Virtual Screening and Molecular Dynamics Study of Potential Negative Allosteric Modulators of mGluR1 from Chinese Herbs

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

AIM

Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics.

METHODS

A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace.

RESULTS

A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications.

CONCLUSIONS

Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).

Functionalized non-woven surfaces for combating the spread of the COVID-19 pandemic

The worldwide outbreak of SARS-CoV-2 infection has necessitated mandatory use of face masks, personal protective equipment and intake of a healthy diet for immunity boosting. As per WHO's recommendation, continuous use of masks has been proven effective in decreasing the SARS-CoV-2 infection rate. The present study reports on the bacterial filtration efficacy (BFE) of a novel 4-ply functionalized non-woven face mask. We synthesized a polypropylene-based fabric with inner layers of melt-blown fine fibres coated with polylactic acid and immune-boosting herbal phytochemicals. Experimental studies on the synthesized face mask demonstrated a BFE of greater than 99% against Staphylococcus aureus (a bacterium species frequently found in mammalian respiratory tract). A thorough computational analysis using LibDock algorithm demonstrated an effective docking performance of herbal phytochemicals against harmful virus structures. More importantly, the face mask also showed sufficient and stable breathability as per regulatory standards. A breathing resistance of 30 Pa at an aerosol flow rate of 30 l h⁻¹ was reported under standard temperature and pressure conditions, indicating a high potential for real-world applications.

Needleless electrospun phytochemicals encapsulated nanofibre based 3-ply biodegradable mask for combating COVID-19 pandemic

The emergence of COVID-19 pandemic has severely affected human health and world economies. According to WHO guidelines, continuous use of face mask is mandatory for personal protection for restricting the spread of bacteria and virus. Here, we report a 3-ply cotton-PLA-cotton layered biodegradable face-mask containing encapsulated phytochemicals in the inner-filtration layer. The nano-fibrous PLA filtration layer was fabricated using needleless electrospinning of PLA & phytochemical-based herbal-extracts. This 3-layred face mask exhibits enhanced air permeability with a differential pressure of 35.78 Pa/cm² and superior bacterial filtration efficiency of 97.9% compared to conventional face masks. Close-packed mesh structure of the nano-fibrous mat results in effective adsorption of particulate matter, aerosol particles, and bacterial targets deep inside the filtration layer. The outer hydrophobic layer of mask exhibited effective blood splash resistance up to a distance of 30 cm, ensuring its utilization for medical practices. Computational analysis of constituent phytochemicals using the LibDock algorithm predicted inhibitory activity of chemicals against the protein structured bacterial sites. The computational analysis projected superior performance of phytochemicals considering the presence of stearic acid, oleic acid, linoleic acid, and Arachidic acid exhibiting structural complementarity to inhibit targeted bacterial interface. Natural cotton fibers and PLA bio-polymer demonstrated promising biodegradable characteristics in the presence of in-house cow-dung based biodegradation slurry. Addition of jaggery to the slurry elevated the biodegradation performance, resulting in increment of change of weight from 07% to 12%. The improved performance was attributed to the increased sucrose content in biodegradation slurry, elevating the bacterial growth in the slurry. An innovative face mask has shown promising results for utilization in day-to-day life and medical frontline workers, considering the post-pandemic environmental impacts.

Chinese Herbal Medicine Interventions in Neurological Disorder Therapeutics by Regulating Glutamate Signaling

Glutamate is the major excitatory neurotransmitter in the central nervous system, and its signaling is critical for excitatory synaptic transmission. The well-established glutamate system involves glutamate synthesis, presynaptic glutamate release, glutamate actions on the ionotropic glutamate receptors (NMDA, AMPA, and kainate receptors) and metabotropic glutamate receptors, and glutamate uptake by glutamate transporters. When the glutamate system becomes dysfunctional, it contributes to the pathogenesis of neurodegenerative and neuropsychiatric diseases such as Alzheimer's disease, Parkinson's disease, depression, epilepsy, and ischemic stroke. In this review, based on regulating glutamate signaling, we summarize the effects and underlying mechanisms of natural constituents from Chinese herbal medicines on neurological disorders. Natural constituents from Chinese herbal medicine can prevent the glutamate-mediated excitotoxicity via suppressing presynaptic glutamate release, decreasing ionotropic and metabotropic glutamate receptors expression in the excitatory synapse, and promoting astroglial glutamate transporter expression to increase glutamate clearance from the synaptic cleft. However, some natural constituents from Chinese herbal medicine have the ability to restore the collapse of excitatory synapses by promoting presynaptic glutamate release and increasing ionotropic and metabotropic glutamate receptors expression. These regulatory processes involve various signaling pathways, which lead to different mechanistic routes of protection against neurological disorders. Hence, our review addresses the underlying mechanisms of natural constituents from Chinese herbal medicines that regulate glutamate systems and serve as promising agents for the treatment of the above-mentioned neurological disorders.

Computational Drug Design Applied to the Study of Metabotropic Glutamate Receptors

Metabotropic glutamate (mGlu) receptors are a family of eight GPCRs that are attractive drug discovery targets to modulate glutamate action and response. Here we review the application of computational methods to the study of this family of receptors. X-ray structures of the extracellular and 7-transmembrane domains have played an important role to enable structure-based modeling approaches, whilst we also discuss the successful application of ligand-based methods. We summarize the literature and highlight the areas where modeling and experiment have delivered important understanding for mGlu receptor drug discovery. Finally, we offer suggestions of future areas of opportunity for computational work.

Progress in Allosteric Database

An allosteric mechanism refers to the biological regulation process wherein macromolecules propagate the effect of ligand binding at one site to a spatially distant orthosteric locus, thus affecting activity. The theory has remained a trending topic in biology research for over 50 years, since the understanding of allostery is fundamental for gleaning numerous biological processes and developing new drug therapies. In the past two decades, the allosteric paradigm has evolved into more descriptive models, with ever-expanding amounts of experimental data pertaining to newly identified allosteric molecules. The AlloSteric Database (ASD, accessible at http://mdl.shsmu.edu.cn/ASD ), which is a comprehensive knowledge repository, has provided the public with integrated information encompassing allosteric proteins, modulators, sites, pathways, and networks to investigate allostery since 2009. In this chapter, we introduce the history and usage of the ASD and give attention to specific applications that have benefited from the ASD.

An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge

Molecular dynamics (MD) has become increasingly popular due to the development of hardware and software solutions and the improvement in algorithms, which allowed researchers to scale up calculations in order to speed them up. MD simulations are usually used to address protein folding issues or protein-ligand complex stability through energy profile analysis over time. In recent years, the development of new tools able to deeply explore a potential energy surface (PES) has allowed researchers to focus on the dynamic nature of the binding recognition process and binding-induced protein conformational changes. Moreover, modern approaches have been demonstrated to be effective and reliable in calculating some kinetic and thermodynamic parameters behind the host-guest recognition process. Starting from all of these considerations, several efforts have been made in order to integrate MD within the virtual screening process in drug discovery. Knowledge retrieved from MD can, in fact, be exploited as a starting point to build pharmacophores or docking constraints in the early stage of the screening campaign as well as to define key features, in order to unravel hidden binding modes and help the optimisation of the molecular structure of a lead compound. Based on these outcomes, researchers are nowadays using MD as an invaluable tool to discover and target previously considered undruggable binding sites, including protein-protein interactions and allosteric sites on a protein surface. As a matter of fact, the use of MD has been recognised as vital to the discovery of selective protein-protein interaction modulators. The use of a dynamic overview on how the host-guest recognition occurs and of the relative conformational modifications induced allows researchers to optimise small molecules and small peptides capable of tightly interacting within the cleft between two proteins. In this review, we aim to present the most recent applications of MD as an integrated tool to be used in the rational design of small molecules or small peptides able to modulate undruggable targets, such as allosteric sites and protein-protein interactions.

Discovery of Potential Inhibitors of Squalene Synthase from Traditional Chinese Medicine Based on Virtual Screening and In Vitro Evaluation of Lipid-Lowering Effect

Squalene synthase (SQS), a key downstream enzyme involved in the cholesterol biosynthetic pathway, plays an important role in treating hyperlipidemia. Compared to statins, SQS inhibitors have shown a very significant lipid-lowering effect and do not cause myotoxicity. Thus, the paper aims to discover potential SQS inhibitors from Traditional Chinese Medicine (TCM) by the combination of molecular modeling methods and biological assays. In this study, cynarin was selected as a potential SQS inhibitor candidate compound based on its pharmacophoric properties, molecular docking studies and molecular dynamics (MD) simulations. Cynarin could form hydrophobic interactions with PHE54, LEU211, LEU183 and PRO292, which are regarded as important interactions for the SQS inhibitors. In addition, the lipid-lowering effect of cynarin was tested in sodium oleate-induced HepG2 cells by decreasing the lipidemic parameter triglyceride (TG) level by 22.50%. Finally. cynarin was reversely screened against other anti-hyperlipidemia targets which existed in HepG2 cells and cynarin was unable to map with the pharmacophore of these targets, which indicated that the lipid-lowering effects of cynarin might be due to the inhibition of SQS. This study discovered cynarin is a potential SQS inhibitor from TCM, which could be further clinically explored for the treatment of hyperlipidemia.

In Silico Analysis of the Association Relationship between Neuroprotection and Flavors of Traditional Chinese Medicine Based on the mGluRs

The metabotropic glutamate receptors (mGluRs) are known as both synaptic receptors and taste receptors. This feature is highly similar to the Property and Flavor theory of Traditional Chinese medicine (TCM), which has the pharmacological effect and flavor. In this study, six ligand based pharmacophore (LBP) models, seven homology modeling models, and fourteen molecular docking models of mGluRs were built based on orthosteric and allosteric sites to screening potential compounds from Traditional Chinese Medicine Database (TCMD). Based on the Pharmacopoeia of the People's Republic of China, TCMs of compounds and their flavors were traced and listed. According to the tracing result, we found that the TCMs of the compounds which bound to orthosteric sites of mGluRs are highly correlated to a sweet flavor, while the allosteric site corresponds to a bitter flavor. Meanwhile, the pharmacological effects of TCMs with highly frequent flavors were further analyzed. We found that those TCMs play a neuroprotective role through the efficiencies of detumescence, promoting blood circulation, analgesic effect, and so on. This study provides a guide for developing new neuroprotective drugs from TCMs which target mGluRs. Moreover, it is the first study to present a novel approach to discuss the association relationship between flavor and the neuroprotective mechanism of TCM based on mGluRs.

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or catalytic enzyme is known experimentally. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to inhibit a particular protein interaction or biological activity. The virtual ligand screening process often relies on docking methods which allow predicting the binding of a molecule into a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of protein flexibility information, no solvation effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions and even in membrane-like environments, and ranking complexes with more accurate binding energy calculations. In this chapter we describe the up-to-date MD protocols that are mandatory supporting tools in the virtual ligand screening (VS) process. Using docking in combination with MD is one of the best computer-aided drug design protocols nowadays. It has proved its efficiency through many examples, described below.

Opportunities and Challenges in the Discovery of Allosteric Modulators of GPCRs

From the pharmacological point of view, allosteric modulators may present numerous advantages over orthosteric ligands. Growing availability of novel tools and experimental data provides a tempting opportunity to apply computational methods to improve known modulators and design novel ones. However, recent progress in understanding of complexity of allostery increases awareness of problems involved in design of modulators with desired properties. Deeper insight into phenomena such as probe dependence, altering signaling bias with minor changes in ligand structure, as well as influence of subtle endogenous allosteric factors turns out to be fundamental. These effects make the design of a modulator with precise pharmacological outcome a very challenging task, and need to be taken into consideration throughout the design process. In this chapter, we focus on nuances of targeting GPCR allosteric sites in computational drug design efforts, in particular with application of docking, virtual screening, and molecular dynamics.

The potential role of in silico approaches to identify novel bioactive molecules from natural resources

In recent years, integration of in silico approaches to natural product (NP) research reawakened the declined interest in NP-based drug discovery efforts. In particular, advancements in cheminformatics enabled comparison of NP databases with contemporary small-molecule libraries in terms of molecular properties and chemical space localizations. Virtual screening and target fishing approaches were successful in recognizing the untold macromolecular targets for NPs to exploit the unmet therapeutic needs. Developments in molecular docking and scoring methods along with molecular dynamics enabled to predict the target-ligand interactions more accurately taking into consideration the remarkable structural complexity of NPs. Hence, innovative in silico strategies have contributed valuably to the NP research in drug discovery processes as reviewed herein. [Formula: see text].

Discovery of Anti-Hypertensive Oligopeptides from Adlay Based on In Silico Proteolysis and Virtual Screening

Adlay (Coix larchryma-jobi L.) was the commonly used Traditional Chinese Medicine (TCM) with high content of seed storage protein. The hydrolyzed bioactive oligopeptides of adlay have been proven to be anti-hypertensive effective components. However, the structures and anti-hypertensive mechanism of bioactive oligopeptides from adlay were not clear. To discover the definite anti-hypertensive oligopeptides from adlay, in silico proteolysis and virtual screening were implemented to obtain potential oligopeptides, which were further identified by biochemistry assay and molecular dynamics simulation. In this paper, ten sequences of adlay prolamins were collected and in silico hydrolyzed to construct the oligopeptide library with 134 oligopeptides. This library was reverse screened by anti-hypertensive pharmacophore database, which was constructed by our research team and contained ten anti-hypertensive targets. Angiotensin-I converting enzyme (ACE) was identified as the main potential target for the anti-hypertensive activity of adlay oligopeptides. Three crystal structures of ACE were utilized for docking studies and 19 oligopeptides were finally identified with potential ACE inhibitory activity. According to mapping features and evaluation indexes of pharmacophore and docking, three oligopeptides were selected for biochemistry assay. An oligopeptide sequence, NPATY (IC50 = 61.88 ± 2.77 µM), was identified as the ACE inhibitor by reverse-phase high performance liquid chromatography (RP-HPLC) assay. Molecular dynamics simulation of NPATY was further utilized to analyze interactive bonds and key residues. ALA354 was identified as a key residue of ACE inhibitors. Hydrophobic effect of VAL518 and electrostatic effects of HIS383, HIS387, HIS513 and Zn2+ were also regarded as playing a key role in inhibiting ACE activities. This study provides a research strategy to explore the pharmacological mechanism of Traditional Chinese Medicine (TCM) proteins based on in silico proteolysis and virtual screening, which could be beneficial to reveal the pharmacological action of TCM proteins and provide new lead compounds for peptides-based drug design.

Identification of potential ACAT-2 selective inhibitors using pharmacophore, SVM and SVR from Chinese herbs

Acyl-coenzyme A cholesterol acyltransferase (ACAT) plays an important role in maintaining cellular and organismal cholesterol homeostasis. Two types of ACAT isozymes with different functions exist in mammals, named ACAT-1 and ACAT-2. Numerous studies showed that ACAT-2 selective inhibitors are effective for the treatment of hypercholesterolemia and atherosclerosis. However, as a typical endoplasmic reticulum protein, ACAT-2 protein has not been purified and revealed, so combinatorial ligand-based methods might be the optimal strategy for discovering the ACAT-2 selective inhibitors. In this study, selective pharmacophore models of ACAT-1 inhibitors and ACAT-2 inhibitors were built, respectively. The optimal pharmacophore model for each subtype was identified and utilized as queries for the Traditional Chinese Medicine Database screening. A total of 180 potential ACAT-2 selective inhibitors were obtained, which were identified using an ACAT-2 pharmacophore and not by our ACAT-1 model. Selective SVM model and bioactive SVR model were generated for further identification of the obtained ACAT-2 inhibitors. Ten compounds were finally obtained with predicted inhibitory activities toward ACAT-2. Hydrogen bond acceptor, 2D autocorrelations, GETAWAY descriptors, and BCUT descriptors were identified as key structural features for selectivity and activity of ACAT-2 inhibitors. This study provides a reasonable ligand-based approach to discover potential ACAT-2 selective inhibitors from Chinese herbs, which could help in further screening and development of ACAT-2 selective inhibitors.

Discovery of Potential Inhibitors of Aldosterone Synthase from Chinese Herbs Using Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Simulation Studies

Aldosterone synthase (CYP11B2) is a key enzyme for the biosynthesis of aldosterone, which plays a significant role for the regulation of blood pressure. Excess aldosterone can cause the dysregulation of the renin-angiotensin-aldosterone system (RAAS) and lead to hypertension. Therefore, research and development of CYP11B2 inhibitor are regarded as a novel approach for the treatment of hypertension. In this study, the pharmacophore models of CYP11B2 inhibitors were generated and the optimal model was used to identify potential CYP11B2 inhibitors from the Traditional Chinese Medicine Database (TCMD, Version 2009). The hits were further refined by molecular docking and the interactions between compounds and CYP11B2 were analyzed. Compounds with high Fitvalue, high docking score, and expected interactions with key residues were selected as potential CYP11B2 inhibitors. Two most promising compounds, ethyl caffeate and labiatenic acid, with high Fitvalue and docking score were reserved for molecular dynamics (MD) study. All of them have stability of ligand binding which suggested that they might perform the inhibitory effect on CYP11B2. This study provided candidates for novel drug-like CYP11B2 inhibitors by molecular simulation methods for the hypertension treatment.

Identification of potential CCR5 inhibitors through pharmacophore-based virtual screening, molecular dynamics simulation and binding free energy analysis

Employing the combined strategy to identify novel CCR5 inhibitors and provide a basis for rational drug design.

Communication: Entropic measure to prevent energy over-minimization in molecular dynamics simulations

This work examines the impact of energy over-minimization on an ensemble of biological molecules subjected to the potential energy minimization procedure in vacuum. In the studied structures, long potential energy minimization stage leads to an increase of the main- and side-chain entropies in proteins. We show that such over-minimization may diverge the protein structures from the near-native attraction basin which possesses a minimum of free energy. We propose a measure based on the Pareto front of total entropy for quality assessment of minimized protein conformation. This measure may help in selection of adequate number of energy minimization steps in protein modelling and, thus, in preservation of the near-native protein conformation.