scholarly journals Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions

2020 ◽  
Vol 8 ◽  
Author(s):  
Yixiao Zhai ◽  
Yu Chen ◽  
Zhixia Teng ◽  
Yuming Zhao
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Antioxidant Proteins

scholarly journals Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

PLoS ONE ◽  
2009 ◽  
Vol 4 (11) ◽  
pp. e7813 ◽  
Author(s):  
Sushmita Roy ◽  
Diego Martinez ◽  
Harriett Platero ◽  
Terran Lane ◽  
Margaret Werner-Washburne
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals Prediction of protein-protein interactions based on elastic net and deep forest

2020 ◽  
Author(s):  
Bin Yu ◽  
Cheng Chen ◽  
Zhaomin Yu ◽  
Anjun Ma ◽  
Bingqiang Liu ◽  
...  
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Protein Interactions ◽  
Predictive Performance ◽  
Elastic Net ◽  
Core Network ◽  
Protein Protein Interactions ◽  
Promising Alternative ◽  
Deep Forest

AbstractPrediction of protein-protein interactions (PPIs) helps to grasp molecular roots of disease. However, web-lab experiments to predict PPIs are limited and costly. Using machine-learning-based frameworks can not only automatically identify PPIs, but also provide new ideas for drug research and development from a promising alternative. We present a novel deep-forest-based method for PPIs prediction. First, pseudo amino acid composition (PAAC), autocorrelation descriptor (Auto), multivariate mutual information (MMI), composition-transition-distribution (CTD), and amino acid composition PSSM (AAC-PSSM), and dipeptide composition PSSM (DPC-PSSM) are adopted to extract and construct the pattern of PPIs. Secondly, elastic net is utilized to optimize the initial feature vectors and boost the predictive performance. Finally, GcForest-PPI model based on deep forest is built up. Benchmark experiments reveal that the accuracy values of Saccharomyces cerevisiae and Helicobacter pylori are 95.44% and 89.26%. We also apply GcForest-PPI on independent test sets and CD9-core network, crossover network, and cancer-specific network. The evaluation shows that GcForest-PPI can boost the prediction accuracy, complement experiments and improve drug discovery. The datasets and code of GcForest-PPI could be downloaded at https://github.com/QUST-AIBBDRC/GcForest-PPI/.

scholarly journals Machine Learning Techniques for Sequence-based Prediction of Viral-Host Interactions between SARS-CoV-2 and Human Proteins

2020 ◽  
Author(s):  
Lopamudra Dey ◽  
Sanjay Chakraborty ◽  
Anirban Mukhopadhyay
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Protein Interactions ◽  
Ensemble Classifier ◽  
Machine Learning Techniques ◽  
World Health ◽  
The World ◽  
Human Proteins ◽  
Health Organization

COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 4.3 million people from more than 200 countries have already been affected throughout the world by this deadly virus, resulting in almost 0.3 millions deaths. Protein-protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with a number of human proteins while many potential interactions still remain to be identified. However, human cells are composed of a large number of proteins. Therefore, it is not possible to experimentally check all possible combinations of interactions. This leads to development of various computational methods to predict the PPIs between the virus and human proteins and further validation of them using biological experiments. This paper presents a prediction model by combining the different sequence-based features of human proteins like the amino acid composition, pseudo amino acid composition, and the conjoint triad. We have built an ensemble voting classifier using $SVM^{Radial}$, $SVM^{Polynomial}$, and Random Forest technique which gives greater accuracy, precision, specificity, recall, and F1 score over all other models used in the work. We have predicted 1326 potential human target proteins using this weighted ensemble classifier. Furthermore, the Gene Ontology (GO) and KEGG pathway enrichments of these predicted human proteins are investigated. This study may encourage the identification of potential targets for more effective anti-COVID drug discovery.

Lipid–protein interactions in abnormal egg yolk: a comparison of the major lipoprotein fractions from normal eggs and from the eggs of hens fed methyl sterculate

1968 ◽  
Vol 46 (8) ◽  
pp. 851-857 ◽  
Author(s):  
R. W. Burley
Keyword(s):  
Amino Acid ◽  
Sodium Chloride ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Protein Interactions ◽  
Chemical Properties ◽  
High Viscosity ◽  
Partial Specific Volume ◽  
Strong Interactions ◽  
Methyl Sterculate

To help explain the known differences in texture and appearance between yolks of normal eggs and of eggs from hens whose diet contains cyclopropenoid compounds such as methyl sterculate, ultra-centrifuge patterns of solutions of the two sorts of yolk have been examined and physical and chemical properties of some of their macromolecular constituents have been compared.Abnormal yolk sedimented in 0.16 M sodium chloride, but it resembled normal yolk in that a major lipoprotein fraction floated in 1.0 M sodium chloride. This abnormal lipoprotein had a lower partial specific volume and flotation coefficient, and gave solutions with a higher viscosity, than the corresponding normal fraction. It had a higher protein content than normal, and its amino acid composition was slightly different, suggesting a different proportion of the constituent apoproteins. The abnormal lipoprotein contained slightly less of a new protein isolated because it remained soluble in a chloroform–methanol mixture. The amino acid composition of this protein differed from that of other yolk proteins. In particular, it contained very little histidine.In the abnormal lipoproteins, combination of protein and lipid appears to be such that strong interactions are possible between ngighboring lipoprotein particles in solution. In whole abnormal yolk, some of the livetins may interact with other yolk constituents thus contributing to the high viscosity. A higher proportion of saturated fatty acid residues in the lipids of abnormal yolk is the only factor so far correlated with the unusual interactions between lipid and protein in this yolk.

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