Incorporating replacement free energy of binding‐site waters in molecular docking

Binding‐site water molecules play a crucial role in protein‐ligand recognition, either being displaced upon ligand binding or forming water bridges to stabilize the complex. However, rigorously treating explicit binding‐site waters is challenging in molecular docking, which requires to fully sample ensembles of waters and to consider the free energy cost of replacing waters. Here, we describe a method to incorporate structural and energetic properties of binding‐site waters into molecular docking. We first developed a solvent property analysis (SPA) program to compute the replacement free energies of binding‐site water molecules by post‐processing molecular dynamics trajectories obtained from ligand‐free protein structure simulation in explicit water. Next, we implemented a distance‐dependent scoring term into DOCK scoring function to take account of the water replacement free energy cost upon ligand binding. We assessed this approach in protein targets containing important binding‐site waters, and we demonstrated that our approach is reliable in reproducing the crystal binding geometries of protein‐ligand‐water complexes, as well as moderately improving the ligand docking enrichment performance. In addition, SPA program (free available to academic users upon request) may be applied in identifying hot‐spot binding‐site residues and structure‐based lead optimization. Proteins 2014; 82:1765–1776. © 2014 Wiley Periodicals, Inc.     

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.     

Molecular docking studies of protein-nucleotide complexes using MOLSDOCK (mutually orthogonal Latin squares DOCK)

Understanding the principles of protein receptor recognition, interaction, and association with molecular substrates and inhibitors is of principal importance in the drug discovery process. MOLSDOCK is a molecular docking method that we have recently developed. It uses mutually orthogonal Latin square sampling (together with a variant of the mean field technique) to identify the optimal docking conformation and pose of a small molecule ligand in the appropriate receptor site. Here we report the application of this method to simultaneously identify both the low energy conformation and the one with the best pose in the case of 62 protein-bound nucleotide ligands. The experimental structures of all these complexes are known. We have compared our results with those obtained from two other well-known molecular docking software, viz. AutoDock 4.2.3 and GOLD 5.1. The results show that the MOLSDOCK method was able to sample a wide range of binding modes for these ligands and also scores them well.     

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     

Docking unbound proteins using shape complementarity, desolvation, and electrostatics

A comprehensive docking study was performed on 27 distinct protein-protein complexes. For 13 test systems, docking was performed with the unbound X-ray structures of both the receptor and the ligand. For the remaining systems, the unbound X-ray structure of only molecule was available; therefore the bound structure for the other molecule was used. Our method optimizes desolvation, shape complementarity, and electrostatics using a Fast Fourier Transform algorithm. A global search in the rotational and translational space without any knowledge of the binding sites was performed for all proteins except nine antibodies recognizing antigens. For these antibodies, we docked their well-characterized binding site-the complementarity-determining region defined without information of the antigen-to the entire surface of the antigen. For 24 systems, we were able to find near-native ligand orientations (interface C(alpha) root mean square deviation less than 2.5 A from the crystal complex) among the top 2,000 choices. For three systems, our algorithm could identify the correct complex structure unambiguously. For 13 other complexes, we either ranked a near-native structure in the top 20 or obtained 20 or more near-native structures in the top 2,000 or both. The key feature of our algorithm is the use of target functions that are highly tolerant to conformational changes upon binding. If combined with a post-processing method, our algorithm may provide a general solution to the unbound docking problem. Our program, called ZDOCK, is freely available to academic users (http://zlab.bu.edu/~rong/dock/).     

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.     

Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites

The use of predicted binding sites (binding sites calculated from the protein structure alone) is evaluated here as a tool to focus the docking of small molecule ligands into protein structures, simulating cases where the real binding sites are unknown. The resulting approach consists of a few independent docking runs carried out on small boxes, centered on the predicted binding sites, as opposed to one larger blind docking run that covers the complete protein structure. The focused and blind approaches were compared using a set of 77 known protein-ligand complexes and 19 ligand-free structures. The focused approach is shown to: (1) identify the correct binding site more frequently than blind docking; (2) produce more accurate docking poses for the ligand; (3) require less computational time. Additionally, the results show that very few real binding sites are missed in spite of focusing on only three predicted binding sites per target protein. Overall the results indicate that, by improving the sampling in regions that are likely to correspond to binding sites, the focused docking approach increases accuracy and efficiency of protein ligand docking for those cases where the ligand-binding site is unknown. This is especially relevant in applications such as reverse virtual screening and structure-based functional annotation of proteins.     

FLIPDock: docking flexible ligands into flexible receptors

Conformational changes of biological macromolecules when binding with ligands have long been observed and remain a challenge for automated docking methods. Here we present a novel protein-ligand docking software called FLIPDock (Flexible LIgand-Protein Docking) allowing the automated docking of flexible ligand molecules into active sites of flexible receptor molecules. In FLIPDock, conformational spaces of molecules are encoded using a data structure that we have developed recently called the Flexibility Tree (FT). While the FT can represent fully flexible ligands, it was initially designed as a hierarchical and multiresolution data structure for the selective encoding of conformational subspaces of large biological macromolecules. These conformational subspaces can be built to span a range of conformations important for the biological activity of a protein. A variety of motions can be combined, ranging from domains moving as rigid bodies or backbone atoms undergoing normal mode-based deformations, to side chains assuming rotameric conformations. In addition, these conformational subspaces are parameterized by a small number of variables which can be searched during the docking process, thus effectively modeling the conformational changes in a flexible receptor. FLIPDock searches the variables using genetic algorithm-based search techniques and evaluates putative docking complexes with a scoring function based on the AutoDock3.05 force-field. In this paper, we describe the concepts behind FLIPDock and the overall architecture of the program. We demonstrate FLIPDock's ability to solve docking problems in which the assumption of a rigid receptor previously prevented the successful docking of known ligands. In particular, we repeat an earlier cross docking experiment and demonstrate an increased success rate of 93.5%, compared to original 72% success rate achieved by AutoDock over the 400 cross-docking calculations. We also demonstrate FLIPDock's ability to handle conformational changes involving backbone motion by docking balanol to an adenosine-binding pocket of protein kinase A.     

Binding Site Identification and Flexible Docking of Single Stranded RNA to Proteins Using a Fragment-Based Approach

Protein-RNA docking is hampered by the high flexibility of RNA, and particularly single-stranded RNA (ssRNA). Yet, ssRNA regions typically carry the specificity of protein recognition. The lack of methodology for modeling such regions limits the accuracy of current protein-RNA docking methods. We developed a fragment-based approach to model protein-bound ssRNA, based on the structure of the protein and the sequence of the RNA, without any prior knowledge of the RNA binding site or the RNA structure. The conformational diversity of each fragment is sampled by an exhaustive RNA fragment library that was created from all the existing experimental structures of protein-ssRNA complexes. A systematic and detailed analysis of fragment-based ssRNA docking was performed which constitutes a proof-of-principle for the fragment-based approach. The method was tested on two 8-homo-nucleotide ssRNA-protein complexes and was able to identify the binding site on the protein within 10 Å. Moreover, a structure of each bound ssRNA could be generated in close agreement with the crystal structure with a mean deviation of ~1.5 Å except for a terminal nucleotide. This is the first time a bound ssRNA could be modeled from sequence with high precision.     

Towards Ligand Docking Including Explicit Interface Water Molecules

Small molecule docking predicts the interaction of a small molecule ligand with a protein at atomic-detail accuracy including position and conformation the ligand but also conformational changes of the protein upon ligand binding. While successful in the majority of cases, docking algorithms including RosettaLigand fail in some cases to predict the correct protein/ligand complex structure. In this study we show that simultaneous docking of explicit interface water molecules greatly improves Rosetta’s ability to distinguish correct from incorrect ligand poses. This result holds true for both protein-centric water docking wherein waters are located relative to the protein binding site and ligand-centric water docking wherein waters move with the ligand during docking. Protein-centric docking is used to model 99 HIV-1 protease/protease inhibitor structures. We find protease inhibitor placement improving at a ratio of 9∶1 when one critical interface water molecule is included in the docking simulation. Ligand-centric docking is applied to 341 structures from the CSAR benchmark of diverse protein/ligand complexes [1]. Across this diverse dataset we see up to 56% recovery of failed docking studies, when waters are included in the docking simulation.     

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.

     

High quality binding modes in docking ligands to proteins

Multiple near-optimal conformations of protein-ligand complexes provide a better chance for accurate representation of biomolecular interactions, compared with a single structure. We present ISE-dock--a docking program which is based on the iterative stochastic elimination (ISE) algorithm. ISE eliminates values that consistently lead to the worst results, thus optimizing the search for docking poses. It constructs large sets of such poses with no additional computational cost compared with single poses. ISE-dock is validated using 81 protein-ligand complexes from the PDB and its performance was compared with those of Glide, GOLD, and AutoDock. ISE-dock has a better chance than the other three to find more than 60% top single poses under RMSD = 2.0 A and more than 80% under RMSD = 3.0 A from experimental. ISE alone produced at least one 3.0 A or better solutions among the top 20 poses in the entire test set. In 98% of the examined molecules, ISE produced solutions that are closer than 2.0 A from experimental. Paired t-tests (PTT) were used throughout to assess the significance of comparisons between the performances of the different programs. ISE-dock provides more than 100-fold docking solutions in a similar time frame as LGA in AutoDock. We demonstrate the usefulness of the large near optimal populations of ligand poses by showing a correlation between the docking results and experiments that support multiple binding modes in p38 MAP kinase (Pargellis et al., Nat Struct Biol 2002;9:268-272] and in Human Transthyretin (Hamilton, Benson, Cell Mol Life Sci 2001;58:1491-1521).     

A coarse-grained force field for Protein-RNA docking

The awareness of important biological role played by functional, non coding (nc) RNA has grown tremendously in recent years. To perform their tasks, ncRNA molecules typically unite with protein partners, forming ribonucleoprotein complexes. Structural insight into their architectures can be greatly supplemented by computational docking techniques, as they provide means for the integration and refinement of experimental data that is often limited to fragments of larger assemblies or represents multiple levels of spatial resolution. Here, we present a coarse-grained force field for protein-RNA docking, implemented within the framework of the ATTRACT program. Complex structure prediction is based on energy minimization in rotational and translational degrees of freedom of binding partners, with possible extension to include structural flexibility. The coarse-grained representation allows for fast and efficient systematic docking search without any prior knowledge about complex geometry.     

AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility

Automated docking of drug-like molecules into receptors is an essential tool in structure-based drug design. While modeling receptor flexibility is important for correctly predicting ligand binding, it still remains challenging. This work focuses on an approach in which receptor flexibility is modeled by explicitly specifying a set of receptor side-chains a-priori. The challenges of this approach include the: 1) exponential growth of the search space, demanding more efficient search methods; and 2) increased number of false positives, calling for scoring functions tailored for flexible receptor docking. We present AutoDockFR–AutoDock for Flexible Receptors (ADFR), a new docking engine based on the AutoDock4 scoring function, which addresses the aforementioned challenges with a new Genetic Algorithm (GA) and customized scoring function. We validate ADFR using the Astex Diverse Set, demonstrating an increase in efficiency and reliability of its GA over the one implemented in AutoDock4. We demonstrate greatly increased success rates when cross-docking ligands into apo receptors that require side-chain conformational changes for ligand binding. These cross-docking experiments are based on two datasets: 1) SEQ17 –a receptor diversity set containing 17 pairs of apo-holo structures; and 2) CDK2 –a ligand diversity set composed of one CDK2 apo structure and 52 known bound inhibitors. We show that, when cross-docking ligands into the apo conformation of the receptors with up to 14 flexible side-chains, ADFR reports more correctly cross-docked ligands than AutoDock Vina on both datasets with solutions found for 70.6% vs. 35.3% systems on SEQ17, and 76.9% vs. 61.5% on CDK2. ADFR also outperforms AutoDock Vina in number of top ranking solutions on both datasets. Furthermore, we show that correctly docked CDK2 complexes re-create on average 79.8% of all pairwise atomic interactions between the ligand and moving receptor atoms in the holo complexes. Finally, we show that down-weighting the receptor internal energy improves the ranking of correctly docked poses and that runtime for AutoDockFR scales linearly when side-chain flexibility is added.     

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.     

Automated docking of ligands to antibodies: methods and applications

Many approaches to studying protein-ligand interactions by computational docking are currently available. Given the structures of a protein and a ligand, the ultimate goal of all docking methods is to predict the structure of the resulting complex. This requires a suitable representation of molecular structures and properties, search algorithms to efficiently scan the configuration space for favorable interaction geometries, and accurate scoring functions to evaluate and rank the generated orientations. For many of the available methods, tests on experimentally known antibody-antigen or antibody-hapten complexes have appeared in the literature. In addition, some of them have been used in predictive studies on antibody-ligand interactions to provide structural insights where adequate experimental information is missing. The AutoDock program is presented as example of a method for flexibly docking ligands to antibodies. Applying parameters of the second-generation AMBER force field, three antibody-hapten complexes (AN02, DB3, NC6.8) are used as new test cases to analyze the ability of the method to reproduce experimental findings. The X-ray structures could be reconstituted and the corresponding solutions were ranked with best energy score in all cases. Docking to the free instead of the complexed NC6.8 structure indicated the limits of the rigid protein treatment, although fairly good guesses about the location of the binding site and the contact residues could still be obtained if conformational flexibility was allowed at least in the ligand.     

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.     

istar: A Web Platform for Large-Scale Protein-Ligand Docking

Protein-ligand docking is a key computational method in the design of starting points for the drug discovery process. We are motivated by the desire to automate large-scale docking using our popular docking engine idock and thus have developed a publicly-accessible web platform called istar. Without tedious software installation, users can submit jobs using our website. Our istar website supports 1) filtering ligands by desired molecular properties and previewing the number of ligands to dock, 2) monitoring job progress in real time, and 3) visualizing ligand conformations and outputting free energy and ligand efficiency predicted by idock, binding affinity predicted by RF-Score, putative hydrogen bonds, and supplier information for easy purchase, three useful features commonly lacked on other online docking platforms like DOCK Blaster or iScreen. We have collected 17,224,424 ligands from the All Clean subset of the ZINC database, and revamped our docking engine idock to version 2.0, further improving docking speed and accuracy, and integrating RF-Score as an alternative rescoring function. To compare idock 2.0 with the state-of-the-art AutoDock Vina 1.1.2, we have carried out a rescoring benchmark and a redocking benchmark on the 2,897 and 343 protein-ligand complexes of PDBbind v2012 refined set and CSAR NRC HiQ Set 24Sept2010 respectively, and an execution time benchmark on 12 diverse proteins and 3,000 ligands of different molecular weight. Results show that, under various scenarios, idock achieves comparable success rates while outperforming AutoDock Vina in terms of docking speed by at least 8.69 times and at most 37.51 times. When evaluated on the PDBbind v2012 core set, our istar platform combining with RF-Score manages to reproduce Pearson''s correlation coefficient and Spearman''s correlation coefficient of as high as 0.855 and 0.859 respectively between the experimental binding affinity and the predicted binding affinity of the docked conformation. istar is freely available at http://istar.cse.cuhk.edu.hk/idock.     

The effect of histidine residue modification on tyrosinase activity and conformation: inhibition kinetics and computational prediction

We found that the histidine chemical modification of tyrosinase conspicuously inactivated enzyme activity. The substrate reactions with diethylpyridinecarbamate showed slow-binding inhibition kinetics (K(I) = 0.24 +/- 0.03 mM). Bromoacetate, as another histidine modifier, was also applied in order to study inhibition kinetics. The bromoacetate directly induced the exposures of hydrophobic surfaces following by complete inactivation via ligand binding. For further insights, we predicted the 3D structure of tyrosinase and simulated the docking between tyrosinase and diethylpyridinecarbamate. The docking simulation was shown to the significant binding energy scores (-3.77 kcal/mol by AutoDock4 and -25.26 kcal/mol by Dock6). The computational prediction was informative to elucidate the role of free histidine residues at the active site, which are related to substrate accessibility during tyrosinase catalysis.