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Special Issue "Molecular Docking in Drug Design 2018"

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

Deadline for manuscript submissions: 31 May 2019

Special Issue Editor

Guest Editor
Dr. Dragos Horvath

Laboratoire de Chemoinformatique; UMR 7141, Université de Strasbourg, 1 rue B. Pascal, Strasbourg 67000, France
Website | E-Mail
Interests: fundamental molecular modeling; chemoinformatics and drug design

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

Manuscript Submission Information

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Keywords

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

Published Papers (1 paper)

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Research

Open AccessArticle
In Silico Peptide Ligation: Iterative Residue Docking and Linking as a New Approach to Predict Protein-Peptide Interactions
Molecules 2019, 24(7), 1351; https://doi.org/10.3390/molecules24071351
Received: 20 January 2019 / Revised: 2 April 2019 / Accepted: 3 April 2019 / Published: 5 April 2019
PDF Full-text (2069 KB) | HTML Full-text | XML Full-text | Supplementary Files
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 [...] Read more.
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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