docking based in silico screening approach for off-target binding has high potential but requires 3D-structure of all human M1-aminopeptidaes. Therefore, in the present study 3D structural models of seven human M1-aminopeptidases were developed. The robustness of docking parameters and quality of predicted human M1-aminopeptidases structural models was evaluated by stereochemical analysis and docking of their respective known inhibitors. The docking scores were in agreement with the inhibitory concentrations elucidated in enzyme assays of respective inhibitor enzyme combinations (r2≈0.70). Further docking analysis of fifteen potential PfA-M1 inhibitors (virtual screening identified) showed that three compounds had less docking affinity for human M1-aminopeptidases as compared to PfA-M1. These three identified potential lead compounds can be validated with enzyme assays and used as a scaffold for designing of new compounds with increased specificity towards PfA-M1.     

Accounting for ligand-bound metal ions in docking small molecules on adenylyl cyclase toxins

The adenylyl cyclase toxins produced by bacteria (such as the edema factor (EF) of Bacillus anthracis and CyaA of Bordetella pertussis) are important virulence factors in anthrax and whooping cough. Co-crystal structures of these proteins differ in the number and positioning of metal ions in the active site. Metal ions bound only to the ligands in the crystal structures are not included during the docking. To determine what effect these "missing" metals have on docking results, the AutoDock, LigandFit/Cerius2, and FlexX programs were compared for their ability to correctly place substrate analogues and inhibitors into the active sites of the crystal structures of EF, CyaA, and mammalian adenylate cyclase. Protonating the phosphates of substrate analogues improved the accuracy of docking into the active site of CyaA, where the grid did not account for one of the three Mg2+ ions in the crystal structure. The AutoDock ranking (based on docking energies) of a test group of compounds was relatively unaffected by protonation of carboxyl groups. However, the ranking by FlexX-ChemScore varied significantly, especially for docking to CyaA, suggesting that alternate protonation states should be tested when screening compound libraries with this program. When the charges on the bound metal were set correctly, AutoDock was the most reliable program of the three tested with respect to positioning substrate analogues and ranking compounds according to their experimentally determined ability to inhibit EF.     

A molecular model for diacylglycerol acyltransferase from Mortierella ramanniana var. angulispora

Acyl CoA diacylglycerol acyltransferase (DGAT, EC 2.3.120) is recognized as a key player of cellular diacylglycerol metabolism. It catalyzes the terminal, yet the committed step in triacylglycerol synthesis using diacylglycerol and fatty acyl CoA as substrates. The protein sequence of diacylglycerol acyltransferse (DGAT) Type 2B in Moretierella ramanniana var. angulispora (Protein_ID = {"type":"entrez-protein","attrs":{"text":"AAK84180.1","term_id":"15099961"}}AAK84180.1) was retrieved from GenBank. However, a structure is not yet available for this sequence. The 3D structure of DGAT Type 2B was modeled using a template structure (PDB ID: 1K30) obtained from Protein databank (PDB) identified by searching with position specific iterative BLAST (PSI-BLAST). The template (PDB ID: 1K30) describes the structure of DGAT from Cucurbita moschata. Modeling was performed using Modeller 9v2 and protein model is hence generated. The DGAT type 2B protein model was subsequently docked with six inhibitors (sphingosine; trifluoroperazine; phosphatidic acid; lysophospatidylserine; KCl; 1, 2-diolein) using AutoDock (a molecular docking program). The binding of inhibitors to the protein model of DGAT type 2B is discussed.     

rDock: A Fast,Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids

Identification of chemical compounds with specific biological activities is an important step in both chemical biology and drug discovery. When the structure of the intended target is available, one approach is to use molecular docking programs to assess the chemical complementarity of small molecules with the target; such calculations provide a qualitative measure of affinity that can be used in virtual screening (VS) to rank order a list of compounds according to their potential to be active. rDock is a molecular docking program developed at Vernalis for high-throughput VS (HTVS) applications. Evolved from RiboDock, the program can be used against proteins and nucleic acids, is designed to be computationally very efficient and allows the user to incorporate additional constraints and information as a bias to guide docking. This article provides an overview of the program structure and features and compares rDock to two reference programs, AutoDock Vina (open source) and Schrödinger''s Glide (commercial). In terms of computational speed for VS, rDock is faster than Vina and comparable to Glide. For binding mode prediction, rDock and Vina are superior to Glide. The VS performance of rDock is significantly better than Vina, but inferior to Glide for most systems unless pharmacophore constraints are used; in that case rDock and Glide are of equal performance. The program is released under the Lesser General Public License and is freely available for download, together with the manuals, example files and the complete test sets, at http://rdock.sourceforge.net/

This is a PLOS Computational Biology Software Article.

     

Integrative computational protocol for the discovery of inhibitors of the <Emphasis Type="Italic">Helicobacter pylori</Emphasis> nickel response regulator (NikR)

In order to identify novel inhibitors of the Helicobacter pylori nickel response regulator (HpNikR) an integrative protocol was performed for half a million compounds retrieved from the ZINC database. We firstly implement a structure-based virtual screening to build a library of potential inhibitors against the HpNikR using a docking analysis (AutoDock Vina). The library was then used to perform a hierarchical clustering of docking poses, based on protein-contact footprints calculation from the multiple conformations given by the AutoDock Vina software, and the drug-protein interaction analyses to identify and remove potential promiscuous compounds likely interacting with human proteins, hence causing drug side effects. 250 drug-like compounds were finally proposed as non-promicuous potential inhibitors for HpNikR. These compounds target the DNA-binding sites of HpNikR so that HpNikR-compound binding could be able to mimic key interactions in the DNA-protein recognition process. HpNikR inhibitors with promising potential against H. pylori could also act against other human bacterial pathogens due to the conservation of targeting motif of NikR involved in DNA-protein interaction.     

MS-DOCK: Accurate multiple conformation generator and rigid docking protocol for multi-step virtual ligand screening

Background  

The number of protein targets with a known or predicted tri-dimensional structure and of drug-like chemical compounds is growing rapidly and so is the need for new therapeutic compounds or chemical probes. Performing flexible structure-based virtual screening computations on thousands of targets with millions of molecules is intractable to most laboratories nor indeed desirable. Since shape complementarity is of primary importance for most protein-ligand interactions, we have developed a tool/protocol based on rigid-body docking to select compounds that fit well into binding sites.     

Structural adaptation of extreme halophilic proteins through decrease of conserved hydrophobic contact surface

Background

Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding.

Results

In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces.

Conclusions

NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.     

Fpocket: An open source platform for ligand pocket detection

Background  

Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces.     

A fast protein-ligand docking algorithm based on hydrogen bond matching and surface shape complementarity

With the rapid development of structural determination of target proteins for human diseases, high throughout virtual screening based drug discovery is gaining popularity gradually. In this paper, a fast docking algorithm (H-DOCK) based on hydrogen bond matching and surface shape complementarity was developed. In H-DOCK, firstly a divide-and-conquer strategy based enumeration approach is applied to rank the intermolecular modes between protein and ligand by maximizing their hydrogen bonds matching, then each docked conformation of the ligand is calculated according to the matched hydrogen bonding geometry, finally a simple but effective scoring function reflecting mainly the van der Waals interaction is used to evaluate the docked conformations of the ligand. H-DOCK is tested for rigid ligand docking and flexible one, the latter is implemented by repeating rigid docking for multiple conformations of a small molecule and ranking all together. For rigid ligands, H-DOCK was tested on a set of 271 complexes where there is at least one intermolecular hydrogen bond, and H-DOCK achieved success rate (RMSD<2.0?Å) of 91.1%. For flexible ligands, H-DOCK was tested on another set of 93 complexes, where each case was a conformation ensemble containing native ligand conformation as well as 100 decoy ones generated by AutoDock [1], and the success rate reached 81.7%. The high success rate of H-DOCK indicates that the hydrogen bonding and steric hindrance can grasp the key interaction between protein and ligand. H-DOCK is quite efficient compared with the conventional docking algorithms, and it takes only about 0.14 seconds for a rigid ligand docking and about 8.25 seconds for a flexible one on average. According to the preliminary docking results, it implies that H-DOCK can be potentially used for large scale virtual screening as a pre-filter for a more accurate but less efficient docking algorithm.     

Computational screening of dipeptidyl peptidase IV inhibitors from micoroalgal metabolites by pharmacophore modeling and molecular docking

Dipeptidyl peptidase IV (DPP‐IV) catalyzes conversion of GLP‐1 (glucagon like peptide 1) to inert structure, which results in insufficient secretion of insulin and increase in postprandial blood glucose level. The present study attempts to identify novel inhibitors from bioactive metabolites present in microalgae against DPP‐IV through virtual screening, molecular docking, and pharmacophore modeling for the active target. Possible binding modes of all 60 ligands against DPP‐IV receptor were constructed using MTiOpenScreen virtual screening server. Pharmacophore model was built based on identified 38 DPP‐IV test ligands by using the web‐based PharmaGist program which encompasses hydrogen‐bond acceptors, hydrophobic groups, spatial features, and aromatic rings. The pharmacophore model having highest scores was selected to screen active DPP‐IV ligands. Highest scoring model was used as a query in ZincPharmer screening. All identified ligands were filtered, based on the Lipinski's rule‐of‐five and were subjected to docking studies. In the process of docking analyses, we considered different bonding modes of one ligand with multiple active cavities of DPP‐IV with the help of AutoDock 4.0. The docking analyses indicate that the bioactive constituents, namely, β‐stigmasterol, barbamide, docosahexaenoic acid, arachidonic acid, and harman showed the best binding energies on DPP‐IV receptor and hydrogen bonding with ASP545, GLY741, TYR754, TYR666, ARG125, TYR547, SER630, and LYS554 residues. This study concludes that docosahexaenoic acid, arachidonic acid, β‐stigmasterol, barbamide, harman, ZINC58564986, ZINC56907325, ZINC69432950, ZINC69431828, ZINC73533041, ZINC84287073, ZINC69849395, and ZINC10508406 act as possible DPP‐IV inhibitors.     

BSP-SLIM: a blind low-resolution ligand-protein docking approach using predicted protein structures

We developed BSP‐SLIM, a new method for ligand–protein blind docking using low‐resolution protein structures. For a given sequence, protein structures are first predicted by I‐TASSER; putative ligand binding sites are transferred from holo‐template structures which are analogous to the I‐TASSER models; ligand–protein docking conformations are then constructed by shape and chemical match of ligand with the negative image of binding pockets. BSP‐SLIM was tested on 71 ligand–protein complexes from the Astex diverse set where the protein structures were predicted by I‐TASSER with an average RMSD 2.92 Å on the binding residues. Using I‐TASSER models, the median ligand RMSD of BSP‐SLIM docking is 3.99 Å which is 5.94 Å lower than that by AutoDock; the median binding‐site error by BSP‐SLIM is 1.77 Å which is 6.23 Å lower than that by AutoDock and 3.43 Å lower than that by LIGSITE^CSC. Compared to the models using crystal protein structures, the median ligand RMSD by BSP‐SLIM using I‐TASSER models increases by 0.87 Å, while that by AutoDock increases by 8.41 Å; the median binding‐site error by BSP‐SLIM increase by 0.69Å while that by AutoDock and LIGSITE^CSC increases by 7.31 Å and 1.41 Å, respectively. As case studies, BSP‐SLIM was used in virtual screening for six target proteins, which prioritized actives of 25% and 50% in the top 9.2% and 17% of the library on average, respectively. These results demonstrate the usefulness of the template‐based coarse‐grained algorithms in the low‐resolution ligand–protein docking and drug‐screening. An on‐line BSP‐SLIM server is freely available at http://zhanglab.ccmb.med.umich.edu/BSP‐SLIM . Proteins 2012. © 2011 Wiley Periodicals, Inc.