Identification of New Inhibitors with Potential Antitumor Activity from Polypeptide Structures via Hierarchical Virtual Screening

Abstract

Leukemias are neoplasms that affect hematopoietic cells, which are developed by genetic alterations (mutations) that lead to the loss of proliferation control mechanisms (maturation and/or cell death). The α4β1 integrin receptor is a therapeutic target for inflammation, autoimmune diseases and lymphoid tumors. This study was carried out to search through the antagonists-based virtual screening for α4β1 receptor. Initially, seventeen (17) structures were selected (based on the inhibitory activity values, IC50) and the structure with the best value was chosen as the pivot. The pharmacophoric pattern was determined from the online PharmaGist server and resulted in a model of score value equal to 97.940 with 15 pharmacophoric characteristics that were statistically evaluated via Pearson correlations, principal component analysis (PCA) and hierarchical clustering analysis (HCA). A refined model generated four pharmacophoric hypotheses totaling 1.478 structures set of Zinc_database. After, the pharmacokinetic, toxicological and biological activity predictions were realized comparing with pivot structure that resulted in five (ZINC72088291, ZINC68842860, ZINC14365931, ZINC09588345 and ZINC91247798) structures with optimal in silico predictions. Therefore, future studies are needed to confirm antitumor potential activity of molecules selected this work with in vitro and in vivo assays.

Identification of Potential COX-2 Inhibitors for the Treatment of Inflammatory Diseases Using Molecular Modeling Approaches

“…Subsequently, the input was made to the PharmaGist Web Server 15 to determine the following characteristics: atoms (ATM), spatial characteristics (SF), characteristics (F), aromatic (ARO), hydrophobic (HYD), acceptor (ACC), and donor of hydrogen (DONN). The initial set presented 25 molecules, which were aligned according to the similarity with the selected pivot molecule, allowing the generation of pharmacophore models with the aid of the Discovery Studio ® v. 4.0 program, following the methodology developed by us [ 10 , 12 , 14 , 57 , 58 , 59 ].…”

“…The inhibitory activity values were transformed into pIC 50 (−log (IC 50 )) in order to reduce the inconsistencies of the data obtained in an experimental way and homogenize the dataset, following the adopted methodological proposal [ 10 , 57 , 59 ]. In parallel, the importance of each pharmacophore descriptor was attributed—atoms (ATM), spatial characteristics (SF), characteristics (F), aromatic (ARO), hydrophobic (HYD), acceptor (ACC) and hydrogen donor (DONN); these were used for prediction in order to assess notoriety regarding the response to the pIC 50 value through the Pearson correlation (p), using the software Statistica 7.0 ® and Minitab 19 ® , adapting the methodology adopted by Santos et al 2015 and Ferreira et al 2019 [ 12 , 59 ]. Pearson’s coefficient (Equation (1)) measures the degree of linearity between two variables, assuming a value between +1 and −1.…”

Virtual Screening Approach to Identify High-Affinity Inhibitors of Serum and Glucocorticoid-Regulated Kinase 1 among Bioactive Natural Products: Combined Molecular Docking and Simulation Studies

“…The pharmacophore features of the selected compounds were generated through the PharmaGist Webserver (https://bioinfo3d.cs.tau.ac.il/PharmaGist/). Detailed information on pharmacophore modeling and structure-based virtual screening have been described elsewhere [47][48][49].…”

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches

“…So, a series of 22 parametric equations (15 tetraparametric models, p = 4; 6 pentaparametric models, p = 5; and 1 hexaparametric model) were obtained through different combinations (no repetitions) using six parameters from the properties indicated by the Pearson correlation. The selected descriptors were used to build the QSAR models, using Equation (1) shown below, based on previous studies [27,28]:…”

“…After evaluating the parameters and the statistical quality, the parametric models (tetra-, pentaand hexaparametric) were applied to the set of seven compounds (22)(23)(24)(25)(26)(27)(28) in this study, called the test set (totaling 43.75% in relation to the number of compounds used in the training set; n = 16). Compounds were selected from the Pubchem database based on their respective EC 50 values, which were converted to pEC 50 .…”