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14 pages, 1172 KiB

Open AccessArticle

Quantitative Structure–Activity Relationship (QSAR) Modeling of Chiral CCR2 Antagonists with a Multidimensional Space of Novel Chirality Descriptors

by Ramanathan Natarajan, Ganapathy S. Natarajan and Subhash C. Basak

Molecules 2025, 30(2), 307; https://doi.org/10.3390/molecules30020307 - 14 Jan 2025

Viewed by 412

Abstract

The development of chirality descriptors for quantitative chirality structure–activity relationship (QCSAR) modeling has always attracted attention, owing to the importance of chiral molecules in pharmaceutical, agriculture, food, and fragrance industries, and environmental toxicology. The utility of a multidimensional space of novel relative chirality [...] Read more.

The development of chirality descriptors for quantitative chirality structure–activity relationship (QCSAR) modeling has always attracted attention, owing to the importance of chiral molecules in pharmaceutical, agriculture, food, and fragrance industries, and environmental toxicology. The utility of a multidimensional space of novel relative chirality indices (RCIs) in the QCSAR modeling of twenty CCR2 antagonists is reported upon in this paper. The numerical characterization of chirality by the RCI approach gives a large pool of chirality descriptors with different degrees of mutual correlation (the correlation coefficient among the computed descriptors varied from 0.02 to 0.99). In the present study, the final data set contains 198 chirality descriptors for each of the twenty CCR2 antagonist molecules, providing a multidimensional space for modeling. The data reduction using principal component analysis resulted in the extraction of eight principal components (PCs). The linear regression using the principal component scores (PCSs) resulted in a three-predictor prediction model with good statistics: R² = 0.823; Adj R² = 0.790. The regression models were rebuilt using the chirality descriptors (RCIs) that are most correlated with each of the scores (PCSs) of the three principal components. The R² value for the regression models with three RCIs as the predictors is 0.742 and the five-fold cross validation, R_cv², is 0.839. The new chirality descriptors, namely, the RCIs calculated using a different weighting scheme, provide a multidimensional space of chirality descriptors for a set of chiral molecules, and such a multidimensional chirality space is a powerful tool to build quantitative chiral structure–activity relationship (QCSAR) models. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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17 pages, 2131 KiB

Open AccessArticle

Structure and Intercalation of Cysteine–Asparagine–Serine Peptide into Montmorillonite as an Anti-Inflammatory Agent Preparation—A DFT Study

by Carolina Barrientos-Salcedo, Catalina Soriano-Correa, Alfonso Hernández-Laguna and Claro Ignacio Sainz-Díaz

Molecules 2024, 29(17), 4250; https://doi.org/10.3390/molecules29174250 - 7 Sep 2024

Viewed by 1014

Abstract

Peptides are receiving significant attention in pharmaceutical sciences due to their applications as anti-inflammatory drugs; however, many aspects of their interactions and mechanisms at the molecular level are not well-known. This work explores the molecular structure of two peptides—(i) cysteine (Cys)–asparagine (Asn)–serine (Ser) [...] Read more.

Peptides are receiving significant attention in pharmaceutical sciences due to their applications as anti-inflammatory drugs; however, many aspects of their interactions and mechanisms at the molecular level are not well-known. This work explores the molecular structure of two peptides—(i) cysteine (Cys)–asparagine (Asn)–serine (Ser) (CNS) as a molecule in the gas phase and solvated in water in zwitterion form, and (ii) the crystal structure of the dipeptide serine–asparagine (SN), a reliable peptide indication whose experimental cell parameters are well known. A search was performed by means of atomistic calculations based on density functional theory (DFT). These calculations matched the experimental crystal structure of SN, validating the CNS results and useful for assignments of our experimental spectroscopic IR bands. Our calculations also explore the intercalation of CNS into the interlayer space of montmorillonite (MNT). Our quantum mechanical calculations show that the conformations of these peptides change significantly during intercalation into the confined interlayer space of MNT. This intercalation is energetically favorable, indicating that this process can be a useful preparation for therapeutic anti-inflammatory applications and showing high stability and controlled release processes. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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18 pages, 1862 KiB

Open AccessArticle

MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction

by Baofang Hu, Zhenmei Yu and Mingke Li

Molecules 2024, 29(11), 2483; https://doi.org/10.3390/molecules29112483 - 24 May 2024

Cited by 1 | Viewed by 1257

Abstract

The combinatorial therapy with multiple drugs may lead to unexpected drug-drug interactions (DDIs) and result in adverse reactions to patients. Predicting DDI events can mitigate the potential risks of combinatorial therapy and enhance drug safety. In recent years, deep models based on heterogeneous [...] Read more.

The combinatorial therapy with multiple drugs may lead to unexpected drug-drug interactions (DDIs) and result in adverse reactions to patients. Predicting DDI events can mitigate the potential risks of combinatorial therapy and enhance drug safety. In recent years, deep models based on heterogeneous graph representation learning have attracted widespread interest in DDI event prediction and have yielded satisfactory results, but there is still room for improvement in prediction performance. In this study, we proposed a meta-path-based heterogeneous graph contrastive learning model, MPHGCL-DDI, for DDI event prediction. The model constructs two contrastive views based on meta-paths: an average graph view and an augmented graph view. The former represents that there are connections between drugs, while the latter reveals how the drugs connect with each other. We defined three levels of data augmentation schemes in the augmented graph view and adopted a combination of three losses in the model training phase: multi-relation prediction loss, unsupervised contrastive loss and supervised contrastive loss. Furthermore, the model incorporates indirect drug information, protein–protein interactions (PPIs), to reveal latent relations of drugs. We evaluated MPHGCL-DDI on three different tasks of two datasets. Experimental results demonstrate that MPHGCL-DDI surpasses several state-of-the-art methods in performance. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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31 pages, 6924 KiB

Open AccessArticle

Modeling of Effectiveness of N³-Substituted Amidrazone Derivatives as Potential Agents against Gram-Positive Bacteria

by Małgorzata Ćwiklińska-Jurkowska, Renata Paprocka, Godwin Munroe Mwaura and Jolanta Kutkowska

Molecules 2024, 29(10), 2369; https://doi.org/10.3390/molecules29102369 - 17 May 2024

Viewed by 1101

Abstract

Prediction of the antibacterial activity of new chemical compounds is an important task, due to the growing problem of bacterial drug resistance. Generalized linear models (GLMs) were created using 85 amidrazone derivatives based on the results of antimicrobial activity tests, determined as the [...] Read more.

Prediction of the antibacterial activity of new chemical compounds is an important task, due to the growing problem of bacterial drug resistance. Generalized linear models (GLMs) were created using 85 amidrazone derivatives based on the results of antimicrobial activity tests, determined as the minimum inhibitory concentration (MIC) against Gram-positive bacteria: Staphylococcus aureus, Enterococcus faecalis, Micrococcus luteus, Nocardia corallina, and Mycobacterium smegmatis. For the analysis of compounds characterized by experimentally measured MIC values, we included physicochemical properties (e.g., molecular weight, number of hydrogen donors and acceptors, topological polar surface area, compound percentages of carbon, nitrogen, and oxygen, melting points, and lipophilicity) as potential predictors. The presence of R1 and R2 substituents, as well as interactions between melting temperature and R1 or R2 substituents, were also considered. The set of potential predictors also included possible biological effects (e.g., antibacterial, antituberculotic) of tested compounds calculated with the PASS (Prediction of Activity Spectra for Substances) program. Using GLMs with least absolute shrinkage and selection (LASSO), least-angle regression, and stepwise selection, statistically significant models with the optimal value of the adjusted determination coefficient and of seven fit criteria were chosen, e.g., Akaike’s information criterion. The most often selected variables were as follows: molecular weight, PASS_antieczematic, PASS_anti-inflam, squared melting temperature, PASS_antitumor, and experimental lipophilicity. Additionally, relevant to the bacterial strain, the interactions between melting temperature and R1 or R2 substituents were selected, indicating that the relationship between MIC and melting temperature depends on the type of R1 or R2 substituent. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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31 pages, 14832 KiB

Open AccessArticle

QSAR Study, Molecular Docking and Molecular Dynamic Simulation of Aurora Kinase Inhibitors Derived from Imidazo[4,5-b]pyridine Derivatives

by Yang-Yang Tian, Jian-Bo Tong, Yuan Liu and Yu Tian

Molecules 2024, 29(8), 1772; https://doi.org/10.3390/molecules29081772 - 13 Apr 2024

Viewed by 2057

Abstract

Cancer is a serious threat to human life and social development and the use of scientific methods for cancer prevention and control is necessary. In this study, HQSAR, CoMFA, CoMSIA and TopomerCoMFA methods are used to establish models of 65 imidazo[4,5-b]pyridine [...] Read more.

Cancer is a serious threat to human life and social development and the use of scientific methods for cancer prevention and control is necessary. In this study, HQSAR, CoMFA, CoMSIA and TopomerCoMFA methods are used to establish models of 65 imidazo[4,5-b]pyridine derivatives to explore the quantitative structure-activity relationship between their anticancer activities and molecular conformations. The results show that the cross-validation coefficients q² of HQSAR, CoMFA, CoMSIA and TopomerCoMFA are 0.892, 0.866, 0.877 and 0.905, respectively. The non-cross-validation coefficients r² are 0.948, 0.983, 0.995 and 0.971, respectively. The externally validated complex correlation coefficients r²_pred of external validation are 0.814, 0.829, 0.758 and 0.855, respectively. The PLS analysis verifies that the QSAR models have the highest prediction ability and stability. Based on these statistics, virtual screening based on R group is performed using the ZINC database by the Topomer search technology. Finally, 10 new compounds with higher activity are designed with the screened new fragments. In order to explore the binding modes and targets between ligands and protein receptors, these newly designed compounds are conjugated with macromolecular protein (PDB ID: 1MQ4) by molecular docking technology. Furthermore, to study the nature of the newly designed compound in dynamic states and the stability of the protein-ligand complex, molecular dynamics simulation is carried out for N3, N4, N5 and N7 docked with 1MQ4 protease structure for 50 ns. A free energy landscape is computed to search for the most stable conformation. These results prove the efficient and stability of the newly designed compounds. Finally, ADMET is used to predict the pharmacology and toxicity of the 10 designed drug molecules. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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18 pages, 19241 KiB

Open AccessArticle

Scaffold Morphing and In Silico Design of Potential BACE-1 (β-Secretase) Inhibitors: A Hope for a Newer Dawn in Anti-Alzheimer Therapeutics

by Shiveena Bhatia, Manjinder Singh, Pratibha Sharma, Somdutt Mujwar, Varinder Singh, Krishna Kumar Mishra, Thakur Gurjeet Singh, Tanveer Singh and Sheikh Fayaz Ahmad

Molecules 2023, 28(16), 6032; https://doi.org/10.3390/molecules28166032 - 12 Aug 2023

Cited by 6 | Viewed by 1931

Abstract

Alzheimer’s disease (AD) is the prime cause of 65–80% of dementia cases and is caused by plaque and tangle deposition in the brain neurons leading to brain cell degeneration. β-secretase (BACE-1) is a key enzyme responsible for depositing extracellular plaques made of β-amyloid [...] Read more.

Alzheimer’s disease (AD) is the prime cause of 65–80% of dementia cases and is caused by plaque and tangle deposition in the brain neurons leading to brain cell degeneration. β-secretase (BACE-1) is a key enzyme responsible for depositing extracellular plaques made of β-amyloid protein. Therefore, efforts are being applied to develop novel BACE-1 enzyme inhibitors to halt plaque build-up. In our study, we analyzed some Elenbecestat analogues (a BACE-1 inhibitor currently in clinical trials) using a structure-based drug design and scaffold morphing approach to achieve a superior therapeutic profile, followed by in silico studies, including molecular docking and pharmacokinetics methodologies. Among all the designed compounds, SB306 and SB12 showed good interactions with the catalytic dyad motifs (Asp228 and Asp32) of the BACE-1 enzyme with drug-likeliness properties and a high degree of thermodynamic stability confirmed by the molecular dynamic and stability of the simulated system indicating the inhibitory nature of the SB306 and SB12 on BACE 1. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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19 pages, 4647 KiB

Open AccessArticle

Decoding the Conformational Selective Mechanism of FGFR Isoforms: A Comparative Molecular Dynamics Simulation

by Mingyang Zhang, Miersalijiang Yasen, Shaoyong Lu, De-Ning Ma and Zongtao Chai

Molecules 2023, 28(6), 2709; https://doi.org/10.3390/molecules28062709 - 17 Mar 2023

Cited by 8 | Viewed by 2993

Abstract

Fibroblast growth factor receptors (FGFRs) play critical roles in the regulation of cell growth, differentiation, and proliferation. Specifically, FGFR2 gene amplification has been implicated in gastric and breast cancer. Pan-FGFR inhibitors often cause large toxic side effects, and the highly conserved ATP-binding pocket [...] Read more.

Fibroblast growth factor receptors (FGFRs) play critical roles in the regulation of cell growth, differentiation, and proliferation. Specifically, FGFR2 gene amplification has been implicated in gastric and breast cancer. Pan-FGFR inhibitors often cause large toxic side effects, and the highly conserved ATP-binding pocket in the FGFR1/2/3 isoforms poses an immense challenge in designing selective FGFR2 inhibitors. Recently, an indazole-based inhibitor has been discovered that can selectively target FGFR2. However, the detailed mechanism involved in selective inhibition remains to be clarified. To this end, we performed extensive molecular dynamics simulations of the apo and inhibitor-bound systems along with multiple analyses, including Markov state models, principal component analysis, a cross-correlation matrix, binding free energy calculation, and community network analysis. Our results indicated that inhibitor binding induced the phosphate-binding loop (P-loop) of FGFR2 to switch from the open to the closed conformation. This effect enhanced extensive hydrophobic FGFR2-inhibitor contacts, contributing to inhibitor selectivity. Moreover, the key conformational intermediate states, dynamics, and driving forces of this transformation were uncovered. Overall, these findings not only provided a structural basis for understanding the closed P-loop conformation for therapeutic potential but also shed light on the design of selective inhibitors for treating specific types of cancer. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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27 pages, 8760 KiB

Open AccessFeature PaperArticle

Quorum Quenchers from Reynoutria japonica in the Battle against Methicillin-Resistant Staphylococcus aureus (MRSA)

by Maliha Fatima, Arshia Amin, Metab Alharbi, Sundas Ishtiaq, Wasim Sajjad, Faisal Ahmad, Sajjad Ahmad, Faisal Hanif, Muhammad Faheem and Atif Ali Khan Khalil

Molecules 2023, 28(6), 2635; https://doi.org/10.3390/molecules28062635 - 14 Mar 2023

Cited by 7 | Viewed by 2796

Abstract

Over the past decade, methicillin-resistant Staphylococcus aureus (MRSA) has become a major source of biofilm formation and a major contributor to antimicrobial resistance. The genes that govern biofilm formation are regulated by a signaling mechanism called the quorum-sensing system. There is a need [...] Read more.

Over the past decade, methicillin-resistant Staphylococcus aureus (MRSA) has become a major source of biofilm formation and a major contributor to antimicrobial resistance. The genes that govern biofilm formation are regulated by a signaling mechanism called the quorum-sensing system. There is a need for new molecules to treat the infections caused by dangerous pathogens like MRSA. The current study focused on an alternative approach using juglone derivatives from Reynoutria japonica as quorum quenchers. Ten bioactive compounds from this plant, i.e., 2-methoxy-6-acetyl-7-methyljuglone, emodin, emodin 8-o-b glucoside, polydatin, resveratrol, physcion, citreorosein, quercetin, hyperoside, and coumarin were taken as ligands and docked with accessory gene regulator proteins A, B, and C and the signal transduction protein TRAP. The best ligand was selected based on docking score, ADMET properties, and the Lipinski rule. Considering all these parameters, resveratrol displayed all required drug-like properties with a docking score of −8.9 against accessory gene regulator protein C. To further assess the effectiveness of resveratrol, it was compared with the commercially available antibiotic drug penicillin. A comparison of all drug-like characteristics showed that resveratrol was superior to penicillin in many aspects. Penicillin showed a binding affinity of −6.7 while resveratrol had a score of −8.9 during docking. This was followed by molecular dynamic simulations wherein inhibitors in complexes with target proteins showed stability inside the active site during the 100 ns simulations. Structural changes due to ligand movement inside the cavity were measured in the protein targets, but they remained static due to hydrogen bonds. The results showed acceptable pharmacokinetic properties for resveratrol as compared to penicillin. Thus, we concluded that resveratrol has protective effects against Staphylococcus aureus infections and that it suppresses the quorum-sensing ability of this bacterium by targeting its infectious proteins. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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14 pages, 3730 KiB

Open AccessArticle

Prediction of ADMET Properties of Anti-Breast Cancer Compounds Using Three Machine Learning Algorithms

by Xinkang Li, Lijun Tang, Zeying Li, Dian Qiu, Zhuoling Yang and Baoqiong Li

Molecules 2023, 28(5), 2326; https://doi.org/10.3390/molecules28052326 - 2 Mar 2023

Cited by 4 | Viewed by 2753

Abstract

In recent years, machine learning methods have been applied successfully in many fields. In this paper, three machine learning algorithms, including partial least squares-discriminant analysis (PLS-DA), adaptive boosting (AdaBoost), and light gradient boosting machine (LGBM), were applied to establish models for predicting the [...] Read more.

In recent years, machine learning methods have been applied successfully in many fields. In this paper, three machine learning algorithms, including partial least squares-discriminant analysis (PLS-DA), adaptive boosting (AdaBoost), and light gradient boosting machine (LGBM), were applied to establish models for predicting the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET for short) properties, namely Caco-2, CYP3A4, hERG, HOB, MN of anti-breast cancer compounds. To the best of our knowledge, the LGBM algorithm was applied to classify the ADMET property of anti-breast cancer compounds for the first time. We evaluated the established models in the prediction set using accuracy, precision, recall, and F1-score. Compared with the performance of the models established using the three algorithms, the LGBM yielded most satisfactory results (accuracy > 0.87, precision > 0.72, recall > 0.73, and F1-score > 0.73). According to the obtained results, it can be inferred that LGBM can establish reliable models to predict the molecular ADMET properties and provide a useful tool for virtual screening and drug design researchers. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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17 pages, 3300 KiB

Open AccessArticle

A Novel Approach to Develop New and Potent Inhibitors for the Simultaneous Inhibition of Protease and Helicase Activities of HCV NS3/4A Protease: A Computational Approach

by Muhammad Riaz, Ashfaq Ur Rehman, Muhammad Waqas, Asaad Khalid, Ashraf N. Abdalla, Arif Mahmood, Junjian Hu and Abdul Wadood

Molecules 2023, 28(3), 1300; https://doi.org/10.3390/molecules28031300 - 29 Jan 2023

Cited by 1 | Viewed by 2084

Abstract

Infection of hepatitis C (HCV) is a major threat to human health throughout the world. The current therapy program suffers from restricted efficiency and low tolerance, and there is serious demand frr novel medication. NS3/4A protease is observed to be very effective target [...] Read more.

Infection of hepatitis C (HCV) is a major threat to human health throughout the world. The current therapy program suffers from restricted efficiency and low tolerance, and there is serious demand frr novel medication. NS3/4A protease is observed to be very effective target for the treatment of HCV. A data set of the already reported HCV NS3/4A protease inhibitors was first docked into the NS3/4A protease (PDB ID: 4A92A) active sites of both protease and helicase sites for calculating the docking score, binding affinity, binding mode, and solvation energy. Then the data set of these reported inhibitors was used in a computer-based program “RECAP Analyses” implemented in MOE to fragment every molecule in the subset according to simple retrosynthetic analysis rules. The RECAP analysis fragments were then used in another computer-based program “RECAP Synthesis” to randomly recombine and generate synthetically reasonable novel chemical structures. The novel chemical structures thus produced were then docked against HCV NS3/4A. After a thorough validation of all undertaken steps, based on Lipinski’s rule of five, docking score, binding affinity, solvation energy, and Van der Waal’s interactions with HCV NS3/4A, 12 novel chemical structures were identified as inhibitors of HCV NS3/4A. The novel structures thus designed are hoped to play a key role in the development of new effective inhibitors of HCV. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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16 pages, 4644 KiB

Open AccessArticle

Pharmacophore Mapping Combined with dbCICA Reveal New Structural Features for the Development of Novel Ligands Targeting α4β2 and α7 Nicotinic Acetylcholine Receptors

by Victor S. Batista, Adriano Marques Gonçalves and Nailton M. Nascimento-Júnior

Molecules 2022, 27(23), 8236; https://doi.org/10.3390/molecules27238236 - 25 Nov 2022

Cited by 3 | Viewed by 2286

Abstract

The neuronal nicotinic acetylcholine receptors (nAChRs) belong to the ligand-gated ion channel (GLIC) group, presenting a crucial role in several biological processes and neuronal disorders. The α4β2 and α7 nAChRs are the most abundant in the central nervous system (CNS), being involved in [...] Read more.

The neuronal nicotinic acetylcholine receptors (nAChRs) belong to the ligand-gated ion channel (GLIC) group, presenting a crucial role in several biological processes and neuronal disorders. The α4β2 and α7 nAChRs are the most abundant in the central nervous system (CNS), being involved in challenging diseases such as epilepsy, Alzheimer’s disease, schizophrenia, and anxiety disorder, as well as alcohol and nicotine dependencies. In addition, in silico-based strategies may contribute to revealing new insights into drug design and virtual screening to find new drug candidates to treat CNS disorders. In this context, the pharmacophore maps were constructed and validated for the orthosteric sites of α4β2 and α7 nAChRs, through a docking-based Comparative Intermolecular Contacts Analysis (dbCICA). In this sense, bioactive ligands were retrieved from the literature for each receptor. A molecular docking protocol was developed for all ligands in both receptors by using GOLD software, considering GoldScore, ChemScore, ASP, and ChemPLP scoring functions. Output GOLD results were post-processed through dbCICA to identify critical contacts involved in protein-ligand interactions. Moreover, Crossminer software was used to construct a pharmacophoric map based on the most well-behaved ligands and negative contacts from the dbCICA model for each receptor. Both pharmacophore maps were validated by using a ROC curve. The results revealed important features for the ligands, such as the presence of hydrophobic regions, a planar ring, and hydrogen bond donor and acceptor atoms for α4β2. Parallelly, a non-planar ring region was identified for α7. These results can enable fragment-based drug design (FBDD) strategies, such as fragment growing, linking, and merging, allowing an increase in the activity of known fragments. Thus, our results can contribute to a further understanding of structural subunits presenting the potential for key ligand-receptor interactions, favoring the search in molecular databases and the design of novel ligands. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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11 pages, 4404 KiB

Open AccessCommunication

Structural Activity and HAD Inhibition Efficiency of Pelargonidin and Its Glucoside—A Theoretical Approach

by Rangasamy Praveena, Athinarayanan Balasankar, Kanakaraj Aruchamy, Taehwan Oh, Veerababu Polisetti, Subramaniyan Ramasundaram and Kandasamy Anbazhakan

Molecules 2022, 27(22), 8016; https://doi.org/10.3390/molecules27228016 - 18 Nov 2022

Cited by 3 | Viewed by 2002

Abstract

Anthocyanins are an important pharmaceutical ingredient possessing diet regulatory, antioxidant, anticancer, antidiabetic, anti-obesity, antimicrobial, and anti-inflammatory properties. Pelargonidin is an important anthocyanin-based orange-red flavonoid compound used in drugs for treating hypoglycemia, retinopathy, skeletal myopathy, etc. The main sources of pelargonidin are strawberries and [...] Read more.

Anthocyanins are an important pharmaceutical ingredient possessing diet regulatory, antioxidant, anticancer, antidiabetic, anti-obesity, antimicrobial, and anti-inflammatory properties. Pelargonidin is an important anthocyanin-based orange-red flavonoid compound used in drugs for treating hypoglycemia, retinopathy, skeletal myopathy, etc. The main sources of pelargonidin are strawberries and food products with red pigmentation. There is a lack of evidence for supporting its use as an independent supplement. In the present study, pelargonidin and pelargonidin-3-O-glucoside are studied for their structural properties using quantum chemical calculations based on density functional theory. The results confirmed that the parent compound and its glycosylated derivative acted as good electron donors. Electrostatic potential, frontier molecular orbitals, and molecular descriptor analyses also substantiated their electron donating properties. Furthermore, based on the probability, a target prediction was performed for pelargonidin and pelargonidin-3-O-glucoside. Hydroxyacyl-coenzyme A dehydrogenase was chosen as an enzymatic target of interest, since the presence work focuses on glucuronidated compounds and their efficacy over diabetes. Possible interactions between these compounds and a target with nominable binding energies were also evaluated. Further, the structural stability of these two compounds were also analyzed using a molecular dynamics simulation. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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16 pages, 4204 KiB

Open AccessArticle

Molecular Docking and Molecular Dynamics Studies Reveal Secretory Proteins as Novel Targets of Temozolomide in Glioblastoma Multiforme

by Sumera, Farha Anwer, Maaz Waseem, Areeba Fatima, Nishat Malik, Amjad Ali and Saadia Zahid

Molecules 2022, 27(21), 7198; https://doi.org/10.3390/molecules27217198 - 24 Oct 2022

Cited by 31 | Viewed by 3872

Abstract

Glioblastoma multiforme (GBM) is a tumor of glial origin and is the most malignant, aggressive and prevalent type, with the highest mortality rate in adult brain cancer. Surgical resection of the tumor followed by Temozolomide (TMZ) therapy is currently available, but the development [...] Read more.

Glioblastoma multiforme (GBM) is a tumor of glial origin and is the most malignant, aggressive and prevalent type, with the highest mortality rate in adult brain cancer. Surgical resection of the tumor followed by Temozolomide (TMZ) therapy is currently available, but the development of resistance to TMZ is a common limiting factor in effective treatment. The present study investigated the potential interactions of TMZ with several secretory proteins involved in various molecular and cellular processes in GBM. Automated docking studies were performed using AutoDock 4.2, which showed an encouraging binding affinity of TMZ towards all targeted proteins, with the strongest interaction and binding affinity with GDF1 and SLIT1, followed by NPTX1, CREG2 and SERPINI, among the selected proteins. Molecular dynamics (MD) simulations of protein–ligand complexes were performed via CABS-flex V2.0 and the iMOD server to evaluate the root-mean-square fluctuations (RMSFs) and measure protein stability, respectively. The results showed that docked models were more flexible and stable with TMZ, suggesting that it may be able to target putative proteins implicated in gliomagenesis that may impact radioresistance. However, additional in vitro and in vivo investigations can ascertain the potential of the selected proteins to serve as novel targets for TMZ for GBM treatment. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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15 pages, 4211 KiB

Open AccessArticle

From Myricetin to the Discovery of Novel Natural Human ENPP1 Inhibitors: A Virtual Screening, Molecular Docking, Molecular Dynamics Simulation, and MM/GBSA Study

by Shaohan Song and Zhiyu Shao

Molecules 2022, 27(19), 6175; https://doi.org/10.3390/molecules27196175 - 21 Sep 2022

Cited by 7 | Viewed by 2528

Abstract

It was recently revealed that naturally occurring myricetin can inhibit ectonucleotidase ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), which, in turn, can treat ischemic cardiac injury. However, due to myricetin’s poor druggability, its further developments are relatively limited, which necessitates the discovery of novel ENPP1-inhibiting myricetin [...] Read more.

It was recently revealed that naturally occurring myricetin can inhibit ectonucleotidase ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), which, in turn, can treat ischemic cardiac injury. However, due to myricetin’s poor druggability, its further developments are relatively limited, which necessitates the discovery of novel ENPP1-inhibiting myricetin analogs as alternatives. In this study, the binding model of myricetin with ENPP1 was elucidated by molecular docking and molecular dynamics studies. Subsequently, virtual screening on the self-developed flavonoid natural product database (FNPD), led to the identification of two flavonoid glycosides (Cas No: 1397173-50-0 and 1169835-58-8), as potential ENPP1 inhibitors. Docking scores and MM/GBSA binding energies predicted that they might have higher inhibitory effects than myricetin. This study provides a strong foundation for the future development of ischemic cardiac injury drugs. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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12 pages, 3198 KiB

Open AccessArticle

Ligand and Structure-Based Virtual Screening in Combination, to Evaluate Small Organic Molecules as Inhibitors for the XIAP Anti-Apoptotic Protein: The Xanthohumol Hypothesis

by Angeliki Mavra, Christos C. Petrou and Manos C. Vlasiou

Molecules 2022, 27(15), 4825; https://doi.org/10.3390/molecules27154825 - 28 Jul 2022

Cited by 5 | Viewed by 2526

Abstract

Herein, we propose two chalcone molecules, (E)-1-(4-methoxyphenyl)-3-(p-tolyl) prop-2-en-1-one and (E)-3-(4-hydroxyphenyl)-1-(2,4,6-trihydroxyphenyl) prop-2-en-1-one, based on the anticancer bioactive molecule Xanthohumol, which are suitable for further in vitro and in vivo studies. Their ability to create stable complexes with the antiapoptotic X-linked IAP (XIAP) protein makes [...] Read more.

Herein, we propose two chalcone molecules, (E)-1-(4-methoxyphenyl)-3-(p-tolyl) prop-2-en-1-one and (E)-3-(4-hydroxyphenyl)-1-(2,4,6-trihydroxyphenyl) prop-2-en-1-one, based on the anticancer bioactive molecule Xanthohumol, which are suitable for further in vitro and in vivo studies. Their ability to create stable complexes with the antiapoptotic X-linked IAP (XIAP) protein makes them promising anticancer agents. The calculations were based on ligand-based and structure-based virtual screening combined with the pharmacophore build. Additionally, the structures passed Lipinski’s rule for drug use, and their reactivity was confirmed using density functional theory studies. ADMET studies were also performed to reveal the pharmacokinetic potential of the compounds. The candidates were chosen from 10,639,400 compounds, and the docking protocols were evaluated using molecular dynamics simulations. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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12 pages, 3023 KiB

Open AccessArticle

Mechanistic Insights into the Mechanism of Inhibitor Selectivity toward the Dark Kinase STK17B against Its High Homology STK17A

by Chang Liu, Yichi Zhang, Yuqing Zhang, Zonghan Liu, Feifei Mao and Zongtao Chai

Molecules 2022, 27(14), 4655; https://doi.org/10.3390/molecules27144655 - 21 Jul 2022

Cited by 4 | Viewed by 1987

Abstract

As a member of the death-associated protein kinase (DAPK) family, STK17B plays an important role in the regulation of cellular apoptosis and has been considered as a promising drug target for hepatocellular carcinoma. However, the highly conserved ATP-binding site of protein kinases represents [...] Read more.

As a member of the death-associated protein kinase (DAPK) family, STK17B plays an important role in the regulation of cellular apoptosis and has been considered as a promising drug target for hepatocellular carcinoma. However, the highly conserved ATP-binding site of protein kinases represents a challenge to design selective inhibitors for a specific DAPK isoform. In this study, molecular docking, multiple large-scale molecular dynamics (MD) simulations, and binding free energy calculations were performed to decipher the molecular mechanism of the binding selectivity of PKIS43 toward STK17B against its high homology STK17A. MD simulations revealed that STK17A underwent a significant conformational arrangement of the activation loop compared to STK17B. The binding free energy predictions suggested that the driving force to control the binding selectivity of PKIS43 was derived from the difference in the protein–ligand electrostatic interactions. Furthermore, the per-residue free energy decomposition unveiled that the energy contribution from Arg41 at the phosphate-binding loop of STK17B was the determinant factor responsible for the binding specificity of PKIS43. This study may provide useful information for the rational design of novel and potent selective inhibitors toward STK17B. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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15 pages, 5069 KiB

Open AccessArticle

Insights into the Allosteric Effect of SENP1 Q597A Mutation on the Hydrolytic Reaction of SUMO1 via an Integrated Computational Study

by Mingfei Ji, Zongtao Chai, Jie Chen, Gang Li, Qiang Li, Miao Li, Yelei Ding, Shaoyong Lu, Guanqun Ju and Jianquan Hou

Molecules 2022, 27(13), 4149; https://doi.org/10.3390/molecules27134149 - 28 Jun 2022

Cited by 4 | Viewed by 1999

Abstract

Small ubiquitin-related modifier (SUMO)-specific protease 1 (SENP1) is a cysteine protease that catalyzes the cleavage of the C-terminus of SUMO1 for the processing of SUMO precursors and deSUMOylation of target proteins. SENP1 is considered to be a promising target for the treatment of [...] Read more.

Small ubiquitin-related modifier (SUMO)-specific protease 1 (SENP1) is a cysteine protease that catalyzes the cleavage of the C-terminus of SUMO1 for the processing of SUMO precursors and deSUMOylation of target proteins. SENP1 is considered to be a promising target for the treatment of hepatocellular carcinoma (HCC) and prostate cancer. SENP1 Gln597 is located at the unstructured loop connecting the helices α4 to α5. The Q597A mutation of SENP1 allosterically disrupts the hydrolytic reaction of SUMO1 through an unknown mechanism. Here, extensive multiple replicates of microsecond molecular dynamics (MD) simulations, coupled with principal component analysis, dynamic cross-correlation analysis, community network analysis, and binding free energy calculations, were performed to elucidate the detailed mechanism. Our MD simulations showed that the Q597A mutation induced marked dynamic conformational changes in SENP1, especially in the unstructured loop connecting the helices α4 to α5 which the mutation site occupies. Moreover, the Q597A mutation caused conformational changes to catalytic Cys603 and His533 at the active site, which might impair the catalytic activity of SENP1 in processing SUMO1. Moreover, binding free energy calculations revealed that the Q597A mutation had a minor effect on the binding affinity of SUMO1 to SENP1. Together, these results may broaden our understanding of the allosteric modulation of the SENP1−SUMO1 complex. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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17 pages, 12447 KiB

Open AccessArticle

Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus

by Wissal Liman, Mehdi Oubahmane, Ismail Hdoufane, Imane Bjij, Didier Villemin, Rachid Daoud, Driss Cherqaoui and Achraf El Allali

Molecules 2022, 27(9), 2729; https://doi.org/10.3390/molecules27092729 - 23 Apr 2022

Cited by 17 | Viewed by 2816

Abstract

Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the [...] Read more.

Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure–activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R² = 0.991 and Q² = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R² = 0.915 and Q² = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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12 pages, 2387 KiB

Open AccessArticle

Computational Strategy for Minimizing Mycotoxins in Cereal Crops: Assessment of the Biological Activity of Compounds Resulting from Virtual Screening

by Vessela Atanasova, Emmanuel Bresso, Bernard Maigret, Natalia Florencio Martins and Florence Richard-Forget

Molecules 2022, 27(8), 2582; https://doi.org/10.3390/molecules27082582 - 16 Apr 2022

Cited by 2 | Viewed by 2430

Abstract

Cereal crops are frequently affected by toxigenic Fusarium species, among which the most common and worrying in Europe are Fusarium graminearum and Fusarium culmorum. These species are the causal agents of grain contamination with type B trichothecene (TCTB) mycotoxins. To help reduce [...] Read more.

Cereal crops are frequently affected by toxigenic Fusarium species, among which the most common and worrying in Europe are Fusarium graminearum and Fusarium culmorum. These species are the causal agents of grain contamination with type B trichothecene (TCTB) mycotoxins. To help reduce the use of synthetic fungicides while guaranteeing low mycotoxin levels, there is an urgent need to develop new, efficient and environmentally-friendly plant protection solutions. Previously, F. graminearum proteins that could serve as putative targets to block the fungal spread and toxin production were identified and a virtual screening undertaken. Here, two selected compounds, M1 and M2, predicted, respectively, as the top compounds acting on the trichodiene synthase, a key enzyme of TCTB biosynthesis, and the 24-sterol-C-methyltransferase, a protein involved in ergosterol biosynthesis, were submitted for biological tests. Corroborating in silico predictions, M1 was shown to significantly inhibit TCTB yield by a panel of strains. Results were less obvious with M2 that induced only a slight reduction in fungal biomass. To go further, seven M1 analogs were assessed, which allowed evidencing of the physicochemical properties crucial for the anti-mycotoxin activity. Altogether, our results provide the first evidence of the promising potential of computational approaches to discover new anti-mycotoxin solutions Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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25 pages, 9081 KiB

Open AccessArticle

Computational Identification of Druggable Bioactive Compounds from Catharanthus roseus and Avicennia marina against Colorectal Cancer by Targeting Thymidylate Synthase

by Md Rashedul Islam, Md Abdul Awal, Ahmed Khames, Mohammad A. S. Abourehab, Abdus Samad, Walid M. I. Hassan, Rahat Alam, Osman I. Osman, Suza Mohammad Nur, Mohammad Habibur Rahman Molla, Abdulrasheed O. Abdulrahman, Sultana Rajia, Foysal Ahammad, Md Nazmul Hasan, Ishtiaq Qadri and Bonglee Kim

Molecules 2022, 27(7), 2089; https://doi.org/10.3390/molecules27072089 - 24 Mar 2022

Cited by 26 | Viewed by 5697

Abstract

Colorectal cancer (CRC) is the second most common cause of death worldwide, affecting approximately 1.9 million individuals in 2020. Therapeutics of the disease are not yet available and discovering a novel anticancer drug candidate against the disease is an urgent need. Thymidylate synthase [...] Read more.

Colorectal cancer (CRC) is the second most common cause of death worldwide, affecting approximately 1.9 million individuals in 2020. Therapeutics of the disease are not yet available and discovering a novel anticancer drug candidate against the disease is an urgent need. Thymidylate synthase (TS) is an important enzyme and prime precursor for DNA biosynthesis that catalyzes the methylation of deoxyuridine monophosphate (dUMP) to deoxythymidine monophosphate (dTMP) that has emerged as a novel drug target against the disease. Elevated expression of TS in proliferating cells promotes oncogenesis as well as CRC. Therefore, this study aimed to identify potential natural anticancer agents that can inhibit the activity of the TS protein, subsequently blocking the progression of colorectal cancer. Initially, molecular docking was implied on 63 natural compounds identified from Catharanthus roseus and Avicennia marina to evaluate their binding affinity to the desired protein. Subsequently, molecular dynamics (MD) simulation, ADME (Absorption, Distribution, Metabolism, and Excretion), toxicity, and quantum chemical-based DFT (density-functional theory) approaches were applied to evaluate the efficacy of the selected compounds. Molecular docking analysis initially identified four compounds (PubChem CID: 5281349, CID: 102004710, CID: 11969465, CID: 198912) that have better binding affinity to the target protein. The ADME and toxicity properties indicated good pharmacokinetics (PK) and toxicity ability of the selected compounds. Additionally, the quantum chemical calculation of the selected molecules found low chemical reactivity indicating the bioactivity of the drug candidate. The global descriptor and HOMO-LUMO energy gap values indicated a satisfactory and remarkable profile of the selected molecules. Furthermore, MD simulations of the compounds identified better binding stability of the compounds to the desired protein. To sum up, the phytoconstituents from two plants showed better anticancer activity against TS protein that can be further developed as an anti-CRC drug. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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16 pages, 45499 KiB

Open AccessArticle

Phytochemical Compound Screening to Identify Novel Small Molecules against Dengue Virus: A Docking and Dynamics Study

by Mst. Sharmin Sultana Shimu, Shafi Mahmud, Trina Ekwati Tallei, Saad Ahmed Sami, Ahmad Akroman Adam, Uzzal Kumar Acharjee, Gobindo Kumar Paul, Talha Bin Emran, Shahriar Zaman, Md. Salah Uddin, Md. Abu Saleh, Sultan Alshehri, Mohammed M Ghoneim, Maha Alruwali, Ahmad J. Obaidullah, Nabilah Rahman Jui, Junghwan Kim and Bonglee Kim

Molecules 2022, 27(3), 653; https://doi.org/10.3390/molecules27030653 - 20 Jan 2022

Cited by 18 | Viewed by 4370

Abstract

The spread of the Dengue virus over the world, as well as multiple outbreaks of different serotypes, has resulted in a large number of deaths and a medical emergency, as no viable medications to treat Dengue virus patients have yet been found. In [...] Read more.

The spread of the Dengue virus over the world, as well as multiple outbreaks of different serotypes, has resulted in a large number of deaths and a medical emergency, as no viable medications to treat Dengue virus patients have yet been found. In this paper, we provide an in silico virtual screening and molecular dynamics-based analysis to uncover efficient Dengue infection inhibitors. Based on a Google search and literature mining, a large phytochemical library was generated and employed as ligand molecules. In this investigation, the protein target NS2B/NS3 from Dengue was employed, and around 27 compounds were evaluated in a docking study. Phellodendroside (−63 kcal/mole), quercimeritrin (−59.5 kcal/mole), and quercetin-7-O-rutinoside (−54.1 kcal/mole) were chosen based on their binding free energy in MM-GBSA. The tested compounds generated numerous interactions at Lys74, Asn152, and Gln167 residues in the active regions of NS2B/NS3, which is needed for the protein’s inhibition. As a result, the stable mode of docked complexes is defined by various descriptors from molecular dynamics simulations, such as RMSD, SASA, Rg, RMSF, and hydrogen bond. The pharmacological properties of the compounds were also investigated, and no toxicity was found in computational ADMET properties calculations. As a result, this computational analysis may aid fellow researchers in developing innovative Dengue virus inhibitors. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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20 pages, 12179 KiB

Open AccessArticle

Insulin Complexation with Cyclodextrins—A Molecular Modeling Approach

by Pálma Bucur, Ibolya Fülöp and Emese Sipos

Molecules 2022, 27(2), 465; https://doi.org/10.3390/molecules27020465 - 11 Jan 2022

Cited by 12 | Viewed by 3407

Abstract

Around 5% of the population of the world is affected with the disease called diabetes mellitus. The main medication of the diabetes is the insulin; the active form is the insulin monomer, which is an instable molecule, because the long storage time, or [...] Read more.

Around 5% of the population of the world is affected with the disease called diabetes mellitus. The main medication of the diabetes is the insulin; the active form is the insulin monomer, which is an instable molecule, because the long storage time, or the high temperature, can cause the monomer insulin to adapt an alternative fold, rich in β-sheets, which is pharmaceutically inactive. The aim of this study is to form different insulin complexes with all the cyclodextrin used for pharmaceutical excipients (native cyclodextrin, methyl, hydroxyethyl, hydroxypropyl and sulfobutylether substituted β-cyclodextrin), in silico condition, with the AutoDock molecular modeling program, to determine the best type of cyclodextrin or cyclodextrin derivate to form a complex with an insulin monomer, to predict the molar ratio, the conformation of the complex, and the intermolecular hydrogen bonds formed between the cyclodextrin and the insulin. From the results calculated by the AutoDock program it can be predicted that insulin can make a stable complex with 5–7 molecules of hydroxypropyl-β-cyclodextrin or sulfobutylether-β-cyclodextrin, and by forming a complex potentially can prevent or delay the amyloid fibrillation of the insulin and increase the stability of the molecule. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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18 pages, 1763 KiB

Open AccessArticle

Evaluation of Virtual Screening Strategies for the Identification of γ-Secretase Inhibitors and Modulators

by Alicia Ioppolo, Melissa Eccles, David Groth, Giuseppe Verdile and Mark Agostino

Molecules 2022, 27(1), 176; https://doi.org/10.3390/molecules27010176 - 28 Dec 2021

Cited by 3 | Viewed by 3313

Abstract

γ-Secretase is an intramembrane aspartyl protease that is important in regulating normal cell physiology via cleavage of over 100 transmembrane proteins, including Amyloid Precursor Protein (APP) and Notch family receptors. However, aberrant proteolysis of substrates has implications in the progression of disease pathologies, [...] Read more.

γ-Secretase is an intramembrane aspartyl protease that is important in regulating normal cell physiology via cleavage of over 100 transmembrane proteins, including Amyloid Precursor Protein (APP) and Notch family receptors. However, aberrant proteolysis of substrates has implications in the progression of disease pathologies, including Alzheimer’s disease (AD), cancers, and skin disorders. While several γ-secretase inhibitors have been identified, there has been toxicity observed in clinical trials associated with non-selective enzyme inhibition. To address this, γ-secretase modulators have been identified and pursued as more selective agents. Recent structural evidence has provided an insight into how γ-secretase inhibitors and modulators are recognized by γ-secretase, providing a platform for rational drug design targeting this protease. In this study, docking- and pharmacophore-based screening approaches were evaluated for their ability to identify, from libraries of known inhibitors and modulators with decoys with similar physicochemical properties, γ-secretase inhibitors and modulators. Using these libraries, we defined strategies for identifying both γ-secretase inhibitors and modulators incorporating an initial pharmacophore-based screen followed by a docking-based screen, with each strategy employing distinct γ-secretase structures. Furthermore, known γ-secretase inhibitors and modulators were able to be identified from an external set of bioactive molecules following application of the derived screening strategies. The approaches described herein will inform the discovery of novel small molecules targeting γ-secretase. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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10 pages, 2244 KiB

Open AccessArticle

Drug Repurposing for Influenza Virus Polymerase Acidic (PA) Endonuclease Inhibitor

by Xin Meng and Ye Wang

Molecules 2021, 26(23), 7326; https://doi.org/10.3390/molecules26237326 - 2 Dec 2021

Cited by 4 | Viewed by 2815

Abstract

Drug repurposing can quickly and effectively identify novel drug repurposing opportunities. The PA endonuclease catalytic site has recently become regarded as an attractive target for the screening of anti-influenza drugs. PA N-terminal (PA_N) inhibitor can inhibit the entire PA endonuclease activity. [...] Read more.

Drug repurposing can quickly and effectively identify novel drug repurposing opportunities. The PA endonuclease catalytic site has recently become regarded as an attractive target for the screening of anti-influenza drugs. PA N-terminal (PA_N) inhibitor can inhibit the entire PA endonuclease activity. In this study, we screened the effectivity of PA_N inhibitors from the FDA database through in silico methods and in vitro experiments. PA_N and mutant PA_N-I38T were chosen as virtual screening targets for overcoming drug resistance. Gel-based PA endonuclease analysis determined that the drug lifitegrast can effectively inhibit PA_N and PA_N-I38T, when the IC₅₀ is 32.82 ± 1.34 μM and 26.81 ± 1.2 μM, respectively. Molecular docking calculation showed that lifitegrast interacted with the residues around PA or PA-I38 T’s active site, occupying the catalytic site pocket. Both PA_N/PA_N-I38T and lifitegrast can acquire good equilibrium in 100 ns molecular dynamic simulation. Because of these properties, lifitegrast, which can effectively inhibit PA endonuclease activity, was screened through in silico and in vitro research. This new research will be of significance in developing more effective and selective drugs for anti-influenza therapy. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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Review

Jump to: Research

23 pages, 2667 KiB

Open AccessReview

Computer-Aided Drug Design Boosts RAS Inhibitor Discovery

by Ge Wang, Yuhao Bai, Jiarui Cui, Zirui Zong, Yuan Gao and Zhen Zheng

Molecules 2022, 27(17), 5710; https://doi.org/10.3390/molecules27175710 - 5 Sep 2022

Cited by 13 | Viewed by 4325

Abstract

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of [...] Read more.

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design. Full article

(This article belongs to the Special Issue Computational Strategy for Drug Design)

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