Plant Proteins

2021 ◽  
pp. 17-47
Author(s):  
William W. Riley
Keyword(s):  
Plant Proteins

Plant proteins mapped

Nature ◽  
2008 ◽  
Author(s):  
Heidi Ledford
Keyword(s):  
Plant Proteins

PEAS AND PRODUCTS OF ITS PROCESSING OF - NONCONVENTIONAL RAW MATERIAL RESOURCES

2020 ◽  
Author(s):  
D.Y. Bolgova ◽  
◽  
N.A. Tarasenko ◽  
Z.S. Mukhametova ◽  
◽  
...  
Keyword(s):  
Chemical Composition ◽  
Human Health ◽  
Food Production ◽  
Food Industry ◽  
Raw Material ◽  
Plant Proteins ◽  
Material Resources ◽  
The Rich

Nutrition is an important factor that affects human health. The use of plant proteins as various additives in food production has now been actively developed. The rich chemical composition of pea grains determines the possibility of application in the food industry. Peas are characterized by good assimilability and degree of digestion.

Penggunaan Standar Bumbu Masakan Lauk Hewani dan Nabati di RSUD Panembahan Senopati Bantul Yogyakarta

2017 ◽  
Vol 19 (2) ◽  
pp. 131-139
Author(s):  
Kukuh Probo Sukmawati ◽  
Setyowati Setyowati ◽  
Th Ninuk Sri Hartini
Keyword(s):  
Animal Protein ◽  
Plant Protein ◽  
Observational Research ◽  
Plant Proteins ◽  
Tabular Form ◽  
Cross Sectional ◽  
Herbs And Spices

  Background: Using of herbs and spices greatly affect the taste of foods, although the ingredients used are the same, different formulations of herbs will produce different flavors. Standardized seasonings are needed to produce a relatively similar food taste. Objective: The research aims to determine the use of herbs in animal and plant proteins. Method: This research was an observational research with cross sectional approach at PanembahanSenopati Hospital Bantul. The objects of this research were standardized seasonings inanimal and plant proteinsin the menu cycle of 10 days in Juny 2015. The data were analyzed descriptively and presented in tabular form and textural. Result: Standardized Seasonings in animal protein were B for satay and C for semur. Standardized seasonings in plant protein were B for bali, terik, rujak and D for bacem. The percentage of the use of seasoning than standardized seasoning for satay (148,7%), bali (130,3%), rujak(372%),for semur, terik and bacem the conformity are not not known because the standardized seasonings unwritten. The conformity of ingredient herbskind for satay (66,7%), semur (100%), bali (54,5%), rujak (50%), bacem (100%) and terik (100%). Conclusion:The conformity of herbs weight foranimal protein (124,3%) and plant protein (175,5%). The conformity of ingredient herbskind for animal protein (83,3%) and plant protein (76,1%). Standardized seasonings used were B, C and D.   Keywords: standardized seasonings, animal protein, plant protein

pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset

2019 ◽  
Vol 24 (34) ◽  
pp. 4013-4022 ◽  
Author(s):  
Xiang Cheng ◽  
Xuan Xiao ◽  
Kuo-Chen Chou
Keyword(s):  
Subcellular Localization ◽  
Basic Research ◽  
Training Dataset ◽  
Sequence Information ◽  
Plant Proteins ◽  
Protein Subcellular Localization ◽  
Computational Tools ◽  
Validation Tests ◽  
User Friendly ◽  
Better Than

Knowledge of protein subcellular localization is vitally important for both basic research and drug development. With the avalanche of protein sequences emerging in the post-genomic age, it is highly desired to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mPlant” was developed for identifying the subcellular localization of plant proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mPlant was trained by an extremely skewed dataset in which some subsets (i.e., the protein numbers for some subcellular locations) were more than 10 times larger than the others. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. To overcome such biased consequence, we have developed a new and bias-free predictor called pLoc_bal-mPlant by balancing the training dataset. Cross-validation tests on exactly the same experimentconfirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mPlant, the existing state-of-the-art predictor in identifying the subcellular localization of plant proteins. To maximize the convenience for the majority of experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mPlant/, by which users can easily get their desired results without the need to go through the detailed mathematics.

scholarly journals Effects of Indigenous Diet Iron Content and Location on Hemoglobin Levels of Ghanaians

Nutrients ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2710 ◽  
Author(s):  
April Callister ◽  
Joanna Gautney ◽  
Christina Aguilar ◽  
Julian Chan ◽  
David Aguilar
Keyword(s):  
Food Group ◽  
Food Type ◽  
Plant Proteins ◽  
Food Groups ◽  
Sex Distribution ◽  
Dietary Interventions ◽  
Food Frequency ◽  
Blood Samples ◽  
Ease Of Access ◽  
The Impact

Multiple studies have demonstrated strong links between diet and anemia, but few have explored the impact of food groups on hemoglobin (Hb). We analyzed the correlation between Ghanaian diet and Hb levels to explore reduction of anemia prevalence through dietary interventions. Demographics, food frequency questionnaires (FFQ), and blood samples were obtained from 140 volunteers (ages 18–65) in five locations across Ghana. Hb was measured; estimated iron consumption was calculated. FFQ items were grouped by food type, and a regression analysis was performed to determine the most important dietary predictors of Hb. Moreover, 47% of total participants were anemic; 64% of females and 28% of males. Hb levels were highest in Mole (13.9 g/dL, SD = ±1.9), independent of sex distribution. The regression model revealed a 62.7% adjusted correlation between food groups and Hb levels. Animal foods (β = 0.016, t = 5.08, p < 0.01) and plant protein (β = 0.013, t = 2.86, p < 0.01) were the most influential groups to Hb levels. It is of vital importance to emphasize the benefits of consuming animal foods and plant proteins within the Ghanaian population. The ease of access to plant proteins makes it likely that this food group will be most influential and have the greatest impact in reduction of anemia in the Ghanaian population.

The use of immunochemical techniques to study plant proteins

1985 ◽  
Vol 3 (1-2) ◽  
pp. 17-23 ◽  
Author(s):  
Monica A. Hughes ◽  
M. Alison Dunn
Keyword(s):  
Plant Proteins ◽  
Immunochemical Techniques

Effectors of biotrophic fungal plant pathogens

2010 ◽  
Vol 37 (10) ◽  
pp. 913 ◽  
Author(s):  
Pamela H. P. Gan ◽  
Maryam Rafiqi ◽  
Adrienne R. Hardham ◽  
Peter N. Dodds
Keyword(s):  
Plant Tissue ◽  
Fungal Pathogen ◽  
Plant Pathogens ◽  
Plant Cells ◽  
Host Cells ◽  
Plant Proteins ◽  
Living Plant ◽  
Host Cytoplasm ◽  
Fungal Plant Pathogens ◽  
Biotrophic Fungi

Plant pathogenic biotrophic fungi are able to grow within living plant tissue due to the action of secreted pathogen proteins known as effectors that alter the response of plant cells to pathogens. The discovery and identification of these proteins has greatly expanded with the sequencing and annotation of fungal pathogen genomes. Studies to characterise effector function have revealed that a subset of these secreted pathogen proteins interact with plant proteins within the host cytoplasm. This review focuses on the effectors of intracellular biotrophic and hemibiotrophic fungal plant pathogens and summarises advances in understanding the roles of these proteins in disease and in elucidating the mechanism of fungal effector uptake into host cells.

scholarly journals Dietary plant proteins and vegetable oil blends increase adiposity and plasma lipids in Atlantic salmon (Salmo salarL.)

2011 ◽  
Vol 106 (5) ◽  
pp. 633-647 ◽  
Author(s):  
Bente E. Torstensen ◽  
Marit Espe ◽  
Ingunn Stubhaug ◽  
Øyvind Lie
Keyword(s):  
Lipid Metabolism ◽  
Atlantic Salmon ◽  
Fish Oil ◽  
Fish Meal ◽  
Dietary Lipids ◽  
The Body ◽  
Whole Body ◽  
Acid Uptake ◽  
Plant Proteins ◽  
Vldl Assembly

In order to study whether lipid metabolism may be affected by maximum replacement of dietary fish oil and fish meal with vegetable oils (VO) and plant proteins (PP), Atlantic salmon (Salmo salarL.) smolts were fed a control diet containing fish oil and fish meal or one of three plant-based diets through the seawater production phase for 12 months. Diets were formulated to meet all known nutrient requirements. The whole-body lipid storage pattern was measured after 12 months, as well as post-absorptive plasma, VLDL and liver TAG. To further understand the effects on lipid metabolism, expression of genes encoding for proteins involved in VLDL assembly (apoB100), fatty acid uptake (FATP1, cd36, LPL and FABP3, FABP10 and FABP11) were measured in liver and visceral adipose tissue. Maximum dietary VO and PP increased visceral lipid stores, liver TAG, and plasma VLDL and TAG concentrations. Increased plasma TAG correlated with an increased expression of apoB100, indicating increased VLDL assembly in the liver of fish fed the high-plant protein- and VO-based diet. Atlantic salmon fed intermediate replacement levels of VO or PP did not have increased body fat or visceral mass. Overall, the present results demonstrate an interaction between dietary lipids and protein on lipid metabolism, increasing overall adiposity and TAG in the body when fish meal and fish oil are replaced concomitantly at maximised levels of VO and PP.

scholarly journals ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning

2017 ◽  
Vol 217 (4) ◽  
pp. 1764-1778 ◽  
Author(s):  
Jana Sperschneider ◽  
Peter N. Dodds ◽  
Karam B. Singh ◽  
Jennifer M. Taylor
Keyword(s):  
Machine Learning ◽  
Plant Proteins
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