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The endoplasmic reticulum plays an important role in many cellular processes, which includes protein synthesis, folding and post-translational processing of newly synthesized proteins. It is also the site for quality control of misfolded proteins and entry point of extracellular proteins to the secretory pathway. Hence at any given point of time, endoplasmic reticulum contains two different cohorts of proteins, (i) proteins involved in endoplasmic reticulum-specific function, which reside in the lumen of the endoplasmic reticulum, called as endoplasmic reticulum resident proteins and (ii) proteins which are in process of moving to the extracellular space. Thus, endoplasmic reticulum resident proteins must somehow be distinguished from newly synthesized secretory proteins, which pass through the endoplasmic reticulum on their way out of the cell. Approximately only 50% of the proteins used in this study as training data had endoplasmic reticulum retention signal, which shows that these signals are not essentially present in all endoplasmic reticulum resident proteins. This also strongly indicates the role of additional factors in retention of endoplasmic reticulum-specific proteins inside the endoplasmic reticulum. This is a support vector machine based method, where we had used different forms of protein features as inputs for support vector machine to develop the prediction models. During training approach of cross-validation was used. Maximum performance was obtained with a combination of amino acid compositions of different part of proteins. In this study, we have reported a novel support vector machine based method for predicting endoplasmic reticulum resident proteins, named as ERPred. During training we achieved a maximum accuracy of 81.42% with approach of cross-validation. When evaluated on independent dataset, ERPred did prediction with sensitivity of 72.31% and specificity of 83.69%. We have also annotated six different proteomes to predict the candidate endoplasmic reticulum resident proteins in them. A webserver, ERPred, was developed to make the method available to the scientific community, which can be accessed at http://proteininformatics.org/mkumar/erpred/index.html. We found that out of 124 proteins of the training dataset, only 66 proteins had endoplasmic reticulum retention signals, which shows that these signals are not an absolute necessity for endoplasmic reticulum resident proteins to remain inside the endoplasmic reticulum. This observation also strongly indicates the role of additional factors in retention of proteins inside the endoplasmic reticulum. Our proposed predictor, ERPred, is a signal independent tool. It is tuned for the prediction of endoplasmic reticulum resident proteins, even if the query protein does not contain specific ER-retention signal.

To explore the effect of density on large yellow croaker (Larimichthys crocea) under intensive aquaculture conditions and determine the appropriate culturing density, this study investigate the effects of different stocking densities on the nutritional composition, stress, and immune levels of large yellow croaker. Through a long-term aquaculture experiment, conducted under flow-through conditions of intensive aquaculture, three initial density groups were set: a low density group [LD], 4.92 kg/m3; a medium density group [MD], 7.56 kg/m3; and a high density group [HD], 10.08 kg/m3, for a 150-day rearing trial. Large yellow croaker were fed to satiation twice daily (6:00, 17:00). At the end of this trial, the final densities were 10.38 ± 0.50, 14.41 ± 1.06, and 18.71 ± 0.99 kg/m3 in the LD, MD, and HD groups, respectively. The results showed that the growth performances were adversely influenced by a high stocking density. Levels of cortisol in serum, superoxide dismutase (SOD) and catalase (CAT) in liver, Na+-K+ ATPase and Na+-K+ ATPase gene in gills, and heat shock protein (HSP70/90) genes and glutathione S-transferase (GST) genes in the liver significantly increased under HD treatment. Results of immune response analyses showed that there was a clear decrease in immunoglobulin M (IgM), complement component 4 (C4), and lysozyme (LZM) in serum, lysozyme (LZM) genes, tumor necrosis factor-alpha (TNF-α) genes and interleukin-1β (IL-1β) genes in the head kidney of large yellow croakers reared in the HD group. An obvious increase in free amino acids and fatty acids in the muscle of large yellow croakers reared in HD group was also observed. Overall, this study showed that the optimal final culturing density of large yellow croaker under flow-through systems should be between 14.41 kg/m3 and 18.71 kg/m3 to improve aquaculture efficiency and product quality.

In the Post Genome Age, there is an urgent need to develop the reliable and effective computational methods to predict the subcellular localization for the explosion of newly found proteins. Here, a novel method of pseudo amino acid (PseAA) composition, the so-called “amino acid composition distribution” (AACD), is introduced. First, a protein sequence is divided equally into multiple segments. Then, amino acid composition of each segment is calculated in series. After that, each protein sequence can be represented by a feature vector. Finally, the feature vectors of all sequences thus obtained are further input into the multi-class support vector machines to predict the subcellular localization. The results show that AACD is quite effective in representing protein sequences for the purpose of predicting protein subcellular localization.

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab–based methods are reliable; however, they are expensive and time consuming and are, therefore, not scalable. In this research, we propose a sequence-based novel machine learning approach for the prediction of ECM proteins. In the proposed method, composition of k-spaced amino acid pair (CKSAAP) features are encoded into a classifiable latent space (LS) with the help of deep latent space encoding (LSE). A comprehensive ablation analysis is conducted for performance evaluation of the proposed method. Results are compared with other state-of-the-art methods on the benchmark dataset, and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.

Background In research on new drug discovery, the traditional wet experiment has a long period. Predicting drug–target interaction (DTI) in silico can greatly narrow the scope of search of candidate medications. Excellent algorithm model may be more effective in revealing the potential connection between drug and target in the bioinformatics network composed of drugs, proteins and other related data. Results In this work, we have developed a heterogeneous graph neural network model, named as HGDTI, which includes a learning phase of network node embedding and a training phase of DTI classification. This method first obtains the molecular fingerprint information of drugs and the pseudo amino acid composition information of proteins, then extracts the initial features of nodes through Bi-LSTM, and uses the attention mechanism to aggregate heterogeneous neighbors. In several comparative experiments, the overall performance of HGDTI significantly outperforms other state-of-the-art DTI prediction models, and the negative sampling technology is employed to further optimize the prediction power of model. In addition, we have proved the robustness of HGDTI through heterogeneous network content reduction tests, and proved the rationality of HGDTI through other comparative experiments. These results indicate that HGDTI can utilize heterogeneous information to capture the embedding of drugs and targets, and provide assistance for drug development. Conclusions The HGDTI based on heterogeneous graph neural network model, can utilize heterogeneous information to capture the embedding of drugs and targets, and provide assistance for drug development. For the convenience of related researchers, a user-friendly web-server has been established at http://bioinfo.jcu.edu.cn/hgdti .

The effects of chemical protein extraction, and enzymatic hydrolysis with Alcalase, papain and pepsin, on the functional properties, antioxidant activity, amino acid composition and protein structure of black soldier fly (H. illucens) larval protein were examined. Alcalase hydrolysates had the highest degree of hydrolysis (p < 0.05), with the highest hydrolysate and oil fraction yield (p < 0.05). Pepsin hydrolysates showed the lowest oil holding capacity (p < 0.05), whereas no significant differences were observed among other enzymes and protein concentrates (p > 0.05). The emulsifying stability and foam capacity were significantly lower in protein hydrolysates than protein concentrate (p < 0.05). The antioxidant activity of protein hydrolysates from protein concentrate and Alcalase was higher than that with papain and pepsin (p < 0.05), owing to the higher hydrophobic amino acid content. Raman spectroscopy indicated structural changes in protein α-helices and β-sheets after enzymatic hydrolysis.

Background Lysine succinylation is a new kind of post-translational modification which plays a key role in protein conformation regulation and cellular function control. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. However, traditional methods, experimental approaches, are labor-intensive and time-consuming. Computational prediction methods have been proposed recent years, and they are popular because of their convenience and high speed. In this study, we developed a new method to predict succinylation sites in protein combining multiple features, including amino acid composition, binary encoding, physicochemical property and grey pseudo amino acid composition, with a feature selection scheme (information gain). And then, it was trained using SVM (Support Vector Machine) and an ensemble learning algorithm. Results The performance of this method was measured with an accuracy of 89.14% and a MCC (Matthew Correlation Coefficient) of 0.79 using 10-fold cross validation on training dataset and an accuracy of 84.5% and a MCC of 0.2 on independent dataset. Conclusions The conclusions made from this study can help to understand more of the succinylation mechanism. These results suggest that our method was very promising for predicting succinylation sites. The source code and data of this paper are freely available at https://github.com/ningq669/PSuccE .

To gain insight into the malfunction of the Golgi apparatus and its relationship to various genetic and neurodegenerative diseases, the identification of sub-Golgi proteins, both cis-Golgi and trans-Golgi proteins, is of great significance. In this study, a state-of-art random forests sub-Golgi protein classifier, rfGPT, was developed. The rfGPT used 2-gap dipeptide and split amino acid composition for the feature vectors and was combined with the synthetic minority over-sampling technique (SMOTE) and an analysis of variance (ANOVA) feature selection method. The rfGPT was trained on a sub-Golgi protein sequence data set (137 sequences), with sequence identity less than 25%. For the optimal rfGPT classifier with 93 features, the accuracy (ACC) was 90.5%; the Matthews correlation coefficient (MCC) was 0.811; the sensitivity (Sn) was 92.6%; and the specificity (Sp) was 88.4%. The independent testing scores for the rfGPT were ACC = 90.6%; MCC = 0.696; Sn = 96.1%; and Sp = 69.2%. Although the independent testing accuracy was 4.4% lower than that for the best reported sub-Golgi classifier trained on a data set with 40% sequence identity (304 sequences), the rfGPT is currently the top sub-Golgi protein predictor utilizing feature vectors without any position-specific scoring matrix and its derivative features. Therefore, the rfGPT is a more practical tool, because no sequence alignment is required with tens of millions of protein sequences. To date, the rfGPT is the Golgi classifier with the best independent testing scores, optimized by training on smaller benchmark data sets. Feature importance analysis proves that the non-polar and aliphatic residues composition, the (aromatic residues) + (non-polar, aliphatic residues) dipeptide and aromatic residues composition between NH2-termial and COOH-terminal of protein sequences are the three top biological features for distinguishing the sub-Golgi proteins.

Insects are becoming increasingly relevant as protein sources in food and feed. The Black Soldier Fly (BSF) is one of the most utilized, thanks to its ability to live on many leftovers. Vegetable processing industries produce huge amounts of by-products, and it is important to efficiently rear BSF on different substrates to assure an economical advantage in bioconversion and to overcome the seasonality of some leftovers. This work evaluated how different substrates affect the protein and amino acid content of BSF. BSF prepupae reared on different substrates showed total protein content varying between 35% and 49% on dry matter. Significant lower protein contents were detected in BSF grown on fruit by-products, while higher contents were observed when autumnal leftovers were employed. BSF protein content was mainly correlated to fibre and protein content in the diet. Among amino acids, lysine, valine and leucine were most affected by the diet. Essential amino acids satisfied the Food and Agricultural Organization (FAO) requirements for human nutrition, except for lysine in few cases. BSF could be a flexible tool to bio-convert a wide range of vegetable by-products of different seasonality in a high-quality protein-rich biomass, even if significant differences in the protein fraction were observed according to the rearing substrate.

Collagen isolated from byproducts of common carp was hydrolyzed with alcalase enzyme to obtain peptide fractions. The resulting >30 kDa (PF1), 10–30 kDa (PF2), 3–10 kDa (PF3) and <1 kDa (PF4) fractions were studied for their antioxidant and functional properties. All peptide fractions illustrated antioxidant activity at different concentrations (1, 5, and 10 mg/mL). Although PF4 indicated the highest DPPH radical-scavenging activity (87%) at a concentration of 1 mg/mL, the highest reducing power (0.34) and hydroxyl radical scavenging activity (95.4%) were also observed in PF4 at a concentration of 10 mg/mL. The solubility of the peptide fractions was influenced by pH. The lowest solubility of the peptide fractions was observed at pH 4. The highest emulsifying activity index (EAI) was observed for PF4 (121.1 m2/g), followed by PF3 (99.6 m2/g), PF2 (89.5 m2/g) and PF1 (78.2 m2/g). In contrast to what has been found in the case of EAI, the emulsion stability of the peptide fractions decreased at lower molecular weight, which ranged from 24.4 to 31.6 min. Furthermore, it was revealed that PF1 had the highest foam capacity (87.4%) and foam stability (28.4 min), followed by PF2 and PF3. Overall, the findings suggest that peptide fractions isolated from byproducts of common carp are a promising source of natural antioxidants for application in functional food and pharmaceutical products.