RESUMO
The spontaneous aggregation of infectious or misfolded forms of prion protein is known to be responsible for neurotoxicity in brain cells, which ultimately leads to the progression of prion disorders. Bovine spongiform encephalopathy (BSE) in animals and Creutzfeldt-Jakob disease (CJD) in humans are glaring examples in this regard. Square-planar complexes with labile ligands and indole-based compounds are found to be efficiently inhibitory against protein aggregation. Herein, we report the synthesis of an indole-based cyclometallated palladium complex. The ligand and complex were characterized by various spectroscopic techniques such as UV-visible, NMR, IR, and HRMS. The molecular structure of the complex was confirmed by single-crystal X-ray crystallography. The interaction of the complex with PrP106-126 was studied using UV-visible spectroscopy, CD spectroscopy, MALDI-TOF MS, and molecular docking. The inhibition effects of the complex on the PrP106-126 aggregation, fibrillization and amyloid formation phenomena were analysed through the ThT assay, CD, TEM and AFM. The effect of the complex on the aggregation process of PrP106-126 was determined kinetically through the ThT assay. The complex presented high binding affinity with the peptide and influenced the peptide's conformation and aggregation in different modes of binding. Furthermore, the MTT assay on neuronal HT-22 cells showed considerable protective properties of the complex against PrP106-126-mediated cytotoxicity. These findings suggest that the compound influences peptide aggregation in different ways, and the anti-aggregation action is primarily associated with the metal's physicochemical properties and the reactivity rather than the ligand. As a result, we propose that this compound be investigated as a potential therapeutic molecule in metallopharmaceutical research to treat prion disease (PD).
RESUMO
The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.
RESUMO
Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.
