Prediction of pi-turns in proteins using PSI-BLAST profiles and secondary structure information.

Due to the structural and functional importance of tight turns, some methods have been proposed to predict gamma-turns, beta-turns, and alpha-turns in proteins. In the past, studies of pi-turns were made, but not a single prediction approach has been developed so far. It will be useful to develop a method for identifying pi-turns in a protein sequence. In this paper, the support vector machine (SVM) method has been introduced to predict pi-turns from the amino acid sequence. The training and testing of this approach is performed with a newly collected data set of 640 non-homologous protein chains containing 1931 pi-turns. Different sequence encoding schemes have been explored in order to investigate their effects on the prediction performance. With multiple sequence alignment and predicted secondary structure, the final SVM model yields a Matthews correlation coefficient (MCC) of 0.556 by a 7-fold cross-validation. A web server implementing the prediction method is available at the following URL: http://210.42.106.80/piturn/.

[1]  Katharina Morik,et al.  Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring , 1999, ICML.

[2]  X. Chen,et al.  SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence , 2003, Nucleic Acids Res..

[3]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[4]  Kuo-Chen Chou,et al.  Support Vector Machine for predicting α-turn types , 2003, Peptides.

[5]  R Sowdhamini,et al.  Termination of right handed helices in proteins by residues in left handed helical conformations , 1993, FEBS letters.

[6]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[7]  Haesun Park,et al.  Prediction of protein relative solvent accessibility with support vector machines and long‐range interaction 3D local descriptor , 2004, Proteins.

[8]  Yan Wang,et al.  Better prediction of the location of α‐turns in proteins with support vector machine , 2006 .

[9]  Saejoon Kim.  Protein ß-turn prediction using nearest-neighbor method , 2004, Bioinform..

[10]  Milner-White Ej.  Recurring loop motif in proteins that occurs in right-handed and left-handed forms. Its relationship with alpha-helices and beta-bulge loops. , 1988 .

[11]  E. Baker,et al.  Hydrogen bonding in globular proteins. , 1984, Progress in biophysics and molecular biology.

[12]  Kevin Burrage,et al.  Prediction of protein solvent accessibility using support vector machines , 2002, Proteins.

[13]  Y.Z. Chen,et al.  Enzyme family classification by support vector machines , 2004, Proteins.

[14]  E. Milner-White,et al.  Situations of gamma-turns in proteins. Their relation to alpha-helices, beta-sheets and ligand binding sites. , 1990, Journal of molecular biology.

[15]  K C Chou,et al.  Prediction of tight turns and their types in proteins. , 2000, Analytical biochemistry.

[16]  S. Ramakumar,et al.  π‐Turns in proteins and peptides: Classification, conformation, occurrence, hydration and sequence , 1996, Protein science : a publication of the Protein Society.

[17]  K. Chou,et al.  Classification and Prediction of β-Turn Types , 1997, Journal of protein chemistry.

[18]  U. Hobohm,et al.  Selection of representative protein data sets , 1992, Protein science : a publication of the Protein Society.

[19]  Gajendra P. S. Raghava,et al.  A neural‐network based method for prediction of γ‐turns in proteins from multiple sequence alignment , 2003, Protein science : a publication of the Protein Society.

[20]  B. Rost,et al.  A modified definition of Sov, a segment‐based measure for protein secondary structure prediction assessment , 1999, Proteins.

[21]  Kuo-Chen Chou,et al.  Predicting subcellular localization of proteins in a hybridization space , 2004, Bioinform..

[22]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[23]  Bernard F. Buxton,et al.  Secondary structure prediction with support vector machines , 2003, Bioinform..

[24]  Zhirong Sun,et al.  Support vector machine approach for protein subcellular localization prediction , 2001, Bioinform..

[25]  K. Chou.  Prediction and classification of α‐turn types , 1997 .

[26]  William J. Welsh,et al.  Improved method for predicting ?-turn using support vector machine , 2005, Bioinform..

[28]  A. Alix,et al.  High accuracy prediction of β‐turns and their types using propensities and multiple alignments , 2005 .

[29]  Gajendra Pal Singh Raghava,et al.  Prediction of β‐turns in proteins from multiple alignment using neural network , 2003, Protein science : a publication of the Protein Society.

[30]  Gajendra P. S. Raghava,et al.  Prediction of α‐turns in proteins using PSI‐BLAST profiles and secondary structure information , 2004 .

[31]  Standard conformations of a polypeptide chain in irregular protein regions , 1986 .

[32]  Kuo-Chen Chou,et al.  Support vector machines for the classification and prediction of β‐turn types , 2002, Journal of peptide science : an official publication of the European Peptide Society.

[33]  D Gorse,et al.  Prediction of the location and type of β‐turns in proteins using neural networks , 1999, Protein science : a publication of the Protein Society.

[34]  S. Hua,et al.  A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. , 2001, Journal of molecular biology.

[35]  D T Jones,et al.  Protein secondary structure prediction based on position-specific scoring matrices. , 1999, Journal of molecular biology.

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