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Special Issue "Virtual Screening in Chemical Biology"

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

Deadline for manuscript submissions: closed (1 May 2022) | Viewed by 3751

Special Issue Editor

Dr. Sérgio F. Sousa
E-Mail Website
Guest Editor
UCIBIO/REQUIMTE, BioSIM - Departamento de BioMedicina, Faculdade de Medicina da Universidade do Porto, 4200-319 Porto, Portugal
Interests: computational enzymatic catalysis; QM/MM; docking; virtual screening; molecular dynamics simulations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lead discovery is a central step in the development of a new drug, requiring the identification of new molecular entities with a confirmed molecular response against a therapeutic target associated with a specific disease or condition. This molecule is then used as a starting point for optimization to yield a new drug molecule, with the desired activity and properties.

Finding such molecules is not easy. Even though the chemical space has been estimated to include as much as 1013 to 10100 molecules, only a fraction of such molecules possess properties that can make them drug-like or lead-like. However, their sheer number and diversity still makes them impossible to synthetize or to screen in high-throughput screening protocols.

For these reasons, virtual screening, which involves the application of computational methodologies to screen in silico” large numbers of molecules for their ability to bind to specific protein targets, has come to represent an important aspect of the initial stages of the drug discovery process. It ensures the large potential exploration of the chemical space, limiting the number of molecules that have to be tested experimentally to a small set of more promising candidates, decreasing costs and ensuring greater chemical diversity.

The present Special Issue aims to cover new approaches involving virtual screening methodologies, as well studies involving their application in the identification of novel drug candidates.

Dr. Sérgio F. Sousa
Guest Editor

Manuscript Submission Information

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Keywords

  • virtual screening
  • computer-aided drug design
  • computer-aided drug discovery
  • inverted virtual screening
  • protein-ligand docking

Published Papers (4 papers)

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Research

Article
In Silico Identification of Potential Sites for a Plastic-Degrading Enzyme by a Reverse Screening through the Protein Sequence Space and Molecular Dynamics Simulations
Molecules 2022, 27(10), 3353; https://doi.org/10.3390/molecules27103353 - 23 May 2022
Viewed by 270
Abstract
The accumulation of polyethylene terephthalate (PET) seriously harms the environment because of its high resistance to degradation. The recent discovery of the bacteria-secreted biodegradation enzyme, PETase, sheds light on PET recycling; however, the degradation efficiency is far from practical use. Here, in silico [...] Read more.
The accumulation of polyethylene terephthalate (PET) seriously harms the environment because of its high resistance to degradation. The recent discovery of the bacteria-secreted biodegradation enzyme, PETase, sheds light on PET recycling; however, the degradation efficiency is far from practical use. Here, in silico alanine scanning mutagenesis (ASM) and site-saturation mutagenesis (SSM) were employed to construct the protein sequence space from binding energy of the PETase–PET interaction to identify the number and position of mutation sites and their appropriate side-chain properties that could improve the PETase–PET interaction. The binding mechanisms of the potential PETase variant were investigated through atomistic molecular dynamics simulations. The results show that up to two mutation sites of PETase are preferable for use in protein engineering to enhance the PETase activity, and the proper side chain property depends on the mutation sites. The predicted variants agree well with prior experimental studies. Particularly, the PETase variants with S238C or Q119F could be a potential candidate for improving PETase. Our combination of in silico ASM and SSM could serve as an alternative protocol for protein engineering because of its simplicity and reliability. In addition, our findings could lead to PETase improvement, offering an important contribution towards a sustainable future. Full article
(This article belongs to the Special Issue Virtual Screening in Chemical Biology)
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Article
Virtual Screening with Gnina 1.0
Molecules 2021, 26(23), 7369; https://doi.org/10.3390/molecules26237369 - 04 Dec 2021
Cited by 1 | Viewed by 1190
Abstract
Virtual screening—predicting which compounds within a specified compound library bind to a target molecule, typically a protein—is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic [...] Read more.
Virtual screening—predicting which compounds within a specified compound library bind to a target molecule, typically a protein—is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions. Full article
(This article belongs to the Special Issue Virtual Screening in Chemical Biology)
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Article
In Silico Identification of Possible Inhibitors for Protein Kinase B (PknB) of Mycobacterium tuberculosis
Molecules 2021, 26(20), 6162; https://doi.org/10.3390/molecules26206162 - 12 Oct 2021
Cited by 2 | Viewed by 945
Abstract
With tuberculosis still being one of leading causes of death in the world and the emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb), researchers have been seeking to find further therapeutic strategies or more specific molecular targets. PknB is one of the 11 [...] Read more.
With tuberculosis still being one of leading causes of death in the world and the emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb), researchers have been seeking to find further therapeutic strategies or more specific molecular targets. PknB is one of the 11 Ser/Thr protein kinases of Mtb and is responsible for phosphorylation-mediated signaling, mainly involved in cell wall synthesis, cell division and metabolism. With the amount of structural information available and the great interest in protein kinases, PknB has become an attractive target for drug development. This work describes the optimization and application of an in silico computational protocol to find new PknB inhibitors. This multi-level computational approach combines protein–ligand docking, structure-based virtual screening, molecular dynamics simulations and free energy calculations. The optimized protocol was applied to screen a large dataset containing 129,650 molecules, obtained from the ZINC/FDA-Approved database, Mu.Ta.Lig Virtual Chemotheca and Chimiothèque Nationale. It was observed that the most promising compounds selected occupy the adenine-binding pocket in PknB, and the main interacting residues are Leu17, Val26, Tyr94 and Met155. Only one of the compounds was able to move the active site residues into an open conformation. It was also observed that the P-loop and magnesium position loops change according to the characteristics of the ligand. This protocol led to the identification of six compounds for further experimental testing while also providing additional structural information for the design of more specific and more effective derivatives. Full article
(This article belongs to the Special Issue Virtual Screening in Chemical Biology)
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Article
Network Pharmacology-Based Study to Uncover Potential Pharmacological Mechanisms of Korean Thistle (Cirsium japonicum var. maackii (Maxim.) Matsum.) Flower against Cancer
Molecules 2021, 26(19), 5904; https://doi.org/10.3390/molecules26195904 - 29 Sep 2021
Cited by 4 | Viewed by 859
Abstract
Cirsium japonicum var. maackii (Maxim.) Matsum. or Korean thistle flower is a herbal plant used to treat tumors in Korean folk remedies, but its essential bioactives and pharmacological mechanisms against cancer have remained unexplored. This study identified the main compounds(s) and mechanism(s) of [...] Read more.
Cirsium japonicum var. maackii (Maxim.) Matsum. or Korean thistle flower is a herbal plant used to treat tumors in Korean folk remedies, but its essential bioactives and pharmacological mechanisms against cancer have remained unexplored. This study identified the main compounds(s) and mechanism(s) of the C. maackii flower against cancer via network pharmacology. The bioactives from the C. maackii flower were revealed by gas chromatography-mass spectrum (GC-MS), and SwissADME evaluated their physicochemical properties. Next, target(s) associated with the obtained bioactives or cancer-related targets were retrieved by public databases, and the Venn diagram selected the overlapping targets. The networks between overlapping targets and bioactives were visualized, constructed, and analyzed by RPackage. Finally, we implemented a molecular docking test (MDT) to explore key target(s) and compound(s) on AutoDockVina and LigPlot+. GC-MS detected a total of 34 bioactives and all were accepted by Lipinski’s rules and therefore classified as drug-like compounds (DLCs). A total of 597 bioactive-related targets and 4245 cancer-related targets were identified from public databases. The final 51 overlapping targets were selected between the bioactive targets network and cancer-related targets. With Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, a total of 20 signaling pathways were manifested, and a hub signaling pathway (PI3K-Akt signaling pathway), a key target (Akt1), and a key compound (Urs-12-en-24-oic acid, 3-oxo, methyl ester) were selected among the 20 signaling pathways via MDT. Overall, Urs-12-en-24-oic acid, 3-oxo, methyl ester from the C. maackii flower has potent anti-cancer efficacy by inactivating Akt1 on the PI3K-Akt signaling pathway. Full article
(This article belongs to the Special Issue Virtual Screening in Chemical Biology)
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