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

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.

Author(s):  
Ciro Balestrieri ◽  
Giovanni Colonna ◽  
Alfonso Giovane ◽  
Gaetano Irace ◽  
Luigi Servillo ◽  
...  

Author(s):  
А.А. Алексеева ◽  
Н.М. Агеева ◽  
В.Е. Струкова ◽  
М.А. Назаренко ◽  
Е.Н. Гонтарева

Исследован аминокислотный состав столового сухого белого виноматериала Пино Блан, полученного сбраживанием виноградного сусла расой активных дрожжей штамма WT-1 (Германия) с последующей выдержкой молодого виноматериала на дрожжевой гуще в течение 30 сут. Установлено, что в анализируемом виноматериале доминирует пролин (422 мг/дм3). Количество аминокислот аланина и аспарагина составило 67,2 и 57,6 мг/дм3 тирозина и серина 18,4 и 17,7 мг/дм3 метионина и изолейцина 16,2 и 14,4 мг/дм3 соответственно. Глутаминовой кислоты содержится в 3 раза меньше пролина. Оклейка молодого виноматериала привела к снижению концентрации аминокислот независимо от строения и химических свойств: глицина в 3,5 раза аланина, изолейцина, серина, фенилаланина, гистидина от 2,0 до 2,7 раза аспарагина, валина, треонина, тирозина, лизина, цистина и цистеина от 1,3 до 1,9 раза. Концентрация пролина снизилась незначительно c 422 до 389 мг/дм3. После выдержки молодого виноматериала на дрожжевой гуще в течение 1 мес. концентрация большинства аминокислот не повысилась. Обработка виноматериала бентонитом привела к дальнейшему снижению концентрации аминокислот в 1,52,0 раза. Отмечено уменьшение в 1,72 раза количества цистина и цистеина, обусловливающих формирование мышиного тона в виноматериалах. В 1,57 раза снизилась концентрация тирозина в 2,7 и 2,3 раза гистидина и серина соответственно. Проведенное исследование будет способствовать дальнейшим работам по стабилизации концентрации основных аминокислот в виноматериалах. The amino acid composition of table dry white wine material Pinot Blanc was studied, obtained by fermentation of grape must with a race of active yeast strain WT-1 (Germany), followed by exposure of the young wine material to yeast for 30 days. It was established that proline (422 mg/dm3) prevails in the analyzed wine material. The number of amino acids of alanine and asparagine was 67,2 and 57,6 mg/dm3 tyrosine and serine 18,4 and 17,7 mg/dm3 methionine and isoleucine 16,2 and 14,4 mg/dm3, respectively. Glutamic acid is 3 times less than proline. Pasting of young wine material led to a decrease in the concentration of amino acids, regardless of structure and chemical properties: glycine by 3,5 times alanine, isoleucine, serine, phenylalanine, histidine from 2,0 to 2,7 times asparagine, valine, threonine, tyrosine, lysine, cystine and cysteine from 1,3 to 1,9 times. The proline concentration decreased slightly from 422 to 389 mg/dm3. After aging the young wine material on yeast for 1 month, the concentration of most amino acids did not increase. Processing of wine material with bentonite led to a further decrease in the concentration of amino acids by 1,52,0 times. There was a 1,72-fold decrease in the amount of cystine and cysteine. The tyrosine concentration decreased 1,57 times 2,7 and 2,3 times histidine and serine, respectively. The study will contribute to further work on stabilizing the concentration of basic amino acids in wine materials.


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

2020 ◽  
Author(s):  
Lopamudra Dey ◽  
Sanjay Chakraborty ◽  
Anirban Mukhopadhyay

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.


1984 ◽  
Vol 105 (3) ◽  
pp. 391-397 ◽  
Author(s):  
Kenneth Sterling ◽  
Gordon A. Campbell ◽  
Milton A. Brenner

Abstract. The thyroid hormone receptor of the inner membrane of rat liver mitochondria was purified by osmotic and freeze-thaw lyses followed by partial purification on Sephadex G-200, and then by affinity chromatography with T3-Sepharose 4B. A single predominant protein band demonstrable on sodium dodecylsulphate (SDS) polyacrylamide gel electrophoresis was present in the first 4 mm NaOH elution peak of affinity chromatography. This was collected from affinity peaks from about 30 rat livers followed by preparative polyacrylamide gel electrophoresis. A single absorbance peak was observed by high pressure liquid chromatography (HPLC). The purified protein was analyzed for binding constants, amino acid composition, and characterized by analytical ultracentrifugation. The association constant (KA) exceeded 1011 m−1. The sedimentation coefficient (S20,W) was 2.2S, partial specific volume (v) 0.72, frictional coefficient (f/fo)s m 1.68 and the molecular weight was estimated at 28000. The amino acid composition was obtained.


2016 ◽  
Vol 8 (1) ◽  
pp. 111-116 ◽  
Author(s):  
M. Harmankaya ◽  
E. Ceyhan ◽  
A.S. Çelik ◽  
H. Sert ◽  
A. Kahraman ◽  
...  

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