Amino acid composition and hydrophobicity patterns of protein domains correlate with their structures

Biopolymers ◽  
1985 ◽  
Vol 24 (10) ◽  
pp. 1995-2023 ◽  
Author(s):  
Robert P. Sheridan ◽  
J. Scott Dixon ◽  
R. Venkataraghavan ◽  
I. D. Kuntz ◽  
K. P. Scott
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Protein Domains

scholarly journals Pathway-Specific Protein Domains (PSPD) Discrimination by Using a Hybrid Feature Space Based on Deep Neural Network(DNN)

2020 ◽  
Author(s):  
Ali Ghulam ◽  
XiuJuan Lei ◽  
Yuchen Zhang ◽  
Zhenqiang Wu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Composition ◽  
Correlation Coefficient ◽  
Acid Composition ◽  
Deep Neural Network ◽  
Protein Domains ◽  
Specific Protein ◽  
Matthew’S Correlation Coefficient

Abstract The Pathway-specific protein domains (PSPDs) are important tools in examining drug growth as they provide a fast, reliable, and inexpensive way of estimating complex new molecular targets in specific diseases. The protein architecture prevents the formation of a direct correlation between signal transduction behavior and cellular structure. Accordingly, protein–tissue factor pathway inhibitor 2 isotypes 1 precursors have been used to encode peptide sequence information into specific feature structures. The measurable structure-activity classification model obtained by machine learning technology can predict pathway-specific protein interactions and new signaling peptides. We introduce deep neural network (DNN)-based PSPDs, abbreviated as DNNPSPDs, as the first pathway-specific protein domain that is built based on five extant models, namely, the AAindex, pseudo-amino acid composition, amino acid composition, composition mood of pseudoamino acids, and dipeptide composition. A total of 900 proteins with undetermined roles collected from the PDB data base are tested to evaluate the predictive power of this model. Various combinations of the available feature selection technologies are also combined to process a hybrid function space. DNNPSPDs predicts PSPDs by using features that are automatically learned from primary protein sequences. The sequences of pathway-associated proteins are sequentially fed into and decoded in neural network layers. Several classifications are also employed. DNNPSPDs achieves a prediction accuracy of 0.957 at a Matthew’s correlation coefficient (MCC) of 91.86%, with DPC, and 2nd achieve high prediction score 0.936 at Matthew’s correlation coefficient (MCC) of 88.02%, accuracy which is probably better. In terms of ROC–AUC, DNNPSPDs achieves a ROC–AUC curve of 0.982, which is larger than that of the other machine learning classifiers. A study using an alternative dataset reveals that our primary pathways, as pathway-specific protein domains, have accurate and reliable associations, thereby proving the viability of the proposed DNNPSPDs.

Effect of maternal diet on the amino acid composition of human uterine fluid

2014 ◽  
Author(s):  
Alexandra Jayne Kermack ◽  
Ying Cheong ◽  
Nick Brook ◽  
Nick Macklon ◽  
Franchesca D Houghton
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Maternal Diet ◽  
Uterine Fluid

Biological Processing of Pea Grain and Secondary Starch Raw Materials to Produce Food and Feed Protein Concentrates

2020 ◽  
Vol 36 (4) ◽  
pp. 49-58
Author(s):  
V.V. Kolpakova ◽  
R.V. Ulanova ◽  
L.V. Chumikina ◽  
V.V. Bessonov
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Mass Fraction ◽  
Geotrichum Candidum ◽  
Protein Concentrate ◽  
Protein Concentrates ◽  
Pea Flour ◽  
Nitrogenous Substances

The goal of the study was to develop a biotechnological process for the production of protein concentrates via bioconversion of pea flour and whey, a secondary product of starch manufacture. Standard and special methods were used to analyze the chemical and biochemical composition of protein concentrates (amino acid, carbohydrate, and fractional) of flour, whey and protein concentrates. It was established that pea flour contains 52.28-57.05% water-soluble nitrogenous substances, 23.04-25.50% salt-soluble, 2.94-4.69% alcohol-soluble compounds, 0-0.61% of soluble glutenine, 6.67-10.40% alkali-soluble glutenine and 5.96-10.86% insoluble sclerotic substances. A mathematical model and optimal parameters of the enzymatic extraction of pea protein with a yield of 65-70% were developed. Ultrasonic exposure increased the yield of nitrogenous substances by 23.16 ± 0.69%, compared with the control without ultrasound. The protein concentrate had a mass fraction of nitrogenous substances of 72.48 ± 0.41% (Nx6.25) and a complete amino acid composition. The microbial conversion by the Saccharomyces cerevisiae 121 and Geotrichum candidum 977 cultures of starch whey which remained after protein precipitation allowed us to obtain feed concentrates from biomass and culture liquid with a protein mass fraction of 61.68-70.48% (Nx6.25). Protein concentrates positively affected the vital signs of rats and their excretory products. A technological scheme was developed to test the complex pea grain and starch whey processing under pilot conditions. pea, protein concentrate, extracts, whey, bioconversion, Geotrichum candidum, Saccharomyces cerevisiae, chemical composition, amino acid composition

Sign in / Sign up

Export Citation Format

Share Document