protr: Protein Sequence Feature Extraction with R

The protr package aims for protein sequence feature extraction, which could be easily applied in Bioinforamtics and Chemogenomics research. The descriptors listed in this package include Amino Acid Composition (Amino Acid Composition/Dipeptide Composition/Tripeptide Composition), Autocorrelation (Normalized Moreau-Broto Autocorrelation/Moran Autocorrelation/Geary Autocorrelation), CTD (Composition/Transition/Distribution), Conjoint Traid, Quasi-Sequence Order (Sequence Order Coupling Number/Quasi-sequence Order Descriptors), and Pseudo Amino Acid Composition (Pseudo Amino Acid Composition/Amphiphilic Pseudo Amino Acid Composition). Total 14 descriptors in 6 categories. The package is collaboratively developed by Computational Biology and Drug Design Group, Central South University.

[1]  Minoru Kanehisa,et al.  AAindex: Amino Acid index database , 2000, Nucleic Acids Res..

[2]  Minoru Kanehisa,et al.  AAindex: amino acid index database, progress report 2008 , 2007, Nucleic Acids Res..

[3]  I. Muchnik,et al.  Prediction of protein folding class using global description of amino acid sequence. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Z. R. Li,et al.  Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence , 2006, Nucleic Acids Res..

[5]  K. Chou,et al.  Prediction of protein subcellular locations by GO-FunD-PseAA predictor. , 2004, Biochemical and biophysical research communications.

[6]  L. Jiang,et al.  PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence , 2006, Nucleic Acids Res..

[7]  G Schneider,et al.  The rational design of amino acid sequences by artificial neural networks and simulated molecular evolution: de novo design of an idealized leader peptidase cleavage site. , 1994, Biophysical journal.

[8]  K. R. Woods,et al.  Prediction of protein antigenic determinants from amino acid sequences. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[9]  K. Chou Prediction of protein cellular attributes using pseudo‐amino acid composition , 2001 .

[10]  I. Muchnik,et al.  Recognition of a protein fold in the context of the SCOP classification , 1999 .

[11]  K. Chou Prediction of protein cellular attributes using pseudo‐amino acid composition , 2001, Proteins.

[12]  Kuo-Chen Chou,et al.  Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes , 2005, Bioinform..

[13]  Gajendra P S Raghava,et al.  Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition* , 2004, Journal of Biological Chemistry.

[14]  J Damborský,et al.  Quantitative structure-function and structure-stability relationships of purposely modified proteins. , 1998, Protein engineering.

[15]  K. Chou,et al.  Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. , 2000, Biochemical and biophysical research communications.

[16]  Hiroyuki Ogata,et al.  AAindex: Amino Acid Index Database , 1999, Nucleic Acids Res..

[17]  Juwen Shen,et al.  Predicting protein–protein interactions based only on sequences information , 2007, Proceedings of the National Academy of Sciences.

[18]  R. Grantham Amino Acid Difference Formula to Help Explain Protein Evolution , 1974, Science.