Predicting secondary structures of proteins

The article presents the application of a new machine-learning algorithm for the prediction of secondary structures of proteins. The logical analysis of data (LAD) algorithm was applied to recognize which amino acids properties could be analyzed to deliver additional information, independent from protein homology, useful in determining the secondary structure of a protein. The study showed that to get better results, LAD should be used as a first stage of analysis in combination with another method that is able to take into account a more detailed understanding of the physical chemistry of proteins and amino acids.

[1]  P. Argos,et al.  Knowledge‐based protein secondary structure assignment , 1995, Proteins.

[2]  Jacek Blazewicz,et al.  Logical Analysis of Data as a Predictor of Protein Secondary Structures , 2004 .

[3]  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.

[4]  Jacek Blazewicz,et al.  Prediction of protein secondary structure using logical analysis of data algorithm , 2001 .

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

[6]  Toshihide Ibaraki,et al.  Logical analysis of numerical data , 1997, Math. Program..

[7]  C. Anfinsen.  Principles that govern the folding of protein chains. , 1973, Science.

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

[9]  Toshihide Ibaraki,et al.  An Implementation of Logical Analysis of Data , 2000, IEEE Trans. Knowl. Data Eng..

[10]  Peter L. Hammer,et al.  Convexity and logical analysis of data , 2000, Theor. Comput. Sci..

[11]  M J Sternberg,et al.  Machine learning approach for the prediction of protein secondary structure. , 1990, Journal of molecular biology.

[12]  Geoffrey J. Barton,et al.  JPred : a consensus secondary structure prediction server , 1999 .

[13]  J. Grzymala-Busse,et al.  Three discretization methods for rule induction , 2001 .

[14]  V. Lim.  Algorithms for prediction of α-helical and β-structural regions in globular proteins , 1974 .

[15]  G J Barton,et al.  Evaluation and improvement of multiple sequence methods for protein secondary structure prediction , 1999, Proteins.

[16]  F. Richards,et al.  Identification of structural motifs from protein coordinate data: Secondary structure and first‐level supersecondary structure * , 1988, Proteins.

[17]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

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

[19]  P. Argos,et al.  Seventy‐five percent accuracy in protein secondary structure prediction , 1997, Proteins.

[20]  R. King,et al.  Identification and application of the concepts important for accurate and reliable protein secondary structure prediction , 1996, Protein science : a publication of the Protein Society.

[21]  J. Garnier,et al.  Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. , 1978, Journal of molecular biology.

[22]  Giovanni Soda,et al.  Exploiting the past and the future in protein secondary structure prediction , 1999, Bioinform..

[23]  B. Rost,et al.  Redefining the goals of protein secondary structure prediction. , 1994, Journal of molecular biology.

[24]  Jerzy W. Grzymala-Busse,et al.  LERS-A System for Learning from Examples Based on Rough Sets , 1992, Intelligent Decision Support.

[25]  B. Rost.  PHD: predicting one-dimensional protein structure by profile-based neural networks. , 1996, Methods in enzymology.

[26]  A A Salamov,et al.  Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignments. , 1995, Journal of molecular biology.

[27]  J. Mesirov,et al.  Hybrid system for protein secondary structure prediction. , 1992, Journal of molecular biology.

[28]  G J Barton,et al.  Application of multiple sequence alignment profiles to improve protein secondary structure prediction , 2000, Proteins.

[29]  B. Rost,et al.  Prediction of protein secondary structure at better than 70% accuracy. , 1993, Journal of molecular biology.

[30]  Y. Crama,et al.  Cause-effect relationships and partially defined Boolean functions , 1988 .