Minghua Cui

Bio: Minghua Cui is an academic researcher from Ewha Womans University. The author has contributed to research in topics: TRPV1 & Docking (molecular). The author has an hindex of 13, co-authored 27 publications receiving 495 citations. Previous affiliations of Minghua Cui include Wonkwang University.

Harnessing the Therapeutic Potential of Capsaicin and Its Analogues in Pain and Other Diseases

Abstract: Capsaicin is the most predominant and naturally occurring alkamide found in Capsicum fruits. Since its discovery in the 19th century, the therapeutic roles of capsaicin have been well characterized. The potential applications of capsaicin range from food flavorings to therapeutics. Indeed, capsaicin and few of its analogues have featured in clinical research covered by more than a thousand patents. Previous records suggest pleiotropic pharmacological activities of capsaicin such as an analgesic, anti-obesity, anti-pruritic, anti-inflammatory, anti-apoptotic, anti-cancer, anti-oxidant, and neuro-protective functions. Moreover, emerging data indicate its clinical significance in treating vascular-related diseases, metabolic syndrome, and gastro-protective effects. The dearth of potent drugs for management of such disorders necessitates the urge for further research into the pharmacological aspects of capsaicin. This review summarizes the historical background, source, structure and analogues of capsaicin, and capsaicin-triggered TRPV1 signaling and desensitization processes. In particular, we will focus on the therapeutic roles of capsaicin and its analogues in both normal and pathophysiological conditions.

Exploring G protein-coupled receptors (GPCRs) ligand space via cheminformatics approaches: Impact on rational drug design

Abstract: The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.

Transient receptor potential vanilloid type 1 antagonists: a patent review (2011 – 2014)

Abstract: Introduction: Transient receptor potential vanilloid type 1 (TRPV1) is a nonselective cation channel that can be activated by noxious heat, low pH and vanilloid compounds such as capsaicin. Since TRPV1 acts as an integrator of painful stimuli, TRPV1 antagonists can be used as promising therapeutics for new types of analgesics.Areas covered: This review article covers the patents that claim TRPV1 antagonists and were published during 2011 – 2014. The patent evaluation is organized according to the applicant companies, and the representative chemical entities with important in vitro and in vivo data are summarized.Expert opinion: Many pharmaceutical companies showed promising results in the discovery of potent small molecule TRPV1 antagonists, and recently, a number of small molecule TRPV1 antagonists have been advanced into clinical trials. Unfortunately, several candidate molecules showed critical side effects such as hyperthermia and impaired noxious heat sensation in humans, leading to their withdrawal ...

Discovery of Potent Human Glutaminyl Cyclase Inhibitors as Anti-Alzheimer's Agents Based on Rational Design.

Abstract: Glutaminyl cyclase (QC) has been implicated in the formation of toxic amyloid plaques by generating the N-terminal pyroglutamate of β-amyloid peptides (pGlu-Aβ) and thus may participate in the pathogenesis of Alzheimer’s disease (AD). We designed a library of glutamyl cyclase (QC) inhibitors based on the proposed binding mode of the preferred substrate, Aβ3E−42. An in vitro structure–activity relationship study identified several excellent QC inhibitors demonstrating 5- to 40-fold increases in potency compared to a known QC inhibitor. When tested in mouse models of AD, compound 212 significantly reduced the brain concentrations of pyroform Aβ and total Aβ and restored cognitive functions. This potent Aβ-lowering effect was achieved by incorporating an additional binding region into our previously established pharmacophoric model, resulting in strong interactions with the carboxylate group of Glu327 in the QC binding site. Our study offers useful insights in designing novel QC inhibitors as a potential tre...

PubChem in 2021: new data content and improved web interfaces.

Abstract: PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Iterative model-building, structure refinement, and density modification with the PHENIX AutoBuild Wizard

Abstract: Iterative model-building, structure refinement, and density modification with the PHENIX AutoBuild Wizard Thomas C. Terwilliger a* , Ralf W. Grosse-Kunstleve b , Pavel V. Afonine b , Nigel W. Moriarty b , Peter Zwart b , Li-Wei Hung a , Randy J. Read c , Paul D. Adams b* a b Los Alamos National Laboratory, Mailstop M888, Los Alamos, NM 87545, USA Lawrence Berkeley National Laboratory, One Cyclotron Road, Bldg 64R0121, Berkeley, CA 94720, USA. c Department of Haematology, University of Cambridge, Cambridge CB2 0XY, UK. * Email: terwill@lanl.gov or PDAdams@lbl.gov Running title: The PHENIX AutoBuild Wizard Abstract The PHENIX AutoBuild Wizard is a highly automated tool for iterative model- building, structure refinement and density modification using RESOLVE or TEXTAL model- building, RESOLVE statistical density modification, and phenix.refine structure refinement. Recent advances in the AutoBuild Wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model completion algorithms, and automated solvent molecule picking. Model completion algorithms in the AutoBuild Wizard include loop-building, crossovers between chains in different models of a structure, and side-chain optimization. The AutoBuild Wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 A to 3.2 A, resulting in a mean R-factor of 0.24 and a mean free R factor of 0.29. The R-factor of the final model is dependent on the quality of the starting electron density, and relatively independent of resolution. Keywords: Model building; model completion; macromolecular models; Protein Data Bank; structure refinement; PHENIX Introduction Iterative model-building and refinement is a powerful approach to obtaining a complete and accurate macromolecular model. The approach consists of cycles of building an atomic model based on an electron density map for a macromolecular structure, refining the structure, using the refined structure as a basis for improving the map, and building a new model. This type of approach has been carried out in a semi-automated fashion for many years, with manual model-building iterating with automated refinement (Jensen, 1997). More recently, with the development first of ARP/wARP (Perrakis et al., 1999), and later other procedures including RESOLVE iterative model-building and refinement (Terwilliger,

Strategies and Approaches of Targeting STAT3 for Cancer Treatment.

Abstract: Signal transducer and activator of transcription 3 (STAT3) is a transcription factor that regulates the expression of genes related to cell cycle, cell survival, and immune response associated with cancer progression and malignancy in a number of cancer types. Once activated, STAT3 forms a homodimer and translocates to the nucleus where it binds DNA promoting the translation of target genes associated with antiapoptosis, angiogenesis, and invasion/migration. In normal cells, levels of activated STAT3 remain transient; however, STAT3 remains constitutively active in approximately 70% of human solid tumors. The pivotal role of STAT3 in tumor progression has promoted a campaign in drug discovery to identify small molecules that disrupt the function of STAT3. A range of approaches have been used to identify novel small molecule inhibitors of STAT3, including high-throughput screening of chemical libraries, computational-based virtual screening, and fragment-based design strategies. The most common approaches ...

Capsaicin: Current Understanding of Its Mechanisms and Therapy of Pain and Other Pre-Clinical and Clinical Uses

Abstract: In this review, we discuss the importance of capsaicin to the current understanding of neuronal modulation of pain and explore the mechanisms of capsaicin-induced pain. We will focus on the analgesic effects of capsaicin and its clinical applicability in treating pain. Furthermore, we will draw attention to the rationale for other clinical therapeutic uses and implications of capsaicin in diseases such as obesity, diabetes, cardiovascular conditions, cancer, airway diseases, itch, gastric, and urological disorders.

A Review on Applications of Computational Methods in Drug Screening and Design

Abstract: Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.