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Computational Medicine and Molecular Drug Design
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Computational Medicine and Molecular Drug Design

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pharmacology".

Deadline for manuscript submissions: closed (20 January 2025) | Viewed by 13905

Special Issue Editor

Dr. Ηaralampos Tzoupis

E-Mail Website
Guest Editor
Department of Chemistry, University of Patras, 26504 Rion, Greece
Interests: computational biology; GPCRs; T-cell receptors; structure-based drug design; biochemistry; peptide conjugates; protein-ligand interactions; molecular dynamics protein simulations; design of non-peptide/peptide mimetics

Special Issue Information

Dear Colleagues,

Drug design comprises an important aspect of our understanding of biochemical pathways inside the cell, since it offers valuable information with regard to the role of various proteins. The process involves numerous steps, from identifying a suitable drug target to the evaluation of the various properties (physicochemical and ADMET) of the designed molecule. In order to facilitate the drug development process, various computational techniques have been and are being developed. These in silico tools address various issues, such as binding affinity predictions and interactions (e.g., molecular docking, molecular dynamics) and the prediction of ADMET properties. Additionally, various curated databases offer an extensive trove of data (experimental and predicted) that can be exploited in order to further empower the predictive strength of available and novel techniques. Moreover, the advent of AI and neural networks offers a powerful tool that can be exploited in order to address the vast quantity of accumulated data and assist in medicinal chemistry.

The aim of this Special Issue is to address the prospects offered by computational tools in the development of new molecules and the impact of innovative techniques on medicinal chemistry via the publication of communications, reviews and full papers focused on the above aspects.

Dr. Ηaralampos Tzoupis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • drug design
  • protein–ligand interactions
  • drug delivery systems
  • binding affinity prediction
  • drug repurposing
  • multidisciplinary approaches
  • toxicity prediction
  • machine learning drug design

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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23 pages, 7703 KiB  
Article
Leveraging the Fragment Molecular Orbital and MM-GBSA Methods in Virtual Screening for the Discovery of Novel Non-Covalent Inhibitors Targeting the TEAD Lipid Binding Pocket
by Jongwan Kim, Haiyan Jin, Jinhyuk Kim, Seon Yeon Cho, Sungho Moon, Jianmin Wang, Jiashun Mao and Kyoung Tai No
Int. J. Mol. Sci. 2024, 25(10), 5358; https://doi.org/10.3390/ijms25105358 - 14 May 2024
Cited by 4 | Viewed by 2748
Abstract
The Hippo pathway controls organ size and homeostasis and is linked to numerous diseases, including cancer. The transcriptional enhanced associate domain (TEAD) family of transcription factors acts as a receptor for downstream effectors, namely yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif [...] Read more.
The Hippo pathway controls organ size and homeostasis and is linked to numerous diseases, including cancer. The transcriptional enhanced associate domain (TEAD) family of transcription factors acts as a receptor for downstream effectors, namely yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ), which binds to various transcription factors and is essential for stimulated gene transcription. YAP/TAZ-TEAD facilitates the upregulation of multiple genes involved in evolutionary cell proliferation and survival. TEAD1–4 overexpression has been observed in different cancers in various tissues, making TEAD an attractive target for drug development. The central drug-accessible pocket of TEAD is crucial because it undergoes a post-translational modification called auto-palmitoylation. Crystal structures of the C-terminal TEAD complex with small molecules are available in the Protein Data Bank, aiding structure-based drug design. In this study, we utilized the fragment molecular orbital (FMO) method, molecular dynamics (MD) simulations, shape-based screening, and molecular mechanics–generalized Born surface area (MM-GBSA) calculations for virtual screening, and we identified a novel non-covalent inhibitor—BC-001—with IC50 = 3.7 μM in a reporter assay. Subsequently, we optimized several analogs of BC-001 and found that the optimized compound BC-011 exhibited an IC50 of 72.43 nM. These findings can be used to design effective TEAD modulators with anticancer therapeutic implications. Full article
(This article belongs to the Special Issue Computational Medicine and Molecular Drug Design)
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21 pages, 5874 KiB  
Article
Inhibitor Trapping in Kinases
by Danislav S. Spassov, Mariyana Atanasova and Irini Doytchinova
Int. J. Mol. Sci. 2024, 25(6), 3249; https://doi.org/10.3390/ijms25063249 - 13 Mar 2024
Cited by 2 | Viewed by 1617
Abstract
Recently, we identified a novel mechanism of enzyme inhibition in N-myristoyltransferases (NMTs), which we have named ‘inhibitor trapping’. Inhibitor trapping occurs when the protein captures the small molecule within its structural confines, thereby preventing its free dissociation and resulting in a dramatic increase [...] Read more.
Recently, we identified a novel mechanism of enzyme inhibition in N-myristoyltransferases (NMTs), which we have named ‘inhibitor trapping’. Inhibitor trapping occurs when the protein captures the small molecule within its structural confines, thereby preventing its free dissociation and resulting in a dramatic increase in inhibitor affinity and potency. Here, we demonstrate that inhibitor trapping also occurs in the kinases. Remarkably, the drug imatinib, which has revolutionized targeted cancer therapy, is entrapped in the structure of the Abl kinase. This effect is also observed in p38α kinase, where inhibitor trapping was found to depend on a ‘magic’ methyl group, which stabilizes the protein conformation and increases the affinity of the compound dramatically. Altogether, these results suggest that inhibitor trapping is not exclusive to N-myristoyltransferases, as it also occurs in the kinase family. Inhibitor trapping could enhance the binding affinity of an inhibitor by thousands of times and is as a key mechanism that plays a critical role in determining drug affinity and potency. Full article
(This article belongs to the Special Issue Computational Medicine and Molecular Drug Design)
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17 pages, 2610 KiB  
Article
Unveiling Novel Urease Inhibitors for Helicobacter pylori: A Multi-Methodological Approach from Virtual Screening and ADME to Molecular Dynamics Simulations
by Paulina Valenzuela-Hormazabal, Romina V. Sepúlveda, Melissa Alegría-Arcos, Elizabeth Valdés-Muñoz, Víctor Rojas-Pérez, Ileana González-Bonet, Reynier Suardíaz, Christian Galarza, Natalia Morales, Verónica Leddermann, Ricardo I. Castro, Bruna Benso, Gabriela Urra, Erix W. Hernández-Rodríguez and Daniel Bustos
Int. J. Mol. Sci. 2024, 25(4), 1968; https://doi.org/10.3390/ijms25041968 - 6 Feb 2024
Cited by 4 | Viewed by 2991
Abstract
Helicobacter pylori (Hp) infections pose a global health challenge demanding innovative therapeutic strategies by which to eradicate them. Urease, a key Hp virulence factor hydrolyzes urea, facilitating bacterial survival in the acidic gastric environment. In this study, a multi-methodological approach combining [...] Read more.
Helicobacter pylori (Hp) infections pose a global health challenge demanding innovative therapeutic strategies by which to eradicate them. Urease, a key Hp virulence factor hydrolyzes urea, facilitating bacterial survival in the acidic gastric environment. In this study, a multi-methodological approach combining pharmacophore- and structure-based virtual screening, molecular dynamics simulations, and MM-GBSA calculations was employed to identify novel inhibitors for Hp urease (HpU). A refined dataset of 8,271,505 small molecules from the ZINC15 database underwent pharmacokinetic and physicochemical filtering, resulting in 16% of compounds for pharmacophore-based virtual screening. Molecular docking simulations were performed in successive stages, utilizing HTVS, SP, and XP algorithms. Subsequent energetic re-scoring with MM-GBSA identified promising candidates interacting with distinct urease variants. Lys219, a residue critical for urea catalysis at the urease binding site, can manifest in two forms, neutral (LYN) or carbamylated (KCX). Notably, the evaluated molecules demonstrated different interaction and energetic patterns in both protein variants. Further evaluation through ADMET predictions highlighted compounds with favorable pharmacological profiles, leading to the identification of 15 candidates. Molecular dynamics simulations revealed comparable structural stability to the control DJM, with candidates 5, 8 and 12 (CA5, CA8, and CA12, respectively) exhibiting the lowest binding free energies. These inhibitors suggest a chelating capacity that is crucial for urease inhibition. The analysis underscores the potential of CA5, CA8, and CA12 as novel HpU inhibitors. Finally, we compare our candidates with the chemical space of urease inhibitors finding physicochemical similarities with potent agents such as thiourea. Full article
(This article belongs to the Special Issue Computational Medicine and Molecular Drug Design)
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Review

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26 pages, 2938 KiB  
Review
Binding Affinity Determination in Drug Design: Insights from Lock and Key, Induced Fit, Conformational Selection, and Inhibitor Trapping Models
by Danislav S. Spassov
Int. J. Mol. Sci. 2024, 25(13), 7124; https://doi.org/10.3390/ijms25137124 - 28 Jun 2024
Cited by 14 | Viewed by 5009
Abstract
Binding affinity is a fundamental parameter in drug design, describing the strength of the interaction between a molecule and its target protein. Accurately predicting binding affinity is crucial for the rapid development of novel therapeutics, the prioritization of promising candidates, and the optimization [...] Read more.
Binding affinity is a fundamental parameter in drug design, describing the strength of the interaction between a molecule and its target protein. Accurately predicting binding affinity is crucial for the rapid development of novel therapeutics, the prioritization of promising candidates, and the optimization of their properties through rational design strategies. Binding affinity is determined by the mechanism of recognition between proteins and ligands. Various models, including the lock and key, induced fit, and conformational selection, have been proposed to explain this recognition process. However, current computational strategies to predict binding affinity, which are based on these models, have yet to produce satisfactory results. This article explores the connection between binding affinity and these protein-ligand interaction models, highlighting that they offer an incomplete picture of the mechanism governing binding affinity. Specifically, current models primarily center on the binding of the ligand and do not address its dissociation. In this context, the concept of ligand trapping is introduced, which models the mechanisms of dissociation. When combined with the current models, this concept can provide a unified theoretical framework that may allow for the accurate determination of the ligands’ binding affinity. Full article
(This article belongs to the Special Issue Computational Medicine and Molecular Drug Design)
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