Current computer-aided drug design最新文献

Background: The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing globally, impacting individuals in Western nations and rapid growing in Asian countries due to sedentary lifestyles; thus, NAFLD has emerged as a significant worldwide health concern. Presently, lifestyle changes represent the primary approach to managing NAFLD.

Methods: This research aimed to identify the potential drug targets for treating NAFLD through comprehensive in silico computational analysis. These include the prediction of the three-dimensional structure of the protein, the prediction of inhibitors by PubChem and ZINC, molecular docking by Autodcok, pharmacophore modeling, molecular dynamics simulation by the OPLS_2005 force field, and the orthorhombic box solvent model Intermolecular Interaction Potential 3 Points Transferable to the selected compound. The toxicity of the lead compounds was analyzed through AdmetSAR software.

Results: The protein associated with the PNPLA3 gene, whose overall three-dimensional structure was 95% accurate, were retrieved following inhibitor selection via PubChem and ZINC. Among the selected inhibitors and docked compounds with ID 10033935 (ellagitannin) showed a minimum E-Score of -17.266. In docking and pharmacophore modeling the compound ellagitannin shows promise as a potential drug candidate. Moreover, the molecular dynamics and structural stability of the protein-ligand complex were evaluated with several metrics such as as root mean square fluctuation and root mean square deviation and resulted in the stability not only of PNPLA3-10033935 (ellagitannin) but also of compound PNPLA3-71448940 and PNPLA3-5748394 complexed proteins at 400 ns with very slight variation.

Conclusion: Overall, ellagitannin was identified as the best druggable target with the best therapeutics profile. The findings of our study can pave the way for the development of a new drug against NALFD.

Background: Blocking Kv 1.2 and Kv 1.3 potassium channels using scorpion venom- derived toxins holds potential therapeutic value. These channels are implicated in autoimmune diseases such as neurodegenerative diseases, multiple sclerosis, rheumatoid arthritis, and type 1 diabetes.

Objective: The present work aims at the discovery and in silico activity analysis of potassium channel blockers (KTxs) from the cDNA library derived from the venom gland of Iranian scorpion Hemiscorpius lepturus (H. lepturus).

Methods: The sequence regarding potassium channel blockers were extracted based on Gene Ontology for H. lepturus venom gland. Homology analyses, superfamily, family, and evolutionary signatures of H. lepturus KTxs (H.L KTxs) were determined by using BLASTP, COBALT, PROSITE, and InterPro servers. The predicted 3D structures of H.L KTxs were superimposed against their homologs to predict structure activity relationship. Molecular docking analysis was also performed to predict the binding affinity of H.L KTxs to Kv 1.2 and Kv 1.3 channels. Finally, the toxicity was predicted.

Results: Seven H.L KTxs, designated as Leptukalin, were extracted from the cDNA library of H. lepturus venom gland. Homology analyses proved that they can act as potassium channel blockers and they belong to the superfamily and family of Scorpion Toxin-like and Short-chain scorpion toxins, respectively. Structural alignment results confirmed the activity of H.L KTxs. Binding affinity of all H.L KTxs to Kv 1.2 and Kv 1.3 channels ranged from -4.4 to -5.5 and -4 to -5.7 Kcal/mol, respectively. In silico toxicity assay showed that Leptukalin 3, Leptukalin 5, and Leptukalin 7 were non-toxic.

Conclusion: Three non-toxic KTxs, Leptukalin 3, 5, and 7, were successfully discovered from the cDNA library of H. lepturus venom gland. Gathering all data together, the discovered peptides are promising potassium channel blockers. Accordingly, Leptukalin 3, 5, and 7 could be suggested for complementary in vitro studies and mouse model of autoimmune diseases.

Introduction: Computational methods are crucial for efficient and cost-effective drug toxicity prediction. Unfortunately, the data used for prediction is often imbalanced, resulting in biased models that favor the majority class. This paper proposes an approach to apply a hybrid class balancing technique and evaluate its performance on computational models for toxicity prediction in Tox21 datasets.

Methods: The process begins by converting chemical compound data structures (SMILES strings) from various bioassay datasets into molecular descriptors that can be processed by algorithms. Subsequently, Undersampling and Oversampling techniques are applied in two different schemes on the training data. In the first scheme (Individual), only one balancing technique (Oversampling or Undersampling) is used. In the second scheme (Hybrid), the training data is divided according to a ratio (e.g., 90-10), applying a different balancing technique to each proportion. We considered eight resampling techniques (four Oversampling and four Undersampling), six molecular descriptors (based on MACCS, ECFP, and Mordred), and five classification models (KNN, MLP, RF, XGB and SVM) over 10 bioassay datasets to determine the configurations that yield the best performance.

Results: We defined three testing scenarios: without balancing techniques (baseline), Individual, and Hybrid. We found that using the ENN technique in the MACCS-MLP combination resulted in a 10.01% improvement in performance. The increase for ECFP6-2048 was 16.47% after incorporating a combination of the SMOTE (10%) and RUS (90%) techniques. Meanwhile, using the same combination of techniques, MORDRED-XGB showed the most significant increase in performance, achieving a 22.62% improvement.

Conclusion: Integrating any of the class balancing schemes resulted in a minimum of 10.01% improvement in prediction performance compared to the best baseline configuration. In this study, Undersampling techniques were more appropriate due to the significant overlap among samples. By eliminating specific samples from the predominant class that are close to the minority class, this overlap is greatly reduced.

Background: Anti-tubercular drug discovery is a critical research area aimed at addressing the global health burden imposed by Mycobacterium tuberculosis. Nowadays, computational techniques have increased the likelihood of drug development compared to traditional, labor-intensive, and time-consuming drug design approaches. The pivotal goal of drug design is to identify compounds capable of selectively targeting protein, thereby disrupting its enzymatic activity. InhA, or NADH-dependent enoyl-acyl carrier protein reductase, stands at the forefront of targeted approaches in the battle against TB. Isatin derivatives have garnered interest for their diverse pharmacological activities.

Objective: To identify novel isatin derivatives that could serve as potential chemical templates for anti-TB drug discovery by targeting InhA.

Methods: The present work utilized various computational approaches, including molecular docking, binding free energy calculations, and conformational alignment studies to investigate the binding mode and interactions of carefully selected dataset of 88 isatin derivatives within InhA active site. Study also employed MD simulations of the most promising molecule to check the stability of the protein-ligand complex and in-silico ADMET profiling of the top compounds to predict their pharmacokinetic and toxicity properties.

Results: Results provided insights into the structural features contributing to InhA inhibition, assessing overall drug-like characteristics of isatin derivatives and identified compound 48 (BA= -10.4 kcal mol-1 ) with potential for further optimization. MD simulation analysis revealed that compound 48 binds firmly within the InhA protein, exhibiting minimal conformational fluctuations and enhanced stability.

Conclusion: Considering the aforementioned, isatin derivatives represents a novel framework for creating targeted InhA inhibitors during anti-TB therapy. However, experimental validations and in-depth analyses are crucial to confirm efficacy and safety of these derivatives as potential InhA inhibitors for TB treatment.

Background: Valproic acid (VPA)-induced hepatotoxicity is among the most common and severe adverse drug reactions, limiting its clinical application. Recent studies have suggested that activating the farnesoid X receptor (FXR) could be a promising therapeutic approach to alleviate VPA-induced hepatotoxicity; however, related research remains limited.

Objective: This study aims to comprehensively investigate the mechanisms underlying FXR activation by obeticholic acid (OCA) for the treatment of VPA-induced hepatotoxicity.

Methods: Network pharmacology was performed to identify potential targets and pathways underlying the amelioration of VPA-induced hepatotoxicity by OCA. The identified pathways were validated through GEO data analysis, and the affinities between OCA and potential key targets were predicted using molecular docking as well as molecular dynamics simulations.

Results: A total of 462 targets associated with VPA-induced hepatotoxicity and 288 targets of OCA were identified, with 81 shared targets. KEGG pathway and GO enrichment analysis indicated that the effect of OCA on VPA-induced hepatotoxicity primarily involved lipid metabolism, as well as oxidative stress and inflammation. The results from GEO data analysis, molecular docking, and molecular dynamics simulations revealed a close association between bile secretion, the PPAR signaling pathway, and the treatment of VPA-induced hepatotoxicity by OCA.

Conclusion: Our findings suggest that OCA exhibits potential therapeutic efficacy against VPAinduced hepatotoxicity through multiple targets and pathways, thereby highlighting the therapeutic potential of FXR as a target for treating VPA-induced hepatotoxicity.

Background: Si-Ni-San (SNS) is the formula prescription of Traditional Chinese Medicine (TCM) with anti-depression properties, but its underlying mechanisms remain unclear.

Objective: This study provides novel approaches for the study of Traditional Chinese Medicine (TCM) and offers new opportunities for exploring the pharmacological properties of SNS.

Methods: The ingredients in SNS implicated in the treatment of depression were identified and studied using network pharmacology. SwissTargetPrediction and molecular docking were used to study the interaction of SNS ingredients and their targets. The protective effect of these ingredients and their cocktail in rat pheochromocytoma cells (PC12) exposed to corticosterone (Cor) were evaluated using the CCK-8 assay, Hoechst 33342 staining, 2',7'-dichlorodihydro fluorescein diacetate (H2DCFDA) staining, 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay, and in-cell Western analysis.

Results: The network pharmacology study showed that the HIF-1 signaling pathway was the most crucial pathway implicated in the anti-depressive property of SNS. MAPK1 (ERK2), MAPK3 (ERK1), AKT1, VEGFA, STAT3, and EGF were identified as hub target proteins in the HIF-1 signaling pathway. Quercetin, naringenin, licochalcone A, and kaempferol from SNS, which targeted the six proteins mentioned above, were used to create a cocktail. This cocktail exerted protective properties, decreased the oxidative stress in PC12 exposed to Cor, and successfully regulated the expressions of AKT1, p-AKT1, ERK1, ERK2, p-ERK1/2, STAT3, p- STAT3, and VEGFA induced by Cor exposure. The SwissTargetPrediction and molecular docking study showed that the cocktail may regulate the HIF-1 signaling pathway by directly binding with AKT1 and MAPK1.

Conclusion: The cocktail from SNS comprised of quercetin, naringenin, licochalcone A, and kaempferol exerts anti-depression potentiality by modulating the HIF-1 signaling pathway via direct interactions with AKT1 and MAPK1.

Introduction: It has been reported that the extension of conjugation in chalcone scaffolds considerably enhanced the potency, selectivity, reversibility, and competitive mode of MAO-B inhibition. In this study, using the experimental results of IC50 values of fifteen halogenated conjugated dienone derivatives (MK1-MK15) against MAO-B, we developed a 3DQSAR model.

Methods: Further, we created a 3D pharmacophore model in active compounds in the series. The built model selected three variables (G2U, RDF115m, RDF155m) among the 653 AlvaDesc molecular descriptors, with a r2 value of 0.87 and a Q2 cv for cross-validation equal to 0.82. The three variables were mostly associated with the direction of symmetry and the likelihood of discovering massive atoms at great distances. The evaluated molecules exhibited a good correlation between experimental and predicted data, indicating that the IC50 value of the structure MK2 was related to the interatomic distances of 15.5 Å between bromine and chloro substituents. Furthermore, the molecules in the series with the highest activity were those with enhanced second component symmetry directional index from the 3D representation, which included the structures MK5 and MK6.

Result: Additionally, a pharmacophore hypothesis was developed and validated using the decoy Schrodinger dataset, with an ROC score of 0.87 and an HHRR 1 fitness score that ranged from 2.783 to 3.00. The MK series exhibited a significant blood-brain barrier (BBB) permeability, according to exploratory analyses and in silico projections, and almost all analogues were expected to have strong BBB permeability.

Conclusion: Further DFT research revealed that electrostatics were important in the interactions with MAO-B.

Introduction: Arthritis is the cause of morbidity associated with Chikungunya virus (CHIKV) infection. It persists even after the virus has been cleared from the body. MBZM-NIBT was earlier shown to inhibit (CHIKV) infection in vitro and in vivo.

Objective: The objective of this study is to determine the ability of MBZM-N-IBT to manage arthritis independent of CHIKV infection.

Method: The acute toxicity of MBZM-N-IBT was determined to find a permissible oral dose. Effects against inflammation and arthritis were determined in relevant preclinical models. Network pharmacology was used to propose possible modes of action.

Result: It showed no acute toxicity orally, with an estimated LD50 of more than 5000 mg/kg in rats. It significantly reduced inflammation. Its effect against Complete Freund's Adjuvant (CFA) induced arthritis was comparable to that of Diclofenac sodium. Network pharmacology analysis revealed that MBZM-N-IBT can potentially interfere with multiple targets and pathways. MMP12 and CTSD were found to be the most probable hub targets of MBZM-N-IBT for its effect against arthritis.

Conclusion: In conclusion, MBZM-N-IBT is safe at 50 mg/kg and can manage arthritis independent of CHIKV infection through modulation of multiple pathways and arthritis-associated targets.

Introduction: The prevalence of breast cancer presents a substantial global health concern, underscoring the ongoing need for the development of inventive therapeutic remedies.

Methods: In this investigation, an array of novel indazole-pyridine hybrids (5a-h) have been designed and synthesized to assess their potential as candidates for treating breast cancer. Subsequently, we have conducted biological evaluations to determine their cytotoxic effects on the human MCF-7 breast cancer cell line. Furthermore, in silico analysis was conducted to estimate the inhibition potential of the compounds against TrkA (Tropomyosin receptor kinase A), a specific molecular target associated with breast cancer, through molecular docking. In silico physicochemical and pharmacokinetic predictions were made to assess the compounds' drug-like properties.

Results: Compound 5a emerged as the most active compound among the others with GI50 < 10 μg/ml. Besides, compound 5a showed high binding energy (BE -10.7 kcal/mol) against TrkA and was stabilized within the TrkA binding pocket through hydrophobic, H-bonding, and van der Waals interactions. In silico physicochemical and pharmacokinetic prediction studies indicated that compound 5a obeyed both Lipinski's and Veber's rule and displayed a versatile pharmacokinetic profile, implying compound 5a to appear as a viable candidate and that it could be further refined to develop therapeutic agents for potentially treating breast cancer.

Conclusion: This study offers a promising direction for the advancement of innovative breast cancer treatments, highlighting the effectiveness of indazole-pyridine hybrids as potential anticancer agents. Further optimization and preclinical development are necessary to advance these compounds to clinical trials.