scholarly journals Dynamic Changes in Glutenin Macropolymer during Different Dough Mixing and Resting Processes

Molecules ◽  
2021 ◽  
Vol 26 (3) ◽  
pp. 541
Author(s):  
Yulin Feng ◽  
Huijuan Zhang ◽  
Jing Wang ◽  
Haitao Chen
Keyword(s):  
Protein Structure ◽  
Disulfide Bond ◽  
Mixing Time ◽  
Protein Secondary Structure ◽  
Protein Subunit ◽  
Dough Mixing ◽  
Negative Results ◽  
Resting Time ◽  
Glutenin Protein ◽  
Glutenin Macropolymer

The glutenin macropolymer (GMP), which is an important component of the glutenin protein in wheat flour, plays a prominent role in governing dough properties and breadmaking quality. This study investigated the changes in GMP properties during the mixing and resting stages of dough processing. The results show that the GMP content decreases by about 20.20% when the mixing time increases from 3 to 5 min, while increasing the resting time can lead to restoration of some GMP contents. Resting promotes greater formation of large-sized GMP particles, which is likely related to the increased disulfide bond content in the GMP during this process. In contrast, the mechanical force of mixing causes GMP depolymerization and formation of smaller particles. Furthermore, after mixing, the protein secondary structure tends to be disordered, the protein morphology becomes irregular, and the protein subunit ratio changes. Thus, mixing has many of the opposite effects to resting, although resting can (to some extent) restore the properties of the GMP after mixing. However, excessive resting time can lead to negative results, reflected in lower disulfide bond (SS) and GMP contents, and more irregular particle sizes. The presented results suggest that dough mixing induces rearrangement of the dough’s protein structure, and resting somewhat restores the chemical bonds and internal protein structure.

scholarly journals Statistical Modelling of Gluten Production by Varying Mixing Time, Salt and Water Levels During Dough Mixing

2009 ◽  
Vol 5 (3) ◽  
Author(s):  
Dayang N Abang Zaidel ◽  
Nyuk L Chin ◽  
Yus Aniza Yusof ◽  
Russly Abdul Rahman ◽  
Roselina Karim
Keyword(s):  
Volume Expansion ◽  
Food Industry ◽  
Mixing Time ◽  
Statistical Modelling ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Dough Mixing ◽  
Modelling Studies ◽  
Main Factors ◽  
Central Composite

Gluten is widely discussed in dough and breadmaking subjects but the focus has not been on the production of gluten itself for usage and application in the food industry. This paper presents some modelling studies of gluten production using response surface methodology (RSM) by varying three main factors that contribute towards gluten formation during dough mixing, which are the mixing time, salt and water levels. The gluten produced was measured for its quantity in terms of wet and dry gluten contents and for quality, in terms of volume expansion of fried gluten and extensibility of gluten. Two wheat flour types, the strong and weak were used as comparison. The experiment was designed following a standard RSM design, known as the central composite design. The results of analysis of variance indicated that the proposed mathematical model obtained can adequately describe the production of gluten within the studied factor limits. The coefficient of determination, R2 of all the responses were higher than 0.80. Results show that salt gave the most significant effect (0.0001

scholarly journals SSNN, a method for neural network protein secondary structure fitting using circular dichroism data

2014 ◽  
Vol 6 (17) ◽  
pp. 6721-6726 ◽  
Author(s):  
Vincent Hall ◽  
Anthony Nash ◽  
Alison Rodger
Keyword(s):  
Neural Network ◽  
Circular Dichroism ◽  
Protein Structure ◽  
Secondary Structure ◽  
Protein Secondary Structure ◽  
Network Approach ◽  
Cd Spectra ◽  
Neural Network Approach ◽  
Self Organising Map ◽  
Circular Dichroïsm

SSNN is a self-organising map neural network approach for estimating protein structure from circular dichroism (CD) spectra. The method for using SSNN is described here, and SSNN is compared with CDSSTR, a well-known methodology for finding secondary structures from CD. SSNN compares well with similar methodologies.

Correlation of glutenin macropolymer with viscoelastic properties during dough mixing

2007 ◽  
Vol 45 (2) ◽  
pp. 128-133 ◽  
Author(s):  
Jin-shui Wang ◽  
Mou-ming Zhao ◽  
Qiang-zhong Zhao
Keyword(s):  
Viscoelastic Properties ◽  
Dough Mixing ◽  
Glutenin Macropolymer

scholarly journals Hermes: an ensemble machine learning architecture for protein secondary structure prediction

2019 ◽  
Author(s):  
Larry Bliss ◽  
Ben Pascoe ◽  
Samuel K Sheppard
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Cross Validation ◽  
Secondary Structure Prediction ◽  
Protein Structures ◽  
Lower Boundary ◽  
Protein Secondary Structure ◽  
Homologous Proteins

AbstractMotivationProtein structure predictions, that combine theoretical chemistry and bioinformatics, are an increasingly important technique in biotechnology and biomedical research, for example in the design of novel enzymes and drugs. Here, we present a new ensemble bi-layered machine learning architecture, that directly builds on ten existing pipelines providing rapid, high accuracy, 3-State secondary structure prediction of proteins.ResultsAfter training on 1348 solved protein structures, we evaluated the model with four independent datasets: JPRED4 - compiled by the authors of the successful predictor with the same name, and CASP11, CASP12 & CASP13 - assembled by the Critical Assessment of protein Structure Prediction consortium who run biannual experiments focused on objective testing of predictors. These rigorous, pre-established protocols included 7-fold cross-validation and blind testing. This led to a mean Hermes accuracy of 95.5%, significantly (p<0.05) better than the ten previously published models analysed in this paper. Furthermore, Hermes yielded a reduction in standard deviation, lower boundary outliers, and reduced dependency on solved structures of homologous proteins, as measured by NEFF score. This architecture provides advantages over other pipelines, while remaining accessible to users at any level of bioinformatics experience.Availability and ImplementationThe source code for Hermes is freely available at: https://github.com/HermesPrediction/Hermes. This page also includes the cross-validation with corresponding models, and all training/testing data presented in this study with predictions and accuracy.

scholarly journals Clean Label in Bread

Foods ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 2054
Author(s):  
Maite Cristina Alava Vargas ◽  
Senay Simsek
Keyword(s):  
Water Absorption ◽  
Shelf Life ◽  
Mixing Time ◽  
Bread Quality ◽  
Staple Food ◽  
Chemical Additives ◽  
Dough Mixing ◽  
Definition Of ◽  
Interest In Research

Bread is considered a staple food worldwide, and therefore there is much interest in research around the topic. The bread industry is usually looking for ways to improve its formulations. Therefore, other ingredients such as dough conditioners, crumb softeners, emulsifiers, and surfactants can be added to enhance bread quality. These ingredients perform functions such as helping standardize processes in the industry, reducing dough-mixing time, increasing water absorption, improving bread quality, and extending its shelf life. Consumers are concerned about the effect of these ingredients on their health, and this has increased the popularity of clean-label bread formulations. A clean label generally indicates that a product is free of chemical additives, has an ingredient list that is easy to understand, has undergone natural or limited processing, and/or is organic and free of additives or preservatives. However, there is no scientific definition of the term “clean label.” Researchers have focused on these clean-label initiatives to replace dough strengtheners and preservatives in bread formulations and give consumers what they perceive as a healthier product.

scholarly journals Literature Survey of Protein Secondary Structure Prediction

2012 ◽  
Author(s):  
Satya Nanda Vel Arjunan ◽  
Safaai Deris ◽  
Rosli Md Illias
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Large Scale ◽  
Secondary Structure Prediction ◽  
Protein Structures ◽  
Protein Secondary Structure ◽  
Fundamental Theory ◽  
Protein Secondary Structure Prediction ◽  
General Guide

Dengan wujudnya projek jujukan DNA secara besar-besaran, teknik yang tepat untuk meramalkan struktur protein diperlukan. Masalah meramalkan struktur protein daripada jujukan DNA pada dasarnya masih belum dapat diselesaikan walaupun kajian intensif telah dilakukan selama lebih daripada tiga dekad. Dalam kertas kerja ini, teori asas struktur protein akan dibincangkan sebagai panduan umum bagi kajian peramalan struktur protein sekunder. Analisis jujukan terkini serta prinsi p yang digunakan dalam teknik-teknik tersebut akan diterangkan. Kata kunci: peramalan stuktur sekunder protein; rangkaian neural. In the wake of large-scale DNA sequencing projects, accurate tools are needed to predict protein structures. The problem of predicting protein structure from DNA sequence remains fundamentally unsolved even after more than three decades of intensive research. In this paper, fundamental theory of the protein structure of the protein structure will be presented as a general guide to protein secondary structure prediction research. An overview of the state-of-theart in sequence analysis and some princi ples of the methods invloved wil be described. Key words: protein secondary structure prediction;neural networks.

Prediction of Protein Secondary Structure

2012 ◽  
Author(s):  
Satya Nanda Vel Arjunan ◽  
Safaai Deris ◽  
Rosli Md Illias
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Large Scale ◽  
Secondary Structure Prediction ◽  
State Of The Art ◽  
Protein Structures ◽  
Protein Secondary Structure ◽  
Protein Secondary Structure Prediction ◽  
General Guide

Dengan wujudnya projek jujukan DNA secara besar–besaran, teknik yang tepat untuk meramalkan struktur protein diperlukan. Masalah meramalkan struktur protein daripada jujukan DNA pada dasarnya masih belum dapat diselesaikan walaupun kajian intensif telah dilakukan selama lebih daripada tiga dekad. Dalam kertas kerja ini, teori asas struktur protein akan dibincangkan sebagai panduan umum bagi kajian peramalan struktur protein sekunder. Analisis jujukan terkini serta prinsip yang digunakan dalam teknik–teknik tersebut akan diterangkan. Kata kunci: Peramalan struktur sekunder protein; Rangkaian Neural In the wake of large-scale DNA sequencing projects, accurate tools are needed to predict protein structures. The problem of predicting protein structure from DNA sequence remains fundamentally unsolved even after more than three decades of intensive research. In this paper, fundamental theory of the protein structure will be presented as a general guide to protein secondary structure prediction research. An overview of the state–of–the–art in sequence analysis and some principles of the methods involved wil be described. Key words: Protein secondary structure prediction; Neural networks

scholarly journals Fourier transform infrared conformational investigation of type I collagen aged by in vitro induced dehydration and non-enzymatic glycation treatments

2017 ◽  
Vol 90 (1) ◽  
Author(s):  
Maria Grazia Bridelli ◽  
Chiaramaria Stani ◽  
Roberta Bedotti
Keyword(s):  
Fourier Transform ◽  
Protein Structure ◽  
Conformational Changes ◽  
Self Assembly ◽  
Type I Collagen ◽  
Protein Secondary Structure ◽  
Fourier Transform Infrared ◽  
Type I ◽  
Absorption Bands

The two main ageing-inducing events in the collagenous tissues are the water loss and the formation of intermolecular crosslinks based on the reaction of collagen with matrix carbohydrates, following a mechanism known as non-enzymatic-glycation. With the aim to mimic the two deleterious processes for the protein structure, rat-tail collagen was submitted to hydration changes and allowed to interact with two sugars characterized by different reducing properties, D-glucose and D-ribose. Fourier transform infrared (FTIR) spectroscopy was employed to investigate the conformational changes induced in the protein by the two treatments by analyzing the subsequent spectra modifications. FTIR spectra monitored: i) the amplitude and position changes of the two characteristic absorption bands OH stretching and Amide I, in dependence on the humidity level: a significant hysteresis effect in the ν(OH) band (ν~3400 cm–1) amplitude of the protein dehydrated and then rehydrated to the initial relative humidity (aw=0.92- 0.06) may be related to the enhancement of the β-sheet fraction in the protein structure as revealed by the parallel modification in the Amide I band (ν~1650 cm–1); ii) the area of the carbohydrate double band peaking at 1080 cm–1 and 1031 cm–1, associated to the accumulation of the glycation products, depending on the sugar concentration and incubation time. The association of both sugars to collagen only minimally affects the protein secondary structure as revealed by Amide I band Gaussian analysis. The whole set of results suggests hints to hypothesize a self-assembly model for collagen molecules induced by ageing.

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