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Amino Acid Composition

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Baishan Fang – One of the best experts on this subject based on the ideXlab platform.

  • Predicting the cofactors of oxidoreductases based on Amino Acid Composition distribution and Chou’s amphiphilic pseudo-Amino Acid Composition.
    Journal of theoretical biology, 2008
    Co-Authors: Guangya Zhang, Baishan Fang
    Abstract:

    Predicting the cofactors of oxidoreductases plays an important role in inferring their catalytic mechanism. Feature extraction is a critical part in the prediction systems, requiring raw sequence data to be transformed into appropriate numerical feature vectors while minimizing information loss. In this paper, we present an Amino Acid Composition distribution method for extracting useful features from primary sequence, and the k-nearest neighbor was used as the classifier. The overall prediction accuracy evaluated by the 10-fold cross-validation reached 90.74%. Comparing our method with other eight feature extraction methods, the improvement of the overall prediction accuracy ranged from 3.49% to 15.74%. Our experimental results confirm that the method we proposed is very useful and may be used for other bioinformatical predictions. Interestingly, when features extracted by our method and Chou’s amphiphilic pseudo-Amino Acid Composition were combined, the overall accuracy could reach 92.53%.

  • Predicting the cofactors of oxidoreductases by the modified pseudo-Amino Acid Composition
    Sheng wu gong cheng xue bao = Chinese journal of biotechnology, 2008
    Co-Authors: Guangya Zhang, Baishan Fang
    Abstract:

    Types of cofactor independency for newly found oxidoreductases sequences are usually determined by experimental analysis. These experimental methods are both time-consuming and costly. With the explosion of oxidoreductases sequences entering into the databanks, it is highly desirable to explore the feasibility of selectively classifying newly found oxidoreductases into their respective cofactor independency classes by means of an automated method. In this study, we proposed a modified Chou’s pseudo-Amino Acid Composition method to extract features from sequences and the k-nearest neighbor was used as the classifier, and the results were very encouraging. When lambda = 48, w = 0.1, the areas under the ROC curve of k-nearest neighbor in 10-fold cross-validation was 0.9536; and the success rate was 92.0%, which was 3.5% higher than that of pseudo-Amino Acid Composition. It was also better than all the other 7 feature extraction methods. Our results showed that predicting the cofactors of oxidoreductases was feasible and the modified pseudo-Amino Acid Composition method may be a useful method for extracting features from protein sequences.

William Ferdinand – One of the best experts on this subject based on the ideXlab platform.

  • Amino Acid Composition of rat and human liver microsomes in normal and pathological conditions
    Bioscience reports, 1995
    Co-Authors: Gheorghe Benga, William Ferdinand
    Abstract:

    The Amino Acid Composition of proteins from liver microsomes has been studied in rats and in human subjects with normal liver, with obstructive jaundice or liver cirrhosis. The pattern of the Amino Acid Composition of microsomes appeared to be species-specific. Phenylalanine, threonine, serine, proline, histidine and [aspartic Acid plus asparagine] were increased, while alanine, tyrosine, glycine and arginine were decreased in the human compared to the rat microsomes. In patients with obstructive jaundice of short duration (less than two months) only a slight decrease in leucine and phenylalanine could be noticed, while in the case of liver cirrhosis Amino Acid Composition was markedly changed.

Kenji Sorimachi – One of the best experts on this subject based on the ideXlab platform.

  • Classification of Non-Animals and Invertebrates Based on Amino Acid Composition of Complete Mitochondrial Genomes
    International Journal of Biology, 2013
    Co-Authors: Kenji Sorimachi, Teiji Okayasu
    Abstract:

    Amino Acid Compositions were predicted from data sets of 47 non-animal and 58 invertebrate animal complete mitochondrial genomes, which were chosen alphabetically based on scientific names without considering biological characteristics. Using Ward’s clustering method with Amino Acid Composition or nucleotide content as traits, non-animals were classified into Plantae, Chromalveolata, and Fungi, and invertebrates were classified into Animalia and primitive groups, Amoebozoa, Excavata, Protista, and Choanozoa. A combined sample set of primitive eukaryotes was also examined by cluster analysis using Amino Acid Composition and nucleotide content. Some Amoebozoa comprised a single cluster, whereas other Amoebozoa were grouped with other organisms (Excavata, Prosista, Chromaleolata, Fungi and Plantae), indicating their close relationships. Choanozoa (choanoflagellates; Monosiga brevicollis ), considered the closest living relatives of animals, were found to be instead closely related to Fungi ( Smittium culisetae , Pleurotus ostreatus , and Epidermophyton floccosum ) and Excavata ( Malawimonas jakobiformis ). Our results demonstrate that Amino Acid Composition and nucleotide content are useful indices for characterizing non-animal and invertebrate complete mitochondrial genomes.

  • Gene assembly consisting of small units with similar Amino Acid Composition in the Saccharomyces cerevisiae genome
    Mycoscience, 2003
    Co-Authors: Kenji Sorimachi, Teiji Okayasu
    Abstract:

    Amino Acid Compositions of all genes in Saccharomyces cerevisiae were determined using a computer analysis of the complete genome. The Amino Acid Composition of an assembly of several genes or a single gene consisted of 3000–7000 Amino Acid residues forming a certain pattern of Amino Acid Composition. This rule was independent not only of the gene size, but also of the gene position. Thus, the small units, consisting of 3000–7000 Amino Acid residues, showed a similar Amino Acid Composition, and they formed all the genes in the complete genome.

  • Conservation of the basic pattern of cellular Amino Acid Composition of archaeobacteria during biological evolution and the putative Amino Acid Composition of primitive life forms
    Amino Acids, 2001
    Co-Authors: Kenji Sorimachi, TAKAHIRO OKAYASU, Kazumi Akimoto, T. Itoh, Y. Kawarabayasi, Akira Niwa
    Abstract:

    Previous studies showed that the cellular Amino Acid Composition obtained by Amino Acid analysis of whole cells, differs such as eubacteria, protozoa, fungi and mammalian cells. These results suggest that the difference in the cellular Amino Acid Composition reflects biological changes as the result of evolution. However, the basic pattern of cellular Amino Acid Composition was relatively constant in all organisms examined. In the present study, we examined archaeobacteria, because they are considered important in understanding the relationship between biological evolution and cellular Amino Acid Composition. The cellular Amino Acid Compositions of Archaeoglobus fulgidus, Pyrococcus horikoshii, Methanobacterium thermoautotrophicum and Methanococcus jannaschii differed slightly from each other, but were similar to those determined from codon usage data, based on the complete genomes. Thus, the cellular Amino Acid Composition reflects biological evolution. We suggest that primitive forms of life appearing on earth at the end of prebiotic evolution had a similar-cellular Amino Acid Composition.

Akira Niwa – One of the best experts on this subject based on the ideXlab platform.

  • Conservation of the basic pattern of cellular Amino Acid Composition of archaeobacteria during biological evolution and the putative Amino Acid Composition of primitive life forms
    Amino Acids, 2001
    Co-Authors: Kenji Sorimachi, TAKAHIRO OKAYASU, Kazumi Akimoto, T. Itoh, Y. Kawarabayasi, Akira Niwa
    Abstract:

    Previous studies showed that the cellular Amino Acid Composition obtained by Amino Acid analysis of whole cells, differs such as eubacteria, protozoa, fungi and mammalian cells. These results suggest that the difference in the cellular Amino Acid Composition reflects biological changes as the result of evolution. However, the basic pattern of cellular Amino Acid Composition was relatively constant in all organisms examined. In the present study, we examined archaeobacteria, because they are considered important in understanding the relationship between biological evolution and cellular Amino Acid Composition. The cellular Amino Acid Compositions of Archaeoglobus fulgidus, Pyrococcus horikoshii, Methanobacterium thermoautotrophicum and Methanococcus jannaschii differed slightly from each other, but were similar to those determined from codon usage data, based on the complete genomes. Thus, the cellular Amino Acid Composition reflects biological evolution. We suggest that primitive forms of life appearing on earth at the end of prebiotic evolution had a similar-cellular Amino Acid Composition.

  • Conservation of the basic pattern of cellular Amino Acid Composition during biological evolution in plants.
    Amino Acids, 2000
    Co-Authors: Kenji Sorimachi, TAKAHIRO OKAYASU, Kazumi Akimoto, Akira Niwa
    Abstract:

    The cellular Amino Acid Composition of plant cells was analyzed. The callus of carrot (Daucus carota), leaves of Torenia fournieri and protocomb-like body of Cymbidium, s.p. were examined as examples of plant cells. The cellular Amino Acid Compositions differed in the plant cells, but their basic patterns were quite similar. It is concluded that the basic pattern of the cellular Amino Acid Composition is conserved in all terrestrial organisms, including plants.

Guangya Zhang – One of the best experts on this subject based on the ideXlab platform.

  • Predicting the cofactors of oxidoreductases based on Amino Acid Composition distribution and Chou’s amphiphilic pseudo-Amino Acid Composition.
    Journal of theoretical biology, 2008
    Co-Authors: Guangya Zhang, Baishan Fang
    Abstract:

    Predicting the cofactors of oxidoreductases plays an important role in inferring their catalytic mechanism. Feature extraction is a critical part in the prediction systems, requiring raw sequence data to be transformed into appropriate numerical feature vectors while minimizing information loss. In this paper, we present an Amino Acid Composition distribution method for extracting useful features from primary sequence, and the k-nearest neighbor was used as the classifier. The overall prediction accuracy evaluated by the 10-fold cross-validation reached 90.74%. Comparing our method with other eight feature extraction methods, the improvement of the overall prediction accuracy ranged from 3.49% to 15.74%. Our experimental results confirm that the method we proposed is very useful and may be used for other bioinformatical predictions. Interestingly, when features extracted by our method and Chou’s amphiphilic pseudo-Amino Acid Composition were combined, the overall accuracy could reach 92.53%.

  • Predicting the cofactors of oxidoreductases by the modified pseudo-Amino Acid Composition
    Sheng wu gong cheng xue bao = Chinese journal of biotechnology, 2008
    Co-Authors: Guangya Zhang, Baishan Fang
    Abstract:

    Types of cofactor independency for newly found oxidoreductases sequences are usually determined by experimental analysis. These experimental methods are both time-consuming and costly. With the explosion of oxidoreductases sequences entering into the databanks, it is highly desirable to explore the feasibility of selectively classifying newly found oxidoreductases into their respective cofactor independency classes by means of an automated method. In this study, we proposed a modified Chou’s pseudo-Amino Acid Composition method to extract features from sequences and the k-nearest neighbor was used as the classifier, and the results were very encouraging. When lambda = 48, w = 0.1, the areas under the ROC curve of k-nearest neighbor in 10-fold cross-validation was 0.9536; and the success rate was 92.0%, which was 3.5% higher than that of pseudo-Amino Acid Composition. It was also better than all the other 7 feature extraction methods. Our results showed that predicting the cofactors of oxidoreductases was feasible and the modified pseudo-Amino Acid Composition method may be a useful method for extracting features from protein sequences.