Practices in Molecular Docking and Structure-Based Virtual Screening

Molecular Docking and Absorption, Distribution, Metabolism, and Excretion (ADME) Analysis: Examining the Binding Modes and Affinities of Myricetin With Insulin Receptor, Glycogen Synthase Kinase, and Glucokinase

Aim By using molecular docking analysis (MDA) to examine its interactions with important regulatory proteins linked to diabetes, such as glycogen synthase kinase 3 beta (GSK3β), insulin receptor (IR), and glucose kinase (GCK), this study seeks to explore the therapeutic potential of myricetin, a naturally occurring flavonoid. Objective The main goal is to determine potential effects on insulin signalling, GSK3β activity, and glucose metabolism by evaluating the binding affinities of myricetin with GCK, IR, and GSK3β through MDA. In order to assess the drug affinity of myricetin, the study also intends to perform absorption, distribution, metabolism, and excretion (ADME) studies. Materials and methods To model the interaction between myricetin and the target proteins (GCK, IR, and GSK3β), we used molecular docking analysis with computational tools. ADME studies were also included in the study to evaluate drug affinity. Identification of binding sites, essential residues, and interaction stability were all part of the structural analysis. Results As evidence of possible interactions with these regulatory proteins, myricetin showed positive binding affinities with GCK, IR, and GSK3β. Strong interactions with important ligand recognition residues were seen in the docking into IR, indicating a potential impact on insulin signalling. Moreover, a strong binding affinity for GCK indicated potential effects on the metabolism of glucose. Studies using ADME confirmed the high drug affinity of myricetin. Conclusion This work sheds light on the multi-target potential of myricetin in the regulation of diabetes. It appears that it has the ability to influence glucose metabolism, suppress GSK3β activity, and regulate insulin signalling based on its interactions with IR, GSK3β, and GCK. Although these computational results show promise, more experimental work is necessary to confirm and fully understand the precise mechanisms that underlie myricetin's effects on the regulation of diabetes.

Analysing potent biomarkers along phytochemicals for breast cancer therapy: an in silico approach

PURPOSE

This research focused on the identification of herbal compounds as potential anti-cancer drugs, especially for breast cancer, that involved the recognition of Notch downstream targets NOTCH proteins (1-4) specifically expressed in breast tumours as biomarkers for prognosis, along with P53 tumour antigens, that were used as comparisons to check the sensitivity of the herbal bio-compounds.

METHODS

After investigating phytochemical candidates, we employed an approach for computer-aided drug design and analysis to find strong breast cancer inhibitors. The present study utilized in silico analyses and protein docking techniques to characterize and rank selected bio-compounds for their efficiency in oncogenic inhibition for use in precise carcinomic cell growth control.

RESULTS

Several of the identified phytocompounds found in herbs followed Lipinski's Rule of Five and could be further investigated as potential medicinal molecules. Based on the Vina score obtained after the docking process, the active compound Epigallocatechin gallate in green tea with NOTCH (1-4) and P53 proteins showed promising results for future drug repurposing. The stiffness and binding stability of green tea pharmacological complexes were further elucidated by the molecular dynamic simulations carried out for the highest scoring phytochemical ligand complex.

CONCLUSION

The target-ligand complex of green tea active compound Epigallocatechin gallate with NOTCH (1-4) had the potential to become potent anti-breast cancer therapeutic candidates following further research involving wet-lab experiments.

A practical guide to machine-learning scoring for structure-based virtual screening

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.

Computer simulation approach to the identification of visfatin-derived angiogenic peptides

Angiogenesis plays an essential role in various normal physiological processes, such as embryogenesis, tissue repair, and skin regeneration. Visfatin is a 52 kDa adipokine secreted by various tissues including adipocytes. It stimulates the expression of vascular endothelial growth factor (VEGF) and promotes angiogenesis. However, there are several issues in developing full-length visfatin as a therapeutic drug due to its high molecular weight. Therefore, the purpose of this study was to develop peptides, based on the active site of visfatin, with similar or superior angiogenic activity using computer simulation techniques.Initially, the active site domain (residues 181∼390) of visfatin was first truncated into small peptides using the overlapping technique. Subsequently, the 114 truncated small peptides were then subjected to molecular docking analysis using two docking programs (HADDOCK and GalaxyPepDock) to generate small peptides with the highest affinity for visfatin. Furthermore, molecular dynamics simulations (MD) were conducted to investigate the stability of the protein-ligand complexes by computing root mean square deviation (RSMD) and root mean square fluctuation(RMSF) plots for the visfatin-peptide complexes. Finally, peptides with the highest affinity were examined for angiogenic activities, such as cell migration, invasion, and tubule formation in human umbilical vein endothelial cells (HUVECs). Through the docking analysis of the 114 truncated peptides, we screened nine peptides with a high affinity for visfatin. Of these, we discovered two peptides (peptide-1: LEYKLHDFGY and peptide-2: EYKLHDFGYRGV) with the highest affinity for visfatin. In an in vitrostudy, these two peptides showed superior angiogenic activity compared to visfatin itself and stimulated mRNA expressions of visfatin and VEGF-A. These results show that the peptides generated by the protein-peptide docking simulation have a more efficient angiogenic activity than the original visfatin.

Beware of Simple Methods for Structure-Based Virtual Screening: The Critical Importance of Broader Comparisons

We discuss how data unbiasing and simple methods such as protein-ligand Interaction FingerPrint (IFP) can overestimate virtual screening performance. We also show that IFP is strongly outperformed by target-specific machine-learning scoring functions, which were not considered in a recent report concluding that simple methods were better than machine-learning scoring functions at virtual screening.

Improving drug discovery through parallelism

Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered.

New putative therapeutic targets against Serratia marcescens using reverse vaccinology and subtractive genomics

The Gram-negative bacillus Serratia marcescens, a member of Enterobacteriaceae family, is an opportunistic nosocomial pathogen commonly found in hospital outbreaks that can cause infections in the urinary tract, bloodstream, central nervous system and pneumonia. Because S. marcescens strains are resistant to several antibiotics, it is critical the need for effective treatments, including new drugs and vaccines. Here, we applied reverse vaccinology and subtractive genomic approaches for the in silico prediction of potential vaccine and drug targets against 59 strains of S. marcescens. We found 759 core non-host homologous proteins, of which 87 are putative surface-exposed proteins, 183 secreted proteins, and 80 membrane proteins. From these proteins, we predicted seven candidates vaccine targets: a sn-glycerol-3-phosphate-binding periplasmic protein UgpB, a vitamin B12 TonB-dependent receptor, a ferrichrome porin FhuA, a divisome-associated lipoprotein YraP, a membrane-bound lytic murein transglycosylase A, a peptidoglycan lytic exotransglycosylase, and a DUF481 domain-containing protein. We also predicted two drug targets: a N(4)-acetylcytidine amidohydrolase, and a DUF1428 family protein. Using the molecular docking approach for each drug target, we identified and selected ZINC04259491 and ZINC04235390 molecules as the most favorable interactions with the target active site residues. Our findings may contribute to the development of vaccines and new drug targets against S. marcescens. Communicated by Ramaswamy H. Sarma.

Boosting the Full Potential of PyMOL with Structural Biology Plugins

Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses.

DockingPie: a consensus docking plugin for PyMOL

MOTIVATION

The primary strategy for predicting the binding mode of small molecules to their receptors and for performing receptor-based virtual screening studies is protein-ligand docking, which is undoubtedly the most popular and successful approach in computer-aided drug discovery. The increased popularity of docking has resulted in the development of different docking algorithms and scoring functions. Nonetheless, it is unlikely that a single approach outperforms the others in terms of reproducibility and precision. In this ground, consensus docking techniques are taking hold.

RESULTS

We have developed DockingPie, an open source PyMOL plugin for individual, as well as consensus docking analyses. Smina, AutoDock Vina, ADFR and RxDock are the four docking engines that DockingPie currently supports in an easy and extremely intuitive way, thanks to its integrated docking environment and its GUI, fully integrated within PyMOL.

AVAILABILITY AND IMPLEMENTATION

https://github.com/paiardin/DockingPie.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Assay development in leishmaniasis drug discovery: a comprehensive review

INTRODUCTION

Cutaneous, muco-cutaneous and visceral leishmaniasis occur due to an infection with the protozoan parasite Leishmania. The current therapeutic options are limited mainly due to extensive toxicity, emerging resistance and variation in efficacy based on species and strain of the Leishmania parasite. There exists a high unmet medical need to identify new chemical starting points for drug discovery to tackle the disease.

AREAS COVERED

The authors have highlighted the recent progress, limitations and successes achieved in assay development for leishmaniasis drug discovery.

EXPERT OPINION

It is true that sophisticated and robust phenotypic in vitro assays have been developed during the last decade, however limitations and challenges remain with respect to variation in activity reported between different research groups and success in translating in vitro outcomes in vivo. The variability is not only due to strain and species differences but also a lack of well-defined criteria and assay conditions, e.g. culture media, host cell type, assay formats, parasite form used, multiplicity of infection and incubation periods. Thus, there is an urgent need for more physiologically relevant assays that encompass multi-species phenotypic approaches to identify new chemical starting points for leishmaniasis drug discovery.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

An Overview of Biological and Computational Methods for Designing Mechanism-Informed Anti-biofilm Agents

Bacterial biofilms are complex and highly antibiotic-resistant aggregates of microbes that form on surfaces in the environment and body including medical devices. They are key contributors to the growing antibiotic resistance crisis and account for two-thirds of all infections. Thus, there is a critical need to develop anti-biofilm specific therapeutics. Here we discuss mechanisms of biofilm formation, current anti-biofilm agents, and strategies for developing, discovering, and testing new anti-biofilm agents. Biofilm formation involves many factors and is broadly regulated by the stringent response, quorum sensing, and c-di-GMP signaling, processes that have been targeted by anti-biofilm agents. Developing new anti-biofilm agents requires a comprehensive systems-level understanding of these mechanisms, as well as the discovery of new mechanisms. This can be accomplished through omics approaches such as transcriptomics, metabolomics, and proteomics, which can also be integrated to better understand biofilm biology. Guided by mechanistic understanding, in silico techniques such as virtual screening and machine learning can discover small molecules that can inhibit key biofilm regulators. To increase the likelihood that these candidate agents selected from in silico approaches are efficacious in humans, they must be tested in biologically relevant biofilm models. We discuss the benefits and drawbacks of in vitro and in vivo biofilm models and highlight organoids as a new biofilm model. This review offers a comprehensive guide of current and future biological and computational approaches of anti-biofilm therapeutic discovery for investigators to utilize to combat the antibiotic resistance crisis.

Assessing multiple score functions in Rosetta for drug discovery

Rosetta is a computational software suite containing algorithms for a wide variety of macromolecular structure prediction and design tasks including small molecule protocols commonly used in drug discovery or enzyme design. Here, we benchmark RosettaLigand score functions and protocols in comparison to results of other software recently published in the Comparative Assessment of Score Functions (CASF-2016). The CASF-2016 benchmark covers a wide variety of tests including scoring and ranking multiple compounds against a target, ligand docking of a small molecule to a target, and virtual screening to extract binders from a compound library. Direct comparison to the score functions provided by CASF-2016 results shows that the original RosettaLigand score function ranks among the top software for scoring, ranking, docking and screening tests. Most notably, the RosettaLigand score function ranked 2/34 among other report score functions in CASF-2016. We additionally perform a ligand docking test with full sampling to mimic typical use cases. Despite improved performance of newer score functions in canonical protein structure prediction and design, we demonstrate here that more recent Rosetta score functions have reduced performance across all small molecule benchmarks. The tests described here have also been uploaded to the Rosetta scientific benchmarking server and will be run weekly to track performance as the code is continually being developed.

Of Drugs and Trypanosomatids: New Tools and Knowledge to Reduce Bottlenecks in Drug Discovery

Leishmaniasis (Leishmania species), sleeping sickness (Trypanosoma brucei), and Chagas disease (Trypanosoma cruzi) are devastating and globally spread diseases caused by trypanosomatid parasites. At present, drugs for treating trypanosomatid diseases are far from ideal due to host toxicity, elevated cost, limited access, and increasing rates of drug resistance. Technological advances in parasitology, chemistry, and genomics have unlocked new possibilities for novel drug concepts and compound screening technologies that were previously inaccessible. In this perspective, we discuss current models used in drug-discovery cascades targeting trypanosomatids (from in vitro to in vivo approaches), their use and limitations in a biological context, as well as different examples of recently discovered lead compounds.

Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set

Protein-peptide interactions play a crucial role in many cellular and biological functions, which justify the increasing interest in the development of peptide-based drugs. However, predicting experimental binding modes and affinities in protein-peptide docking remains a great challenge for most docking programs due to some particularities of this class of ligands, such as the high degree of flexibility. In this paper, we present the performance of the DockThor program on the LEADS-PEP data set, a benchmarking set composed of 53 diverse protein-peptide complexes with peptides ranging from 3 to 12 residues and with up to 51 rotatable bonds. The DockThor performance for pose prediction on redocking studies was compared with some state-of-the-art docking programs that were also evaluated on the LEADS-PEP data set, AutoDock, AutoDock Vina, Surflex, GOLD, Glide, rDock, and DINC, as well as with the task-specific docking protocol HPepDock. Our results indicate that DockThor could dock 40% of the cases with an overall backbone RMSD below 2.5 Å when the top-scored docking pose was considered, exhibiting similar results to Glide and outperforming other protein-ligand docking programs, whereas rDock and HPepDock achieved superior results. Assessing the docking poses closest to the crystal structure (i.e., best-RMSD pose), DockThor achieved a success rate of 60% in pose prediction. Due to the great overall performance of handling peptidic compounds, the DockThor program can be considered as suitable for docking highly flexible and challenging ligands, with up to 40 rotatable bonds. DockThor is freely available as a virtual screening Web server at https://www.dockthor.lncc.br/ .

Why hasn't there been more progress in new Chagas disease drug discovery?

Introduction: Chagas disease (CD) is a neglected disease caused by the protozoan parasite Trypanosoma cruzi. In terms of novel drug discovery, there has been no progress since the 1960s with the same two drugs, benznidazole and nifurtimox, still in use. The complex life cycle, genetic diversity of T. cruzi strains, different sensitivities to the available drugs, as well as little interest from pharmaceutical companies and inadequate methodologies for translating in vitro and in vivo findings to the discovery of new drugs have all contributed to the lack of progress.Areas covered: In this perspective, the authors give discussion to the relevant points connected to the lack of developments in CD drug discovery and provide their expert perspectives.Expert opinion: There are few drugs currently in the preclinical pipeline for the treatment of CD. Only three classes of compounds have been shown to achieve high cure rates in mouse models of infection: nitroimidazoles (fexinidazole), oxaborole DNDi-6148 and proteasome inhibitors (GNF6702). New biomarkers for Chagas' disease are urgently needed for the diagnosis and detection of cure/treatment efficacy. Efforts from academia and pharmaceutical companies are in progress and more intense interaction to accelerate the process of new drugs development is necessary.

Cruzain structures: apocruzain and cruzain bound to S-methyl thiomethanesulfonate and implications for drug design

Chagas disease, which is caused by Trypanosoma cruzi, affects more than six million people worldwide. Cruzain is the major cysteine protease involved in the survival of this parasite. Here, the expression, purification and crystallization of this enzyme are reported. The cruzain crystals diffracted to 1.2 Å resolution, yielding two novel cruzain structures: apocruzain and cruzain bound to the reversible covalent inhibitor S-methyl thiomethanesulfonate. Mass-spectrometric experiments confirmed the presence of a methylthiol group attached to the catalytic cysteine. Comparison of these structures with previously published structures indicates the rigidity of the cruzain structure. These results provide further structural information about the enzyme and may help in new in silico studies to identify or optimize novel prototypes of cruzain inhibitors.

ImmtorLig_DB: repertoire of virtually screened small molecules against immune receptors to bolster host immunity

Host directed therapies to boost immunity against infection are gaining considerable impetus following the observation that use of antibiotics has become a continuous source for the emergence of drug resistant strains of pathogens. Receptors expressed by the cells of immune system play a cardinal role in initiating sequence of events necessary to ameliorate many morbid conditions. Although, ligands for the immune receptors are available; but their use is limited due to complex structure, synthesis and cost-effectiveness. Virtual screening (VS) is an integral part of chemoinformatics and computer-aided drug design (CADD) and aims to streamline the process of drug discovery. ImmtorLig_DB is a repertoire of 5000 novel small molecules, screened from ZINC database and ranked using structure based virtual screening (SBVS) against 25 immune receptors which play a pivotal role in defending and initiating the activation of immune system. Consequently, in the current study, small molecules were screened by docking on the essential domains present on the receptors expressed by cells of immune system. The screened molecules exhibited efficacious binding to immune receptors, and indicated a possibility of discovering novel small molecules. Other features of ImmtorLig_DB include information about availability, clustering analysis, and estimation of absorption, distribution, metabolism, and excretion (ADME) properties of the screened small molecules. Structural comparisons indicate that predicted small molecules may be considered novel. Further, this repertoire is available via a searchable graphical user interface (GUI) through http://bioinfo.imtech.res.in/bvs/immtor/ .

Structure investigation, enrichment analysis and structure-based repurposing of FDA-approved drugs as inhibitors of BET-BRD4

We report herein detailed structural insights into the ligand recognition modes guiding bromodomain selectivity, enrichment analysis and docking-based database screening for the identification of the FDA-approved drugs that have potential to be the human BRD4 inhibitors. Analysis of multiple X-ray structures prevailed that the lysine-recognition sites are highly conserved, and apparently, the dynamic ZA loop guides the specific ligand-recognition. The protein-ligand interaction profiling revealed that both BRD2 and BRD4 shared hydrophobic interaction of bound ligands with PRO-98/PRO-82, PHE-99/PHE-83, LEU-108/LEU-92 and direct H-bonding with ASN-156/ASN-140 (BRD2/BRD4), while on the other hand the water-mediated H-bonding of bound ligands with PRO-82, GLN-85, PRO-86, VAL-87, ASP-88, LEU-92, TYR-97 and MET-132, and aromatic π-π stacking with TRP-81 prevailed as unique interaction in BRD4, and were not observed in BRD2. Subsequently, through ROC curve analysis, the best enrichment was found with PDB-ID 4QZS of BRD4 structures. Finally, through docking-based database screening study, we found that several drugs have better binding affinity than the control candidate lead (+)-JQ1 (Binding affinity = -7.9 kcal/mol), a well-known BRD4 inhibitor. Among the top-ranked drugs, azelastine, a selective histamine H1 receptor antagonist, showed the best binding affinity of -9.3 kcal/mol and showed interactions with several key residues of the acetyl lysine binding pocket. Azelastine may serve as a promising template for further medicinal chemistry. These insights may serve as basis for structure-based drug design, drug repurposing and the discovery of novel BRD4 inhibitors. Communicated by Ramaswamy H. Sarma.

Chemoinformatics Strategies for Leishmaniasis Drug Discovery

Leishmaniasis is a fatal neglected tropical disease (NTD) that is caused by more than 20 species of Leishmania parasites. The disease kills approximately 20,000 people each year and more than 1 billion are susceptible to infection. Although counting on a few compounds, the therapeutic arsenal faces some drawbacks such as drug resistance, toxicity issues, high treatment costs, and accessibility problems, which highlight the need for novel treatment options. Worldwide efforts have been made to that aim and, as well as in other therapeutic areas, chemoinformatics have contributed significantly to leishmaniasis drug discovery. Breakthrough advances in the comprehension of the parasites’ molecular biology have enabled the design of high-affinity ligands for a number of macromolecular targets. In addition, the use of chemoinformatics has allowed highly accurate predictions of biological activity and physicochemical and pharmacokinetics properties of novel antileishmanial compounds. This review puts into perspective the current context of leishmaniasis drug discovery and focuses on the use of chemoinformatics to develop better therapies for this life-threatening condition.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

Luteolin Inhibits Vascular Smooth Muscle Cell Proliferation and Migration by Inhibiting TGFBR1 Signaling

Vascular smooth muscle cell (VSMC) proliferation and migration play a critical role in the development of arterial remodeling during various vascular diseases including atherosclerosis, hypertension, and related diseases. Luteolin is a food-derived flavonoid that exerts protective effects on cardiovascular diseases. Here, we investigated whether transforming growth factor-β receptor 1 (TGFBR1) signaling underlies the inhibitory effects of luteolin on VSMC proliferation and migration. We found that luteolin reduced the proliferation and migration of VSMCs, specifically A7r5 and HASMC cells, in a dose-dependent manner, based on MTS and EdU, and Transwell and wound healing assays, respectively. We also demonstrated that it inhibited the expression of proliferation-related proteins including PCNA and Cyclin D1, as well as the migration-related proteins MMP2 and MMP9, in a dose-dependent manner by western blotting. In addition, luteolin dose-dependently inhibited the phosphorylation of TGFBR1, Smad2, and Smad3. Notably, adenovirus-mediated overexpression of TGFBR1 enhanced TGFBR1, Smad2, and Smad3 activation in VSMCs and partially blocked the inhibitory effect of luteolin on TGFBR1, Smad2, and Smad3. Moreover, overexpression of TGFBR1 rescued the inhibitory effects of luteolin on the proliferation and migration of VSMCs. Additionally, molecular docking showed that this compound could dock onto an agonist binding site of TGFBR1, and that the binding energy between luteolin and TGFBR1 was -10.194 kcal/mol. Simulations of molecular dynamics showed that TGFBR1-luteolin binding was stable. Collectively, these data demonstrated that luteolin might inhibit VSMC proliferation and migration by suppressing TGFBR1 signaling.