scholarly journals Development of a novel monosaccharide substitution matrix for improved comparison of glycan structures

2022 ◽  
pp. 108496
Author(s):  
Akihiro Fujita ◽  
Kiyoko F. Aoki-Kinoshita
Keyword(s):  
Substitution Matrix

Hubsm: A Novel Amino Acid Substitution Matrix for Comparing Hub Proteins

2017 ◽  
Vol 7 (8) ◽  
pp. 212
Author(s):  
Renganayaki G. ◽  
Achuthsankar S. Nair
Keyword(s):  
Amino Acid ◽  
Amino Acid Substitution ◽  
Low Complexity ◽  
Database Search ◽  
Substitution Matrix ◽  
Compositional Bias ◽  
Sequence Alignments ◽  
Amino Acid Substitution Matrix ◽  
Alignment Algorithms ◽  
Hub Proteins

Sequence alignment algorithms and  database search methods use BLOSUM and PAM substitution matrices constructed from general proteins. These de facto matrices are not optimal to align sequences accurately, for the proteins with markedly different compositional bias in the amino acid.   In this work, a new amino acid substitution matrix is calculated for the disorder and low complexity rich region of Hub proteins, based on residue characteristics. Insights into the amino acid background frequencies and the substitution scores obtained from the Hubsm unveils the  residue substitution patterns which differs from commonly used scoring matrices .When comparing the Hub protein sequences for detecting homologs,  the use of this Hubsm matrix yields better results than PAM and BLOSUM matrices. Usage of Hubsm matrix can be optimal in database search and for the construction of more accurate sequence alignments of Hub proteins.

Determination of Glutamate and GABA Released by Mouse Embryonic Stem Cells Using HILIC-ESI-MS/MS

2020 ◽  
Vol 10 (2) ◽  
pp. 122-129
Author(s):  
Haoyu Lv ◽  
Yabin Tang ◽  
Fan Sun ◽  
Shimin An ◽  
Xinjie Yang ◽  
...  
Keyword(s):  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Embryonic Stem ◽  
Mouse Embryonic Stem Cells ◽  
Reversed Phase ◽  
Water Soluble ◽  
Substitution Matrix ◽  
Neural Cells ◽  
Esi Ms

Background:In recent years, more and more researches have shown that neurotransmitters can also be synthesized and released by peripheral non-neural cells. However, specificity and high sensitivity detection means were required for confirming ESCs autocrine glutamate and γ - aminobutyric acid (GABA). Glutamate and GABA are water-soluble and polar compounds which cannot be retained on a reversed phase C18 column, and their contents are often at a trace level. On the other hand, the biological matrix such as cell culture fluid contains a large number of amino acids, vitamins, carbohydrates, inorganic ions and other substances. Therefore, the main problem is the selection of the chromatographic column to avoid matrix interference.Objective:To establish a rapid and reliable method for the simultaneous determination of glutamate and GABA released from embryonic stem cells based on analytical chemistry.Methods:Glutamate and GABA released from mouse embryonic stem cells were determined on the basis of hydrophilic interaction chromatography coupled with electrospray ionization tandem Mass Spectrometry (HILIC- ESI- MS/MS), using isotope internal standards and substitution matrix method.Results:Undifferentiated embryonic stem cells autocrine glutamate and GABA and will reach releasing- reuptacking dynamic equilibriums at different time points. In contrast, neither glutamate nor GABA releasing could be detected from the MEFs, indicating the specificity release of the mESCs in the applied analytic method.Conclusion:A novel, simple, sensitive, selective and quantitative method was developed for determination of the glutamate and GABA from mouse embryonic stem cells.

A pair-to-pair amino acids substitution matrix and its applications for protein structure prediction

2007 ◽  
Vol 67 (1) ◽  
pp. 142-153 ◽  
Author(s):  
Eran Eyal ◽  
Milana Frenkel-Morgenstern ◽  
Vladimir Sobolev ◽  
Shmuel Pietrokovski
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Substitution Matrix

scholarly journals A novel substitution matrix fitted to the compositional bias in Mollicutes improves the prediction of homologous relationships

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Claire Lemaitre ◽  
Aurélien Barré ◽  
Christine Citti ◽  
Florence Tardy ◽  
François Thiaucourt ◽  
...  
Keyword(s):  
Substitution Matrix ◽  
Compositional Bias

scholarly journals The Ancient Operational Code is Embedded in the Amino Acid Substitution Matrix and aaRS Phylogenies

2019 ◽  
Vol 88 (2) ◽  
pp. 136-150 ◽  
Author(s):  
Julia A. Shore ◽  
Barbara R. Holland ◽  
Jeremy G. Sumner ◽  
Kay Nieselt ◽  
Peter R. Wills
Keyword(s):  
Amino Acid ◽  
Amino Acid Substitution ◽  
Substitution Matrix ◽  
Operational Code ◽  
Amino Acid Substitution Matrix

scholarly journals Sequence alignment using machine learning for accurate template-based protein structure prediction

2019 ◽  
Vol 36 (1) ◽  
pp. 104-111
Author(s):  
Shuichiro Makigaki ◽  
Takashi Ishida
Keyword(s):  
Machine Learning ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Structural Alignment ◽  
Protein Structures ◽  
Substitution Matrix ◽  
Detection Methods ◽  
Supplementary Information ◽  
Homology Detection ◽  
Sequence Alignments

Abstract Motivation Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high sensitivity, the accuracy of template-based models generated from homology-detection-based alignments is often lower than that from ideal alignments. Results In this study, we propose a new method that generates pairwise sequence alignments for more accurate template-based modeling. The proposed method trains a machine learning model using the structural alignment of known homologs. It is difficult to directly predict sequence alignments using machine learning. Thus, when calculating sequence alignments, instead of a fixed substitution matrix, this method dynamically predicts a substitution score from the trained model. We evaluate our method by carefully splitting the training and test datasets and comparing the predicted structure’s accuracy with that of state-of-the-art methods. Our method generates more accurate tertiary structure models than those produced from alignments obtained by other methods. Availability and implementation https://github.com/shuichiro-makigaki/exmachina. Supplementary information Supplementary data are available at Bioinformatics online.

PROGRESSIVE MULTIPLE ALIGNMENT USING SEQUENCE TRIPLET OPTIMIZATIONS AND THREE-RESIDUE EXCHANGE COSTS

2004 ◽  
Vol 02 (04) ◽  
pp. 719-745 ◽  
Author(s):  
ARUN SIDDHARTH KONAGURTHU ◽  
JAMES WHISSTOCK ◽  
PETER J. STUCKEY
Keyword(s):  
Amino Acid ◽  
Amino Acid Substitution ◽  
Multiple Alignment ◽  
Substitution Matrix ◽  
Practical Approach ◽  
Sequence Alignments ◽  
Optimal Sequence ◽  
Amino Acid Substitution Matrix ◽  
Multiple Alignments ◽  
First Time

In this paper we demonstrate a practical approach to construct progressive multiple alignments using sequence triplet optimizations rather than a conventional pairwise approach. Using the sequence triplet alignments progressively provides a scope for the synthesis of a three-residue exchange amino acid substitution matrix. We develop such a 20×20×20 matrix for the first time and demonstrate how its use in optimal sequence triplet alignments increases the sensitivity of building multiple alignments. Various comparisons were made between alignments generated using the progressive triplet methods and the conventional progressive pairwise procedure. The assessment of these data reveal that, in general, the triplet based approaches generate more accurate sequence alignments than the traditional pairwise based procedures, especially between more divergent sets of sequences.

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