Dataset Information

Model of P-Glycoprotein Ligand Binding and Validation with Efflux Substrate Matched Pairs.

ABSTRACT: The blood-brain barrier (BBB) poses a significant obstacle in developing therapeutics for neurodegenerative diseases and central nervous system (CNS) disorders. P-glycoprotein (P-gp), a multidrug resistance protein, is a critical gatekeeper in the BBB and plays a role in cancer chemoresistance. This paper uses cryo-EM P-gp structures as starting points with an induced fit docking (IFD) model to evaluate 19 pairs of compounds with known P-gp efflux data. The study reveals significant differences in binding energy and sheds light on structural modifications' impact on efflux properties. In the cases examined, fluorine incorporation influences the efflux by altering the molecular conformation rather than proximal heteroatom basicity. Although there are limitations in addressing covalent interactions or when binding extends into the more flexible vestibule region of the protein, the results provide valuable insights and potential strategies to overcome P-gp efflux, contributing to the advancement of drug development for both CNS disorders and cancer therapies.

SUBMITTER: Conrad J

PROVIDER: S-EPMC11017244 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Model of P-Glycoprotein Ligand Binding and Validation with Efflux Substrate Matched Pairs.

Conrad Jay J Paras Nick A NA Vaz Roy J RJ

Journal of medicinal chemistry 20240327 7

The blood-brain barrier (BBB) poses a significant obstacle in developing therapeutics for neurodegenerative diseases and central nervous system (CNS) disorders. P-glycoprotein (P-gp), a multidrug resistance protein, is a critical gatekeeper in the BBB and plays a role in cancer chemoresistance. This paper uses cryo-EM P-gp structures as starting points with an induced fit docking (IFD) model to evaluate 19 pairs of compounds with known P-gp efflux data. The study reveals significant differences ...[more]

PMID: 38544305

Similar Datasets

Project description:Galactofuranose (Galf) residues are present in cell wall glycoconjugates of numerous pathogenic microbes. Uridine 5'-diphosphate (UDP) Galf, the biosynthetic precursor of Galf-containing glycoconjugates, is produced from UDP-galactopyranose (UDP-Galp) by the flavoenzyme UDP-galactopyranose mutase (UGM). The gene encoding UGM (glf) is essential for the viability of pathogens, including Mycobacterium tuberculosis, and this finding underscores the need to understand how UGM functions. Considerable effort has been devoted to elucidating the catalytic mechanism of UGM, but progress has been hindered by a lack of structural data for an enzyme-substrate complex. Such data could reveal not only substrate binding interactions but how UGM can act preferentially on two very different substrates, UDP-Galp and UDP-Galf, yet avoid other structurally related UDP sugars present in the cell. Herein, we describe the first structure of a UGM-ligand complex, which provides insight into the catalytic mechanism and molecular basis for substrate selectivity. The structure of UGM from Klebsiella pneumoniae bound to the substrate analog UDP-glucose (UDP-Glc) was solved by X-ray crystallographic methods and refined to 2.5 A resolution. The ligand is proximal to the cofactor, a finding that is consistent with a proposed mechanism in which the reduced flavin engages in covalent catalysis. Despite this proximity, the glucose ring of the substrate analog is positioned such that it disfavors covalent catalysis. This orientation is consistent with data indicating that UDP-Glc is not a substrate for UGM. The relative binding orientations of UDP-Galp and UDP-Glc were compared using saturation transfer difference NMR. The results indicate that the uridine moiety occupies a similar location in both ligand complexes, and this relevant binding mode is defined by our structural data. In contrast, the orientations of the glucose and galactose sugar moieties differ. To understand the consequences of these differences, we derived a model for the productive UGM-substrate complex that highlights interactions that can contribute to catalysis and substrate discrimination.

Project description:Melanoma is a malignancy of melanocytes, responsible for a high percentage of skin cancer mortality. Ligand-Receptor pairs, a type of cellular communication, are essential for tumor genesis, growth, metastasis, and prognosis. Yet, the role of Ligand-Receptor pairs in melanoma has not been fully elucidated. Our research focused on the function of Ligand-Receptor pairs in melanoma prognosis. We screened 131 melanoma prognosis corresponded ligand-receptor pairs by analyzing the TCGA data of melanoma and the 2293 LR pairs retrieved from the connectomeDB2020 database. And further developed subtypes of melanoma according to the expression of these ligand-receptor pairs by Consensus Clustering. Then we using lasso cox regression and stepwise multivariate regression analysis established a ligand-receptor pairs-based scoring model for the evaluation of melanoma prognosis. Our study demonstrated that the ligand-receptor pairs are vital to the molecular heterogeneity of melanoma, and characterized three different melanoma ligand-receptor pairs subtypes. Among them, the C3 subtype showed a better prognosis, while the C1 subtype exhibited a low prognosis state. And our analysis then found out that this could be related to the differed activation and inhabitation of the cell cycle and immune-related pathways. Using lasso cox regression and stepwise multivariate regression analysis, we further identified 9 key ligand-receptor pairs and established a scoring model that effectively correlated with the prognosis, immune pathways, and therapy of melanoma, showing that the LR.score model was a trustworthy and independent biomarker for melanoma prognosis evaluation. In sum, we found that ligand-receptor pairs are significantly associated with the prognosis and therapy of melanoma. And our ligand-receptor-based scoring model showed potential for the evaluation of melanoma prognosis and immune therapy outcome prediction, which is crucial to the survival for the patients.

Dataset Information

Model of P-Glycoprotein Ligand Binding and Validation with Efflux Substrate Matched Pairs.

Publications

Model of P-Glycoprotein Ligand Binding and Validation with Efflux Substrate Matched Pairs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets