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21 pages, 11756 KiB

Open AccessArticle

Computer-Aided Prediction of the Interactions of Viral Proteases with Antiviral Drugs: Antiviral Potential of Broad-Spectrum Drugs

by Pengxuan Ren, Shiwei Li, Shihang Wang, Xianglei Zhang and Fang Bai

Molecules 2024, 29(1), 225; https://doi.org/10.3390/molecules29010225 - 31 Dec 2023

Viewed by 1268

Abstract

Human society is facing the threat of various viruses. Proteases are promising targets for the treatment of viral infections. In this study, we collected and profiled 170 protease sequences from 125 viruses that infect humans. Approximately 73 of them are viral 3-chymotrypsin-like proteases [...] Read more.

Human society is facing the threat of various viruses. Proteases are promising targets for the treatment of viral infections. In this study, we collected and profiled 170 protease sequences from 125 viruses that infect humans. Approximately 73 of them are viral 3-chymotrypsin-like proteases (3CL^pro), and 11 are pepsin-like aspartic proteases (PAPs). Their sequences, structures, and substrate characteristics were carefully analyzed to identify their conserved nature for proposing a pan-3CL^pro or pan-PAPs inhibitor design strategy. To achieve this, we used computational prediction and modeling methods to predict the binding complex structures for those 73 3CL^pro with 4 protease inhibitors of SARS-CoV-2 and 11 protease inhibitors of HCV. Similarly, the complex structures for the 11 viral PAPs with 9 protease inhibitors of HIV were also obtained. The binding affinities between these compounds and proteins were also evaluated to assess their pan-protease inhibition via MM-GBSA. Based on the drugs targeting viral 3CL^pro and PAPs, repositioning of the active compounds identified several potential uses for these drug molecules. As a result, Compounds 1–2, modified based on the structures of Ray1216 and Asunaprevir, indicate potential inhibition of DENV protease according to our computational simulation results. These studies offer ideas and insights for future research in the design of broad-spectrum antiviral drugs. Full article

(This article belongs to the Special Issue Theoretical and Computational Research in Drug Discovery, Design and Development)

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22 pages, 5240 KiB

Open AccessArticle

In Silico Studies of Novel Vemurafenib Derivatives as BRAF Kinase Inhibitors

by Teresa Żołek, Adam Mazurek and Ireneusz P. Grudzinski

Molecules 2023, 28(13), 5273; https://doi.org/10.3390/molecules28135273 - 07 Jul 2023

Viewed by 1489

Abstract

BRAF inhibitors have improved the treatment of advanced or metastatic melanoma in patients that harbor a BRAF^T1799A mutation. Because of new insights into the role of aberrant glycosylation in drug resistance, we designed and studied three novel vemurafenib derivatives possessing pentose-associated aliphatic [...] Read more.

BRAF inhibitors have improved the treatment of advanced or metastatic melanoma in patients that harbor a BRAF^T1799A mutation. Because of new insights into the role of aberrant glycosylation in drug resistance, we designed and studied three novel vemurafenib derivatives possessing pentose-associated aliphatic ligands—methyl-, ethyl-, and isopropyl-ketopentose moieties—as potent BRAF_V600E kinase inhibitors. The geometries of these derivatives were optimized using the density functional theory method. Molecular dynamic simulations were performed to find interactions between the ligands and BRAF_V600E kinase. Virtual screening was performed to assess the fate of derivatives and their systemic toxicity, genotoxicity, and carcinogenicity. The computational mapping of the studied ligand–BRAF_V600E complexes indicated that the central pyrrole and pyridine rings of derivatives were located within the hydrophobic ATP-binding site of the BRAF_V600E protein kinase, while the pentose ring and alkyl chains were mainly included in hydrogen bonding interactions. The isopropyl-ketopentose derivative was found to bind the BRAF_V600E oncoprotein with more favorable energy interaction than vemurafenib. ADME-TOX in silico studies showed that the derivatives possessed some desirable pharmacokinetic and toxicologic properties. The present results open a new avenue to study the carbohydrate derivatives of vemurafenib as potent BRAF_V600E kinase inhibitors to treat melanoma. Full article

(This article belongs to the Special Issue Theoretical and Computational Research in Drug Discovery, Design and Development)

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34 pages, 7583 KiB

Open AccessArticle

Establishing the Role of Iridoids as Potential Kirsten Rat Sarcoma Viral Oncogene Homolog G12C Inhibitors Using Molecular Docking; Molecular Docking Simulation; Molecular Mechanics Poisson–Boltzmann Surface Area; Frontier Molecular Orbital Theory; Molecular Electrostatic Potential; and Absorption, Distribution, Metabolism, Excretion, and Toxicity Analysis

by Mubarak A. Alamri, Abdullah S. Alawam, Mohammed Merae Alshahrani, Sarkar M. A. Kawsar, Prinsa and Supriyo Saha

Molecules 2023, 28(13), 5050; https://doi.org/10.3390/molecules28135050 - 28 Jun 2023

Cited by 5 | Viewed by 1268

Abstract

The RAS gene family is one of the most frequently mutated oncogenes in human cancers. In KRAS, mutations of G12D and G12C are common. Here, 52 iridoids were selected and docked against 8AFB (KRAS G12C receptor) using Sotorasib as the standard. As per [...] Read more.

The RAS gene family is one of the most frequently mutated oncogenes in human cancers. In KRAS, mutations of G12D and G12C are common. Here, 52 iridoids were selected and docked against 8AFB (KRAS G12C receptor) using Sotorasib as the standard. As per the docking interaction data, 6-O-trans-p-coumaroyl-8-O-acetylshanzhiside methyl ester (dock score: −9.9 kcal/mol), 6′-O-trans-para-coumaroyl geniposidic acid (dock score: −9.6 kcal/mol), 6-O-trans-cinnamoyl-secologanoside (dock score: −9.5 kcal/mol), Loganic acid 6′-O-beta-d-glucoside (dock score: −9.5 kcal/mol), 10-O-succinoylgeniposide (dock score: −9.4), Loganic acid (dock score: −9.4 kcal/mol), and Amphicoside (dock score: −9.2 kcal/mol) showed higher dock scores than standard Sotorasib (dock score: −9.1 kcal/mol). These common amino acid residues between iridoids and complexed ligands confirmed that all the iridoids perfectly docked within the receptor’s active site. The 100 ns MD simulation data showed that RMSD, RMSF, radius of gyration, and SASA values were within range, with greater numbers of hydrogen bond donors and acceptors. MM/PBSA analysis showed maximum binding energy values of −7309 kJ/mol for 6-O-trans-p-coumaroyl-8-O-acetylshanzhiside methyl ester. FMO analysis showed that 6-O-trans-p-coumaroyl-8-O-acetylshanzhiside methyl ester was the most likely chemically reactive molecule. MEP analysis data highlighted the possible electrophilic and nucleophilic attack regions of the best-docked iridoids. Of all the best-docked iridoids, Loganic acid passed Lipinski, Pfizer, and GSK filters with a similar toxicity profile to Sotorasib. Thus, if we consider these iridoids to be KRAS G12C inhibitors, they will be a boon to mankind. Full article

(This article belongs to the Special Issue Theoretical and Computational Research in Drug Discovery, Design and Development)

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11 pages, 1800 KiB

Open AccessArticle

Stability Analysis of the Asiatic Acid-COX-2 Complex Using 100 ns Molecular Dynamic Simulations and Its Selectivity against COX-2 as a Potential Anti-Inflammatory Candidate

by Ida Musfiroh, Rahmana E. Kartasasmita, Slamet Ibrahim, Muchtaridi Muchtaridi, Syahrul Hidayat and Nur Kusaira Khairul Ikram

Molecules 2023, 28(9), 3762; https://doi.org/10.3390/molecules28093762 - 27 Apr 2023

Cited by 1 | Viewed by 1333

Abstract

Asiatic acid, a triterpenoid compound, has been shown to have anti-inflammatory activity through the inhibition of the formation of cyclooxygenase-2 (COX-2) in vitro and in vivo. This study was conducted to determine the binding stability and the inhibitory potential of asiatic acid as [...] Read more.

Asiatic acid, a triterpenoid compound, has been shown to have anti-inflammatory activity through the inhibition of the formation of cyclooxygenase-2 (COX-2) in vitro and in vivo. This study was conducted to determine the binding stability and the inhibitory potential of asiatic acid as an anti-inflammatory candidate. The study involved in vitro testing utilizing a colorimetric kit as well as in silico testing for the pharmacophore modeling and molecular dynamic (MD) simulation of asiatic acid against COX-2 (PDB ID: 3NT1). The MD simulations showed a stable binding of asiatic acid to COX-2 and an RMSD range of 1–1.5 Å with fluctuations at the residues of Phe41, Leu42, Ile45, Arg44, Asp367, Val550, Glu366, His246, and Gly227. The total binding energy of the asiatic acid–COX-2 complex is −7.371 kcal/mol. The anti-inflammatory activity of the asiatic acid inhibition of COX-2 was detected at IC₅₀ values of 120.17 µM. Based on pharmacophore modeling, we discovered that carboxylate and hydroxyl are the two main functional groups that act as hydrogen bond donors and acceptors interacting with the COX-2 enzyme. From the results, it is evident that asiatic acid is a potential anti-inflammatory candidate with high inhibitory activity in relation to the COX-2 enzyme. Full article

(This article belongs to the Special Issue Theoretical and Computational Research in Drug Discovery, Design and Development)

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11 pages, 1827 KiB

Open AccessArticle

DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning

by Shihang Wang, Zhehan Shen, Taigang Liu, Wei Long, Linhua Jiang and Sihua Peng

Molecules 2023, 28(5), 2284; https://doi.org/10.3390/molecules28052284 - 01 Mar 2023

Cited by 3 | Viewed by 1918

Abstract

The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA’s subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. [...] Read more.

The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA’s subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. In this study, a deep neural network-based eukaryotic mRNA subcellular location prediction method, DeepmRNALoc, was proposed, utilizing a two-stage feature extraction strategy that featured bimodal information splitting and fusing for the first stage and a VGGNet-like CNN module for the second stage. The five-fold cross-validation accuracies of DeepmRNALoc in the cytoplasm, endoplasmic reticulum, extracellular region, mitochondria, and nucleus were 0.895, 0.594, 0.308, 0.944, and 0.865, respectively, demonstrating that it outperforms existing models and techniques. Full article

(This article belongs to the Special Issue Theoretical and Computational Research in Drug Discovery, Design and Development)

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16 pages, 9858 KiB

Open AccessArticle

Structural Analysis, Multi-Conformation Virtual Screening and Molecular Simulation to Identify Potential Inhibitors Targeting pS273R Proteases of African Swine Fever Virus

by Gen Lu, Kang Ou, Yihan Zhang, Huan Zhang, Shouhua Feng, Zuofeng Yang, Guo Sun, Jinling Liu, Shu Wei, Shude Pan and Zeliang Chen

Molecules 2023, 28(2), 570; https://doi.org/10.3390/molecules28020570 - 06 Jan 2023

Cited by 2 | Viewed by 2078

Abstract

The African Swine Fever virus (ASFV) causes an infectious viral disease in pigs of all ages. The development of antiviral drugs primarily aimed at inhibition of proteases required for the proteolysis of viral polyproteins. In this study, the conformation of the pS273R protease [...] Read more.

The African Swine Fever virus (ASFV) causes an infectious viral disease in pigs of all ages. The development of antiviral drugs primarily aimed at inhibition of proteases required for the proteolysis of viral polyproteins. In this study, the conformation of the pS273R protease in physiological states were investigated, virtually screened the multi-protein conformation of pS273R target proteins, combined various molecular docking scoring functions, and identified five potential drugs from the Food and Drug Administration drug library that may inhibit pS273R. Subsequent validation of the dynamic interactions of pS273R with the five putative inhibitors was achieved using molecular dynamics simulations and binding free energy calculations using the molecular mechanics/Poison-Boltzmann (Generalized Born) (MM/PB(GB)SA) surface area. These findings demonstrate that the arm domain and Thr159-Lys167 loop region of pS273R are significantly more flexible compared to the core structural domain, and the Thr159-Lys167 loop region can serve as a “gatekeeper” in the substrate channel. Leucovorin, Carboprost, Protirelin, Flavin Mononucleotide, and Lovastatin Acid all have Gibbs binding free energies with pS273R that were less than −20 Kcal/mol according to the MM/PBSA analyses. In contrast to pS273R in the free energy landscape, the inhibitor and drug complexes of pS273R showed distinct structural group distributions. These five drugs may be used as potential inhibitors of pS273R and may serve as future drug candidates for treating ASFV. Full article

(This article belongs to the Special Issue Theoretical and Computational Research in Drug Discovery, Design and Development)

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17 pages, 3974 KiB

Open AccessArticle

Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation

by Ruoqi Yang, Guiping Zhao and Bin Yan

Molecules 2022, 27(19), 6249; https://doi.org/10.3390/molecules27196249 - 22 Sep 2022

Cited by 4 | Viewed by 1901

Abstract

c-Jun N-terminal kinase 1 (JNK1) is currently considered a critical therapeutic target for type-2 diabetes. In recent years, there has been a great interest in naturopathic molecules, and the discovery of active ingredients from natural products for specific targets has received increasing attention. [...] Read more.

c-Jun N-terminal kinase 1 (JNK1) is currently considered a critical therapeutic target for type-2 diabetes. In recent years, there has been a great interest in naturopathic molecules, and the discovery of active ingredients from natural products for specific targets has received increasing attention. Based on the above background, this research aims to combine emerging Artificial Intelligence technologies with traditional Computer-Aided Drug Design methods to find natural products with JNK1 inhibitory activity. First, we constructed three machine learning models (Support Vector Machine, Random Forest, and Artificial Neural Network) and performed model fusion based on Voting and Stacking strategies. The integrated models with better performance (AUC of 0.906 and 0.908, respectively) were then employed for the virtual screening of 4112 natural products in the ZINC database. After further drug-likeness filtering, we calculated the binding free energy of 22 screened compounds using molecular docking and performed a consensus analysis of the two methodologies. Subsequently, we identified the three most promising candidates (Lariciresinol, Tricin, and 4′-Demethylepipodophyllotoxin) according to the obtained probability values and relevant reports, while their binding characteristics were preliminarily explored by molecular dynamics simulations. Finally, we performed in vitro biological validation of these three compounds, and the results showed that Tricin exhibited an acceptable inhibitory activity against JNK1 (IC₅₀ = 17.68 μM). This natural product can be used as a template molecule for the design of novel JNK1 inhibitors. Full article

(This article belongs to the Special Issue Theoretical and Computational Research in Drug Discovery, Design and Development)

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24 pages, 3567 KiB

Open AccessArticle

Identification of Novel Dopamine D₂ Receptor Ligands—A Combined In Silico/In Vitro Approach

by Lukas Zell, Constanze Lainer, Jakub Kollár, Veronika Temml and Daniela Schuster

Molecules 2022, 27(14), 4435; https://doi.org/10.3390/molecules27144435 - 11 Jul 2022

Cited by 4 | Viewed by 2138

Abstract

Diseases of the central nervous system are an alarming global problem showing an increasing prevalence. Dopamine receptor D₂ (D₂R) has been shown to be involved in central nervous system diseases. While different D₂R-targeting drugs have been approved by [...] Read more.

Diseases of the central nervous system are an alarming global problem showing an increasing prevalence. Dopamine receptor D₂ (D₂R) has been shown to be involved in central nervous system diseases. While different D₂R-targeting drugs have been approved by the FDA, they all suffer from major drawbacks due to promiscuous receptor activity leading to adverse effects. Increasing the number of potential D₂R-targeting drug candidates bears the possibility of discovering molecules with less severe side-effect profiles. In dire need of novel D₂R ligands for drug development, combined in silico/in vitro approaches have been shown to be efficient strategies. In this study, in silico pharmacophore models were generated utilizing both ligand- and structure-based approaches. Subsequently, different databases were screened for novel D₂R ligands. Selected virtual hits were investigated in vitro, quantifying their binding affinity towards D₂R. This workflow successfully identified six novel D₂R ligands exerting micro- to nanomolar (most active compound K_I = 4.1 nM) activities. Thus, the four pharmacophore models showed prospective true-positive hit rates in between 4.5% and 12%. The developed workflow and identified ligands could aid in developing novel drug candidates for D₂R-associated pathologies. Full article

(This article belongs to the Special Issue Theoretical and Computational Research in Drug Discovery, Design and Development)

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