Pharmacophore modeling, virtual screening and molecular docking of ATPase inhibitors of HSP70

Sangeetha, K. N.; Sasikala, R.; Meena, Kalawati

doi:10.1016/j.compbiolchem.2017.05.011

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Gerhard et al (2005) developed multiple pharmacophore models based on different binding modes using LigandScout [ 262 ], and observed different pharmacophore models comprised of different chemical features. The built models were further adopted to virtually screen a number of commercially available chemical databases, totaling ca.…”

Section: In Silico Approachesmentioning

confidence: 99%

In Silico Approaches to Identify Polyphenol Compounds as α-Glucosidase and α-Amylase Inhibitors against Type-II Diabetes

Riyaphan¹,

Pham

Leong

et al. 2021

Biomolecules

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Type-II diabetes mellitus (T2DM) results from a combination of genetic and lifestyle factors, and the prevalence of T2DM is increasing worldwide. Clinically, both α-glucosidase and α-amylase enzymes inhibitors can suppress peaks of postprandial glucose with surplus adverse effects, leading to efforts devoted to urgently seeking new anti-diabetes drugs from natural sources for delayed starch digestion. This review attempts to explore 10 families e.g., Bignoniaceae, Ericaceae, Dryopteridaceae, Campanulaceae, Geraniaceae, Euphorbiaceae, Rubiaceae, Acanthaceae, Rutaceae, and Moraceae as medicinal plants, and folk and herb medicines for lowering blood glucose level, or alternative anti-diabetic natural products. Many natural products have been studied in silico, in vitro, and in vivo assays to restrain hyperglycemia. In addition, natural products, and particularly polyphenols, possess diverse structures for exploring them as inhibitors of α-glucosidase and α-amylase. Interestingly, an in silico discovery approach using natural compounds via virtual screening could directly target α-glucosidase and α-amylase enzymes through Monte Carto molecular modeling. Autodock, MOE-Dock, Biovia Discovery Studio, PyMOL, and Accelrys have been used to discover new candidates as inhibitors or activators. While docking score, binding energy (Kcal/mol), the number of hydrogen bonds, or interactions with critical amino acid residues have been taken into concerning the reliability of software for validation of enzymatic analysis, in vitro cell assay and in vivo animal tests are required to obtain leads, hits, and candidates in drug discovery and development.

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Section: In Silico Approachesmentioning

confidence: 99%

In Silico Approaches to Identify Polyphenol Compounds as α-Glucosidase and α-Amylase Inhibitors against Type-II Diabetes

Riyaphan¹,

Pham

Leong

et al. 2021

Biomolecules

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“…The rational design of innovative pharmaceuticals, with the aim of creating pharmaceutical products with more specificity by calculated simulation, has emerged as a crucial aspect of medicinal chemistry (Mouchlis et al, 2020). Pharmacophore-based and docking-based screening are two classic CADD approaches, which were usually applied in virtual screening to select the potential bioactive derivatives (Niu et al, 2012;Sangeetha et al, 2017). Recently, the discovery of a novel drug has benefited greatly from the use of pharmacophore-based virtual screening (PBVS), especially when there is a lack of information regarding the three-dimensional structure of the desired protein target (Sharma et al, 2020;Zhu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Ligand and structure-based approaches for the exploration of structure–activity relationships of fusidic acid derivatives as antibacterial agents

Zheng

Zhang

et al. 2023

Front. Chem.

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Introduction: Fusidic acid (FA) has been widely applied in the clinical prevention and treatment of bacterial infections. Nonetheless, its clinical application has been limited due to its narrow antimicrobial spectrum and some side effects.Purpose: Therefore, it is necessary to explore the structure–activity relationships of FA derivatives as antibacterial agents to develop novel ones possessing a broad antimicrobial spectrum.Methods and result: First, a pharmacophore model was established on the nineteen FA derivatives with remarkable antibacterial activities reported in previous studies. The common structural characteristics of the pharmacophore emerging from the FA derivatives were determined as those of six hydrophobic centers, two atom centers of the hydrogen bond acceptor, and a negative electron center around the C-21 field. Then, seven FA derivatives have been designed according to the reported structure–activity relationships and the pharmacophore characteristics. The designed FA derivatives were mapped on the pharmacophore model, and the Qfit values of all FA derivatives were over 50 and FA-8 possessed the highest value of 82.66. The molecular docking studies of the partial target compounds were conducted with the elongation factor G (EF-G) of S. aureus. Furthermore, the designed FA derivatives have been prepared and their antibacterial activities were evaluated by the inhibition zone test and the minimum inhibitory concentration (MIC) test. The derivative FA-7 with a chlorine group as the substituent group at C-25 of FA displayed the best antibacterial property with an MIC of 3.125 µM. Subsequently, 3D-QSAR was carried on all the derivatives by using the CoMSIA mode of SYBYL-X 2.0.Conclusion: Hence, a computer-aided drug design model was developed for FA, which can be further used to optimize FA derivatives as highly potent antibacterial agents.

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“…These have been employed to study Hsp70 binding to substrates 51,52 or to inhibitors. 53,54 These energy functions have also been successfully combined with binding array data to improve their predictive power and augmented with additional insights obtained directly from the growing number of Hsp70−client structures. 55,56 Additionally, as computer processor speeds have increased, molecular dynamics (MD) simulations have provided new insights into substrate binding 51,57 and chaperone dynamics.…”

Section: ■ Introductionmentioning

confidence: 99%

“…The first predictive model of Hsp70s binding preferences was based on the statistical analysis of peptide array data and revealed a preference for branched hydrophobics and positively charged residues, an observation consistent with the structure of the SBD of DnaK (the E. coli Hsp70) in a complex with a model peptide (NR, NRLLLTG). , Subsequent predictive models have incorporated empirical and statistical energy functions , developed for protein folding and structure prediction, which have consistently improved in the last couple of decades. These have been employed to study Hsp70 binding to substrates , or to inhibitors. , These energy functions have also been successfully combined with binding array data to improve their predictive power and augmented with additional insights obtained directly from the growing number of Hsp70–client structures. , …”

Section: Introductionmentioning

confidence: 99%

Computationally-Aided Modeling of Hsp70–Client Interactions: Past, Present, and Future

et al. 2022

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Hsp70 molecular chaperones play central roles in maintaining a healthy cellular proteome. Hsp70s function by binding to short peptide sequences in incompletely folded client proteins, thus preventing them from misfolding and/or aggregating, and in many cases holding them in a state that is competent for subsequent processes like translocation across membranes. There is considerable interest in predicting the sites where Hsp70s may bind their clients, as the ability to do so sheds light on the cellular functions of the chaperone. In addition, the capacity of the Hsp70 chaperone family to bind to a broad array of clients and to identify accessible sequences that enable discrimination of those that are folded from those that are not fully folded, which is essential to their cellular roles, is a fascinating puzzle in molecular recognition. In this article we discuss efforts to harness computational modeling with input from experimental data to develop a predictive understanding of the promiscuous yet selective binding of Hsp70 molecular chaperones to accessible sequences within their client proteins. We trace how an increasing understanding of the complexities of Hsp70−client interactions has led computational modeling to new underlying assumptions and design features. We describe the trend from purely data-driven analysis toward increased reliance on physics-based modeling that deeply integrates structural information and sequence-based functional data with physics-based binding energies. Notably, new experimental insights are adding to our understanding of the molecular origins of "selective promiscuity" in substrate binding by Hsp70 chaperones and challenging the underlying assumptions and design used in earlier predictive models. Taking the new experimental findings together with exciting progress in computational modeling of protein structures leads us to foresee a bright future for a predictive understanding of selective-yet-promiscuous binding exploited by Hsp70 molecular chaperones; the resulting new insights will also apply to substrate binding by other chaperones and by signaling proteins.

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Pharmacophore modeling, virtual screening and molecular docking of ATPase inhibitors of HSP70

Cited by 6 publications

References 22 publications

In Silico Approaches to Identify Polyphenol Compounds as α-Glucosidase and α-Amylase Inhibitors against Type-II Diabetes

In Silico Approaches to Identify Polyphenol Compounds as α-Glucosidase and α-Amylase Inhibitors against Type-II Diabetes

Ligand and structure-based approaches for the exploration of structure–activity relationships of fusidic acid derivatives as antibacterial agents

Computationally-Aided Modeling of Hsp70–Client Interactions: Past, Present, and Future

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