scholarly journals 3D Interaction Homology: Computational Titration of Aspartic Acid, Glutamic Acid and Histidine Can Create pH-Tunable Hydropathic Environment Maps

2021 ◽  
Vol 8 ◽  
Author(s):  
Noah B. Herrington ◽  
Glen E. Kellogg
Keyword(s):  
Glutamic Acid ◽  
Aspartic Acid ◽  
Protein Design ◽  
Solvent Accessibility ◽  
Scoring Function ◽  
Relative Solvent Accessibility ◽  
Solvent Exposure ◽  
3D Interaction ◽  
Ionizable Residues ◽  
Environment Maps

Aspartic acid, glutamic acid and histidine are ionizable residues occupying various protein environments and perform many different functions in structures. Their roles are tied to their acid/base equilibria, solvent exposure, and backbone conformations. We propose that the number of unique environments for ASP, GLU and HIS is quite limited. We generated maps of these residue's environments using a hydropathic scoring function to record the type and magnitude of interactions for each residue in a 2703-protein structural dataset. These maps are backbone-dependent and suggest the existence of new structural motifs for each residue type. Additionally, we developed an algorithm for tuning these maps to any pH, a potentially useful element for protein design and structure building. Here, we elucidate the complex interplay between secondary structure, relative solvent accessibility, and residue ionization states: the degree of protonation for ionizable residues increases with solvent accessibility, which in turn is notably dependent on backbone structure.

scholarly journals Dietary glutamic acid and aspartic acid as biomarkers for predicting diabetic retinopathy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
So Young Park ◽  
Jieun Kim ◽  
Jung Il Son ◽  
Sang Youl Rhee ◽  
Do-Yeon Kim ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Glutamic Acid ◽  
Aspartic Acid ◽  
Education Level ◽  
Screening Rate ◽  
Food Records ◽  
Acid Intake ◽  
Lower Education Level ◽  
Lower Education

AbstractThe screening rate of diabetic retinopathy (DR) is low despite the importance of early diagnosis. We investigated the predictive value of dietary glutamic acid and aspartic acid for diagnosis of DR using the Korea National Diabetes Program cohort study. The 2067 patients with type 2 diabetes without DR were included. The baseline intakes of energy, glutamic acid and aspartic acid were assessed using a 3-day food records. The risk of DR incidence based on intake of glutamic acid and aspartic acid was analyzed. The DR group was older, and had higher HbA1c, longer DM duration, lower education level and income than non-DR group (all p < 0.05). The intake of total energy, glutamic acid and aspartic acid were lower in DR group than non-DR group (p = 0.010, p = 0.025 and p = 0.042, respectively). There was no difference in the risk of developing DR according to the intake of glutamic acid and ascorbic acid. But, aspartic acid intake had a negative correlation with PDR. Hence, the intake of glutamic acid and aspartic acid did not affect in DR incidence. However, lower aspartic acid intake affected the PDR incidence.

Biodegradation of copoly(L-aspartic acid/L-glutamic acid) in vitro

Biopolymers ◽  
1990 ◽  
Vol 29 (3) ◽  
pp. 549-557 ◽  
Author(s):  
Toshio Hayashi ◽  
Makoto Iwatsuki
Keyword(s):  
Glutamic Acid ◽  
Aspartic Acid

scholarly journals Utilization in vivo of glucose and volatile fatty acids by sheep brain for the synthesis of acidic amino acids

1966 ◽  
Vol 101 (3) ◽  
pp. 591-597 ◽  
Author(s):  
R M O'Neal ◽  
R E Koeppe ◽  
E I Williams
Keyword(s):  
Amino Acids ◽  
Fatty Acids ◽  
Glutamic Acid ◽  
Aspartic Acid ◽  
Free Amino Acids ◽  
Free Amino ◽  
Specific Activity ◽  
Tricarboxylic Acid Cycle ◽  
Sheep Brain ◽  
The Brain

1. Free glutamic acid, aspartic acid, glutamic acid from glutamine and, in some instances, the glutamic acid from glutathione and the aspartic acid from N-acetyl-aspartic acid were isolated from the brains of sheep and assayed for radioactivity after intravenous injection of [2-(14)C]glucose, [1-(14)C]acetate, [1-(14)C]butyrate or [2-(14)C]propionate. These brain components were also isolated and analysed from rats that had been given [2-(14)C]propionate. The results indicate that, as in rat brain, glucose is by far the best precursor of the free amino acids of sheep brain. 2. Degradation of the glutamate of brain yielded labelling patterns consistent with the proposal that the major route of pyruvate metabolism in brain is via acetyl-CoA, and that the short-chain fatty acids enter the brain without prior metabolism by other tissue and are metabolized in brain via the tricarboxylic acid cycle. 3. When labelled glucose was used as a precursor, glutamate always had a higher specific activity than glutamine; when labelled fatty acids were used, the reverse was true. These findings add support and complexity to the concept of the metabolic; compartmentation' of the free amino acids of brain. 4. The results from experiments with labelled propionate strongly suggest that brain metabolizes propionate via succinate and that this metabolic route may be a limited but important source of dicarboxylic acids in the brain.

Nanocomposites of hydroxyapatite with aspartic acid and glutamic acid and their interaction with osteoblast-like cells

Biomaterials ◽  
2006 ◽  
Vol 27 (25) ◽  
pp. 4428-4433 ◽  
Author(s):  
E BOANINI ◽  
P TORRICELLI ◽  
M GAZZANO ◽  
R GIARDINO ◽  
A BIGI
Keyword(s):  
Glutamic Acid ◽  
Aspartic Acid

scholarly journals Solvent Accessibility of Residues Undergoing Pathogenic Variations in Humans: From Protein Structures to Protein Sequences

2021 ◽  
Vol 7 ◽  
Author(s):  
Castrense Savojardo ◽  
Matteo Manfredi ◽  
Pier Luigi Martelli ◽  
Rita Casadio
Keyword(s):  
Solvent Accessibility ◽  
Protein Structures ◽  
Three Dimensional ◽  
Protein Sequences ◽  
Large Data ◽  
Human Protein ◽  
Dimensional Structure ◽  
Wild Type ◽  
Solvent Exposure ◽  
Data Set

Solvent accessibility (SASA) is a key feature of proteins for determining their folding and stability. SASA is computed from protein structures with different algorithms, and from protein sequences with machine-learning based approaches trained on solved structures. Here we ask the question as to which extent solvent exposure of residues can be associated to the pathogenicity of the variation. By this, SASA of the wild-type residue acquires a role in the context of functional annotation of protein single-residue variations (SRVs). By mapping variations on a curated database of human protein structures, we found that residues targeted by disease related SRVs are less accessible to solvent than residues involved in polymorphisms. The disease association is not evenly distributed among the different residue types: SRVs targeting glycine, tryptophan, tyrosine, and cysteine are more frequently disease associated than others. For all residues, the proportion of disease related SRVs largely increases when the wild-type residue is buried and decreases when it is exposed. The extent of the increase depends on the residue type. With the aid of an in house developed predictor, based on a deep learning procedure and performing at the state-of-the-art, we are able to confirm the above tendency by analyzing a large data set of residues subjected to variations and occurring in some 12,494 human protein sequences still lacking three-dimensional structure (derived from HUMSAVAR). Our data support the notion that surface accessible area is a distinguished property of residues that undergo variation and that pathogenicity is more frequently associated to the buried property than to the exposed one.

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