Harmonic modes as variables to approximately account for receptor flexibility in ligand–receptor docking simulations: Application to DNA minor groove ligand complex

An approach to approximately account for receptor flexibility in ligand–receptor docking simulations is described and applied to a DNA/Hoechst 33258 analogue complex. Harmonic modes corresponding to eigenvectors with small eigenvalues of the Hessian matrix of the potential energy function were used as independent variables to describe receptor flexibility. For the DNA minor groove ligand case most of the conformational difference between an energy minimized free DNA and ligand-bound structure could be assigned to 5–40 harmonic receptor modes with small eigenvalues. During docking, deformations of the DNA receptor structure in the subset of harmonic modes were limited using a simple penalty function that avoided the summation over all intrareceptor atom pairs. Significant improvement of the sterical fit between ligand and receptor was found upon relaxation of the DNA in the subset of harmonic modes after docking of the ligand at the position found in the known crystal structure. In addition, the harmonic mode relaxation resulted in DNA structures that were more similar to the energy minimized ligand-bound form. Although harmonic mode relaxation also leads to improved sterical fit for other ligand placements, the placement as observed in the crystal structure could still be identified as the site with the most favorable sterical interactions. Because relaxation in the harmonic modes is orders of magnitude faster than conventional energy minimization using all atom coordinates as independent variables, the approach might be useful as a preselection tool to recognize ligand binding sites accessible only upon small conformational changes of the receptor. The harmonic mode relaxed structures can only be considered as approximate structures because deformation of the receptor in the harmonic modes can lead to small perturbations of the stereochemical geometry of the molecule. Energy minimization of preselected ligand–DNA docking candidates in all atom coordinates is required to reduce these deviations. ©1999 John Wiley & Sons, Inc. J Comput Chem 20: 287–300, 1999     

Automated docking of highly flexible ligands by genetic algorithms: a critical assessment

An improved version of the fragment-based flexible ligand docking approach SEED-FFLD is tested on inhibitors of human immunodeficiency virus type 1 protease, human alpha-thrombin and the estrogen receptor beta. The docking results indicate that it is possible to correctly reproduce the binding mode of inhibitors with more than ten rotatable bonds if the strain in their covalent geometry upon binding is not large. A high degree of convergence towards a unique binding mode in multiple runs of the genetic algorithm is proposed as a necessary condition for successful docking.     

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     

GalaxyDock2: Protein–ligand docking using beta‐complex and global optimization

In this article, an enhanced version of GalaxyDock protein–ligand docking program is introduced. GalaxyDock performs conformational space annealing (CSA) global optimization to find the optimal binding pose of a ligand both in the rigid‐receptor mode and the flexible‐receptor mode. Binding pose prediction has been improved compared to the earlier version by the efficient generation of high‐quality initial conformations for CSA using a predocking method based on a beta‐complex derived from the Voronoi diagram of receptor atoms. Binding affinity prediction has also been enhanced by using the optimal combination of energy components, while taking into consideration the energy of the unbound ligand state. The new version has been tested in terms of binding mode prediction, binding affinity prediction, and virtual screening on several benchmark sets, showing improved performance over the previous version and AutoDock, on which the GalaxyDock energy function is based. GalaxyDock2 also performs better than or comparable to other state‐of‐the‐art docking programs. GalaxyDock2 is freely available at http://galaxy.seoklab.org/softwares/galaxydock.html . © 2013 Wiley Periodicals, Inc.     

Assessment of programs for ligand binding affinity prediction

The prediction of the binding free energy between a ligand and a protein is an important component in the virtual screening and lead optimization of ligands for drug discovery. To determine the quality of current binding free energy estimation programs, we examined FlexX, X-Score, AutoDock, and BLEEP for their performance in binding free energy prediction in various situations including cocrystallized complex structures, cross docking of ligands to their non-cocrystallized receptors, docking of thermally unfolded receptor decoys to their ligands, and complex structures with "randomized" ligand decoys. In no case was there a satisfactory correlation between the experimental and estimated binding free energies over all the datasets tested. Meanwhile, a strong correlation between ligand molecular weight-binding affinity correlation and experimental predicted binding affinity correlation was found. Sometimes the programs also correctly ranked ligands' binding affinities even though native interactions between the ligands and their receptors were essentially lost because of receptor deformation or ligand randomization, and the programs could not decisively discriminate randomized ligand decoys from their native ligands; this suggested that the tested programs miss important components for the accurate capture of specific ligand binding interactions.     

Efficient inclusion of receptor flexibility in grid-based protein-ligand docking

Accounting for receptor flexibility is an essential component of successful protein-ligand docking but still marks a major computational challenge. For many target molecules of pharmaceutical relevance, global backbone conformational changes are relevant during the ligand binding process. However, popular methods that represent the protein receptor molecule as a potential grid typically assume a rigid receptor structure during ligand-receptor docking. A new approach has been developed that combines inclusion of global receptor flexibility with the efficient potential grid representation of the receptor molecule. This is achieved using interpolation between grid representations of the receptor protein deformed in selected collective degrees of freedom. The method was tested on the docking of three ligands to apo protein kinase A (PKA), an enzyme that undergoes global structural changes upon inhibitor binding. Structural variants of PKA were generated along the softest normal mode of an elastic network representation of apo PKA. Inclusion of receptor deformability during docking resulted in a significantly improved docking performance compared with rigid PKA docking, thus allowing for systematic virtual screening applications at small additional computational cost.     

Maximum common binding modes (MCBM): consensus docking scoring using multiple ligand information and interaction fingerprints

Improving the scoring functions for small molecule-protein docking is a highly challenging task in current computational drug design. Here we present a novel consensus scoring concept for the prediction of binding modes for multiple known active ligands. Similar ligands are generally believed to bind to their receptor in a similar fashion. The presumption of our approach was that the true binding modes of similar ligands should be more similar to each other compared to false positive binding modes. The number of conserved (consensus) interactions between similar ligands was used as a docking score. Patterns of interactions were modeled using ligand receptor interaction fingerprints. Our approach was evaluated for four different data sets of known cocrystal structures (CDK-2, dihydrofolate reductase, HIV-1 protease, and thrombin). Docking poses were generated with FlexX and rescored by our approach. For comparison the CScore scoring functions from Sybyl were used, and consensus scores were calculated thereof. Our approach performed better than individual scoring functions and was comparable to consensus scoring. Analysis of the distribution of docking poses by self-organizing maps (SOM) and interaction fingerprints confirmed that clusters of docking poses composed of multiple ligands were preferentially observed near the native binding mode. Being conceptually unrelated to commonly used docking scoring functions our approach provides a powerful method to complement and improve computational docking experiments.     

Fluctuation analysis and accuracy of a large-scale in silico screen

Using a cascadic version of the stochastic tunneling method we perform an all-atom database screen over 186,000 flexible ligands of the NCI 3D database against the thymidine kinase receptor. By analyzing the errors in the binding energy we demonstrate how the cascadic technique is superior to conventional sequential docking techniques and how reliable results for the determination of the top-scoring ligands could be achieved. The substrate corresponding to the crystal structure used in the screen ranks in the upper 0.05% of the database, validating both docking methodology and the applicability of the scoring function to this substrate. Several high ranking ligands of the database display significant structural similarity with known substrates. A detailed analysis of the accuracy of the screening method is carried out, and its dependence on the flexibility of the ligand is quantified.     

FDS: flexible ligand and receptor docking with a continuum solvent model and soft-core energy function

The docking of flexible small molecule ligands to large flexible protein targets is addressed in this article using a two-stage simulation-based method. The methodology presented is a hybrid approach where the first component is a dock of the ligand to the protein binding site, based on deriving sets of simultaneously satisfied intermolecular hydrogen bonds using graph theory and a recursive distance geometry algorithm. The output structures are reduced in number by cluster analysis based on distance similarities. These structures are submitted to a modified Monte Carlo algorithm using the AMBER-AA molecular mechanics force field with the Generalized Born/Surface Area (GB/SA) continuum model. This solvent model is not only less expensive than an explicit representation, but also yields increased sampling. Sampling is also increased using a rotamer library to direct some of the protein side-chain movements along with large dihedral moves. Finally, a softening function for the nonbonded force field terms is used, enabling the potential energy function to be slowly turned on throughout the course of the simulation. The docking procedure is optimized, and the results are presented for a single complex of the arabinose binding protein. It was found that for a rigid receptor model, the X-ray binding geometry was reproduced and uniquely identified based on the associated potential energy. However, when side-chain flexibility was included, although the X-ray structure was identified, it was one of three possible binding geometries that were energetically indistinguishable. These results suggest that on relaxing the constraint on receptor flexibility, the docking energy hypersurface changes from being funnel-like to rugged. A further 14 complexes were then examined using the optimized protocol. For each complex the docking methodology was tested for a fully flexible ligand, both with and without protein side-chain flexibility. For the rigid protein docking, 13 out of the 15 test cases were able to find the experimental binding mode; this number was reduced to 11 for the flexible protein docking. However, of these 11, in the majority of cases the experimental binding mode was not uniquely identified, but was present in a cluster of low energy structures that were energetically indistinguishable. These results not only support the presence of a rugged docking energy hypersurface, but also suggest that it may be necessary to consider the possibility of more than one binding conformation during ligand optimization.     

A molecular docking study of potential inhibitors and repurposed drugs against SARS-CoV-2 main protease enzyme

COVID-19 has affected millions of people. Although many drugs are in use to combat disease, there is not any sufficient treatment yet. Having critical role in propagation of the novel coronavirus (SARS-CoV-2) works Main Protease up into a significant drug target. We have performed a molecular docking study to define possible inhibitor candidates against SARS-CoV-2 Main Protease enzyme. Besides docking Remdesivir, Ribavirin, Chloroquine and 28 other antiviral inhibitors (totally 31 inhibitors) to Main Protease enzyme, we have also performed a molecular docking study of 2177 ligands, which are used against Main Protease for the first time by using molecular docking program Autodock4. All ligands were successfully docked into Main Protease enzyme binding site. Among all ligands, EY16 coded ligand which previously used as EBNA1-DNA binding blocker candidate showed the best score for Main Protease with a binding free energy of −10.83 ​kcal/mol which was also lower than re-docking score of N3 ligand (−10.72 ​kcal/mol) contained in crystal structure of Main Protease. After analyzing the docking modes and docking scores we have found that our ligands have better binding free energy values than the inhibitors in use of treatment. We believe that further studies such as molecular dynamics or Molecular Mechanic Poisson Boltzmann Surface Area studies can make contribution that is more exhaustive to the docking results.     

Representing receptor flexibility in ligand docking through relevant normal modes

Inspired by the current representation of the ligand-receptor binding process, a normal-mode-based methodology is presented to incorporate receptor flexibility in ligand docking and virtual screening. However, the systematic representation of the deformation space grows geometrically with the number of modes, and furthermore, midscale loop rearrangements like those found in protein kinase binding pockets cannot be accounted for with the first lowest-frequency modes. We thus introduced a measure of relevance of normal modes on a given region of interest and showed that only very few modes in the low-frequency range are necessary and sufficient to describe loop flexibility in cAMP-dependent protein kinase. We used this approach to generate an ensemble of representative receptor backbone conformations by perturbing the structure along a combination of relevant modes. Each ensemble conformation is complexed with known non-native binders to optimize the position of the binding-pocket side chains through a full flexible docking procedure. The multiple receptor conformations thus obtained are used in a small-scale virtual screening using receptor ensemble docking. We evaluated this algorithm on holo and apo structures of cAMP-dependent protein kinase that exhibit backbone rearrangements on two independent loop regions close to the binding pocket. Docking accuracy is improved, since the ligands considered in the virtual screening docked within 1.5 A to at least one of the structures. The discrimination between binders and nonbinders is also enhanced, as shown by the improvement of the enrichment factor. This constitutes a new step toward the systematic integration of flexible ligand-flexible receptor docking tools in structure-based drug discovery.     

Modeling loop backbone flexibility in receptor‐ligand docking simulations

The relevance of receptor conformational change during ligand binding is well documented for many pharmaceutically relevant receptors, but is still not fully accounted for in in silico docking methods. While there has been significant progress in treatment of receptor side chain flexibility sampling of backbone flexibility remains challenging because the conformational space expands dramatically and the scoring function must balance protein–protein and protein–ligand contributions. Here, we investigate an efficient multistage backbone reconstruction algorithm for large loop regions in the receptor and demonstrate that treatment of backbone receptor flexibility significantly improves binding mode prediction starting from apo structures and in cross docking simulations. For three different kinase receptors in which large flexible loops reconstruct upon ligand binding, we demonstrate that treatment of backbone flexibility results in accurate models of the complexes in simulations starting from the apo structure. At the example of the DFG‐motif in the p38 kinase, we also show how loop reconstruction can be used to model allosteric binding. Our approach thus paves the way to treat the complex process of receptor reconstruction upon ligand binding in docking simulations and may help to design new ligands with high specificity by exploitation of allosteric mechanisms. © 2012 Wiley Periodicals, Inc.     

Investigating cyclic peptides inhibiting CD2–CD58 interactions through molecular dynamics and molecular docking methods

The CD2–CD58 protein–protein interaction is known to favor the recognition of antigen presenting cells by T cells. The structural, energetics, and dynamical properties of three known cyclic CD58 ligands, named P6, P7, and RTD-c, are studied through molecular dynamics (MD) simulations and molecular docking calculations. The ligands are built so as to mimic the C and F β-strands of protein CD2, connected via turn inducers. The MD analyses focus on the location of the ligands with respect to the experimental binding site and on the direct and water-mediated hydrogen bonds (H bonds) they form with CD58. Ligand P6, with a sequence close to the experimental β-strands of CD2, presents characteristics that explain its higher experimental affinity, e.g., the lower mobility and flexibility at the CD58 surface, and the larger number and occurrence frequency of ligand-CD58 H bonds. For the two other ligands, the structural modifications lead to changes in the binding pattern with CD58 and its dynamics. In parallel, a large set of molecular docking calculations, carried out with various search spaces and docking algorithms, are compared to provide a consensus view of the preferred ligand binding modes. The analysis of the ligand side chain locations yields results that are consistent with the CD2–CD58 crystal structure and suggests various binding modes of the experimentally identified hot spot of the ligands, i.e., Tyr86. P6 is shown to form a number of contacts that are also present in the experimental CD2–CD58 structure.     

A QXP-based multistep docking procedure for accurate prediction of protein-ligand complexes

The two great challenges of the docking process are the prediction of ligand poses in a protein binding site and the scoring of the docked poses. Ligands that are composed of extended chains in their molecular structure display the most difficulties, predominantly because of the torsional flexibility. On the basis of the molecular docking program QXP-Flo+0802, we have developed a procedure particularly for ligands with a high degree of rotational freedom that allows the accurate prediction of the orientation and conformation of ligands in protein binding sites. Starting from an initial full Monte Carlo docking experiment, this was achieved by performing a series of successive multistep docking runs using a local Monte Carlo search with a restricted rotational angle, by which the conformational search space is limited. The method was established by using a highly flexible acetylcholinesterase inhibitor and has been applied to a number of challenging protein-ligand complexes known from the literature.     

SPOT‐Ligand: Fast and effective structure‐based virtual screening by binding homology search according to ligand and receptor similarity

Structure‐based virtual screening usually involves docking of a library of chemical compounds onto the functional pocket of the target receptor so as to discover novel classes of ligands. However, the overall success rate remains low and screening a large library is computationally intensive. An alternative to this “ab initio” approach is virtual screening by binding homology search. In this approach, potential ligands are predicted based on similar interaction pairs (similarity in receptors and ligands). SPOT‐Ligand is an approach that integrates ligand similarity by Tanimoto coefficient and receptor similarity by protein structure alignment program SPalign. The method was found to yield a consistent performance in DUD and DUD‐E docking benchmarks even if model structures were employed. It improves over docking methods (DOCK6 and AUTODOCK Vina) and has a performance comparable to or better than other binding‐homology methods (FINDsite and PoLi) with higher computational efficiency. The server is available at http://sparks-lab.org . © 2016 Wiley Periodicals, Inc.     

Response surface methodology in drug design: A case study on docking analysis of a potent antifungal fluconazole

Molecular docking is a valuable in silico technique for discovery/design of bioactive compounds. A current challenge within docking simulations is the incorporation of receptor flexibility. A useful strategy toward solving such problem would be the docking of a typical ligand into the multiple conformations of the target. In this study, a multifactor response surface model was constructed to estimate the AutoDock based binding free energy of fluconazole within multiple conformations of 14α-demethylase (CYP51) (cross docking) as a validated antifungal target. On the basis of developed models, individual and interactive effects of important experimental parameters on cross docking of fluconazole were elucidated. For this purpose, a set of high-resolution holo crystallographic structures from CYP51 of human pathogen Trypanosoma cruzi were retrieved to statistically model the binding mode and affinity of fluconazole. The changes of AutoDock binding free energy for the complexes of CYP51-fluconazole were elucidated with the simultaneous variations of six independent variables including grid size, grid spacing, number of genetic algorithm (GA) runs, maximum number of energy evaluations, torsion degrees for ligand and quaternion degrees for ligand. It was revealed that grid spacing (distance between adjacent grid points) and maximum number of energy evaluations were two significant model terms. It was also revealed that grid size, torsion degrees for ligand and quaternion degrees for ligand had insignificant effects on estimated binding energy while the effect of GA runs was non-significant. The interactive effect of “torsion degrees for ligand” with number of GA runs was found to be the significant factor. Other important interactive effects were the interaction of “number of GA runs” with “grid spacing” and “number of energy evaluations” with “grid size”. Furthermore; results of modeling studies within several CYP51 conformations exhibited that “number of GA runs” and “number of energy evaluations” were less sensitive to varied target conformations.     

Prediction of binding poses to FXR using multi-targeted docking combined with molecular dynamics and enhanced sampling

Advanced molecular docking methods often aim at capturing the flexibility of the protein upon binding to the ligand. In this study, we investigate whether instead a simple rigid docking method can be applied, if combined with multiple target structures to model the backbone flexibility and molecular dynamics simulations to model the sidechain and ligand flexibility. The methods are tested for the binding of 35 ligands to FXR as part of the first stage of the Drug Design Data Resource (D3R) Grand Challenge 2 blind challenge. The results show that the multiple-target docking protocol performs surprisingly well, with correct poses found for 21 of the ligands. MD simulations started on the docked structures are remarkably stable, but show almost no tendency of refining the structure closer to the experimentally found binding pose. Reconnaissance metadynamics enhances the exploration of new binding poses, but additional collective variables involving the protein are needed to exploit the full potential of the method.     

Automated docking of ligands to an artificial active site: augmenting crystallographic analysis with computer modeling

The W191G cavity of cytochrome c peroxidase is useful as a model system for introducing small molecule oxidation in an artificially created cavity. A set of small, cyclic, organic cations was previously shown to bind in the buried, solvent-filled pocket created by the W191G mutation. We docked these ligands and a set of non-binders in the W191G cavity using AutoDock 3.0. For the ligands, we compared docking predictions with experimentally determined binding energies and X-ray crystal structure complexes. For the ligands, predicted binding energies differed from measured values by +/- 0.8 kcal/mol. For most ligands, the docking simulation clearly predicted a single binding mode that matched the crystallographic binding mode within 1.0 A RMSD. For 2 ligands, where the docking procedure yielded an ambiguous result, solutions matching the crystallographic result could be obtained by including an additional crystallographically observed water molecule in the protein model. For the remaining 2 ligands, docking indicated multiple binding modes, consistent with the original electron density, suggesting disordered binding of these ligands. Visual inspection of the atomic affinity grid maps used in docking calculations revealed two patches of high affinity for hydrogen bond donating groups. Multiple solutions are predicted as these two sites compete for polar hydrogens in the ligand during the docking simulation. Ligands could be distinguished, to some extent, from non-binders using a combination of two trends: predicted binding energy and level of clustering. In summary, AutoDock 3.0 appears to be useful in predicting key structural and energetic features of ligand binding in the W191G cavity.     

Flexible CDOCKER: Development and application of a pseudo‐explicit structure‐based docking method within CHARMM

Protein‐ligand docking is a commonly used method for lead identification and refinement. While traditional structure‐based docking methods represent the receptor as a rigid body, recent developments have been moving toward the inclusion of protein flexibility. Proteins exist in an interconverting ensemble of conformational states, but effectively and efficiently searching the conformational space available to both the receptor and ligand remains a well‐appreciated computational challenge. To this end, we have developed the Flexible CDOCKER method as an extension of the family of complete docking solutions available within CHARMM. This method integrates atomically detailed side chain flexibility with grid‐based docking methods, maintaining efficiency while allowing the protein and ligand configurations to explore their conformational space simultaneously. This is in contrast to existing approaches that use induced‐fit like sampling, such as Glide or Autodock, where the protein or the ligand space is sampled independently in an iterative fashion. Presented here are developments to the CHARMM docking methodology to incorporate receptor flexibility and improvements to the sampling protocol as demonstrated with re‐docking trials on a subset of the CCDC/Astex set. These developments within CDOCKER achieve docking accuracy competitive with or exceeding the performance of other widely utilized docking programs. © 2015 Wiley Periodicals, Inc.     

Use of MCSS to design small targeted libraries: application to picornavirus ligands.

Computational methods were used to design structure-based combinatorial libraries of antipicornaviral capsid-binding ligands. The multiple copy simultaneous search (MCSS) program was employed to calculate functionality maps for many diverse functional groups for both the poliovirus and rhinovirus capsid structures in the region of the known drug binding pocket. Based on the results of the MCSS calculations, small combinatorial libraries consisting of 10s or 100s of three-monomer compounds were designed and synthesized. Ligand binding was demonstrated by a noncell-based mass spectrometric assay, a functional immuno-precipitation assay, and crystallographic analysis of the complexes of the virus with two of the candidate ligands. The P1/Mahoney poliovirus strain was used in the experimental studies. A comparison showed that the MCSS calculations had correctly identified the observed binding site for all three monomer units in one ligand and for two out of three in the other ligand. The correct central monomer position in the second ligand was reproduced in calculations in which the several key residues lining the pocket were allowed to move. This study validates the computational methodology. It also illustrates that subtle changes in protein structure can lead to differences in docking results and points to the importance of including target flexibility, as well as ligand flexibility, in the design process.