scholarly journals Inferring Selection on Amino Acid Preference in Protein Domains

2008 ◽  
Vol 26 (3) ◽  
pp. 527-536 ◽  
Author(s):  
A. M. Moses ◽  
R. Durbin
Keyword(s):  
Amino Acid ◽  
Protein Domains ◽  
Amino Acid Preference

LARVAL NUTRITION AFFECTS FEMALE NECTAR AMINO ACID PREFERENCE IN THE MAP BUTTERFLY (ARASCHNIA LEVANA)

Ecology ◽  
2003 ◽  
Vol 84 (10) ◽  
pp. 2788-2794 ◽  
Author(s):  
Jovanne Mevi-Schütz ◽  
Andreas Erhardt
Keyword(s):  
Amino Acid ◽  
Larval Nutrition ◽  
Amino Acid Preference

scholarly journals Active Site-Induced Evolutionary Constraints Follow Fold Polarity Principles in Soluble Globular Enzymes

2019 ◽  
Vol 36 (8) ◽  
pp. 1728-1733 ◽  
Author(s):  
Alexander Mayorov ◽  
Matteo Dal Peraro ◽  
Luciano A Abriata
Keyword(s):  
Amino Acid ◽  
Active Sites ◽  
Metabolic Cost ◽  
Evolutionary Rates ◽  
Recent Analysis ◽  
Evolutionary Constraints ◽  
Evolutionary Models ◽  
Data Set ◽  
Soluble Enzymes ◽  
Amino Acid Preference

Abstract A recent analysis of evolutionary rates in >500 globular soluble enzymes revealed pervasive conservation gradients toward catalytic residues. By looking at amino acid preference profiles rather than evolutionary rates in the same data set, we quantified the effects of active sites on site-specific constraints for physicochemical traits. We found that conservation gradients respond to constraints for polarity, hydrophobicity, flexibility, rigidity and structure in ways consistent with fold polarity principles; while sites far from active sites seem to experience no physicochemical constraint, rather being highly variable and favoring amino acids of low metabolic cost. Globally, our results highlight that amino acid variation contains finer information about protein structure than usually regarded in evolutionary models, and that this information is retrievable automatically with simple fits. We propose that analyses of the kind presented here incorporated into models of protein evolution should allow for better description of the physical chemistry that underlies molecular evolution.

Critical protein domains and amino acid residues for gating the KIR6.2 channel by intracellular ATP

2003 ◽  
Vol 198 (1) ◽  
pp. 73-81 ◽  
Author(s):  
Jianping Wu ◽  
Hailan Piao ◽  
Asheebo Rojas ◽  
Runping Wang ◽  
Ying Wang ◽  
...  
Keyword(s):  
Amino Acid ◽  
Protein Domains ◽  
Amino Acid Residues ◽  
Intracellular Atp

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.

scholarly journals Plant and Bacterial Metaxin-like Proteins: Novel Proteins Related to Vertebrate Metaxins Involved in Uptake of Nascent Proteins into Mitochondria

2020 ◽  
Author(s):  
Kenneth W. Adolph
Keyword(s):  
Amino Acid ◽  
Protein Import ◽  
Protein Domains ◽  
Amino Acid Sequences ◽  
Similar Degree ◽  
Glutathione S Transferase ◽  
Phylogenetic Groups ◽  
Bacterial Phyla ◽  
Novel Proteins ◽  
Plant Groups

ABSTRACTThe metaxins were originally identified as vertebrate proteins of the outer mitochondrial membrane involved in protein import into mitochondria. Metaxin proteins have also been found in diverse invertebrate phyla. The present study is concerned with examining whether metaxin-like proteins occur in plants and bacteria. Metaxin-like proteins were revealed by their homology with human metaxins and the possession of characteristic GST_Metaxin protein domains. The results demonstrate that metaxin-like proteins exist in plants that include a wide variety of angiosperms, both eudicots and monocots, and other plant groups. Metaxin-like proteins can also be detected in bacteria, particularly in the Proteobacteria phylum, but also in different bacterial phyla. Phylogenetic analysis indicates that plant metaxin-like proteins, bacterial metaxin-like proteins, and vertebrate metaxins form distinct phylogenetic groups, but are related. Metaxin-like proteins, however, are only distantly related to GSTs (glutathione S-transferase proteins). A similar degree of homology is found in aligning the amino acid sequences of plant and bacterial metaxin-like proteins with human metaxins 1, 2, and 3 and other vertebrate metaxins. The amino acid identities range from about 22%-28% for each alignment. The presence of two conserved protein domains, GST_N_Metaxin and GST_C_Metaxin, in both plant and bacterial metaxin-like proteins provides evidence that these proteins are related to the vertebrate and invertebrate metaxins. The metaxin-like proteins have predicted secondary structures that are dominated by alpha-helical segments, like the vertebrate and invertebrate metaxins.

Sugar and amino acid preference in the black garden ant Lasius niger (L.)

2017 ◽  
Vol 100 ◽  
pp. 140-145 ◽  
Author(s):  
Natalia E.L. Madsen ◽  
Peter B. Sørensen ◽  
Joachim Offenberg
Keyword(s):  
Amino Acid ◽  
Lasius Niger ◽  
Amino Acid Preference
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