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New Insight into Computational Drug Discovery: From Single Protein Modulators to Multi-Target Ligand Discovery

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A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Chemical Biology".

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 13069

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Prof. Dr. Alejandro Speck-Planche

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Departamento de Ingeniería Química, Universidad San Francisco de Quito, Diego de Robles y vía Interoceánica, Quito 170901, Ecuador
Interests: multi-target drug discovery, chemoinformatics, QSAR-based approaches, virtual screening, multi-scale de novo drug design, machine learning.
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Dear Colleagues,

Proteins are biomolecules with a vast array of biological functions within living systems. Alterations of such functions can lead to the emergence/progression of multiple diseases in humans, which include (but are not limited to) cancers, as well as neurological, psychiatric, cardiovascular, autoimmune, and metabolic disorders. On the other hand, proteins are essential for the virulence and/or survival of pathogens (e.g., bacteria, fungi, viruses, and protozoans). Consequently, proteins are usually prioritized as therapeutic targets for disease eradication and/or reduction. In this context, discovering protein modulators (e.g., antagonists, agonists, inhibitors, etc.) is of paramount importance at the early stages of the drug development process.

Since the chemical space encompassing drug-like molecules is huge (10⁶⁰ small molecules), finding protein modulators can be a challenging and time-consuming task. In this sense, computational approaches have become an integral part of the drug discovery campaigns, speeding up the search for potent protein modulators with the potential to become therapeutic agents. Computational approaches have also demonstrated their usefulness as tools for the design and prediction of new molecular entities with desired activity profiles, either selective modulators of key proteins or versatile ligands able to simultaneously regulate the activity of multiple proteins associated with different biochemical pathways.

To highlight recent cutting-edge advances regarding the application of computational approaches toward the discovery of protein modulators, in this Special Issue of Biomolecules, we invite scientists around the world to submit their original contributions in the form of research or review articles.

Prof. Alejandro Speck-Planche
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. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). 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.

Prof. Alejandro Speck-Planche
Guest Editor

Manuscript Submission Information

Keywords

PTML Modeling
QSAR and Multi-Target QSAR
Structure-based drug design
Ligand-based drug design
Virtual screening
Fragment-based computational design
De novo design
Chemoinformatics
Machine Learning
Agonists
Antagonists
Inhibitors

Published Papers (4 papers)

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Research

19 pages, 3446 KiB

Open AccessArticle

In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha

by Alejandro Speck-Planche, Valeria V. Kleandrova and Marcus T. Scotti

Biomolecules 2021, 11(12), 1832; https://0-doi-org.brum.beds.ac.uk/10.3390/biom11121832 - 04 Dec 2021

Cited by 11 | Viewed by 2966

Abstract

Inflammation involves a complex biological response of the body tissues to damaging stimuli. When dysregulated, inflammation led by biomolecular mediators such as caspase-1 and tumor necrosis factor-alpha (TNF-alpha) can play a detrimental role in the progression of different medical conditions such as cancer, neurological disorders, autoimmune diseases, and cytokine storms caused by viral infections such as COVID-19. Computational approaches can accelerate the search for dual-target drugs able to simultaneously inhibit the aforementioned proteins, enabling the discovery of wide-spectrum anti-inflammatory agents. This work reports the first multicondition model based on quantitative structure–activity relationships and a multilayer perceptron neural network (mtc-QSAR-MLP) for the virtual screening of agency-regulated chemicals as versatile anti-inflammatory therapeutics. The mtc-QSAR-MLP model displayed accuracy higher than 88%, and was interpreted from a physicochemical and structural point of view. When using the mtc-QSAR-MLP model as a virtual screening tool, we could identify several agency-regulated chemicals as dual inhibitors of caspase-1 and TNF-alpha, and the experimental information later retrieved from the scientific literature converged with our computational results. This study supports the capabilities of our mtc-QSAR-MLP model in anti-inflammatory therapy with direct applications to current health issues such as the COVID-19 pandemic. Full article

(This article belongs to the Special Issue New Insight into Computational Drug Discovery: From Single Protein Modulators to Multi-Target Ligand Discovery)

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17 pages, 2269 KiB

Open AccessArticle

Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases

by Amit Kumar Halder and M. Natália D. S. Cordeiro

Biomolecules 2021, 11(11), 1670; https://0-doi-org.brum.beds.ac.uk/10.3390/biom11111670 - 10 Nov 2021

Cited by 13 | Viewed by 1912

Abstract

The inhibitors of two isoforms of mitogen-activated protein kinase-interacting kinases (i.e., MNK-1 and MNK-2) are implicated in the treatment of a number of diseases including cancer. This work reports, for the first time, a multi-target (or multi-tasking) in silico modeling approach (mt-QSAR) for probing the inhibitory potential of these isoforms against MNKs. Linear and non-linear mt-QSAR classification models were set up from a large dataset of 1892 chemicals tested under a variety of assay conditions, based on the Box–Jenkins moving average approach, along with a range of feature selection algorithms and machine learning tools, out of which the most predictive one (>90% overall accuracy) was used for mechanistic interpretation of the likely inhibition of MNK-1 and MNK-2. Considering that the latter model is suitable for virtual screening of chemical libraries—i.e., commercial, non-commercial and in-house sets, it was made publicly accessible as a ready-to-use FLASK-based application. Additionally, this work employed a focused kinase library for virtual screening using an mt-QSAR model. The virtual hits identified in this process were further filtered by using a similarity search, in silico prediction of drug-likeness, and ADME profiles as well as synthetic accessibility tools. Finally, molecular dynamic simulations were carried out to identify and select the most promising virtual hits. The information gathered from this work can supply important guidelines for the discovery of novel MNK-1/2 inhibitors as potential therapeutic agents. Full article

(This article belongs to the Special Issue New Insight into Computational Drug Discovery: From Single Protein Modulators to Multi-Target Ligand Discovery)

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16 pages, 2130 KiB

Open AccessArticle

Identification of Potential Kinase Inhibitors within the PI3K/AKT Pathway of Leishmania Species

by Rodrigo Ochoa, Amaya Ortega-Pajares, Florencia A. Castello, Federico Serral, Darío Fernández Do Porto, Janny A. Villa-Pulgarin, Rubén E. Varela-M and Carlos Muskus

Biomolecules 2021, 11(7), 1037; https://0-doi-org.brum.beds.ac.uk/10.3390/biom11071037 - 16 Jul 2021

Cited by 5 | Viewed by 3428

Abstract

Leishmaniasis is a public health disease that requires the development of more effective treatments and the identification of novel molecular targets. Since blocking the PI3K/AKT pathway has been successfully studied as an effective anticancer strategy for decades, we examined whether the same approach would also be feasible in Leishmania due to their high amount and diverse set of annotated proteins. Here, we used a best reciprocal hits protocol to identify potential protein kinase homologues in an annotated human PI3K/AKT pathway. We calculated their ligandibility based on available bioactivity data of the reported homologues and modelled their 3D structures to estimate the druggability of their binding pockets. The models were used to run a virtual screening method with molecular docking. We found and studied five protein kinases in five different Leishmania species, which are AKT, CDK, AMPK, mTOR and GSK3 homologues from the studied pathways. The compounds found for different enzymes and species were analysed and suggested as starting point scaffolds for the design of inhibitors. We studied the kinases’ participation in protein–protein interaction networks, and the potential deleterious effects, if inhibited, were supported with the literature. In the case of Leishmania GSK3, an inhibitor of its human counterpart, prioritized by our method, was validated in vitro to test its anti-Leishmania activity and indirectly infer the presence of the enzyme in the parasite. The analysis contributes to improving the knowledge about the presence of similar signalling pathways in Leishmania, as well as the discovery of compounds acting against any of these kinases as potential molecular targets in the parasite. Full article

(This article belongs to the Special Issue New Insight into Computational Drug Discovery: From Single Protein Modulators to Multi-Target Ligand Discovery)

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20 pages, 9458 KiB

Open AccessArticle

Binding of SARS-CoV Covalent Non-Covalent Inhibitors to the SARS-CoV-2 Papain-Like Protease and Ovarian Tumor Domain Deubiquitinases

by Dakshinamurthy Sivakumar and Matthias Stein

Biomolecules 2021, 11(6), 802; https://0-doi-org.brum.beds.ac.uk/10.3390/biom11060802 - 28 May 2021

Cited by 6 | Viewed by 3361

Abstract

The urgent need for novel and effective drugs against the SARS-CoV-2 coronavirus pandemic has stimulated research worldwide. The Papain-like protease (PLpro), which is essential for viral replication, shares a similar active site structural architecture to other cysteine proteases. Here, we have used representatives of the Ovarian Tumor Domain deubiquitinase family OTUB1 and OTUB2 along with the PLpro of SARS-CoV-2 to validate and rationalize the binding of inhibitors from previous SARS-CoV candidate compounds. By forming a new chemical bond with the cysteine residue of the catalytic triad, covalent inhibitors irreversibly suppress the protein’s activity. Modeling covalent inhibitor binding requires detailed knowledge about the compounds’ reactivities and binding. Molecular Dynamics refinement simulations of top poses reveal detailed ligand-protein interactions and show their stability over time. The recently discovered selective OTUB2 covalent inhibitors were used to establish and validate the computational protocol. Structural parameters and ligand dynamics are in excellent agreement with the ligand-bound OTUB2 crystal structures. For SARS-CoV-2 PLpro, recent covalent peptidomimetic inhibitors were simulated and reveal that the ligand-protein interaction is very dynamic. The covalent and non-covalent docking plus subsequent MD refinement of known SARS-CoV inhibitors into DUBs and the SARS-CoV-2 PLpro point out a possible approach to target the PLpro cysteine protease from SARS-CoV-2. The results show that such an approach gives insight into ligand-protein interactions, their dynamic character, and indicates a path for selective ligand design. Full article

(This article belongs to the Special Issue New Insight into Computational Drug Discovery: From Single Protein Modulators to Multi-Target Ligand Discovery)

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