High throughput docking for library design and library prioritization

The prioritization of the screening of combinatorial libraries is an extremely important task for the rapid identification of tight binding ligands and ultimately pharmaceutical compounds. When structural information for the target is available, molecular docking is an approach that can be used for prioritization. Here, we present the initial validation of a new rapid approach to molecular docking developed for prioritizing combinatorial libraries. The algorithm is tested on 103 individual cases from the protein data bank and in nearly 90% of these cases docks the ligand to within 2.0 A of the observed binding mode. Because the mean CPU time is <5 s/mol, this approach can process hundreds of thousands of compounds per week. Furthermore, if a somewhat less thorough search is performed, the search time drops to 1 s/mol, thus allowing millions of compounds to be docked per week and tested for potential activity. Proteins 2001;43:113-124.     

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.     

Accelerating protein docking in ZDOCK using an advanced 3D convolution library

Computational prediction of the 3D structures of molecular interactions is a challenging area, often requiring significant computational resources to produce structural predictions with atomic-level accuracy. This can be particularly burdensome when modeling large sets of interactions, macromolecular assemblies, or interactions between flexible proteins. We previously developed a protein docking program, ZDOCK, which uses a fast Fourier transform to perform a 3D search of the spatial degrees of freedom between two molecules. By utilizing a pairwise statistical potential in the ZDOCK scoring function, there were notable gains in docking accuracy over previous versions, but this improvement in accuracy came at a substantial computational cost. In this study, we incorporated a recently developed 3D convolution library into ZDOCK, and additionally modified ZDOCK to dynamically orient the input proteins for more efficient convolution. These modifications resulted in an average of over 8.5-fold improvement in running time when tested on 176 cases in a newly released protein docking benchmark, as well as substantially less memory usage, with no loss in docking accuracy. We also applied these improvements to a previous version of ZDOCK that uses a simpler non-pairwise atomic potential, yielding an average speed improvement of over 5-fold on the docking benchmark, while maintaining predictive success. This permits the utilization of ZDOCK for more intensive tasks such as docking flexible molecules and modeling of interactomes, and can be run more readily by those with limited computational resources.     

Prediction of ligand-binding sites of proteins by molecular docking calculation for a random ligand library

A new approach to predicting the ligand-binding sites of proteins was developed, using protein-ligand docking computation. In this method, many compounds in a random library are docked onto the whole protein surface. We assumed that the true ligand-binding site would exhibit stronger affinity to the compounds in the random library than the other sites, even if the random library did not include the ligand corresponding to the true binding site. We also assumed that the affinity of the true ligand-binding site would be correlated to the docking scores of the compounds in the random library, if the ligand-binding site was correctly predicted. We call this method the molecular-docking binding-site finding (MolSite) method. The MolSite method was applied to 89 known protein-ligand complex structures extracted from the Protein Data Bank, and it predicted the correct binding sites with about 80-99% accuracy, when only the single top-ranked site was adopted. In addition, the average docking score was weakly correlated to the experimental protein-ligand binding free energy, with a correlation coefficient of 0.44.     

OpenMEEG: opensource software for quasistatic bioelectromagnetics

Background  

Interpreting and controlling bioelectromagnetic phenomena require realistic physiological models and accurate numerical solvers. A semi-realistic model often used in practise is the piecewise constant conductivity model, for which only the interfaces have to be meshed. This simplified model makes it possible to use Boundary Element Methods. Unfortunately, most Boundary Element solutions are confronted with accuracy issues when the conductivity ratio between neighboring tissues is high, as for instance the scalp/skull conductivity ratio in electro-encephalography. To overcome this difficulty, we proposed a new method called the symmetric BEM, which is implemented in the OpenMEEG software. The aim of this paper is to present OpenMEEG, both from the theoretical and the practical point of view, and to compare its performances with other competing software packages.     

Progress in molecular docking

Background: In recent years, since the molecular docking technique can greatly improve the efficiency and reduce the research cost, it has become a key tool in computer-assisted drug design to predict the binding affinity and analyze the interactive mode. Results: This study introduces the key principles, procedures and the widely-used applications for molecular docking. Also, it compares the commonly used docking applications and recommends which research areas are suitable for them. Lastly, it briefly reviews the latest progress in molecular docking such as the integrated method and deep learning. Conclusion: Limited to the incomplete molecular structure and the shortcomings of the scoring function, current docking applications are not accurate enough to predict the binding affinity. However, we could improve the current molecular docking technique by integrating the big biological data into scoring function.     

PAL: an object-oriented programming library for molecular evolution and phylogenetics.

Phylogenetic Analysis Library (PAL) is a collection of Java classes for use in molecular evolution and phylogenetics. PAL provides a modular environment for the rapid construction of both special-purpose and general analysis programs. PAL version 1.1 consists of 145 public classes or interfaces in 13 packages, including classes for models of character evolution, maximum-likelihood estimation, and the coalescent, with a total of more than 27000 lines of code. The PAL project is set up as a collaborative project to facilitate contributions from other researchers. AVAILIABILTY: The program is free and is available at http://www.pal-project.org. It requires Java 1.1 or later. PAL is licensed under the GNU General Public License.     

FireDock: fast interaction refinement in molecular docking

Here, we present FireDock, an efficient method for the refinement and rescoring of rigid-body docking solutions. The refinement process consists of two main steps: (1) rearrangement of the interface side-chains and (2) adjustment of the relative orientation of the molecules. Our method accounts for the observation that most interface residues that are important in recognition and binding do not change their conformation significantly upon complexation. Allowing full side-chain flexibility, a common procedure in refinement methods, often causes excessive conformational changes. These changes may distort preformed structural signatures, which have been shown to be important for binding recognition. Here, we restrict side-chain movements, and thus manage to reduce the false-positive rate noticeably. In the later stages of our procedure (orientation adjustments and scoring), we smooth the atomic radii. This allows for the minor backbone and side-chain movements and increases the sensitivity of our algorithm. FireDock succeeds in ranking a near-native structure within the top 15 predictions for 83% of the 30 enzyme-inhibitor test cases, and for 78% of the 18 semiunbound antibody-antigen complexes. Our refinement procedure significantly improves the ranking of the rigid-body PatchDock algorithm for these cases. The FireDock program is fully automated. In particular, to our knowledge, FireDock's prediction results are comparable to current state-of-the-art refinement methods while its running time is significantly lower. The method is available at http://bioinfo3d.cs.tau.ac.il/FireDock/.     

Lead discovery using molecular docking

As the structures of more and more proteins and nucleic acids become available, molecular docking is increasingly considered for lead discovery. Recent studies consider the hit-rate enhancement of docking screens and the accuracy of docking structure predictions. As more structures are determined experimentally, docking against homology-modeled targets also becomes possible for more proteins. With more docking studies being undertaken, the 'drug-likeness' and specificity of docking hits is also being examined.     

Ligand solvation in molecular docking

Solvation plays an important role in ligand‐protein association and has a strong impact on comparisons of binding energies for dissimilar molecules. When databases of such molecules are screened for complementarity to receptors of known structure, as often occurs in structure‐based inhibitor discovery, failure to consider ligand solvation often leads to putative ligands that are too highly charged or too large. To correct for the different charge states and sizes of the ligands, we calculated electrostatic and non‐polar solvation free energies for molecules in a widely used molecular database, the Available Chemicals Directory (ACD). A modified Born equation treatment was used to calculate the electrostatic component of ligand solvation. The non‐polar component of ligand solvation was calculated based on the surface area of the ligand and parameters derived from the hydration energies of apolar ligands. These solvation energies were subtracted from the ligand‐receptor interaction energies. We tested the usefulness of these corrections by screening the ACD for molecules that complemented three proteins of known structure, using a molecular docking program. Correcting for ligand solvation improved the rankings of known ligands and discriminated against molecules with inappropriate charge states and sizes. Proteins 1999;34:4–16. © 1999 Wiley‐Liss, Inc.     

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.     

GEMDOCK: a generic evolutionary method for molecular docking

We have developed an evolutionary approach for flexible ligand docking. This approval, GEMDOCK, uses a Generic Evolutionary Method for molecular DOCKing and an empirical scoring function. The former combines both discrete and continuous global search strategies with local search strategies to speed up convergence, whereas the latter results in rapid recognition of potential ligands. GEMDOCK was tested on a diverse data set of 100 protein-ligand complexes from the Protein Data Bank. In 79% of these complexes, the docked lowest energy ligand structures had root-mean-square derivations (RMSDs) below 2.0 A with respect to the corresponding crystal structures. The success rate increased to 85% if the structure water molecules were retained. We evaluated GEMDOCK on two cross-docking experiments in which each ligand of a protein ensemble was docked into each protein of the ensemble. Seventy-six percent of the docked structures had RMSDs below 2.0 A when the ligands were docked into foreign structures. We analyzed and validated GEMDOCK with respect to various search spaces and scoring functions, and found that if the scoring function was perfect, then the predicted accuracy was also essentially perfect. This study suggests that GEMDOCK is a useful tool for molecular recognition and may be used to systematically evaluate and thus improve scoring functions.     

Distance-constrained molecular docking by simulated annealing

An optimized method based on the principle of simulated annealing is presented for determining the relative position and orientation of interacting molecules. The spatial relationships of these molecules are described by intermolecular distance constraints between specific pairs of atoms, such as found in hydrogen bonds or from experimentally determined data. The method makes use of a random walk through six rotational and translational degrees of freedom where the constituent molecules are treated as rigid bodies. Van der Waals repulsions are used only to define a lower bound on distances between constrained atom pairs within the docking procedure. A cost function comprised of purely geometric constraints is optimized via simulated annealing, in order to search for the best orientation and position of the two molecules. Our docking procedure is applied to eight serine proteinase complexes from the Brookhaven Protein Data Bank. For each simulation 100 computations were performed. A typical docking computation requires only a few seconds of CPU time on a VAXserver 3500. The influence of the number of constraints on the final docked positions was studied. The sensitivity of the docking procedure to a ligand structure which is not well defined is also addressed. Possible applications of this method include using approximate distances incorporating complete energy functions.     

Anti-SARS drug screening by molecular docking

Summary. Starting from a collection of 1386 druggable compounds obtained from the 3D pharmacophore search, we performed a similarity search to narrow down the scope of docking studies. The template molecule is KZ7088 (Chou et al., 2003, Biochem Biophys Res Commun 308: 148–151). The MDL MACCS keys were used to fingerprint the molecules. The Tanimoto coefficient is taken as the metric to compare fingerprints. If the similarity threshold was 0.8, a set of 50 unique hits and 103 conformers were retrieved as a result of similarity search. The AutoDock 3.011 was used to carry out molecular docking of 50 ligands to their macromolecular protein receptors. Three compounds, i.e., C₂₈H₃₄O₄N₇Cl, C₂₁H₃₆O₅N₆, and C₂₁H₃₆O₅N₆, were found that may be promising candidates for further investigation. The main feature shared by these three potential inhibitors as well as the information of the involved side chains of SARS Cov Mpro may provide useful insights for the development of potent inhibitors against SARS enzyme.     

Hydrophobic docking: A proposed enhancement to molecular recognition techniques

In the classical procedures for predicting the structure of protein complexes two molecules are brought in contact at multiple relative positions, the extent of complementarity (geometric and/or energy) at the surface of contact is assessed at each position, and the best fits are retrieved. In view of the higher occurrence of hydrophobic groups at contact sites, their contribution results in more intermolecular atom–atom contacts per unit area for correct matches than for false positive fits. The hydrophobic groups are also potentially less flexible at the surface. Thus, from a practical point of view, a partial representation of the molecules based on hydrophobic groups should improve the quality of the results in finding molecular recognition sites, as compared to full representation. We tested this proposal by applying the idea to an existing geometric fit procedure and compared the results obtained with full vs. hydrophobic representations of molecules in known molecular complexes. The hydrophobic docking yielded distinctly higher signal-to-noise ratio so that the correct match is discriminated better from false positive fits. It appears that nonhydrophobic groups contribute more to false matches. The results are discussed in terms of their relevance to molecular recognition techniques as compared to energy calculations. © 1994 Wiley-Liss, Inc.     

3D docking device for molecular modelling

A device to aid in the process of docking molecules has been incorporated into a molecular modelling system. It provides a superb correspondence between motions of a single electromagnetic control wand and motions of a molecule displayed on the graphics screen. The commercially available hardware device is described, the new algorithms necessary to implement it are also described, and an example is given of how it has been used to dock a drug with its receptor.     

Identification of nonplanar small molecule for G-quadruplex grooves: molecular docking and molecular dynamic study

DNA G-quadruplex is an attractive drug target for anticancer therapy. Most G-quadruplex ligands have little selectivity, due to π-stacking interaction with common G-tetrads surface. Thanks to the varieties of G-quadruplex grooves, the groove-binding ligand is expected to create high selectivity. Therefore, developing novel molecular geometries that target G-quadruplex groove has been paid growing attention. In this work, steroid FG, a special nonplanar and nonaromatic small molecule, interacting with different conformations of G-quadruplexes has been studied by molecular docking and molecular dynamics simulations. The results showed the selectivity of the hydrophobic group of steroid FG for the wide groove of antiparallel G-quadruplex. The methyl groups on the tetracyclic ring of steroid represent the specific binding ability for the small hydrophobic cavity formed by reversed stacking of G-tetrads in antiparallel G-quadruplex groove. This work provides new insight for developing new classes of G-quadruplex groove-binding ligands.