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18 pages, 7212 KiB

Open AccessArticle

Identification of Potential Allosteric Site Binders of Indoleamine 2,3-Dioxygenase 1 from Plants: A Virtual and Molecular Dynamics Investigation

by Vitor Martins de Almeida and Osvaldo Andrade Santos-Filho

Pharmaceuticals 2022, 15(9), 1099; https://doi.org/10.3390/ph15091099 - 02 Sep 2022

Viewed by 1686

Abstract

Ligand and structure-based computational screenings were carried out to identify flavonoids with potential anticancer activity. Kushenol E, a flavonoid with proven anticancer activity and, at the same time, an allosteric site binder of the enzyme indoleamine 2,3-dioxygenase-1 (IDO1), was used as the reference [...] Read more.

Ligand and structure-based computational screenings were carried out to identify flavonoids with potential anticancer activity. Kushenol E, a flavonoid with proven anticancer activity and, at the same time, an allosteric site binder of the enzyme indoleamine 2,3-dioxygenase-1 (IDO1), was used as the reference compound. Molecular docking and molecular dynamics simulations were performed for the screened flavonoids with known anticancer activity. The following two of these flavonoids were identified as potential inhibitors of IDO1: dichamanetin and isochamanetin. Molecular dynamics simulations were used to assess the conformational profile of IDO1-flavonoids complexes, as well as for calculating the bind-free energies. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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15 pages, 11793 KiB

Open AccessArticle

Computational Investigations of Traditional Chinese Medicinal Compounds against the Omicron Variant of SARS-CoV-2 to Rescue the Host Immune System

by Ziad Tareq Naman, Salim Kadhim, Zahraa J. K. Al-Isawi, Christopher J. Butch and Ziyad Tariq Muhseen

Pharmaceuticals 2022, 15(6), 741; https://doi.org/10.3390/ph15060741 - 13 Jun 2022

Cited by 3 | Viewed by 2085

Abstract

Macrodomain-I of the NSP3 (non-structural protein 3) is responsible for immune response hijacking in the SARS-CoV-2 infection known as COVID-19. In the omicron variant (B.1.1.529), this domain harbors a new mutation, V1069I, which may increase the binding of ADPr and consequently the infection [...] Read more.

Macrodomain-I of the NSP3 (non-structural protein 3) is responsible for immune response hijacking in the SARS-CoV-2 infection known as COVID-19. In the omicron variant (B.1.1.529), this domain harbors a new mutation, V1069I, which may increase the binding of ADPr and consequently the infection severity. This macrodomain-I, due to its significant role in infection, is deemed to be an important drug target. Hence, using structural bioinformatics and molecular simulation approaches, we performed a virtual screening of the traditional Chinese medicines (TCM) database for potential anti-viral drugs. The screening of 57,000 compounds yielded the 10 best compounds with docking scores better than the control ADPr. Among the top ten, the best three hits—TCM42798, with a docking score of −13.70 kcal/mol, TCM47007 of −13.25 kcal/mol, and TCM30675 of −12.49 kcal/mol—were chosen as the best hits. Structural dynamic features were explored including stability, compactness, flexibility, and hydrogen bonding, further demonstrating the anti-viral potential of these hits. Using the MM/GBSA approach, the total binding free energy for each complex was reported to be −69.78 kcal/mol, −50.11 kcal/mol, and −47.64 kcal/mol, respectively, which consequently reflect the stronger binding and inhibitory potential of these compounds. These agents might suppress NSP3 directly, allowing the host immune system to recuperate. The current study lays the groundwork for the development of new drugs to combat SARS-CoV-2 and its variants. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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34 pages, 13321 KiB

Open AccessArticle

In Silico Antiprotozoal Evaluation of 1,4-Naphthoquinone Derivatives against Chagas and Leishmaniasis Diseases Using QSAR, Molecular Docking, and ADME Approaches

by Lina S. Prieto Cárdenas, Karen A. Arias Soler, Diana L. Nossa González, Wilson E. Rozo Núñez, Agobardo Cárdenas-Chaparro, Pablo R. Duchowicz and Jovanny A. Gómez Castaño

Pharmaceuticals 2022, 15(6), 687; https://doi.org/10.3390/ph15060687 - 31 May 2022

Cited by 2 | Viewed by 2342

Abstract

Chagas and leishmaniasis are two neglected diseases considered as public health problems worldwide, for which there is no effective, low-cost, and low-toxicity treatment for the host. Naphthoquinones are ligands with redox properties involved in oxidative biological processes with a wide variety of activities, [...] Read more.

Chagas and leishmaniasis are two neglected diseases considered as public health problems worldwide, for which there is no effective, low-cost, and low-toxicity treatment for the host. Naphthoquinones are ligands with redox properties involved in oxidative biological processes with a wide variety of activities, including antiparasitic. In this work, in silico methods of quantitative structure–activity relationship (QSAR), molecular docking, and calculation of ADME (absorption, distribution, metabolism, and excretion) properties were used to evaluate naphthoquinone derivatives with unknown antiprotozoal activity. QSAR models were developed for predicting antiparasitic activity against Trypanosoma cruzi, Leishmania amazonensis, and Leishmania infatum, as well as the QSAR model for toxicity activity. Most of the evaluated ligands presented high antiparasitic activity. According to the docking results, the family of triazole derivatives presented the best affinity with the different macromolecular targets. The ADME results showed that most of the evaluated compounds present adequate conditions to be administered orally. Naphthoquinone derivatives show good biological activity results, depending on the substituents attached to the quinone ring, and perhaps the potential to be converted into drugs or starting molecules. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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22 pages, 4859 KiB

Open AccessArticle

QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the Treatment of Schizophrenia

by Mohamed El fadili, Mohammed Er-Rajy, Mohammed Kara, Amine Assouguem, Assia Belhassan, Amal Alotaibi, Nidal Naceiri Mrabti, Hafize Fidan, Riaz Ullah, Sezai Ercisli, Sara Zarougui and Menana Elhallaoui

Pharmaceuticals 2022, 15(6), 670; https://doi.org/10.3390/ph15060670 - 27 May 2022

Cited by 26 | Viewed by 3805

Abstract

Forty-four bicyclo ((aryl) methyl) benzamides, acting as glycine transporter type 1 (GlyT1) inhibitors, are developed using molecular modeling techniques. QSAR models generated by multiple linear and non-linear regressions affirm that the biological inhibitory activity against the schizophrenia disease is strongly and significantly correlated [...] Read more.

Forty-four bicyclo ((aryl) methyl) benzamides, acting as glycine transporter type 1 (GlyT1) inhibitors, are developed using molecular modeling techniques. QSAR models generated by multiple linear and non-linear regressions affirm that the biological inhibitory activity against the schizophrenia disease is strongly and significantly correlated with physicochemical, geometrical and topological descriptors, in particular: Hydrogen bond donor, polarizability, surface tension, stretch and torsion energies and topological diameter. According to in silico ADMET properties, the most active ligands (L6, L9, L30, L31 and L37) are the molecules having the highest probability of penetrating the central nervous system (CNS), but the molecule 32 has the highest probability of being absorbed by the gastrointestinal tract. Molecular docking results indicate that Tyr124, Phe43, Phe325, Asp46, Phe319 and Val120 amino acids are the active sites of the dopamine transporter (DAT) membrane protein, in which the most active ligands can inhibit the glycine transporter type 1 (GlyT1). The results of molecular dynamics (MD) simulation revealed that all five inhibitors remained stable in the active sites of the DAT protein during 100 ns, demonstrating their promising role as candidate drugs for the treatment of schizophrenia. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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22 pages, 5281 KiB

Open AccessArticle

Structural Elucidation of Rift Valley Fever Virus L Protein towards the Discovery of Its Potential Inhibitors

by Mubarak A. Alamri, Muhammad Usman Mirza, Muhammad Muzammal Adeel, Usman Ali Ashfaq, Muhammad Tahir ul Qamar, Farah Shahid, Sajjad Ahmad, Eid A. Alatawi, Ghadah M. Albalawi, Khaled S. Allemailem and Ahmad Almatroudi

Pharmaceuticals 2022, 15(6), 659; https://doi.org/10.3390/ph15060659 - 25 May 2022

Cited by 12 | Viewed by 2445

Abstract

Rift valley fever virus (RVFV) is the causative agent of a viral zoonosis that causes a significant clinical burden in domestic and wild ruminants. Major outbreaks of the virus occur in livestock, and contaminated animal products or arthropod vectors can transmit the virus [...] Read more.

Rift valley fever virus (RVFV) is the causative agent of a viral zoonosis that causes a significant clinical burden in domestic and wild ruminants. Major outbreaks of the virus occur in livestock, and contaminated animal products or arthropod vectors can transmit the virus to humans. The viral RNA-dependent RNA polymerase (RdRp; L protein) of the RVFV is responsible for viral replication and is thus an appealing drug target because no effective and specific vaccine against this virus is available. The current study reported the structural elucidation of the RVFV-L protein by in-depth homology modeling since no crystal structure is available yet. The inhibitory binding modes of known potent L protein inhibitors were analyzed. Based on the results, further molecular docking-based virtual screening of Selleckchem Nucleoside Analogue Library (156 compounds) was performed to find potential new inhibitors against the RVFV L protein. ADME (Absorption, Distribution, Metabolism, and Excretion) and toxicity analysis of these compounds was also performed. Besides, the binding mechanism and stability of identified compounds were confirmed by a 50 ns molecular dynamic (MD) simulation followed by MM/PBSA binding free energy calculations. Homology modeling determined a stable multi-domain structure of L protein. An analysis of known L protein inhibitors, including Monensin, Mycophenolic acid, and Ribavirin, provide insights into the binding mechanism and reveals key residues of the L protein binding pocket. The screening results revealed that the top three compounds, A-317491, Khasianine, and VER155008, exhibited a high affinity at the L protein binding pocket. ADME analysis revealed good pharmacodynamics and pharmacokinetic profiles of these compounds. Furthermore, MD simulation and binding free energy analysis endorsed the binding stability of potential compounds with L protein. In a nutshell, the present study determined potential compounds that may aid in the rational design of novel inhibitors of the RVFV L protein as anti-RVFV drugs. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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20 pages, 7078 KiB

Open AccessArticle

Virtual Screening Based on Machine Learning Explores Mangrove Natural Products as KRAS^G12C Inhibitors

by Lianxiang Luo, Tongyu Zheng, Qu Wang, Yingling Liao, Xiaoqi Zheng, Ai Zhong, Zunnan Huang and Hui Luo

Pharmaceuticals 2022, 15(5), 584; https://doi.org/10.3390/ph15050584 - 08 May 2022

Cited by 4 | Viewed by 2861

Abstract

Mangrove secondary metabolites have many unique biological activities. We identified lead compounds among them that might target KRAS^G12C. KRAS is considered to be closely related to various cancers. A variety of novel small molecules that directly target KRAS are being developed, [...] Read more.

Mangrove secondary metabolites have many unique biological activities. We identified lead compounds among them that might target KRAS^G12C. KRAS is considered to be closely related to various cancers. A variety of novel small molecules that directly target KRAS are being developed, including covalent allosteric inhibitors for KRAS^G12C mutant, protein–protein interaction inhibitors that bind in the switch I/II pocket or the A59 site, and GTP-competitive inhibitors targeting the nucleotide-binding site. To identify a candidate pool of mangrove secondary metabolic natural products, we tested various machine learning algorithms and selected random forest as a model for predicting the targeting activity of compounds. Lead compounds were then subjected to virtual screening and covalent docking, integrated absorption, distribution, metabolism and excretion (ADME) testing, and structure-based pharmacophore model validation to select the most suitable compounds. Finally, we performed molecular dynamics simulations to verify the binding mode of the lead compound to KRAS^G12C. The lazypredict function package was initially used, and the Accuracy score and F1 score of the random forest algorithm exceeded 60%, which can be considered to carry a strong ability to distinguish the data. Four marine natural products were obtained through machine learning identification and covalent docking screening. Compound 44 and compound 14 were selected for further validation after ADME and toxicity studies, and pharmacophore analysis indicated that they had a favorable pharmacodynamic profile. Comparison with the positive control showed that they stabilized switch I and switch II, and like MRTX849, retained a novel binding mechanism at the molecular level. Molecular dynamics analysis showed that they maintained a stable conformation with the target protein, so compound 44 and compound 14 may be effective inhibitors of the G12C mutant. These findings reveal that the mangrove-derived secondary metabolite compound 44 and compound 14 might be potential therapeutic agents for KRAS^G12C. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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17 pages, 6086 KiB

Open AccessArticle

Chromene Derivatives as Selective TERRA G-Quadruplex RNA Binders with Antiproliferative Properties

by Roberta Rocca, Francesca Scionti, Matteo Nadai, Federica Moraca, Annalisa Maruca, Giosuè Costa, Raffaella Catalano, Giada Juli, Maria Teresa Di Martino, Francesco Ortuso, Stefano Alcaro, Pierosandro Tagliaferri, Pierfrancesco Tassone, Sara N. Richter and Anna Artese

Pharmaceuticals 2022, 15(5), 548; https://doi.org/10.3390/ph15050548 - 28 Apr 2022

Cited by 8 | Viewed by 2339

Abstract

In mammalian cells, telomerase transcribes telomeres in large G-rich non-coding RNA, known as telomeric repeat-containing RNA (TERRA), which folds into noncanonical nucleic acid secondary structures called G-quadruplexes (G4s). Since TERRA G4 has been shown to be involved in telomere length and translation regulation, [...] Read more.

In mammalian cells, telomerase transcribes telomeres in large G-rich non-coding RNA, known as telomeric repeat-containing RNA (TERRA), which folds into noncanonical nucleic acid secondary structures called G-quadruplexes (G4s). Since TERRA G4 has been shown to be involved in telomere length and translation regulation, it could provide valuable insight into fundamental biological processes, such as cancer growth, and TERRA G4 binders could represent an innovative strategy for cancer treatment. In this work, the three best candidates identified in our previous virtual screening campaign on bimolecular DNA/RNA G4s were investigated on the monomolecular Tel DNA and TERRA G4s by means of molecular modelling simulations and in vitro and in cell analysis. The results obtained in this work highlighted the stabilizing power of all the three candidates on TERRA G4. In particular, the two compounds characterized by a chromene scaffold were selective TERRA G4 binders, while the compound with a naphthyridine core acted as a dual Tel/TERRA G4-binder. A biophysical investigation by circular dichroism confirmed the relative stabilization efficiency of the compounds towards TERRA and Tel G4s. The TERRA G4 stabilizing hits showed good antiproliferative activity against colorectal and lung adenocarcinoma cell lines. Lead optimization to increase TERRA G4 stabilization may provide new powerful tools against cancer. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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12 pages, 1837 KiB

Open AccessArticle

Improved Database Filtering Technology Enables More Efficient Ab Initio Design of Potent Peptides against Ebola Viruses

by Thomas Ripperda, Yangsheng Yu, Atul Verma, Elizabeth Klug, Michellie Thurman, St Patrick Reid and Guangshun Wang

Pharmaceuticals 2022, 15(5), 521; https://doi.org/10.3390/ph15050521 - 24 Apr 2022

Cited by 3 | Viewed by 2011

Abstract

The rapid mutations of viruses such as SARS-CoV-2 require vaccine updates and the development of novel antiviral drugs. This article presents an improved database filtering technology for a more effective design of novel antiviral agents. Different from the previous approach, where the most [...] Read more.

The rapid mutations of viruses such as SARS-CoV-2 require vaccine updates and the development of novel antiviral drugs. This article presents an improved database filtering technology for a more effective design of novel antiviral agents. Different from the previous approach, where the most probable parameters were obtained stepwise from the antimicrobial peptide database, we found it possible to accelerate the design process by deriving multiple parameters in a single step during the peptide amino acid analysis. The resulting peptide DFTavP1 displays the ability to inhibit Ebola virus. A deviation from the most probable peptide parameters reduces antiviral activity. The designed peptides appear to block viral entry. In addition, the amino acid signature provides a clue to peptide engineering to gain cell selectivity. Like human cathelicidin LL-37, our engineered peptide DDIP1 inhibits both Ebola and SARS-CoV-2 viruses. These peptides, with broad antiviral activity, may selectively disrupt viral envelopes and offer the lasting efficacy required to treat various RNA viruses, including their emerging mutants. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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23 pages, 8170 KiB

Open AccessArticle

Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches

by Amer H. Asseri, Md. Jahidul Alam, Faisal Alzahrani, Ahmed Khames, Mohammad Turhan Pathan, Mohammed A. S. Abourehab, Salman Hosawi, Rubaiat Ahmed, Sifat Ara Sultana, Nazia Fairooz Alam, Nafee-Ul Alam, Rahat Alam, Abdus Samad, Sushil Pokhrel, Jin Kyu Kim, Foysal Ahammad, Bonglee Kim and Shing Cheng Tan

Pharmaceuticals 2022, 15(5), 501; https://doi.org/10.3390/ph15050501 - 20 Apr 2022

Cited by 9 | Viewed by 4294

Abstract

Merkel cell carcinoma (MCC) is a rare form of aggressive skin cancer mainly caused by Merkel cell polyomavirus (MCPyV). Most MCC tumors express MCPyV large T (LT) antigens and play an important role in the growth-promoting activities of oncoproteins. Truncated LT promotes tumorigenicity [...] Read more.

Merkel cell carcinoma (MCC) is a rare form of aggressive skin cancer mainly caused by Merkel cell polyomavirus (MCPyV). Most MCC tumors express MCPyV large T (LT) antigens and play an important role in the growth-promoting activities of oncoproteins. Truncated LT promotes tumorigenicity as well as host cell proliferation by activating the viral replication machinery, and inhibition of this protein in humans drastically lowers cellular growth linked to the corresponding cancer. Our study was designed with the aim of identifying small molecular-like natural antiviral candidates that are able to inhibit the proliferation of malignant tumors, especially those that are aggressive, by blocking the activity of viral LT protein. To identify potential compounds against the target protein, a computational drug design including molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized Born surface area (MM-GBSA) approaches were applied in this study. Initially, a total of 2190 phytochemicals isolated from 104 medicinal plants were screened using the molecular docking simulation method, resulting in the identification of the top five compounds having the highest binding energy, ranging between −6.5 and −7.6 kcal/mol. The effectiveness and safety of the selected compounds were evaluated based on ADME and toxicity features. A 250 ns MD simulation confirmed the stability of the selected compounds bind to the active site (AS) of the target protein. Additionally, MM-GBSA analysis was used to determine the high values of binding free energy (ΔG bind) of the compounds binding to the target protein. The five compounds identified by computational approaches, Paulownin (CID: 3084131), Actaealactone (CID: 11537736), Epigallocatechin 3-O-cinnamate (CID: 21629801), Cirsilineol (CID: 162464), and Lycoricidine (CID: 73065), can be used in therapy as lead compounds to combat MCPyV-related cancer. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the virus. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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14 pages, 2392 KiB

Open AccessArticle

Rethinking Protein Drug Design with Highly Accurate Structure Prediction of Anti-CRISPR Proteins

by Ho-Min Park, Yunseol Park, Joris Vankerschaver, Arnout Van Messem, Wesley De Neve and Hyunjin Shim

Pharmaceuticals 2022, 15(3), 310; https://doi.org/10.3390/ph15030310 - 04 Mar 2022

Cited by 10 | Viewed by 3937

Abstract

Protein therapeutics play an important role in controlling the functions and activities of disease-causing proteins in modern medicine. Despite protein therapeutics having several advantages over traditional small-molecule therapeutics, further development has been hindered by drug complexity and delivery issues. However, recent progress in [...] Read more.

Protein therapeutics play an important role in controlling the functions and activities of disease-causing proteins in modern medicine. Despite protein therapeutics having several advantages over traditional small-molecule therapeutics, further development has been hindered by drug complexity and delivery issues. However, recent progress in deep learning-based protein structure prediction approaches, such as AlphaFold2, opens new opportunities to exploit the complexity of these macro-biomolecules for highly specialised design to inhibit, regulate or even manipulate specific disease-causing proteins. Anti-CRISPR proteins are small proteins from bacteriophages that counter-defend against the prokaryotic adaptive immunity of CRISPR-Cas systems. They are unique examples of natural protein therapeutics that have been optimized by the host-parasite evolutionary arms race to inhibit a wide variety of host proteins. Here, we show that these anti-CRISPR proteins display diverse inhibition mechanisms through accurate structural prediction and functional analysis. We find that these phage-derived proteins are extremely distinct in structure, some of which have no homologues in the current protein structure domain. Furthermore, we find a novel family of anti-CRISPR proteins which are structurally similar to the recently discovered mechanism of manipulating host proteins through enzymatic activity, rather than through direct inference. Using highly accurate structure prediction, we present a wide variety of protein-manipulating strategies of anti-CRISPR proteins for future protein drug design. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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14 pages, 5052 KiB

Open AccessArticle

Exploring the Prominent and Concealed Inhibitory Features for Cytoplasmic Isoforms of Hsp90 Using QSAR Analysis

by Magdi E. A. Zaki, Sami A. Al-Hussain, Syed Nasir Abbas Bukhari, Vijay H. Masand, Mithilesh M. Rathore, Sumer D. Thakur and Vaishali M. Patil

Pharmaceuticals 2022, 15(3), 303; https://doi.org/10.3390/ph15030303 - 01 Mar 2022

Cited by 4 | Viewed by 2458

Abstract

Cancer is a major life-threatening disease with a high mortality rate in many countries. Even though different therapies and options are available, patients generally prefer chemotherapy. However, serious side effects of anti-cancer drugs compel us to search for a safer drug. To achieve [...] Read more.

Cancer is a major life-threatening disease with a high mortality rate in many countries. Even though different therapies and options are available, patients generally prefer chemotherapy. However, serious side effects of anti-cancer drugs compel us to search for a safer drug. To achieve this target, Hsp90 (heat shock protein 90), which is responsible for stabilization of many oncoproteins in cancer cells, is a promising target for developing an anti-cancer drug. The QSAR (Quantitative Structure–Activity Relationship) could be useful to identify crucial pharmacophoric features to develop a Hsp90 inhibitor. Therefore, in the present work, a larger dataset encompassing 1141 diverse compounds was used to develop a multi-linear QSAR model with a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The new developed six-parameter model satisfies the recommended values for a good number of validation parameters such as R2tr = 0.78, Q2LMO = 0.77, R2ex = 0.78, and CCCex = 0.88. The present analysis reveals that the Hsp90 inhibitory activity is correlated with different types of nitrogen atoms and other hidden structural features such as the presence of hydrophobic ring/aromatic carbon atoms within a specific distance from the center of mass of the molecule, etc. Thus, the model successfully identified a variety of reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with Hsp90. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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16 pages, 6481 KiB

Open AccessArticle

Drug Discovery of New Anti-Inflammatory Compounds by Targeting Cyclooxygenases

by Shady Burayk, Kentaro Oh-hashi and Mahmoud Kandeel

Pharmaceuticals 2022, 15(3), 282; https://doi.org/10.3390/ph15030282 - 24 Feb 2022

Cited by 11 | Viewed by 3750

Abstract

The goal of achieving anti-inflammatory efficacy with the fewest possible adverse effects through selective COX-2 inhibition is still being investigated in order to develop drugs with safe profiles. This work shows the efficacy and safety profile of two novel benzimidazole piperidine and phenoxy [...] Read more.

The goal of achieving anti-inflammatory efficacy with the fewest possible adverse effects through selective COX-2 inhibition is still being investigated in order to develop drugs with safe profiles. This work shows the efficacy and safety profile of two novel benzimidazole piperidine and phenoxy pyridine derivatives in reaching this goal, which would be considered a major achievement in inflammatory therapy. The compounds were evaluated by virtual screening campaign, in vitro cyclooxygenase 1 and 2 (COX-1 and COX-2) inhibition, in vivo carrageenan-induced rat paw edema assay, cytotoxicity against Raw264.7 cells, and histopathological examination of rat paw and stomach. Two new compounds, compound 1 ([(2-{[3-(4-methyl-1H-benzimidazol-2-yl)piperidin-1-yl]carbonyl}phenyl)amino]acetic acid) and compound 2 (ethyl 1-(5-cyano-2-hydroxyphenyl)-4-oxo-5-phenoxy-1,4-dihydropyridine-3-carboxylate) showed high selectivity against COX-2, favourable drug-likeness and ADME descriptors, a lack of cytotoxicity, relived paw edema, and inflammation without noticeable side effects on the stomach. These two compounds are promising new NSAIDs. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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18 pages, 8221 KiB

Open AccessArticle

Re-Exploring the Ability of Common Docking Programs to Correctly Reproduce the Binding Modes of Non-Covalent Inhibitors of SARS-CoV-2 Protease M^pro

by Davide Bassani, Matteo Pavan, Giovanni Bolcato, Mattia Sturlese and Stefano Moro

Pharmaceuticals 2022, 15(2), 180; https://doi.org/10.3390/ph15020180 - 31 Jan 2022

Cited by 19 | Viewed by 2561

Abstract

In the latest few decades, molecular docking has imposed itself as one of the most used approaches for computational drug discovery. Several docking benchmarks have been published, comparing the performance of different algorithms in respect to a molecular target of interest, usually evaluating [...] Read more.

In the latest few decades, molecular docking has imposed itself as one of the most used approaches for computational drug discovery. Several docking benchmarks have been published, comparing the performance of different algorithms in respect to a molecular target of interest, usually evaluating their ability in reproducing the experimental data, which, in most cases, comes from X-ray structures. In this study, we elucidated the variation of the performance of three docking algorithms, namely GOLD, Glide, and PLANTS, in replicating the coordinates of the crystallographic ligands of SARS-CoV-2 main protease (M^pro). Through the comparison of the data coming from docking experiments and the values derived from the calculation of the solvent exposure of the crystallographic ligands, we highlighted the importance of this last variable for docking performance. Indeed, we underlined how an increase in the percentage of the ligand surface exposed to the solvent in a crystallographic complex makes it harder for the docking algorithms to reproduce its conformation. We further validated our hypothesis through molecular dynamics simulations, showing that the less stable protein–ligand complexes (in terms of root-mean-square deviation and root-mean-square fluctuation) tend to be derived from the cases in which the solvent exposure of the ligand in the starting system is higher. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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15 pages, 5684 KiB

Open AccessArticle

Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems

by Tiago Alves de Oliveira, Lucas Rolim Medaglia, Eduardo Habib Bechelane Maia, Letícia Cristina Assis, Paulo Batista de Carvalho, Alisson Marques da Silva and Alex Gutterres Taranto

Pharmaceuticals 2022, 15(2), 132; https://doi.org/10.3390/ph15020132 - 22 Jan 2022

Cited by 8 | Viewed by 4155

Abstract

DNA is a molecular target for the treatment of several diseases, including cancer, but there are few docking methodologies exploring the interactions between nucleic acids with DNA intercalating agents. Different docking methodologies, such as AutoDock Vina, DOCK 6, and Consensus, implemented into Molecular [...] Read more.

DNA is a molecular target for the treatment of several diseases, including cancer, but there are few docking methodologies exploring the interactions between nucleic acids with DNA intercalating agents. Different docking methodologies, such as AutoDock Vina, DOCK 6, and Consensus, implemented into Molecular Architect (MolAr), were evaluated for their ability to analyze those interactions, considering visual inspection, redocking, and ROC curve. Ligands were refined by Parametric Method 7 (PM7), and ligands and decoys were docked into the minor DNA groove (PDB code: 1VZK). As a result, the area under the ROC curve (AUC-ROC) was 0.98, 0.88, and 0.99 for AutoDock Vina, DOCK 6, and Consensus methodologies, respectively. In addition, we proposed a machine learning model to determine the experimental ∆T_m value, which found a 0.84 R² score. Finally, the selected ligands mono imidazole lexitropsin (42), netropsin (45), and N,N′-(1H-pyrrole-2,5-diyldi-4,1-phenylene)dibenzenecarboximidamide (51) were submitted to Molecular Dynamic Simulations (MD) through NAMD software to evaluate their equilibrium binding pose into the groove. In conclusion, the use of MolAr improves the docking results obtained with other methodologies, is a suitable methodology to use in the DNA system and was proven to be a valuable tool to estimate the ∆T_m experimental values of DNA intercalating agents. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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21 pages, 3247 KiB

Open AccessArticle

In Silico Design, Synthesis, and Biological Evaluation of Anticancer Arylsulfonamide Endowed with Anti-Telomerase Activity

by Giulia Culletta, Mario Allegra, Anna Maria Almerico, Ignazio Restivo and Marco Tutone

Pharmaceuticals 2022, 15(1), 82; https://doi.org/10.3390/ph15010082 - 10 Jan 2022

Cited by 11 | Viewed by 2356

Abstract

Telomerase, a reverse transcriptase enzyme involved in DNA synthesis, has a tangible role in tumor progression. Several studies have evidenced telomerase as a promising target for developing cancer therapeutics. The main reason is due to the overexpression of telomerase in cancer cells (85–90%) [...] Read more.

Telomerase, a reverse transcriptase enzyme involved in DNA synthesis, has a tangible role in tumor progression. Several studies have evidenced telomerase as a promising target for developing cancer therapeutics. The main reason is due to the overexpression of telomerase in cancer cells (85–90%) compared with normal cells where it is almost unexpressed. In this paper, we used a structure-based approach to design potential inhibitors of the telomerase active site. The MYSHAPE (Molecular dYnamics SHared PharmacophorE) approach and docking were used to screen an in-house library of 126 arylsulfonamide derivatives. Promising compounds were synthesized using classical and green methods. Compound 2C revealed an interesting IC₅₀ (33 ± 4 µM) against the K-562 cell line compared with the known telomerase inhibitor BIBR1532 IC₅₀ (208 ± 11 µM) with an SI ~10 compared to the BALB/3-T3 cell line. A 100 ns MD simulation of 2C in the telomerase active site evidenced Phe494 as the key residue as well as in BIBR1532. Each moiety of compound 2C was involved in key interactions with some residues of the active site: Arg557, Ile550, and Gly553. Compound 2C, as an arylsulfonamide derivative, is an interesting hit compound that deserves further investigation in terms of optimization of its structure to obtain more active telomerase inhibitors Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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22 pages, 897 KiB

Open AccessArticle

A Review on Parallel Virtual Screening Softwares for High-Performance Computers

by Natarajan Arul Murugan, Artur Podobas, Davide Gadioli, Emanuele Vitali, Gianluca Palermo and Stefano Markidis

Pharmaceuticals 2022, 15(1), 63; https://doi.org/10.3390/ph15010063 - 04 Jan 2022

Cited by 28 | Viewed by 7564

Abstract

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, [...] Read more.

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein–ligand complexes (on the order of 10

^{6}

to 10

^{12}

), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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21 pages, 7199 KiB

Open AccessArticle

Discovery of Small Molecules as Membrane-Bound Catechol-O-methyltransferase Inhibitors with Interest in Parkinson’s Disease: Pharmacophore Modeling, Molecular Docking and In Vitro Experimental Validation Studies

by Pedro Cruz-Vicente, Ana M. Gonçalves, Octávio Ferreira, João A. Queiroz, Samuel Silvestre, Luís A. Passarinha and Eugenia Gallardo

Pharmaceuticals 2022, 15(1), 51; https://doi.org/10.3390/ph15010051 - 31 Dec 2021

Cited by 4 | Viewed by 2610

Abstract

A pharmacophore-based virtual screening methodology was used to discover new catechol-O-methyltransferase (COMT) inhibitors with interest in Parkinson’s disease therapy. To do so, pharmacophore models were constructed using the structure of known inhibitors and then they were used in a screening in [...] Read more.

A pharmacophore-based virtual screening methodology was used to discover new catechol-O-methyltransferase (COMT) inhibitors with interest in Parkinson’s disease therapy. To do so, pharmacophore models were constructed using the structure of known inhibitors and then they were used in a screening in the ZINCPharmer database to discover hit molecules with the desired structural moieties and drug-likeness properties. Following this, the 50 best ranked molecules were submitted to molecular docking to better understand their atomic interactions and binding poses with the COMT (PDB#6I3C) active site. Additionally, the hits’ ADMET properties were also studied to improve the obtained results and to select the most promising compounds to advance for in-vitro studies. Then, the 10 compounds selected were purchased and studied regarding their in-vitro inhibitory potency on human recombinant membrane-bound COMT (MBCOMT), as well as their cytotoxicity in rat dopaminergic cells (N27) and human dermal fibroblasts (NHDF). Of these, the compound ZIN27985035 displayed the best results: For MBCOMT inhibition an IC₅₀ of 17.6 nM was determined, and low cytotoxicity was observed in both cell lines (61.26 and 40.32 μM, respectively). Therefore, the promising results obtained, combined with the structure similarity with commercial COMT inhibitors, can allow for the future development of a potential new Parkinson’s disease drug candidate with improved properties. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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15 pages, 3261 KiB

Open AccessArticle

Unravelling the Interaction of Piperlongumine with the Nucleotide-Binding Domain of HSP70: A Spectroscopic and In Silico Study

by Ana Paula Ribeiro Povinelli, Gabriel Zazeri, Alan M. Jones and Marinnio Lopes Cornélio

Pharmaceuticals 2021, 14(12), 1298; https://doi.org/10.3390/ph14121298 - 13 Dec 2021

Cited by 1 | Viewed by 2164

Abstract

Piperlongumine (PPL) is an alkaloid extracted from several pepper species that exhibits anti-inflammatory and anti-carcinogenic properties. Nevertheless, the molecular mode of action of PPL that confers such powerful pharmacological properties remains unknown. From this perspective, spectroscopic methods aided by computational modeling were employed [...] Read more.

Piperlongumine (PPL) is an alkaloid extracted from several pepper species that exhibits anti-inflammatory and anti-carcinogenic properties. Nevertheless, the molecular mode of action of PPL that confers such powerful pharmacological properties remains unknown. From this perspective, spectroscopic methods aided by computational modeling were employed to characterize the interaction between PPL and nucleotide-binding domain of heat shock protein 70 (NBD/HSP70), which is involved in the pathogenesis of several diseases. Steady-state fluorescence spectroscopy along with time-resolved fluorescence revealed the complex formation based on a static quenching mechanism. Van’t Hoff analyses showed that the binding of PPL toward NBD is driven by equivalent contributions of entropic and enthalpic factors. Furthermore, IDF and Scatchard methods applied to fluorescence intensities determined two cooperative binding sites with K_b of (6.3 ± 0.2) × 10⁴ M⁻¹. Circular dichroism determined the thermal stability of the NBD domain and showed that PPL caused minor changes in the protein secondary structure. Computational simulations elucidated the microenvironment of these interactions, showing that the binding sites are composed mainly of polar amino acids and the predominant interaction of PPL with NBD is Van der Waals in nature. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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18 pages, 8261 KiB

Open AccessArticle

Fragment-Based Ligand Discovery Applied to the Mycolic Acid Methyltransferase Hma (MmaA4) from Mycobacterium tuberculosis: A Crystallographic and Molecular Modelling Study

by Romain Galy, Stéphanie Ballereau, Yves Génisson, Lionel Mourey, Jean-Christophe Plaquevent and Laurent Maveyraud

Pharmaceuticals 2021, 14(12), 1282; https://doi.org/10.3390/ph14121282 - 08 Dec 2021

Cited by 2 | Viewed by 2408

Abstract

The mycolic acid biosynthetic pathway represents a promising source of pharmacological targets in the fight against tuberculosis. In Mycobacterium tuberculosis, mycolic acids are subject to specific chemical modifications introduced by a set of eight S-adenosylmethionine dependent methyltransferases. Among these, Hma (MmaA4) is [...] Read more.

The mycolic acid biosynthetic pathway represents a promising source of pharmacological targets in the fight against tuberculosis. In Mycobacterium tuberculosis, mycolic acids are subject to specific chemical modifications introduced by a set of eight S-adenosylmethionine dependent methyltransferases. Among these, Hma (MmaA4) is responsible for the introduction of oxygenated modifications. Crystallographic screening of a library of fragments allowed the identification of seven ligands of Hma. Two mutually exclusive binding modes were identified, depending on the conformation of residues 147–154. These residues are disordered in apo-Hma but fold upon binding of the S-adenosylmethionine (SAM) cofactor as well as of analogues, resulting in the formation of the short η1-helix. One of the observed conformations would be incompatible with the presence of the cofactor, suggesting that allosteric inhibitors could be designed against Hma. Chimeric compounds were designed by fusing some of the bound fragments, and the relative binding affinities of initial fragments and evolved compounds were investigated using molecular dynamics simulation and generalised Born and Poisson–Boltzmann calculations coupled to the surface area continuum solvation method. Molecular dynamics simulations were also performed on apo-Hma to assess the structural plasticity of the unliganded protein. Our results indicate a significant improvement in the binding properties of the designed compounds, suggesting that they could be further optimised to inhibit Hma activity. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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18 pages, 10695 KiB

Open AccessArticle

A Deep-Learning Proteomic-Scale Approach for Drug Design

by Brennan Overhoff, Zackary Falls, William Mangione and Ram Samudrala

Pharmaceuticals 2021, 14(12), 1277; https://doi.org/10.3390/ph14121277 - 07 Dec 2021

Cited by 5 | Viewed by 3961

Abstract

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures [...] Read more.

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug–proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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11 pages, 4436 KiB

Open AccessArticle

De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach

by Shuheng Huang, Hu Mei, Laichun Lu, Minyao Qiu, Xiaoqi Liang, Lei Xu, Zuyin Kuang, Yu Heng and Xianchao Pan

Pharmaceuticals 2021, 14(12), 1249; https://doi.org/10.3390/ph14121249 - 30 Nov 2021

Cited by 5 | Viewed by 1981

Abstract

Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel [...] Read more.

Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel caspase-6 candidate inhibitors. Herein, a gated recurrent unit (GRU)-based recurrent neural network (RNN) combined with transfer learning was used to build a molecular generative model of caspase-6 inhibitors. The results showed that the GRU-based RNN model can accurately learn the SMILES grammars of about 2.4 million chemical molecules including ionic and isomeric compounds and can generate potential caspase-6 inhibitors after transfer learning of the known 433 caspase-6 inhibitors. Based on the novel molecules derived from the molecular generative model, an optimal logistic regression model and Surflex-dock were employed for predicting and ranking the inhibitory activities. According to the prediction results, three potential caspase-6 inhibitors with different scaffolds were selected as the promising candidates for further research. In general, this paper provides an efficient combinational strategy for de novo molecular design of caspase-6 inhibitors. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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24 pages, 4970 KiB

Open AccessArticle

A Rational Design of α-Helix-Shaped Peptides Employing the Hydrogen-Bond Surrogate Approach: A Modulation Strategy for Ras-RasGRF1 Interaction in Neuropsychiatric Disorders

by Maria Rita Gulotta, Riccardo Brambilla, Ugo Perricone and Andrea Brancale

Pharmaceuticals 2021, 14(11), 1099; https://doi.org/10.3390/ph14111099 - 28 Oct 2021

Cited by 3 | Viewed by 2292

Abstract

In the last two decades, abnormal Ras (rat sarcoma protein)–ERK (extracellular signal-regulated kinase) signalling in the brain has been involved in a variety of neuropsychiatric disorders, including drug addiction, certain forms of intellectual disability, and autism spectrum disorder. Modulation of membrane-receptor-mediated Ras activation [...] Read more.

In the last two decades, abnormal Ras (rat sarcoma protein)–ERK (extracellular signal-regulated kinase) signalling in the brain has been involved in a variety of neuropsychiatric disorders, including drug addiction, certain forms of intellectual disability, and autism spectrum disorder. Modulation of membrane-receptor-mediated Ras activation has been proposed as a potential target mechanism to attenuate ERK signalling in the brain. Previously, we showed that a cell penetrating peptide, RB3, was able to inhibit downstream signalling by preventing RasGRF1 (Ras guanine nucleotide-releasing factor 1), a neuronal specific GDP/GTP exchange factor, to bind Ras proteins, both in brain slices and in vivo, with an IC₅₀ value in the micromolar range. The aim of this work was to mutate and improve this peptide through computer-aided techniques to increase its inhibitory activity against RasGRF1. The designed peptides were built based on the RB3 peptide structure corresponding to the α-helix of RasGRF1 responsible for Ras binding. For this purpose, the hydrogen-bond surrogate (HBS) approach was exploited to maintain the helical conformation of the designed peptides. Finally, residue scanning, MD simulations, and MM-GBSA calculations were used to identify 18 most promising α-helix-shaped peptides that will be assayed to check their potential activity against Ras-RasGRF1 and prevent downstream molecular events implicated in brain disorders. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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16 pages, 4430 KiB

Open AccessArticle

In Silico Studies of Potential Selective Inhibitors of Thymidylate Kinase from Variola virus

by Danielle R. Garcia, Felipe R. Souza, Ana P. Guimarães, Martin Valis, Zbyšek Pavelek, Kamil Kuca, Teodorico C. Ramalho and Tanos C. C. França

Pharmaceuticals 2021, 14(10), 1027; https://doi.org/10.3390/ph14101027 - 09 Oct 2021

Cited by 9 | Viewed by 3119

Abstract

Continuing the work developed by our research group, in the present manuscript, we performed a theoretical study of 10 new structures derived from the antivirals cidofovir and ribavirin, as inhibitor prototypes for the enzyme thymidylate kinase from Variola virus (VarTMPK). The [...] Read more.

Continuing the work developed by our research group, in the present manuscript, we performed a theoretical study of 10 new structures derived from the antivirals cidofovir and ribavirin, as inhibitor prototypes for the enzyme thymidylate kinase from Variola virus (VarTMPK). The proposed structures were subjected to docking calculations, molecular dynamics simulations, and free energy calculations, using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method, inside the active sites of VarTMPK and human TMPK (HssTMPK). The docking and molecular dynamic studies pointed to structures 2, 3, 4, 6, and 9 as more selective towards VarTMPK. In addition, the free energy data calculated through the MM-PBSA method, corroborated these results. This suggests that these compounds are potential selective inhibitors of VarTMPK and, thus, can be considered as template molecules to be synthesized and experimentally evaluated against smallpox. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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18 pages, 4121 KiB

Open AccessFeature PaperArticle

Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes

by Jiaying You, Michael Hsing and Artem Cherkasov

Pharmaceuticals 2021, 14(10), 948; https://doi.org/10.3390/ph14100948 - 22 Sep 2021

Cited by 2 | Viewed by 9951

Abstract

Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In [...] Read more.

Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In this work, we further developed our previous approach—DeepCOP, a quantitative chemogenomic model that predicts gene regulating effects, and extended its application across multiple cell lines presented in LINCS to predict aging gene regulating effects induced by small molecules. As a result, a quantitative chemogenomic Deep Model was trained using gene ontology labels, molecular fingerprints, and cell line descriptors to predict gene expression responses to chemical perturbations. Other state-of-the-art machine learning approaches were also evaluated as benchmarks. Among those, the deep neural network (DNN) classifier has top-ranked known drugs with beneficial effects on aging genes, and some of these drugs were previously shown to promote longevity, illustrating the potential utility of this methodology. These results further demonstrate the capability of “hybrid” chemogenomic models, incorporating quantitative descriptors from biomarkers to capture cell specific drug–gene interactions. Such models can therefore be used for discovering drugs with desired gene regulatory effects associated with longevity. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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19 pages, 6152 KiB

Open AccessArticle

High-Throughput Screening and Molecular Dynamics Simulation of Natural Product-like Compounds against Alzheimer’s Disease through Multitarget Approach

by Danish Iqbal, Md Tabish Rehman, Abdulaziz Bin Dukhyil, Syed Mohd Danish Rizvi, Mohamed F. Al Ajmi, Bader Mohammed Alshehri, Saeed Banawas, M. Salman Khan, Wael Alturaiki and Mohammed Alsaweed

Pharmaceuticals 2021, 14(9), 937; https://doi.org/10.3390/ph14090937 - 18 Sep 2021

Cited by 30 | Viewed by 3351

Abstract

Alzheimer’s disease (AD) is a progressive neurological disorder that affects 50 million people. Despite this, only two classes of medication have been approved by the FDA. Therefore, we have planned to develop therapeutics by multitarget approach. We have explored the library of 2029 [...] Read more.

Alzheimer’s disease (AD) is a progressive neurological disorder that affects 50 million people. Despite this, only two classes of medication have been approved by the FDA. Therefore, we have planned to develop therapeutics by multitarget approach. We have explored the library of 2029 natural product-like compounds for their multi-targeting potential against AD by inhibiting AChE, BChE (cholinergic pathway) MAO-A, and MOA-B (oxidative stress pathway) through in silico high-throughput screening and molecular dynamics simulation. Based on the binding energy of these target enzymes, approximately 189 compounds exhibited a score of less than −10 kcal/mol against all targets. However, none of the control inhibitors exhibited a binding affinity of less than −10 kcal/mol. Among these, the top 10 hits of compounds against all four targets were selected for ADME-T analysis. As a result, only F0850-4777 exhibited an acceptable range of physicochemical properties, drug-likeness, pharmacokinetics, and suitability for BBB permeation with high GI-A and non-toxic effects. The molecular dynamics study confirmed that F0850-4777 remained inside the binding cavity of targets in a stable conformation throughout the simulation and Prime-MM/GBSA study revealed that van der Waals’ energy (ΔG_vdW) and non-polar solvation or lipophilic energy (ΔG_{Sol_Lipo}) contribute favorably towards the formation of a stable protein–ligand complex. Thus, F0850-4777 could be a potential candidate against multiple targets of two pathophysiological pathways of AD and opens the doors for further confirmation through in vitro and in vivo systems. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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24 pages, 7648 KiB

Open AccessArticle

In Silico Prediction of Novel Inhibitors of SARS-CoV-2 Main Protease through Structure-Based Virtual Screening and Molecular Dynamic Simulation

by Sobia Ahsan Halim, Muhammad Waqas, Ajmal Khan and Ahmed Al-Harrasi

Pharmaceuticals 2021, 14(9), 896; https://doi.org/10.3390/ph14090896 - 03 Sep 2021

Cited by 20 | Viewed by 3860

Abstract

The unprecedented pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is threatening global health. SARS-CoV-2 has caused severe disease with significant mortality since December 2019. The enzyme chymotrypsin-like protease (3CLpro) or main protease (M^pro) of the virus is considered to [...] Read more.

The unprecedented pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is threatening global health. SARS-CoV-2 has caused severe disease with significant mortality since December 2019. The enzyme chymotrypsin-like protease (3CLpro) or main protease (M^pro) of the virus is considered to be a promising drug target due to its crucial role in viral replication and its genomic dissimilarity to human proteases. In this study, we implemented a structure-based virtual screening (VS) protocol in search of compounds that could inhibit the viral M^pro. A library of >eight hundred compounds was screened by molecular docking into multiple structures of M^pro, and the result was analyzed by consensus strategy. Those compounds that were ranked mutually in the ‘Top-100’ position in at least 50% of the structures were selected and their analogous binding modes predicted simultaneously in all the structures were considered as bioactive poses. Subsequently, based on the predicted physiological and pharmacokinetic behavior and interaction analysis, eleven compounds were identified as ‘Hits’ against SARS-CoV-2 M^pro. Those eleven compounds, along with the apo form of M^pro and one reference inhibitor (X77), were subjected to molecular dynamic simulation to explore the ligand-induced structural and dynamic behavior of M^pro. The MM-GBSA calculations reflect that eight out of eleven compounds specifically possess high to good binding affinities for M^pro. This study provides valuable insights to design more potent and selective inhibitors of SARS-CoV-2 M^pro. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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22 pages, 3994 KiB

Open AccessArticle

Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors

by Anke Wilm, Marina Garcia de Lomana, Conrad Stork, Neann Mathai, Steffen Hirte, Ulf Norinder, Jochen Kühnl and Johannes Kirchmair

Pharmaceuticals 2021, 14(8), 790; https://doi.org/10.3390/ph14080790 - 11 Aug 2021

Cited by 5 | Viewed by 3342

Abstract

In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use [...] Read more.

In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model (“Skin Doctor CP:Bio”) obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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17 pages, 4384 KiB

Open AccessFeature PaperArticle

Should We Embed in Chemistry? A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints

by Mario Lovrić, Tomislav Đuričić, Han T. N. Tran, Hussain Hussain, Emanuel Lacić, Morten A. Rasmussen and Roman Kern

Pharmaceuticals 2021, 14(8), 758; https://doi.org/10.3390/ph14080758 - 02 Aug 2021

Cited by 8 | Viewed by 5138

Abstract

Methods for dimensionality reduction are showing significant contributions to knowledge generation in high-dimensional modeling scenarios throughout many disciplines. By achieving a lower dimensional representation (also called embedding), fewer computing resources are needed in downstream machine learning tasks, thus leading to a faster training [...] Read more.

Methods for dimensionality reduction are showing significant contributions to knowledge generation in high-dimensional modeling scenarios throughout many disciplines. By achieving a lower dimensional representation (also called embedding), fewer computing resources are needed in downstream machine learning tasks, thus leading to a faster training time, lower complexity, and statistical flexibility. In this work, we investigate the utility of three prominent unsupervised embedding techniques (principal component analysis—PCA, uniform manifold approximation and projection—UMAP, and variational autoencoders—VAEs) for solving classification tasks in the domain of toxicology. To this end, we compare these embedding techniques against a set of molecular fingerprint-based models that do not utilize additional pre-preprocessing of features. Inspired by the success of transfer learning in several fields, we further study the performance of embedders when trained on an external dataset of chemical compounds. To gain a better understanding of their characteristics, we evaluate the embedders with different embedding dimensionalities, and with different sizes of the external dataset. Our findings show that the recently popularized UMAP approach can be utilized alongside known techniques such as PCA and VAE as a pre-compression technique in the toxicology domain. Nevertheless, the generative model of VAE shows an advantage in pre-compressing the data with respect to classification accuracy. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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18 pages, 15325 KiB

Open AccessArticle

Rational Design of Novel Inhibitors of α-Glucosidase: An Application of Quantitative Structure Activity Relationship and Structure-Based Virtual Screening

by Sobia Ahsan Halim, Sumaira Jabeen, Ajmal Khan and Ahmed Al-Harrasi

Pharmaceuticals 2021, 14(5), 482; https://doi.org/10.3390/ph14050482 - 19 May 2021

Cited by 22 | Viewed by 3206

Abstract

α-Glucosidase is considered a prime drug target for Diabetes Mellitus and its inhibitors are used to delay carbohydrate digestion for the treatment of diabetes mellitus. With the aim to design α-glucosidase inhibitors with novel chemical scaffolds, three folds ligand and structure based virtual [...] Read more.

α-Glucosidase is considered a prime drug target for Diabetes Mellitus and its inhibitors are used to delay carbohydrate digestion for the treatment of diabetes mellitus. With the aim to design α-glucosidase inhibitors with novel chemical scaffolds, three folds ligand and structure based virtual screening was applied. Initially linear quantitative structure activity relationship (QSAR) model was developed by a molecular operating environment (MOE) using a training set of thirty-two known inhibitors, which showed good correlation coefficient (r² = 0.88), low root mean square error (RMSE = 0.23), and cross-validated correlation coefficient r² (q² = 0.71 and RMSE = 0.31). The model was validated by predicting the biological activities of the test set which depicted r² value of 0.82, indicating the robustness of the model. For virtual screening, compounds were retrieved from zinc is not commercial (ZINC) database and screened by molecular docking. The best docked compounds were chosen to assess their pharmacokinetic behavior. Later, the α-glucosidase inhibitory potential of the selected compounds was predicted by their mode of binding interactions. The predicted pharmacokinetic profile, docking scores and protein-ligand interactions revealed that eight compounds preferentially target the catalytic site of α-glucosidase thus exhibit potential α-glucosidase inhibition in silico. The α-glucosidase inhibitory activities of those Hits were predicted by QSAR model, which reflect good inhibitory activities of these compounds. These results serve as a guidelines for the rational drug design and development of potential novel anti-diabetic agents. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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17 pages, 3342 KiB

Open AccessArticle

Marine-Derived Natural Products as ATP-Competitive mTOR Kinase Inhibitors for Cancer Therapeutics

by Shraddha Parate, Vikas Kumar, Gihwan Lee, Shailima Rampogu, Jong Chan Hong and Keun Woo Lee

Pharmaceuticals 2021, 14(3), 282; https://doi.org/10.3390/ph14030282 - 21 Mar 2021

Cited by 17 | Viewed by 3861

Abstract

The mammalian target of rapamycin (mTOR) is a serine/threonine kinase portraying a quintessential role in cellular proliferation and survival. Aberrations in the mTOR signaling pathway have been reported in numerous cancers including thyroid, lung, gastric and ovarian cancer, thus making it a therapeutic [...] Read more.

The mammalian target of rapamycin (mTOR) is a serine/threonine kinase portraying a quintessential role in cellular proliferation and survival. Aberrations in the mTOR signaling pathway have been reported in numerous cancers including thyroid, lung, gastric and ovarian cancer, thus making it a therapeutic target. To attain this objective, an in silico investigation was designed, employing a pharmacophore modeling approach. A structure-based pharmacophore (SBP) model exploiting the key features of a selective mTOR inhibitor, Torkinib directed at the ATP-binding pocket was generated. A Marine Natural Products (MNP) library was screened using SBP model as a query. The retrieved compounds after consequent drug-likeness filtration were subjected to molecular docking with mTOR, thus revealing four MNPs with better scores than Torkinib. Successive refinement via molecular dynamics simulations demonstrated that the hits formed crucial interactions with key residues of the pocket. Furthermore, the four identified hits exhibited good binding free energy scores through MM-PBSA calculations and the subsequent in silico toxicity assessments displayed three hits deemed essentially non-carcinogenic and non-mutagenic. The hits presented in this investigation could act as potent ATP-competitive mTOR inhibitors, representing a platform for the future discovery of drugs from marine natural origin. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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16 pages, 2104 KiB

Open AccessArticle

A New Computer Model for Evaluating the Selective Binding Affinity of Phenylalkylamines to T-Type Ca²⁺ Channels

by You Lu and Ming Li

Pharmaceuticals 2021, 14(2), 141; https://doi.org/10.3390/ph14020141 - 10 Feb 2021

Cited by 6 | Viewed by 1859

Abstract

To establish a computer model for evaluating the binding affinity of phenylalkylamines (PAAs) to T-type Ca²⁺ channels (TCCs), we created new homology models for both TCCs and a L-type calcium channel (LCC). We found that PAAs have a high affinity for domains [...] Read more.

To establish a computer model for evaluating the binding affinity of phenylalkylamines (PAAs) to T-type Ca²⁺ channels (TCCs), we created new homology models for both TCCs and a L-type calcium channel (LCC). We found that PAAs have a high affinity for domains I and IV of TCCs and a low affinity for domains III and IV of the LCC. Therefore, they should be considered as favorable candidates for TCC blockers. The new homology models were validated with some commonly recognized TCC blockers that are well characterized. Additionally, examples of the TCC blockers created were also evaluated using these models. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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15 pages, 1876 KiB

Open AccessReview

Opportunities and Challenges for In Silico Drug Discovery at Delta Opioid Receptors

by Yazan J. Meqbil and Richard M. van Rijn

Pharmaceuticals 2022, 15(7), 873; https://doi.org/10.3390/ph15070873 - 15 Jul 2022

Cited by 5 | Viewed by 3097

Abstract

The delta opioid receptor is a Gi-protein-coupled receptor (GPCR) with a broad expression pattern both in the central nervous system and the body. The receptor has been investigated as a potential target for a multitude of significant diseases including migraine, alcohol use disorder, [...] Read more.

The delta opioid receptor is a Gi-protein-coupled receptor (GPCR) with a broad expression pattern both in the central nervous system and the body. The receptor has been investigated as a potential target for a multitude of significant diseases including migraine, alcohol use disorder, ischemia, and neurodegenerative diseases. Despite multiple attempts, delta opioid receptor-selective molecules have not been translated into the clinic. Yet, the therapeutic promise of the delta opioid receptor remains and thus there is a need to identify novel delta opioid receptor ligands to be optimized and selected for clinical trials. Here, we highlight recent developments involving the delta opioid receptor, the closely related mu and kappa opioid receptors, and in the broader area of the GPCR drug discovery research. We focus on the validity and utility of the available delta opioid receptor structures. We also discuss the increased ability to perform ultra-large-scale docking studies on GPCRs, the rise in high-resolution cryo-EM structures, and the increased prevalence of machine learning and artificial intelligence in drug discovery. Overall, we pose that there are multiple opportunities to enable in silico drug discovery at the delta opioid receptor to identify novel delta opioid modulators potentially with unique pharmacological properties, such as biased signaling. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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16 pages, 6937 KiB

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Drug Design by Pharmacophore and Virtual Screening Approach

by Deborah Giordano, Carmen Biancaniello, Maria Antonia Argenio and Angelo Facchiano

Pharmaceuticals 2022, 15(5), 646; https://doi.org/10.3390/ph15050646 - 23 May 2022

Cited by 63 | Viewed by 10686

Abstract

Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of [...] Read more.

Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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99 pages, 23166 KiB

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Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

by Tomasz Róg, Mykhailo Girych and Alex Bunker

Pharmaceuticals 2021, 14(10), 1062; https://doi.org/10.3390/ph14101062 - 19 Oct 2021

Cited by 22 | Viewed by 11689

Abstract

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard “lock [...] Read more.

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard “lock and key” paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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19 pages, 1902 KiB

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In Silico Approaches: A Way to Unveil Novel Therapeutic Drugs for Cervical Cancer Management

by Diana Gomes, Samuel Silvestre, Ana Paula Duarte, Aldo Venuti, Christiane P. Soares, Luís Passarinha and Ângela Sousa

Pharmaceuticals 2021, 14(8), 741; https://doi.org/10.3390/ph14080741 - 29 Jul 2021

Cited by 19 | Viewed by 5186

Abstract

Cervical cancer (CC) is the fourth most common pathology in women worldwide and presents a high impact in developing countries due to limited financial resources as well as difficulties in monitoring and access to health services. Human papillomavirus (HPV) is the leading cause [...] Read more.

Cervical cancer (CC) is the fourth most common pathology in women worldwide and presents a high impact in developing countries due to limited financial resources as well as difficulties in monitoring and access to health services. Human papillomavirus (HPV) is the leading cause of CC, and despite the approval of prophylactic vaccines, there is no effective treatment for patients with pre-existing infections or HPV-induced carcinomas. High-risk (HR) HPV E6 and E7 oncoproteins are considered biomarkers in CC progression. Since the E6 structure was resolved, it has been one of the most studied targets to develop novel and specific therapeutics to treat/manage CC. Therefore, several small molecules (plant-derived or synthetic compounds) have been reported as blockers/inhibitors of E6 oncoprotein action, and computational-aided methods have been of high relevance in their discovery and development. In silico approaches have become a powerful tool for reducing the time and cost of the drug development process. Thus, this review will depict small molecules that are already being explored as HR HPV E6 protein blockers and in silico approaches to the design of novel therapeutics for managing CC. Besides, future perspectives in CC therapy will be briefly discussed. Full article

(This article belongs to the Special Issue In Silico Approaches in Drug Design)

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