Structure-Based Pharmacophore Modeling, Virtual Screening, Molecular Docking, ADMET, and Molecular Dynamics (MD) Simulation of Potential Inhibitors of PD-L1 from the Library of Marine Natural Products

In silico approaches to identify novel anti-diabetic type 2 agents against dipeptidyl peptidase IV from isoxazole derivatives of usnic acid

Diabetes mellitus (DM) is a serious worldwide health issue in the twenty-first century. Additionally, DM, a metabolic endocrine illness that affects the digestion of proteins, carbohydrates, and lipids, has a death rate of 4.9 million individuals globally. This study aims to find anti-diabetic inhibitor for type 2 diabetes (T2D) that inhibits the dipeptidyl peptidase IV (DPP-IV) enzyme using in silico methods. From a range of published literature sources, thirty (30) isoxazole derivatives of UA (IDUA) were selected for this study. To ascertain the possible inhibitory effects of IDUA, ADMET, molecular docking, density functional theory analyses, molecular dynamic simulation and MM/PBSA were conducted. Eleven compounds (1, 2, 3, 4, 7, 13, 18, 21, 22, 24, and 27) were selected from the ADMET study, which were subjected to perform molecular docking against the DPP-IV enzyme of T2D, and findings indicated two compounds (compound 2 and compound 3) showed comparable binding affinity with the reference compound "Linagliptin". In contrast to the reference molecule, which had a binding affinity of - 8.6 kcal/mol against DPP-IV, compound 2 and compound 3 have binding affinities of - 8.1 and - 8.0 kcal/mol, respectively. Furthermore, based on Lipinski's Rule of Five, ELUMO, EHOMO, band energy gap, drug-likeness and DFT-based studies demonstrated druggability and high reactivity for these compounds. In addition, the molecular dynamic (MD) techniques to confirm that docked complexes remained stable and that the binding orientation obtained during docking tests were accurate. These compounds may be investigated in vitro and in vivo for the development of potential DPP-IV of T2D inhibitors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-025-04287-5.

Marine Natural Products in Inflammation-Related Diseases: Opportunities and Challenges

In recent decades, the potentiality of marine natural products (MNPs) in the medical field has been increasingly recognized. Natural compounds derived from marine microorganisms, algae, and invertebrates have shown significant promise for treating inflammation-related diseases. In this review, we cover the three primary sources of MNPs and their diverse and unique chemical structures and bioactivities. This review aims to summarize the progress of MNPs in combating inflammation-related diseases. Moreover, we cover the functions and mechanisms of MNPs in diseases, highlighting their functions in regulating inflammatory signaling pathways, cellular stress responses, and gut microbiota, among others. Meanwhile, we focus on key technologies and scientific methods to address the current limitations and challenges in MNPs.

A Comparative Molecular Dynamics Study of Food-Derived Compounds as PD-L1 Inhibitors: Insights Across Six Flavonoid Subgroups

In this study, we investigated the inhibitory potential of 60 flavonoids from six distinct subgroups on the programmed cell death ligand 1 (PD-L1) dimer through molecular docking and dynamics simulations. Using AutoDock Vina for docking, the binding poses and affinities were evaluated, revealing an average binding affinity of -8.5 kcal/mol for the flavonoids. Among them, ginkgetin exhibited the highest binding free energy of -46.73 kcal/mol, indicating a strong interaction with PD-L1, while diosmin followed closely, with -44.96 kcal/mol. Molecular dynamics simulations were used to further elucidate the dynamic interactions and stability of the flavonoid-PD-L1 complexes, with the analyses showing minimal root mean square deviation (RMSD) and favorable root mean square fluctuation (RMSF) profiles for several compounds, particularly formononetin, idaein, and neohesperidin. Additionally, contact number and hydrogen bond analyses were performed, which highlighted ginkgetin and diosmin as key flavonoids with significant binding interactions, evidenced by their stable conformations and robust molecular interactions throughout the simulations. Ultimately, a cell-based assay confirmed their ability to inhibit the proliferation of cancer cells. These results, validated through cell-based assays, indicate that the strategy of identifying natural compounds with anticancer activity using computational modeling is highly effective.

Advances in bioinformatic methods for the acceleration of the drug discovery from nature

BACKGROUND

Drug discovery from nature has a long, ethnopharmacologically-based background. Today, natural resources are undeniably vital reservoirs of active molecules or drug leads. Advances in (bio)informatics and computational biology emphasized the role of herbal medicines in the drug discovery pipeline.

PURPOSE

This review summarizes bioinformatic approaches applied in recent drug discovery from nature.

STUDY DESIGN

It examines advancements in molecular networking, pathway analysis, network pharmacology within a systems biology framework and AI for assessing the therapeutic potential of herbal preparations.

METHODS

A comprehensive literature search was conducted using Pubmed, SciFinder, and Google Database. Obtained data was analyzed and organized in subsections: AI, systems biology integrative approach, network pharmacology, pathway analysis, molecular networking, structure-based virtual screening.

RESULTS

Bioinformatic approaches is now essential for high-throughput data analysis in drug target identification, mechanism-based drug discovery, drug repurposing and side-effects prediction. Large datasets obtained from "omics" approaches require bioinformatic calculations to unveil interactions, and patterns in disease-relevant conditions. These tools enable databases annotations, pattern-matching, connections discovery, molecular relationship exploration, and data visualisation.

CONCLUSION

Despite the complexity of plant metabolites, bioinformatic approaches assist in characterization of herbal preparations and selection of bioactive molecule. It is perceived as powerful tool for uncovering multi-target effects and potential molecular mechanisms of compounds. By integrating multiple networks that connect gene-disease, drug-target and gene-drug-target, drug discovery from natural sources is experiencing a remarkable comeback.

Network Pharmacology Analysis and Experimental Validation of Tectoridin in the Treatment of Ischemic Stroke by Inhibiting Apoptosis and Regulating Inflammation

The flowers of Pueraria lobate (Puerariae Flos) have served as a traditional Chinese medicinal and food herbage plant for many years. Tectoridin is one of the most active metabolites extracted from flowers of Pueraria lobate and has a variety of beneficial activities, including antioxidative, hypoglycemic, and anti-inflammatory activities. Nevertheless, the functions and potential mechanisms underlying tectoridin in cerebral ischemia/reperfusion injury have not been well interpreted; thus, a network analysis strategy was performed to systematically evaluate its pharmacological mechanisms, which were further validated in rats with cerebral ischemia. Network analysis predicted that tectoridin could attenuate brain damage after stroke by modulating signaling pathways associated with redox, inflammation, and autophagy. The experimental results demonstrated an improvement in neurological function in rats treated with tectoridin, along with a significant reduction in cerebral infarction volume. The neuroprotective benefits of tectoridin stem, in part, from its antioxidant capabilities, which include the upregulation of Nrf2/HO-1 protein expression, reduction of the TLR4/MYD88/NF-κB inflammatory pathway, and inhibition of the PI3K/Akt/mTOR pathway, contributing to its anti-apoptotic effects. This investigation offers a thorough examination of the pathways and targets linked to the therapeutic effects of tectoridin on ischemic stroke, highlighting its anti-inflammatory, antioxidative, and anti-apoptotic mechanisms. These findings serve as a valuable reference for the development and exploration of effective anti-ischemic stroke medications.

In vitro and in silico exploration of newly synthesized triazolyl- isonicotinohydrazides as potent antitubercular agents

In the present study, we have reported the synthesis of novel isoniazid-triazole derivatives (4a-r), via the click chemistry approach. The synthesized isoniazid-triazole derivatives have potent in vitro antitubercular activity against the Mycobacterium tuberculosis (MTB) H37Rv strain. Among these compounds, 4b, 4f, 4g, 4j, 4k, 4m, 4o, 4p, and 4r were found to be the most active ones with a MIC value of 0.78 μg/mL. This activity is better than ciprofloxacin (MIC value = 1.56 μg/mL) and ethambutol (MIC value = 3.12 μg/mL). The compounds, 4a, 4c, 4d, 4e, 4h, 4i, 4l, and 4n have displayed activity equal to ciprofloxacin (MIC value = 1.56 μg/mL). The cytotoxicity of the active isoniazid-triazole derivatives was studied against RAW 264.7 cell line by MTT assay at 25 μg/mL concentration and no toxicity was observed. Moreover, in-vitro results were supported by in-silico studies with the known antitubercular target (PanK). The drug-likeness, density functional study, molecular docking, and molecular dynamics simulation studies of isoniazid-triazole derivatives validated the ability to form a stable complex with Pantothenate kinase (PanK), which will result in inhibiting the Pantothenate kinase (PanK). Therefore, the results obtained indicate that this class of compounds may offer candidates for future development, and positively provide drug alternatives for tuberculosis treatment.Communicated by Ramaswamy H. Sarma.

Advancements in drug discovery: integrating CADD tools and drug repurposing for PD-1/PD-L1 axis inhibition

Despite significant strides in improving cancer survival rates, the global cancer burden remains substantial, with an anticipated rise in new cases. Immune checkpoints, key regulators of immune responses, play a crucial role in cancer evasion mechanisms. The discovery of immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 has revolutionized cancer treatment, with monoclonal antibodies (mAbs) becoming widely prescribed. However, challenges with current mAb ICIs, such as limited oral bioavailability, adverse effects, and high costs, underscore the need to explore alternative small-molecule inhibitors. In this work, we aimed to identify new potential ICI among all FDA-approved drugs. We employed QSAR models to predict PD-1/PD-L1 inhibition, utilizing a diverse dataset of 29 197 molecules sourced from ChEMBL, PubChem, and recent literature. Machine learning techniques, including Random Forest, Support Vector Machine, and Convolutional Neural Network, were employed for benchmarking to assess model performance. Additionally, we undertook a drug repurposing strategy, leveraging the best in silico model for a virtual screening campaign involving 1576 off-patent approved drugs. Only two virtual screening hits were proposed based on the criteria established for this approach, including: (1) QSAR probability of being active against PD-L1; (2) QSAR applicability domain; (3) prediction of the affinity between the PD-L1 and ligands through molecular docking. One of the proposed hits was sonidegib, an anticancer drug, featuring a biphenyl system. Sonidegib was subsequently validated for in vitro PD-1/PD-L1 binding modulation using ELISA and flow cytometry. This integrated approach, which combines computer-aided drug design (CADD) tools, QSAR modelling, drug repurposing, and molecular docking, offers a pioneering strategy to expedite drug discovery for PD-1/PD-L1 axis inhibition. The findings underscore the potential to identify a wider range small molecules to contribute to the ongoing efforts to advancing cancer immunotherapy.

Multilayered screening for multi-targeted anti-Alzheimer's and anti-Parkinson's agents through structure-based pharmacophore modelling, MCDM, docking, molecular dynamics and DFT: a case study of HDAC4 inhibitors

Abstract

Alzheimer's disease (AD) and Parkinson's disease (PD) are neurological conditions that primarily impact the elderly having distinctive traits and some similarities in terms of symptoms and progression. The multifactorial nature of AD and PD encourages exploring potentiality of multi-target therapy for addressing these conditions to conventional, the "one drug one target" strategy. This study highlights the searching of potential HDAC4 inhibitors through multiple screening approaches. In this context, structure-based pharmacophore model, ligand profiler mapping and MCDM approaches were performed for target prioritization. Similarly, ligand profiler, MCDM and Docking studies were performed to prioritize multi-targeted HDAC4 inhibitors. These comprehensive approaches unveiled 5 common targets and 5 multi-targeted prioritized compounds consensually. MD simulations, DFT and binding free energy calculations corroborated the stability and robustness of propitious compound 774 across 5 prioritized targets. In conclusion, the screened compound 774 (ChEMBL 4063938) could be a promising multi-targeted therapy for managing AD and PD further rendering experimental validation.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00302-4.

Identification of Novel LCN2 Inhibitors Based on Construction of Pharmacophore Models and Screening of Marine Compound Libraries by Fragment Design

LCN2, a member of the lipocalin family, is associated with various tumors and inflammatory conditions. Despite the availability of known inhibitors, none have been approved for clinical use. In this study, marine compounds were screened for their ability to inhibit LCN2 using pharmacophore models. Six compounds were optimized for protein binding after being docked against the positive control Compound A. Two compounds showed promising results in ADMET screening. Molecular dynamics simulations were utilized to predict binding mechanisms, with Compound 69081_50 identified as a potential LCN2 inhibitor. MM-PBSA analysis revealed key amino acid residues that are involved in interactions, suggesting that Compound 69081_50 could be a candidate for drug development.

In silico-driven identification of Pranlukast as a Stabilizer of PD-L1 Homodimers

INTRODUCTION

Programmed cell death protein 1 (PD-1) and programmed cell death ligand 1 (PD-L1) are critical immune checkpoints in cancer biology. Multiple small-molecule drugs have been developed as inhibitors of the PD-1/PD-L1 axis. Those drugs promote the formation of PD-L1 homodimers, causing their stabilization, internalization, and subsequent degradation. Drug repurposing is a strategy that expedites the clinical translation by identifying new effects of drugs with clinical use. Herein, we aimed to repurpose drugs as inductors of PD-L1 homodimerization and, therefore, as potential inhibitors of PD-L1.

METHODS

We generated a hybrid pharmacophore model by analyzing the structures of reported ligands that induce PD-L1 homodimerization and their target-binding mode. Pharmacophore-matching compounds were selected from a chemical library of Food and Drug Administration (FDA)-approved drugs. Their binding modes to PDL1 homodimers were assessed by molecular docking and the stability of the complexes and the corresponding binding energies were evaluated by molecular dynamics (MD) simulations. Finally, the activity of one drug as promoter of PD-L1 homodimerization was assessed in protein crosslinking assays.

RESULTS

We identified 12 pharmacophore-matching compounds, but only 4 reproduced the binding mode of the reference inhibitors. Further characterization by MD showed that pranlukast, an antagonist of leukotriene receptors that is used to treat asthma, generated stable and energy-favorable interactions with PD-L1 homodimers and induced homodimerization of recombinant PD-L1.

CONCLUSION

Our results suggest that pranlukast inhibits the PD-1/PD-L1 axis, meriting its repurposing as an antitumor drug.

Theoretical investigation of selective inhibitory activity of chromone-based compounds against monoamine oxidase (MAO)-A and -B

Monoamine oxidase (MAO) is crucial for the breakdown of monoamine neurotransmitters, making it a promising target for treating neurodegenerative disorders, such as depression, Alzheimer's disease, and Parkinson's disease. In this study, we investigated the selective inhibitory activity of chromone-based compounds against MAO-A and MAO-B for neurodegenerative disease treatment. In literary sources, thirty chromone derivatives have been identified as potential ligands for MAO-A and MAO-B inhibitors. We utilized molecular docking to evaluate how the most active compound interacted with the targeted MAO-A and MAO-B. Compound 2 g, the most active for MAO-A, demonstrated a lower CDOCKER energy compared to the co-crystallized ligand. Meanwhile, compound 2f, the most active for MAO-B, showed a CDOCKER energy similar to the co-crystallized ligand and exhibited similar binding patterns. Furthermore, we constructed a quantitative structure-activity relationship (QSAR) model to predict the properties and estimate IC50 values for 30 chromone derivatives functioning as MAO-A and MAO-B inhibitors. The model predictions were validated against experimental measurements. Our 2D QSAR model demonstrated robustness, with a statistically significant non-cross-validated coefficient (r2 < 0.9), cross-validated correlation coefficient (q2 < 0.6), and predictive squared correlation coefficient (r2pred < 0.8). Additionally, MD simulations confirmed the stable binding of compounds 2 g and 2f with MAO-A and MAO-B, respectively, displaying substantial binding energy. The most effective pharmacophore model identified key features, such as hydrogen bond acceptors and hydrophobic interactions, that contribute significantly to inhibitory potency. This study offers valuable insight into the selection of compounds with improved selectivity for MAO inhibition.

Structure-activity relationship of pharmacophores and toxicophores: the need for clinical strategy

OBJECTIVES

Sometimes clinical efficacy and potential risk of therapeutic and toxic agents are difficult to predict over a long period of time. Hence there is need for literature search with a view to assessing cause of toxicity and less efficacy of drugs used in clinical practice.

METHOD

Hence literatures were searched for physicochemical properties, chemical formulas, molecular masses, pH values, ionization, receptor type, agonist and antagonist, therapeutic, toxic and structure-activity relationship of chemical compounds with pharmacophore and toxicophore, with a view to identifying high efficacious and relative low toxic agents. Inclusion criteria were manuscripts published on PubMed, Scopus, Web of Science, PubMed Central, Google Scholar among others, between 1960 and 2023. Keywords such as pharmacophore, toxicophore, structure-activity-relationship and disease where also searched. The exclusion criteria were the chemicals that lack pharmacophore, toxicophore and manuscripts published before 1960.

RESULTS

Findings have shown that pharmacophore and toxicophore functional groups determine clinical efficacy and safety of therapeutics, but if they overlap therapeutic and toxicity effects go concurrently. Hence the functional groups, dose, co-administration and concentration of drugs at receptor, drug-receptor binding and duration of receptor binding are the determining factors of pharmacophore and toxicophore activity. Molecular mass, chemical configuration, pH value, receptor affinity and binding capacity, multiple pharmacophores, hydrophilic/lipophilic nature of the chemical contribute greatly to functionality of pharmacophore and toxicophore.

CONCLUSION

Daily single therapy, avoidance of reversible pharmacology, drugs with covalent adduct, maintenance of therapeutic dose, and the use of multiple pharmacophores for terminal diseases will minimize toxicity and improve efficacy.

Towards novel small-molecule inhibitors blocking PD-1/PD-L1 pathway: From explainable machine learning models to molecular dynamics simulation

Molecular design of small-molecule inhibitors targeting programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) pathway has been recognized as an active research area by the clinical success of cancer immunotherapy. In recent years, using machine learning (ML) methods to accelerate drug design have been confirmed. However, the black box character of ML methods makes model interpretation and ligands optimization obscured. Herein, five explainable ML models were constructed by integrating five ML models with the SHAP method, where these ML models were pretrained with >4000 molecules and their R2 ranged from 0.835 to 0.86 on test set. Subsequently, the explainable ML models were employed to identify the relationship between fragments and bio-activity of a small molecule inhibitor BMS-1166, leading to the modification of BMS-1166 into 60 novel compounds. After consensus docking and ADMET test, 3 small molecules (C27, C52 and C54) with better docking scores and lower toxicity than BMS-1166 were screened out further. Finally, the improved binding affinity of C27, C52 to the PD-L1 dimer was validated by the MD simulation. Overall, this work proposed an efficient protocol on the basis of explainable ML models for designing small-molecule inhibitors targeting PD-1/PD-L1 pathway in a rational way.

PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening

As ultra-large-scale virtual screening becomes critical for early-stage drug discovery, highly efficient screening methods are gaining prominence. Deep-learning-based approaches which directly estimate binding affinities without binding conformation have attracted great attention as an alternative solution to molecular docking, but the generalization capability of existing methods in vast chemical space remains uncertain due to restricted training data. Here, we introduce PharmacoNet, the first deep-learning framework for pharmacophore modeling toward ultra-fast virtual screening. PharmacoNet offers fully automated protein-based pharmacophore modeling and evaluates the potency of ligands with a parameterized analytical scoring function, ensuring high generalization ability across unseen targets and ligands. Our benchmark study shows that PharmacoNet is extremely fast yet reasonably accurate compared to traditional docking methods and existing deep learning-based scoring models. We successfully identified selective inhibitors from 187 million compounds against cannabinoid receptors within 21 hours on a single CPU. This study uncovers the hitherto untapped potential of deep learning in pharmacophore modeling.

Identification and interaction mechanism of novel small molecule antagonists targeting CC chemokine receptor 1/3/5 for treatment of non-small cell lung cancer

Non-Small Cell Lung Cancer (NSCLC) was one of the most prevalent forms of lung cancer. Due to its ease of invasion and migration, the five-year survival rate was relatively low. Therefore, new strategies for NSCLC treatment were needed. CC chemokine receptor 1/3/5 (CCR1/CCR3/CCR5), a member of the G-protein coupled receptor family, could promote the migration and invasion of NSCLC cells by binding to related chemokines. Consequently, targeting CCR1, CCR3 and CCR5 might prevent the progression of the disease. So far, no compound had been reported as a common antagonist for CCR1, CCR3, and CCR5. In this research, we utilized virtual screening and structural optimization to obtain compound 5, which effectively inhibited the migration and invasion of NSCLC cells. Meanwhile, Western Blot and Enzyme linked immunosorbent assay (ELISA) manifested that compound 5 suppressed migration and invasion of NSCLC cells by suppressing the nuclear factor κB (NF-κB) and the consequently decreased Matrix Metalloproteinase-9(MMP-9) secretion. Moreover, drug affinity responsive target stability (DARTS) experiment and molecular simulations confirmed that compound 5 was capable of binding with CCR1/CCR3/CCR5, and Van der Waals forces were instrumental in the binding process. Ile91, Tyr113, Gln284, and Ser184(CCR1-ligand5), Ile189, Met213, and Leu209(CCR3-ligand5), Phe109, Gln194, and Thr195(CCR5-ligand5) had Van der Waals interactions with ligand 5. Dynamic cross-correlation matrix (DCCM) and free energy landscape (FEL) showed that compound 5 could stably bind to CCR1/CCR3/CCR5 to change conformation of the protein and the tendency of residue movements, leading to a persistent inhibitory effect. This study aimed to provide assistance in the rational design of common antagonists for CCR1, CCR3, and CCR5.

Innovative virtual screening of PD-L1 inhibitors: the synergy of molecular similarity, neural networks and GNINA docking

Aims: Immune checkpoint inhibitors targeting PD-L1 are crucial in cancer research for preventing cancer cells from evading the immune system.Materials & methods: This study developed a screening model combining ANN, molecular similarity, and GNINA 1.0 docking to target PD-L1. A database of 2044 substances was compiled from patents.Results: For molecular similarity, the AVALON emerged as the most effective fingerprint, demonstrating an AUC-ROC of 0.963. The ANN model outperformed the Random Forest and Support Vector Classifier in cross-validation and external validation, achieving an average precision of 0.851 and an F1 score of 0.790. GNINA 1.0 was validated through redocking and retrospective control, achieving an AUC of 0.975.Conclusions: From 15235 DrugBank compounds, 22 candidates were shortlisted. Among which (3S)-1-(4-acetylphenyl)-5-oxopyrrolidine-3-carboxylic acid emerged as the most promising.

Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines

With the COVID-19 pandemic, the importance of vaccines has been widely recognized and has led to increased research and development efforts. Vaccines also play a crucial role in cancer treatment by activating the immune system to target and destroy cancer cells. However, enhancing the efficacy of cancer vaccines remains a challenge. Adjuvants, which enhance the immune response to antigens and improve vaccine effectiveness, have faced limitations in recent years, resulting in few novel adjuvants being identified. The advancement of artificial intelligence (AI) technology in drug development has provided a foundation for adjuvant screening and application, leading to a diversification of adjuvants. This article reviews the significant role of tumor vaccines in basic research and clinical treatment and explores the use of AI technology to screen novel adjuvants from databases. The findings of this review offer valuable insights for the development of new adjuvants for next-generation vaccines.

Atomistic Origins of Resurrection of Aged Acetylcholinesterase by Quinone Methide Precursors

Nerve agents are organophosphates (OPs) that act as potent inhibitors of acetylcholinesterase (AChE), the enzyme responsible for the hydrolysis of acetylcholine. After inhibition, a dealkylation reaction of the phosphorylated serine, known as the aging of AChE, can occur. When aged, reactivators of OP-inhibited AChE are no longer effective. Therefore, the realkylation of aged AChE may offer a pathway to reverse AChE aging. In this study, molecular modeling was conducted to propose new ligands as realkylators of aged AChE. We applied a methodology involving docking and quantum mechanics/molecular mechanics (QM/MM) calculations to evaluate the resurrection kinetic constants and ligand interactions with OP-aged AChE, comparing them to data found in the literature. The results obtained confirm that this method is suitable for predicting kinetic and thermodynamic parameters of ligands, which can be useful in the design and selection of new and more effective ligands for AChE realkylation.

Computational Approach for the Development of pH-Selective PD-1/PD-L1 Signaling Pathway Inhibition in Fight with Cancer

Immunotherapy, particularly targeting the PD-1/PD-L1 pathway, holds promise in cancer treatment by regulating the immune response and preventing cancer cells from evading immune destruction. Nonetheless, this approach poses a risk of unwanted immune system activation against healthy cells. To minimize this risk, our study proposes a strategy based on selective targeting of the PD-L1 pathway within the acidic microenvironment of tumors. We employed in silico methods, such as virtual screening, molecular mechanics, and molecular dynamics simulations, analyzing approximately 10,000 natural compounds from the MolPort database to find potential hits with the desired properties. The simulations were conducted under two pH conditions (pH = 7.4 and 5.5) to mimic the environments of healthy and cancerous cells. The compound MolPort-001-742-690 emerged as a promising pH-selective inhibitor, showing a significant affinity for PD-L1 in acidic conditions and lower toxicity compared to known inhibitors like BMS-202 and LP23. A detailed 1000 ns molecular dynamics simulation confirmed the stability of the inhibitor-PD-L1 complex under acidic conditions. This research highlights the potential of using in silico techniques to discover novel pH-selective inhibitors, which, after experimental validation, may enhance the precision and reduce the toxicity of immunotherapies, offering a transformative approach to cancer treatment.

TRAF6 Inhibitors from Marine Compound Library: Pharmacophore, Virtual Screening, Fragment Replacement, ADMET, and Molecular Dynamics

TRAF6 is an E3 ubiquitin ligase that plays a crucial role in cell signaling. It is known that MMP is involved in tumor metastasis, and TRAF6 induces MMP-9 expression by binding to BSG. However, inhibiting TRAF6's ubiquitinase activity without disrupting the RING domain is a challenge that requires further research. To address this, we conducted computer-based drug screening to identify potential TRAF6 inhibitors. Using a ligand-receptor complex pharmacophore based on the inhibitor EGCG, known for its anti-tumor properties, we screened 52,765 marine compounds. After the molecular docking of 405 molecules with TRAF6, six compounds were selected for further analysis. By replacing fragments of non-binding compounds and conducting second docking, we identified two promising molecules, CMNPD9212-16 and CMNPD12791-8, with strong binding activity and favorable pharmacological properties. ADME and toxicity predictions confirmed their potential as TRAF6 inhibitors. Molecular dynamics simulations showed that CMNPD12791-8 maintained a stable structure with the target protein, comparable to EGCG. Therefore, CMNPD12791-8 holds promise as a potential inhibitor of TRAF6 for inhibiting tumor growth and metastasis.

Discovery of Substituted 2-oxoquinolinylthiazolidin-4-one Analogues as Potential EGFRK Inhibitors in Lung Cancer Treatment

PURPOSE

Cancer is the second leading cause of death globally and is responsible for an estimated 9.6 million deaths in 2018. Globally, about 1 in 6 deaths is due to cancer and the chemotherapeutic drugs available have high toxicity and have reported side effects hence, there is a need for the synthesis of novel drugs in the treatment of cancer.

METHODS

The current research work dealt with the synthesis of a series of 3-(3-acetyl-2-oxoquinolin-1-(2H)-yl-2-(substitutedphenyl)thiazolidin-4-one (Va-j) derivatives and evaluation of their in-vitro anticancer activity. All the synthesized compounds were satisfactorily characterized by IR and NMR data. Compounds were further evaluated for their in-vitro anticancer activity against A-549 (lung cancer) cell lines. The in-vitro anticancer activity was based upon the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazoliumbromide (MTT) assay method.

RESULTS

The synthesized compounds exhibited satisfactory anticancer properties against the A-549 cell line. The compound (VH): showed the highest potency amongst the tested derivatives against the A-549 cell line with IC50 values of 100 µg/ml respectively and was also found to be more potent than Imatinib (150 µg/ml) which was used as a standard drug. Molecular docking studies of the titled compounds (Va-j) were carried out using AutoDock Vina/PyRx software. The synthesized compounds exhibited well-conserved hydrogen bonds with one or more amino acid residues in the active pocket of the EGFRK tyrosine kinase domain (PDB 1m17).

CONCLUSION

Among all the synthesized analogues, the binding affinity of the compound (Vh) was found to be higher than other synthesized derivatives and a molecular dynamics simulation study explored the stability of the docked complex system.

Discovery of a potent inhibitor, D-132, targeting AsfvPolX, via protein-DNA complex-guided pharmacophore screening and in vitro molecular characterizations

The heightened transmissibility and capacity of African swine fever virus (ASFV) induce fatal diseases in domestic pigs and wild boars, posing significant economic repercussions and global threats. Despite extensive research efforts, the development of potent vaccines or treatments for ASFV remains a persistent challenge. Recently, inhibiting the AsfvPolX, a key DNA repair enzyme, emerges as a feasible strategy to disrupt viral replication and control ASFV infections. In this study, a comprehensive approach involving pharmacophore-based inhibitor screening, coupled with biochemical and biophysical analyses, were implemented to identify, characterize, and validate potential inhibitors targeting AsfvPolX. The constructed pharmacophore model, Phar-PolX-S, demonstrated efficacy in identifying a potent inhibitor, D-132 (IC50 = 2.8 ± 0.2 µM), disrupting the formation of the AsfvPolX-DNA complex. Notably, D-132 exhibited strong binding to AsfvPolX (KD = 6.9 ± 2.2 µM) through a slow-on-fast-off binding mechanism. Employing molecular modeling, it was elucidated that D-132 predominantly binds in-between the palm and finger domains of AsfvPolX, with crucial residues (R42, N48, Q98, E100, F102, and F116) identified as hotspots for structure-based inhibitor optimization. Distinctively characterized by a 1,2,5,6-tetrathiocane with modifications at the 3 and 8 positions involving ethanesulfonates, D-132 holds considerable promise as a lead compound for the development of innovative agents to combat ASFV infections.

Progress in small-molecule inhibitors targeting PD-L1

PD-L1 is a transmembrane protein overexpressed by tumor cells. It binds to PD-1 on the surface of T-cells, suppresses T-cell activity and hinders the immune response against cancer. Clinically, several monoclonal antibodies targeting PD-1/PD-L1 have achieved significant success in cancer immunotherapy. Nevertheless, their disadvantages, such as unchecked immune responses, high cost and long half-life, stimulated pharmacologists to develop small-molecule inhibitors targeting PD-1/PD-L1. After a batch of excellent inhibitors with a biphenyl core structure were firstly reported by BMS, more and more researchers focused on small-molecule inhibitors targeting PD-L1 rather than PD-1. Numerous small-molecule inhibitors were extensively designed and synthesized in the past few years. In this paper, the structural characteristics of PD-L1 and complexes of PD-L1 with its inhibitors are elaborated and small molecule inhibitors developed in the last decade are summarized as well. This paper aims to provide insights into further designing and synthesis of small molecule inhibitors targeting PD-L1.

Investigating the Mechanistic of Danhong Injection in Brain Damage Caused by Cardiac I/R Injury via Bioinformatics, Computer Simulation, and Experimental Validation

OBJECTIVE

Cardiac ischemia-reperfusion (I/R) injury has negative effects on the brain and can even lead to the occurrence of ischemic stroke. Clinical evidence shows that Danhong injection (DHI) protects the heart and brain following ischemic events. This study investigated the mechanisms and key active compounds underlying the therapeutic effect of DHI against brain damage induced by cardiac I/R injury.

METHODS

The gene expression omnibus database provided GSE66360 and GSE22255 data sets. The R programming language was used to identify the common differentially expressed genes (cDEGs). Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed, and protein-protein interaction network was constructed. Active compounds of DHI were collected from the Traditional Chinese Medicine Systems Pharmacology database. Molecular docking and molecular dynamics simulations were performed. The MMPBSA method was used to calculate the binding-free energy. The pkCSM server and DruLiTo software were used for Absorption, Distribution, metabolism, excretion, and toxicity (ADMET) analysis and drug-likeness analysis. Finally, in vitro experiments were conducted to validate the results.

RESULTS

A total of 27 cDEGs had been identified. The PPI and enrichment results indicated that TNF-α was considered to be the core target. A total of 80 active compounds were retrieved. The molecular docking results indicated that tanshinone I (TSI), tanshinone IIA (TSIIA), and hydroxyl safflower yellow A (HSYA) were selected as core active compounds. Molecular dynamics verification revealed that the conformations were relatively stable without significant fluctuations. MMPBSA analysis revealed that the binding energies of TSI, TSIIA, and HSYA with TNF-α were -36.01, -21.71, and -14.80 kcal/mol, respectively. LEU57 residue of TNF-α has the highest contribution. TSI and TSIIA passed both the ADMET analysis and drug-likeness screening, whereas HSYA did not. Experimental verification confirmed that DHI and TSIIA reduced the expression of TNF-α, NLRP3, and IL-1β in the injured H9C2 and rat brain microvascular endothelial cells.

CONCLUSION

TNF-α can be considered to be a key target for BD-CI/R. TSIIA in DHI exerts a significant inhibitory effect on the inflammatory damage of BD-CI/R, providing new insights for future drug development.

Computational analysis of PD-L1 dimerization mechanism induced by small molecules and potential dynamical properties

The design of small molecule inhibitors that target the programmed death ligand-1 (PD-L1) is a forefront issue in immune checkpoint blocking therapy. Small-molecule inhibitors have been shown to exert therapeutic effects by inducing dimerization of the PD-L1 protein, however, the specific mechanisms underlying this dimerization process remain largely unexplored. Furthermore, there is a notable lack of comparative studies examining the binding modes of structurally diverse inhibitors. In view of the research gaps, this work employed molecular dynamics simulations to meticulously examine the interactions between two distinct types of inhibitors and PD-L1 in both monomeric and dimeric forms, and predicted the dimerization mechanism. The results revealed that inhibitors initially bind to a PD-L1 monomer, subsequently attracting another monomer to form a dimer. Notably, symmetric inhibitors observed superior binding efficiency compared to other inhibitors. Key residues, including Ile54, Tyr56, Met115 and Tyr123 played a leading role in binding. Structurally, symmetric inhibitors were capable of thoroughly engaging the binding pocket, promoting a more symmetrical formation of PD-L1 dimers. Furthermore, symmetric inhibitors formed more extensive hydrophobic interactions with protein residues. The insights garnered from this research are expected to significantly contribute to the rational design and optimization of small molecule inhibitors targeting PD-L1.

In silico analysis of small molecule compounds based on pharmacophore- and backbone-leaping peroxiredoxin 1 (PRDX1) inhibitors

Abnormal expression of PRDX has been found to play a significant role in the growth of colorectal cancer and other types of tumors. Despite the identification of several PRDX1 inhibitory compounds in recent years, none of them have been utilized in clinical treatments. Therefore, we conducted a virtual screening of 210,331 small molecules from the SPECS library using PRDX1 and multiple methods. From this screening, we identified 13 compounds with the highest scores from the molecular docking analysis. To further validate the accuracy of our pharmacophore model, we constructed a structure-based pharmacophore model and analyzed the receiver operating characteristic curve (ROC curve). Through this process, we selected nine compounds using skeleton jumping and virtual screening based on the highest pharmacophore model scores. Subsequently, we examined the ADMET properties of these nine compounds to assess their drug-forming potential, resulting in three compounds with the best drug properties. Finally, we assessed the binding stability of these three candidate molecules to proteins using molecular dynamics and MM-PBSA calculations. After a comprehensive evaluation, we found that compounds 6 and 9 formed stable complexes with PRDX1 proteins and could potentially serve as competitive inhibitors of PRDX1 substrates.

Exploration of potential hit compounds targeting 1-deoxy-d-xylulose 5-phosphate reductoisomerase (IspC) from Acinetobacter baumannii: an in silico investigation

The emergence of carbapenem-resistant Acinetobacter baumannii, a highly concerning bacterial species designated as a Priority 1: Critical pathogen by the WHO, has become a formidable global threat. In this study, we utilised computational methods to explore the potent molecules capable of inhibiting the IspC enzyme, which plays a crucial role in the methylerythritol 4-phosphate (MEP) biosynthetic pathway. Employing high-throughput virtual screening of small molecules from the Enamine library, we focused on the highly conserved substrate binding site of the DXR target protein, resulting in the identification of 1000 potential compounds. Among these compounds, we selected the top two candidates (Z2615855584 and Z2206320703) based on Lipinski's rule of Five and ADMET filters, along with FR900098, a known IspC inhibitor, and DXP, the substrate of IspC, for molecular dynamics (MD) simulations. The MD simulation trajectories revealed remarkable structural and thermodynamic stability, as well as strong binding affinity, for all the IspC-ligand complexes. Furthermore, binding free energy calculations based on MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) methodology demonstrated significant interactions between the selected ligand molecules and IspC. Taking into consideration all the aforementioned criteria, we suggest Z2206320703 as the potent lead candidate against IspC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03923-w.

Identification of PLK1-PBD Inhibitors from the Library of Marine Natural Products: 3D QSAR Pharmacophore, ADMET, Scaffold Hopping, Molecular Docking, and Molecular Dynamics Study

PLK1 is found to be highly expressed in various types of cancers, but the development of inhibitors for it has been slow. Most inhibitors are still in clinical stages, and many lack the necessary selectivity and anti-tumor effects. This study aimed to create new inhibitors for the PLK1-PBD by focusing on the PBD binding domain, which has the potential for greater selectivity. A 3D QSAR model was developed using a dataset of 112 compounds to evaluate 500 molecules. ADMET prediction was then used to select three molecules with strong drug-like characteristics. Scaffold hopping was employed to reconstruct 98 new compounds with improved drug-like properties and increased activity. Molecular docking was used to compare the efficient compound abbapolin, confirming the high-activity status of [(14S)-14-hydroxy-14-(pyridin-2-yl)tetradecyl]ammonium,[(14S)-15-(2-furyl)-14-hydroxypentadecyl]ammonium and [(14S)-14-hydroxy-14-phenyltetradecyl]ammonium. Molecular dynamics simulations and MMPBSA were conducted to evaluate the stability of the compounds in the presence of proteins. An in-depth analysis of [(14S)-15-(2-furyl)-14-hydroxypentadecyl]ammonium and [(14S)-14-hydroxy-14-phenyltetradecyl]ammonium identified them as potential candidates for PLK1 inhibitors.

Multidimensional Criteria for Virtual Screening of PqsR Inhibitors Based on Pharmacophore, Docking, and Molecular Dynamics

Pseudomonas aeruginosa is a clinically challenging pathogen due to its high resistance to antibiotics. Quorum sensing inhibitors (QSIs) have been proposed as a promising strategy to overcome this resistance by interfering with the bacterial communication system. Among the potential targets of QSIs, PqsR is a key regulator of quorum sensing in Pseudomonas aeruginosa. However, the current research on PqsR inhibitors is limited by the lack of diversity in the chemical structures and the screening methods. Therefore, this study aims to develop a multidimensional screening model for PqsR inhibitors based on both ligand- and receptor-based approaches. First, a pharmacophore model was constructed from a training set of PqsR inhibitors to identify the essential features and spatial arrangement for the activity. Then, molecular docking and dynamics simulations were performed to explore the core interactions between PqsR inhibitors and their receptor. The results indicate that an effective PqsR inhibitor should possess two aromatic rings, one hydrogen bond acceptor, and two hydrophobic groups and should form strong interactions with the following four amino acid residues: TYR_258, ILE_236, LEU_208, and GLN_194. Moreover, the docking score and the binding free energy should be lower than -8 kcal/mol and -40 kcal/mol, respectively. Finally, the validity of the multidimensional screening model was confirmed by a test set of PqsR inhibitors, which showed a higher accuracy than the existing screening methods based on single characteristics. This multidimensional screening model would be a useful tool for the discovery and optimization of PqsR inhibitors in the future.

Oxidative stress is involved in immunosuppression and macrophage regulation in glioblastoma

Oxidative stress dually affected cancer progression, while its effect on glioblastomas remained unclear. Herein, we clustered the multicenter glioblastoma cohorts based on the oxidative-stress-responsive genes (OSS) expression. We found that cluster 2 with high OSS levels suffered a worse prognosis. Functional analyses and immune-related analyses results exhibited that M2-like pro-tumoral macrophages and neutrophils were enriched in cluster 2, while Natural killer cells' infiltration was decreased. The increased M2-like pro-tumoral macrophages in cluster 2 was confirmed by immunofluorescence. An integrated single-cell analysis validated the malignant features of cluster 2 neoplastic cells and discovered their crosstalk with M2-like pro-tumoral macrophages. Moreover, we observed that SOD3 knockdown might decrease the M2-like pro-tumoral transformation of macrophage in vitro and in vivo. Comprehensively, we revealed oxidative stress' prognostic and immunosuppressive potential in glioblastoma and discovered SOD3's potential role in regulating macrophage M2-like pro-tumoral transformation.

Discovery of potential scaffolds for glutaminyl cyclase inhibitors: Virtual screening, synthesis, and evaluation

Glutaminyl cyclase (QC) plays a crucial role in the early stages of Alzheimer's disease (AD), thus inhibition of QC may be a promising strategy for the treatment of early AD. Therefore, QC inhibitors with novel chemical scaffolds may contribute to the development of additional anti-AD agents. We conducted a virtual screening of 3 million compounds from the Chemdiv and Enamine databases, to discover potential scaffolds for QC inhibitors. Three scaffolds, 120974, 147706, and 141449, were selected from this structure-based virtual screening through a combination of pharmacophore modeling, a receptor-ligand pharmacophore model, and the GALAHAD model, and furtherly filtered by chelation with zinc ion and docking properties. Consequently, three compounds, 1, 2, and 3, were designed and synthesized based on these three scaffolds, respectively. The IC50 of compounds 1 and 3 against QC were 14.19 ± 4.21 and 4.34 ± 0.35 μM, respectively. Our results indicate that the new scaffolds selected using a virtual screening process exhibit potential as novel QC inhibitors.

In Silico Analysis of USP7 Inhibitors Based on Building QSAR Models and Fragment Design for Screening Marine Compound Libraries

USP7 is highly expressed in a variety of tumors and is thought to play a major role in cancer development. However, there are no drugs available to target USP7, so there is a need to develop new USP7 inhibitors. In this study, AutoQSAR, multiple linear regression, and Naive Bayesian models were constructed using 543 compounds and used to analyze marine compounds. After selecting 240 small molecules for molecular docking with Maestro, MOE, and GOLD, better small molecules than the positive compound P217564 were screened. The molecular structure of "1, 2-dibromobenzene" was optimized to improve the binding effect of the protein, and 10 optimized compounds in ADMET performed well during the screening process. To study the dynamic combination of protein-ligand effect consistency with static molecular docking, 100ns molecular dynamics simulations of candidate compound 1008-1, reference compound P217564, and negative-positive GNE2917 were conducted. The results of molecular docking and molecular dynamics simulation analysis showed that compound 1008-1 maintained a stable conformation with the target protein. Thus, the comprehensive analysis suggests that compound 1008-1 could provide new possibilities for USP7 covalent inhibitor candidates.

Cosmosiin Induces Apoptosis in Colorectal Cancer by Inhibiting PD-L1 Expression and Inducing ROS

Immunotherapies, particularly those concerning immune checkpoint inhibitors, have transformed cancer treatment in recent years. Programmed death-ligand 1 (PD-L1) is a key target for immunotherapy that is overexpressed in the cells of colorectal cancer, a widespread malignant cancer that poses a significant healthcare challenge. This study investigated the effects of cosmosiin treatment on colorectal cancer cell lines. Cosmosiin is a naturally occurring flavone glycoside compound that has potential health benefits, including antioxidant and immunomodulatory effects. This study showed that cosmosiin effectively suppresses the expression of PD-L1 and triggers apoptosis, which is facilitated through pathways that are related to reactive oxygen species. These outcomes suggest that cosmosiin could be a promising candidate for an immune checkpoint inhibitor in the treatment of colorectal cancer.

A Natural Compound Containing a Disaccharide Structure of Glucose and Rhamnose Identified as Potential N-Glycanase 1 (NGLY1) Inhibitors

N-glycanase 1 (NGLY1) is an essential enzyme involved in the deglycosylation of misfolded glycoproteins through the endoplasmic reticulum (ER)-associated degradation (ERAD) pathway, which could hydrolyze N-glycan from N-glycoprotein or N-glycopeptide in the cytosol. Recent studies indicated that NGLY1 inhibition is a potential novel drug target for antiviral therapy. In this study, structure-based virtual analysis was applied to screen candidate NGLY1 inhibitors from 2960 natural compounds. Three natural compounds, Poliumoside, Soyasaponin Bb, and Saikosaponin B2 showed significantly inhibitory activity of NGLY1, isolated from traditional heat-clearing and detoxifying Chinese herbs. Furthermore, the core structural motif of the three NGLY1 inhibitors was a disaccharide structure with glucose and rhamnose, which might exert its action by binding to important active sites of NGLY1, such as Lys238 and Trp244. In traditional Chinese medicine, many compounds containing this disaccharide structure probably targeted NGLY1. This study unveiled the leading compound of NGLY1 inhibitors with its core structure, which could guide future drug development.

Construction and Evaluation of Novel Dual-function Antifungal Inhibitors and Covalent Organic Framework Carriers Based on the Infection Microenvironment

In this study, PD-L1 and CYP51 were selected as key dual-target enzymes, which play an important role in the process of fungal proliferation and immune suppression. A series of novel bifonazole dual-target compounds were designed through the method of fragment combination. Their chemical structure was synthesized, characterized, and evaluated. Among them, the compounds (10c-1, 14a-2, 17c-2) exhibited excellent antifungal and antidrug-resistant fungal activity in vitro. In particular, the preferred compound 14a-2 with high-efficiency dual-target inhibitor ability could block the fungal proliferation and activate the organism's immune efficacy. Moreover, the corresponding covalent organic framework carrier was also successfully constructed to improve its bioavailability. This significantly accelerated the body's recovery process from fungal infection in vivo. In summary, this study expanded the scientific frontier of antifungal drugs and provided a feasible candidate pathway for clinical treatment of fungal infections.

Enhancing personalized immune checkpoint therapy by immune archetyping and pharmacological targeting

Immune checkpoint inhibitors (ICIs) are an expanding class of immunotherapeutic agents with the potential to cure cancer. Despite the outstanding clinical response in patient subsets, most individuals become refractory or develop resistance. Patient stratification and personalized immunotherapies are limited by the absence of predictive response markers. Recent findings show that dominant patterns of immune cell composition, T-cell status and heterogeneity, and spatiotemporal distribution of immune cells within the tumor microenvironment (TME) are becoming essential determinants of prognosis and therapeutic response. In this context, ICIs also function as investigational tools and proof of concept, allowing the validation of the identified mechanisms. After reviewing the current state of ICIs, this article will explore new comprehensive predictive markers for ICIs based on recent discoveries. We will discuss the recent establishment of a classification of TMEs into immune archetypes as a tool for personalized immune profiling, allowing patient stratification before ICI treatment. We will discuss the developing comprehension of T-cell diversity and its role in shaping the immune profile of patients. We describe the potential of strategies that score the mutual spatiotemporal modulation between T-cells and other cellular components of the TME. Additionally, we will provide an overview of a range of synthetic and naturally occurring or derived small molecules. We will compare compounds that were recently identified by in silico prediction to wet lab-validated drug candidates with the potential to function as ICIs and/or modulators of the cellular components of the TME.

mPGES-1 Inhibitor Discovery Based on Computer-Aided Screening: Pharmacophore Models, Molecular Docking, ADMET, and MD Simulations

mPGES-1 is an enzyme, which, when activated by inflammatory factors, can cause prostaglandin E synthesis. Traditional non-steroidal anti-inflammatory drugs are capable of inhibiting prostaglandin production, yet they can also cause gastrointestinal reactions and coagulation disorders. mPGES-1, the enzyme at the conclusion of prostaglandin production, does not cause any adverse reactions when inhibited. Numerous studies have demonstrated that mPGES-1 is more abundant in cancerous cells than in healthy cells, indicating that decreasing the expression of mPGES-1 could be a potential therapeutic strategy for cancer. Consequently, the invention of mPGES-1 inhibitors presents a fresh avenue for the treatment of inflammation and cancer. Incorporating a database of TCM compounds, we collected a batch of compounds that had an inhibitory effect on mPGES-1 and possessed IC50 value. Firstly, a pharmacophore model was constructed, and the TCM database was screened, and the compounds with score cut-off values of more than 1 were retained. Then, the compounds retained after being screened via the pharmacodynamic model were screened for docking at the mPGES-1 binding site, followed by high-throughput virtual screening [HTVS] and standard precision [SP] and super-precision [XP] docking, and the compounds in the top 20% of the XP docking score were selected to calculate the total free binding energy of MM-GBSA. The best ten compounds were chosen by comparing their score against the reference ligand 4U9 and the MM-GBSA_dG_Bind score. ADMET analysis resulted in the selection of ten compounds, three of which had desirable medicinal properties. Finally, the binding energy of the target protein mPGES-1 and the candidate ligand compound was analyzed using a 100 ns molecular dynamics simulation of the reference ligand 4U9 and three selected compounds. After a gradual screening study and analysis, we identified a structure that is superior to the reference ligand 4U9 in all aspects, namely compound 15643. Taken together, the results of this study reveal a structure that can be used to inhibit mPGES-1 compound 15643, thereby providing a new option for anti-inflammatory and anti-tumor drugs.

Investigating small molecule compounds targeting psoriasis based on cMAP database and molecular dynamics simulation

OBJECTIVE

Psoriasis (PSO) is a chronic inflammatory skin disease that severely affects the physical and mental health of patients. Drug resistance has been developed upon current drug treatments, and there is no specific therapy. The aim of this study was to screen promising novel drug candidates for PSO using molecular dynamics (MD) simulations.

METHODS

The data of PSO were downloaded from gene expression omnibus (GEO) database and subjected to variance analysis. Target proteins and small molecule compounds targeting PSO were predicted in the connective map (cMAP) database. Molecular docking, MD simulation, and trajectory analysis were conducted to predict the binding of target proteins to compounds.

RESULTS

1999 differentially expressed genes in PSO were obtained by differential analysis. Through cMAP database prediction, a low Score value of -45.69 for lymphocyte cell-specific protein-tyrosine kinase (LCK) was revealed, and aminogenistein was identified as the compound targeting LCK, and LCK was notably highly expressed in the PSO samples. The drugScore of the binding pocket P_0 was 0.814656, which was docked with aminogenistein. The results showed that there were more than one binding site between LCK and aminogenistein with binding energy less than -7.0 kJ/mol, and the docking was relatively stable. The results of root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), Gyrate, number of hydrogen bonds and total free binding energy in MD simulations showed that the binding of aminogenistein to LCK was relatively solid.

CONCLUSION

Aminogenistein has good protein-ligand interaction and stability with LCK, a target of PSO, and is a novel drug candidate for PSO.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

Advances in the discovery of new chemotypes through ultra-large library docking

INTRODUCTION

The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (M^pro).

AREAS COVERED

This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel M^pro inhibitors targeting the SARS-CoV-2 virus.

EXPERT OPINION

With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.

Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors

Targeting SARS-CoV-2 papain-like protease using inhibitors is a suitable approach for inhibition of virus replication and dysregulation of host anti-viral immunity. Engaging all five binding sites far from the catalytic site of PLpro is essential for developing a potent inhibitor. We developed and validated a structure-based pharmacophore model with 9 features of a potent PLpro inhibitor. The pharmacophore model-aided virtual screening of the comprehensive marine natural product database predicted 66 initial hits. This hit library was downsized by filtration through a molecular weight filter of ≤ 500 g/mol. The 50 resultant hits were screened by comparative molecular docking using AutoDock and AutoDock Vina. Comparative molecular docking enables benchmarking docking and relieves the disparities in the search and scoring functions of docking engines. Both docking engines retrieved 3 same compounds at different positions in the top 1 % rank, hence consensus scoring was applied, through which CMNPD28766, aspergillipeptide F emerged as the best PLpro inhibitor. Aspergillipeptide F topped the 50-hit library with a pharmacophore-fit score of 75.916. Favorable binding interactions were predicted between aspergillipeptide F and PLpro similar to the native ligand XR8-24. Aspergillipeptide F was able to engage all the 5 binding sites including the newly discovered BL2 groove, site V. Molecular dynamics for quantification of Cα-atom movements of PLpro after ligand binding indicated that it exhibits highly correlated domain movements contributing to the low free energy of binding and a stable conformation. Thus, aspergillipeptide F is a promising candidate for pharmaceutical and clinical development as a potent SARS-CoV-2 PLpro inhibitor.

LY3041658/ interleukin-8 complex structure as targets for IL-8 small molecule inhibitors discovery using a combination of in silico methods

Since interleukin-8 (IL-8/CXCL8) and its receptor, CXCR1 and CXCR2, were known in the early 1990s, biological pathways related to these proteins were proven to have high clinical value in cancer and inflammatory/autoimmune conditions treatment. Recently, IL-8 has been identified as biomarker for severe COVID-19 patients and COVID-19 prognosis. Boyles et al. (mAbs 12 (2020), pp. 1831880) have published a high-resolution X-ray crystal structure of the LY3041658 Fab in a complex human CXCL8. They described the ability to bind to IL-8 and the blocking of IL-8/its receptors interaction by the LY3041658 monoclonal antibody. Therefore, the study has been designed to identify potential small molecules inhibiting interleukin-8 by targeting LY3041658/IL-8 complex structure using an in silico approach. A structure‑based pharmacophore and molecular docking models of the protein active site cavity were generated to identify possible candidates, followed by virtual screening with the ZINC database. ADME analysis of hit compounds was also conducted. Molecular dynamics simulations were then performed to survey the behaviour and stability of the ligand-protein complexes. Furthermore, the MM/PBSA technique has been utilized to evaluate the free binding energy. The final data confirmed that one newly obtained compound, ZINC21882765, may serve as the best potential inhibitor for IL-8.

The Inhibitors of CDK4/6 from a Library of Marine Compound Database: A Pharmacophore, ADMET, Molecular Docking and Molecular Dynamics Study

BACKGROUND

CDK4/6 (Cyclin-dependent kinases 4/6) are the key promoters of cell cycle transition from G1 phase to S phase. Thus, selective inhibition of CDK4/6 is a promising cancer treatment.

METHODS

A total of 52,765 marine natural products were screened for CDK4/6. To screen out better natural compounds, pharmacophore models were first generated, then the absorption, distribution, metabolism, elimination, and toxicity (ADMET) were tested, followed by molecular docking. Finally, molecular dynamics simulation was carried out to verify the binding characteristics of the selected compounds.

RESULTS

Eighty-seven marine small molecules were screened based on the pharmacophore model. Then, compounds 41369 and 50843 were selected according to the ADMET and molecular docking score for further kinetic simulation evaluation. Finally, through molecular dynamics analysis, it was confirmed that compound 50843 maintained a stable conformation with the target protein, so it has the opportunity to become an inhibitor of CDK4/6.

CONCLUSION

Through structure-based pharmacophore modeling, ADMET, the molecular docking method and molecular dynamics (MD) simulation, marine natural compound 50843 was proposed as a promising marine inhibitor of CDK4/6.

2', 4'-Dihydroxy-2,3-dimethoxychalcone: A pharmacological inverse agonist of RORγt ameliorating Th17-driven inflammatory diseases by regulating Th17/Treg

Multiple sclerosis, inflammatory bowel disease and organ transplant rejection are related to Th17 cell development and inflammatory respond. RORγt, a specific transcription factor regulating Th17 cell differentiation, is a pivotal target for the treatment of diseases. However, the clinical application of RORγt inverse agonists reported so far has been hindered due to limited efficacy and toxic side effects. Plant-derived natural products with drug-like properties and safety are wide and valuable resources for candidate drug discovery. Herein, structure-based virtual screening was used to find out 2',4'-Dihydroxy-2,3-dimethoxychalcone (DDC), a chalcone derivative rich in plants and food, located in the binding pocket of RORγt and targeted to inhibit RORγt activity. DDC repressed murine Th17 differentiation and promoted Treg differentiation remarkably in a dose-dependent manner. In addition, DDC treatment improved experimental autoimmune encephalomyelitis recovery, ameliorated experimental colitis severity, and prevented graft rejection significantly. Mechanically, DDC indirectly stabilized Foxp3 expression by inhibiting RORγt activity and the expression of its target gene profile in vitro and in vivo, which realized its regulation of Th17/Treg balance. In conclusion, our study provides a scientific basis that DDC, as an inverse agonist of RORγt with simple structure, rich sources, low cost, high efficiency, and low toxicity, has great potential for the development of a novel effective immunomodulator for the treatment of Th17-mediated inflammatory diseases.