A two-stage approach towards protein secondary structure classification

The masking phenomenon of microplastics additives on oxidative stress responses in freshwater food chains

The variability and intrinsic mechanisms of oxidative stress induced by microplastics at different trophic levels in freshwater food chains are not well understood. To comprehensively assess the oxidative stress induced by polystyrene microplastics (PS-MPs) in freshwater food chains, the present study first quantified the oxidative stress induced by PS-MPs in organisms at different trophic levels using factorial experimental design and molecular dynamics methods. Then focuses on analyzing the variability of these responses across different trophic levels using mathematical statistical analysis. Notably, higher trophic level organisms exhibit diminished responses under PS-MPs exposure. Furthermore, the coexistence of multiple additives was found to mask these responses, with antioxidant plastic additives significantly influencing oxidative stress responses. Mechanism analysis using computational chemistry simulation determines that protein structure and amino acid characteristics are key factors driving PS-MPs induced oxidative stress variation in freshwater organisms at different nutrient levels. Increased hydrophobic additives induce protein helicalization and amino acid residue aggregation. This study systematically reveals the variability of biological oxidative stress response under different nutrient levels, emphasizing the pivotal role of chemical additives. Overall, this study offers crucial insights into PS-MPs' impact on oxidative stress responses in freshwater ecosystems, informing future environmental risk assessment.

Bingham deep neural and oppositional fish swarm optimized protein structure prediction

It is familiar that essential proteins take part in managing cellular activities in living organisms. Moreover, protein structure prediction from its amino acid sequence is advantageous to the comprehending of cellular functions. Formerly, several essential protein prediction methods have been proposed. However, those existing prediction methods were not satisfactory because to low sensitivity to imbalance characteristics. To address this issue, this paper presents a novel secondary protein structure prediction method, called, Bingham Deep Convolutional-based Oppositional Artificial Fish Optimized (BDC-OAFO). First, a protein structure identification framework, called, Bingham Distributed Deep Convolutional (BDDC) is designed to identify the essential proteins by eliminating the imbalanced learning issue. Next, secondary structure prediction framework, called, Oppositional Artificial Fish Swarm Optimization is proposed that obtain precise prediction results. Then, predicting secondary protein structure by emulating three biological behaviors of artificial fishes, including foraging behavior, following behavior, swarming behavior in which process, proximal count, oppositional function and Gaussian function are utilized. To evaluate the performance of BDC-OAFO method, we conduct experiments on Protein Data Bank dataset the experimental results show that our method BDC-OAFO achieves a better performance for identifying essential proteins and precise prediction in comparison with several other well-known prediction methods, which confirms the significance of BDC-OAFO.Communicated by Ramaswamy H. Sarma.

A novel fusion based on the evolutionary features for protein fold recognition using support vector machines

Protein fold recognition plays a crucial role in discovering three-dimensional structure of proteins and protein functions. Several approaches have been employed for the prediction of protein folds. Some of these approaches are based on extracting features from protein sequences and using a strong classifier. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In recent years, finding an efficient technique for integrating discriminate features have been received advancing attention. In this study, we integrate Auto-Cross-Covariance and Separated dimer evolutionary feature extraction methods. The results’ features are scored by Information gain to define and select several discriminated features. According to three benchmark datasets, DD, RDD ,and EDD, the results of the support vector machine show more than 6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% improvement in accuracy on these benchmark datasets.