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Literature DB >> 10592278

AAindex: amino acid index database.

Abstract

AAindex is a database of amino acid indices and amino acid mutation matrices. An amino acid index is a set of 20 numerical values representing various physico--chemical and biochemical properties of amino acids. An amino acid mutation matrix is generally 20 x 20 numerical values representing similarity of amino acids. AAindex consists of two sections: AAindex1 for the collection of published amino acid indices and AAindex2 for the collection of published amino acid mutation matrices. Each entry of either AAindex1 or AAindex2 consists of the definition, the reference information, a list of related entries in terms of the correlation coefficient and the actual data. The database may be accessed through the DBGET/LinkDB system at GenomeNet (http://www. genome.ad.jp/aaindex/ ) or may be downloaded by anonymous FTP (ftp://ftp.genome.ad.jp/db/genomenet/aaindex/ ).

Mesh：

Substances：
Amino Acids

Year: 2000 PMID： 10592278 PMCID： PMC102411 DOI： 10.1093/nar/28.1.374

Source DB: PubMed Journal: Nucleic Acids Res ISSN： 0305-1048 Impact factor: 16.971

5 in total

1. AAindex: Amino Acid Index Database.

Authors: S Kawashima; H Ogata; M Kanehisa
Journal: Nucleic Acids Res Date: 1999-01-01 Impact factor: 16.971

2. DBGET/LinkDB: an integrated database retrieval system.

Authors: W Fujibuchi; S Goto; H Migimatsu; I Uchiyama; A Ogiwara; Y Akiyama; M Kanehisa
Journal: Pac Symp Biocomput Date: 1998

Review 3. Linking databases and organisms: GenomeNet resources in Japan.

Authors: M Kanehisa
Journal: Trends Biochem Sci Date: 1997-11 Impact factor: 13.807

4. Analysis of amino acid indices and mutation matrices for sequence comparison and structure prediction of proteins.

Authors: K Tomii; M Kanehisa
Journal: Protein Eng Date: 1996-01

5. Cluster analysis of amino acid indices for prediction of protein structure and function.

Authors: K Nakai; A Kidera; M Kanehisa
Journal: Protein Eng Date: 1988-07

5 in total

212 in total

1. An optimal structure-discriminative amino acid index for protein fold recognition.

Authors: R H Leary; J B Rosen; P Jambeck
Journal: Biophys J Date: 2004-01 Impact factor: 4.033

2. CRASP: a program for analysis of coordinated substitutions in multiple alignments of protein sequences.

Authors: Dmitry A Afonnikov; Nikolay A Kolchanov
Journal: Nucleic Acids Res Date: 2004-07-01 Impact factor: 16.971

3. On the classes of aminoacyl-tRNA synthetases, amino acids and the genetic code.

Authors: Andre R O Cavalcanti; Elisa Soares Leite; Benício B Neto; Ricardo Ferreira
Journal: Orig Life Evol Biosph Date: 2004-08 Impact factor: 1.950

4. Prediction of mucin-type O-glycosylation sites by a two-staged strategy.

Authors: YuDong Cai; JianFeng He; Lin Lu
Journal: Mol Divers Date: 2010-07-22 Impact factor: 2.943

Review 5. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).

Authors: Michael Fernandez; Julio Caballero; Leyden Fernandez; Akinori Sarai
Journal: Mol Divers Date: 2010-03-20 Impact factor: 2.943

AAindex: amino acid index database.

1. AAindex: Amino Acid Index Database.

2. DBGET/LinkDB: an integrated database retrieval system.

Review 3. Linking databases and organisms: GenomeNet resources in Japan.

4. Analysis of amino acid indices and mutation matrices for sequence comparison and structure prediction of proteins.

5. Cluster analysis of amino acid indices for prediction of protein structure and function.

1. An optimal structure-discriminative amino acid index for protein fold recognition.

2. CRASP: a program for analysis of coordinated substitutions in multiple alignments of protein sequences.

3. On the classes of aminoacyl-tRNA synthetases, amino acids and the genetic code.

4. Prediction of mucin-type O-glycosylation sites by a two-staged strategy.

Review 5. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).

6. The necessity of adjusting tests of protein category enrichment in discovery proteomics.

7. Benchmarking B cell epitope prediction: underperformance of existing methods.

8. The exchangeability of amino acids in proteins.

9. Prediction of mitochondrial proteins using discrete wavelet transform.

10. Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors.