Binding affinity toward human prion protein of some anti-prion compounds — Assessment based on QSAR modeling, molecular docking and non-parametric ranking

Multiobject Optimization of National Football League Drafts: Comparison of Teams and Experts

Predicting the success of National Football League drafts has always been an exciting issue for the teams, fans and even for scientists. Among the numerous approaches, one of the best techniques is to ask the opinion of sport experts, who have the knowledge and past experiences to rate the drafts of the teams. When asking a set of sport experts to evaluate the performances of teams, a multicriteria decision making problem arises unavoidably. The current paper uses the draft evaluations of the 32 NFL teams given by 18 experts: a novel multicriteria decision making tool has been applied: the sum of ranking differences (SRD). We introduce a quick and easy-to-follow approach on how to evaluate the performance of the teams and the experts at the same time. Our results on the 2021 NFL draft data indicate that Green Bay Packers has the most promising drafts for 2021, while the experts have been grouped into three distinct groups based on the distance to the hypothetical best evaluation. Even the coding options can be tailored according to the experts’ opinions. Statistically correct (pairwise or group) comparisons can be made using analysis of variance (ANOVA). A comparison to TOPSIS ranking revealed that SRD gives a more objective ranking due to the lack of predefined weights.

Small Molecules with Anti-Prion Activity

Prion pathologies are fatal neurodegenerative diseases caused by the misfolding of the physiological Prion Protein (PrP^C) into a β-structure-rich isoform called PrP^Sc. To date, there is no available cure for prion diseases and just a few clinical trials have been carried out. The initial approach in the search of anti-prion agents had PrP^Sc as a target, but the existence of different prion strains arising from alternative conformations of PrP^Sc, limited the efficacy of the ligands to a straindependent ability. That has shifted research to PrP^C ligands, which either act as chaperones, by stabilizing the native conformation, or inhibit its interaction with PrP^Sc. The role of transition-metal mediated oxidation processes in prion misfolding has also been investigated. Another promising approach is the indirect action via other cellular targets, like membrane domains or the Protein- Folding Activity of Ribosomes (PFAR). Also, new prion-specific high throughput screening techniques have been developed. However, so far no substance has been found to be able to extend satisfactorily survival time in animal models of prion diseases. This review describes the main features of the Structure-Activity Relationship (SAR) of the various chemical classes of anti-prion agents.

Design, Synthesis, and Screening of 5-Aryl-3-(2-(pyrrolyl)thiophenyl)-1,2,4-oxadiazoles as Potential Antitumor Molecules on Breast Cancer MCF-7 Cell Line

The work reported the design and cytotoxic screening of synthetic small molecules: carbonitriles 3a-c, carboximidamides 4a-c, and oxadiazoles 5-19 as antitumor molecules. Molecules 4c, 9, 12, and 14 show promising cytotoxicity profiles against two cell lines higher than prodigiosin (PG). The results of topoisomerase enzyme inhibition assay show that carboximidamide 4c and oxadiazole 14 display potent inhibitory activity in nano-molar concentration higher than PG. In addition, carboximidamide 4c and oxadiazoles 9, 12, and 14 exhibit antiproliferative activities over MCF-7 cells by cell cycle arrest at G₁ phase and apoptosis inducing activity by increasing cell population percentages at pre G₁ and G₂/M phases as shown by DNA-flow cytometry assay and annexin V analysis. Moreover, measurement of p53 and cell death mediators, show that carboximidamide 4c and oxadiazoles 9, 12, and 14 significantly up-regulate p53, Puma and Bax/Bcl-2 ratio levels. Subsequently, pro-apoptotic activities are confirmed by active caspase 3/7 percentages green fluorescence assay.

Predictive models for tyrosinase inhibitors: Challenges from heterogeneous activity data determined by different experimental protocols

Quantitative Structure-Activity Relationship (QSAR) models of tyrosinase inhibitors were built using Random Forest (RF) algorithm and evaluated by the out-of-bag estimation (R²_OOB) and 10-fold cross validation (Q²_CV). We found that the performances of QSAR models were closely correlated with the systematic errors of inhibitory activities of tyrosinase inhibitors arising from the different measuring protocols. By defining ERR_sys, outliers with larger errors can be efficiently identified and removed from heterogeneous activity data. A reasonable QSAR model (R²_OOB of 0.74 and Q²_CV of 0.80) was obtained by the exclusion of 13 outliers with larger systematic errors. It is a clear example of the challenge for QSAR model that can overwhelm heterogeneous data from different experimental protocols.