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Molecular Docking in Drug Design 2018

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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: closed (15 June 2019) | Viewed by 22830

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Dr. Dragos Horvath

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Laboratoire de Chemoinformatique, UMR 7140, Université de Strasbourg/CNRS, 1 rue B. Pascal, 67000 Strasbourg, France
Interests: chemoinformatics-methodology & applications; computer-aided drug design; flexible docking; nature-inspired computing

Special Issue Information

Dear Colleagues,

Nowadays, we have tens of various computational docking strategies, with an even wider choice of scoring functions. It is getting difficult, even for the docking-focused expert, to intimately know all the features of all the docking tools. Thus, "docking" grows more and more into a stand-alone discipline of molecular modeling, and its users are increasingly becoming overspecialized. This is unfortunate, since docking is actually at the cross-roads of (quantitative) Structure–Activity Relationships (Q)SAR and physical molecular simulations, such as molecular dynamics. In principle, docking simulations should be able to discover actives with novel site binding modes—molecules that are structurally different (thus, not eligible to be found by similarity searches based on known ligands), and not matching already known binding pharmacophores. In practice, docking is a trained QSAR model, with a limited applicability domain.

In your opinion, where would you situate docking on this scale of empiricism—do you consider it as a simplified physical simulation, or a rather sophisticated 3D-QSAR approach with ligand-site interaction descriptors? What is, in your experience, the key strength of docking over other methods - is it the ability to propose binding modes to inspire medicinal chemists in search for the best substitution patterns? Did you encounter examples of completely novel, "paradigm-breaking" binders discovered in docking-driven virtual screening? Have you encountered situations (benchmarks, prospective predictions) when docking was the only successful method, "seeing" SAR patterns which could not have been captured by 2D-QSAR? On the contrary, did you run time-consuming docking calculations only in order to discover that results highlight an obvious SAR trend which could have been learned by ultrafast 2D-QSAR, or simply by looking at the compound series?

I would thus encourage the members of community to report original work, or review papers that place docking in the wider context of various other chemoinformatics and modeling approaches, in an attempt to pinpoint the "ecological niche" best covered by this approach. In this respect, both docking success and failure stories can be enlightening. Original docking procedures are of course welcome, as their comparison with other methods are necessary for publication.

Dr. Dragos Horvath
Guest Editor

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Keywords

Docking strategies
Scoring functions versus force field energies
Docking versus 2D QSAR
Molecular Dynamics in Docking

Published Papers (6 papers)

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Research

22 pages, 19147 KiB

Open AccessArticle

A Chemosensory GPCR as a Potential Target to Control the Root-Knot Nematode Meloidogyne incognita Parasitism in Plants

by Emmanuel Bresso, Diana Fernandez, Deisy X. Amora, Philippe Noel, Anne-Sophie Petitot, Maria-Eugênia Lisei de Sa, Erika V. S. Albuquerque, Etienne G. J. Danchin, Bernard Maigret and Natália F. Martins

Molecules 2019, 24(20), 3798; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules24203798 - 22 Oct 2019

Cited by 11 | Viewed by 4365

Abstract

Root-knot nematodes (RKN), from the Meloidogyne genus, have a worldwide distribution and cause severe economic damage to many life-sustaining crops. Because of their lack of specificity and danger to the environment, most chemical nematicides have been banned from use. Thus, there is a great need for new and safe compounds to control RKN. Such research involves identifying beforehand the nematode proteins essential to the invasion. Since G protein-coupled receptors GPCRs are the target of a large number of drugs, we have focused our research on the identification of putative nematode GPCRs such as those capable of controlling the movement of the parasite towards (or within) its host. A datamining procedure applied to the genome of Meloidogyne incognita allowed us to identify a GPCR, belonging to the neuropeptide GPCR family that can serve as a target to carry out a virtual screening campaign. We reconstructed a 3D model of this receptor by homology modeling and validated it through extensive molecular dynamics simulations. This model was used for large scale molecular dockings which produced a filtered limited set of putative antagonists for this GPCR. Preliminary experiments using these selected molecules allowed the identification of an active compound, namely C260-2124, from the ChemDiv provider, which can serve as a starting point for further investigations. Full article

(This article belongs to the Special Issue Molecular Docking in Drug Design 2018)

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17 pages, 5566 KiB

Open AccessArticle

Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1

by Eliane Briand, Ragnar Thomsen, Kristian Linnet, Henrik Berg Rasmussen, Søren Brunak and Olivier Taboureau

Molecules 2019, 24(15), 2747; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules24152747 - 29 Jul 2019

Cited by 6 | Viewed by 4293

Abstract

The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 µM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 µM, 366.8 µM and 391.6 µM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure. Full article

(This article belongs to the Special Issue Molecular Docking in Drug Design 2018)

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13 pages, 2778 KiB

Open AccessArticle

Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking

by Dávid Bajusz, Anita Rácz and Károly Héberger

Molecules 2019, 24(15), 2690; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules24152690 - 24 Jul 2019

Cited by 12 | Viewed by 3161

Abstract

Ensemble docking is a widely applied concept in structure-based virtual screening—to at least partly account for protein flexibility—usually granting a significant performance gain at a modest cost of speed. From the individual, single-structure docking scores, a consensus score needs to be produced by data fusion: this is usually done by taking the best docking score from the available pool (in most cases— and in this study as well—this is the minimum score). Nonetheless, there are a number of other fusion rules that can be applied. We report here the results of a detailed statistical comparison of seven fusion rules for ensemble docking, on five case studies of current drug targets, based on four performance metrics. Sevenfold cross-validation and variance analysis (ANOVA) allowed us to highlight the best fusion rules. The results are presented in bubble plots, to unite the four performance metrics into a single, comprehensive image. Notably, we suggest the use of the geometric and harmonic means as better alternatives to the generally applied minimum fusion rule. Full article

(This article belongs to the Special Issue Molecular Docking in Drug Design 2018)

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16 pages, 6663 KiB

Open AccessFeature PaperArticle

Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses

by Célien Jacquemard, Viet-Khoa Tran-Nguyen, Malgorzata N. Drwal, Didier Rognan and Esther Kellenberger

Molecules 2019, 24(14), 2610; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules24142610 - 18 Jul 2019

Cited by 4 | Viewed by 2979

Abstract

Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time. Full article

(This article belongs to the Special Issue Molecular Docking in Drug Design 2018)

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22 pages, 5324 KiB

Open AccessFeature PaperArticle

Generative Topographic Mapping of the Docking Conformational Space

by Dragos Horvath, Gilles Marcou and Alexandre Varnek

Molecules 2019, 24(12), 2269; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules24122269 - 18 Jun 2019

Cited by 4 | Viewed by 3037

Abstract

Following previous efforts to render the Conformational Space (CS) of flexible compounds by Generative Topographic Mapping (GTM), this polyvalent mapping technique is here adapted to the docking problem. Contact fingerprints (CF) characterize ligands from the perspective of the binding site by monitoring protein atoms that are “touched” by those of the ligand. A “Contact” (CF) map was built by GTM-driven dimensionality reduction of the CF vector space. Alternatively, a “Hybrid” (Hy) map used a composite descriptor of CFs concatenated with ligand fragment descriptors. These maps indirectly represent the active site and integrate the binding information of multiple ligands. The concept is illustrated by a docking study into the ATP-binding site of CDK2, using the S4MPLE program to generate thousands of poses for each ligand. Both maps were challenged to (1) Discriminate native from non-native ligand poses, e.g., create RMSD-landscapes “colored” by the conformer ensemble of ligands of known binding modes in order to highlight “native” map zones (poses with RMSD to PDB structures < 2Å). Then, projection of poses of other ligands on such landscapes might serve to predict those falling in native zones as being well-docked. (2) Distinguish ligands–characterized by their ensemble of conformers–by their potency, e.g., testing the hypotheses whether zones privileged by potent binders are clearly separated from the ones preferred by decoys on the maps. Hybrid maps were better in both challenges and outperformed the classical energy and individual contact satisfaction scores in discriminating ligands by potency. Moreover, the intuitive visualization and analysis of docking CS may, as already mentioned, have several applications–from highlighting of key contacts to monitoring docking calculation convergence. Full article

(This article belongs to the Special Issue Molecular Docking in Drug Design 2018)

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14 pages, 2069 KiB

Open AccessArticle

In Silico Peptide Ligation: Iterative Residue Docking and Linking as a New Approach to Predict Protein-Peptide Interactions

by Julien Diharce, Mickaël Cueto, Massimiliano Beltramo, Vincent Aucagne and Pascal Bonnet

Molecules 2019, 24(7), 1351; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules24071351 - 05 Apr 2019

Cited by 9 | Viewed by 4183

Abstract

Peptide–protein interactions are corner-stones of living functions involved in essential mechanisms, such as cell signaling. Given the difficulty of obtaining direct experimental structural biology data, prediction of those interactions is of crucial interest for the rational development of new drugs, notably to fight diseases, such as cancer or Alzheimer’s disease. Because of the high flexibility of natural unconstrained linear peptides, prediction of their binding mode in a protein cavity remains challenging. Several theoretical approaches have been developed in the last decade to address this issue. Nevertheless, improvements are needed, such as the conformation prediction of peptide side-chains, which are dependent on peptide length and flexibility. Here, we present a novel in silico method, Iterative Residue Docking and Linking (IRDL), to efficiently predict peptide–protein interactions. In order to reduce the conformational space, this innovative method splits peptides into several short segments. Then, it uses the performance of intramolecular covalent docking to rebuild, sequentially, the complete peptide in the active site of its protein target. Once the peptide is constructed, a rescoring step is applied in order to correctly rank all IRDL solutions. Applied on a set of 11 crystallized peptide–protein complexes, the IRDL method shows promising results, since it is able to retrieve experimental binding conformations with a Root Mean Square Deviation (RMSD) below 2 Å in the top five ranked solutions. For some complexes, IRDL method outperforms two other docking protocols evaluated in this study. Hence, IRDL is a new tool that could be used in drug design projects to predict peptide–protein interactions. Full article

(This article belongs to the Special Issue Molecular Docking in Drug Design 2018)

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