Maximum Entropy Weighting of Aligned Sequences of Proteins or DNA

In a family of proteins or other biological sequences like DNA the various subfamilies are often very unevenly represented. For this reason a scheme for assigning weights to each sequence can greatly improve performance at tasks such as database searching with profiles or other consensus models based on multiple alignments. A new weighting scheme for this type of database search is proposed. In a statistical description of the searching problem it is derived from the maximum entropy principle. It can be proved that, in a certain sense, it corrects for uneven representation. It is shown that finding the maximum entropy weights is an easy optimization problem for which standard techniques are applicable.

[1]  D. Haussler,et al.  Hidden Markov models in computational biology. Applications to protein modeling. , 1993, Journal of molecular biology.

[2]  Sean R. Eddy,et al.  Maximum Discrimination Hidden Markov Models of Sequence Consensus , 1995, J. Comput. Biol..

[3]  Amos Bairoch,et al.  The PROSITE dictionary of sites and patterns in proteins, its current status , 1993, Nucleic Acids Res..

[4]  M Vingron,et al.  Weighting in sequence space: a comparison of methods in terms of generalized sequences. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[5]  P. Argos,et al.  Weighting aligned protein or nucleic acid sequences to correct for unequal representation. , 1990, Journal of molecular biology.

[6]  S. Henikoff,et al.  Position-based sequence weights. , 1994, Journal of molecular biology.

[7]  J. Thompson,et al.  CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. , 1994, Nucleic acids research.

[8]  S F Altschul,et al.  Weights for data related by a tree. , 1989, Journal of molecular biology.

[9]  C. Chothia,et al.  Volume changes in protein evolution. , 1994, Journal of molecular biology.

[10]  S. Henikoff,et al.  Automated assembly of protein blocks for database searching. , 1991, Nucleic acids research.

[11]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[12]  A. D. McLachlan,et al.  Profile analysis: detection of distantly related proteins. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[13]  G. Barton.  Protein multiple sequence alignment and flexible pattern matching. , 1990, Methods in enzymology.

[14]  P. Argos,et al.  Quantification of secondary structure prediction improvement using multiple alignments. , 1993, Protein engineering.

[15]  J. Felsenstein.  Maximum-likelihood estimation of evolutionary trees from continuous characters. , 1973, American journal of human genetics.

[16]  William H. Press,et al.  Numerical recipes , 1990 .

[17]  Julie Dawn Thompson,et al.  Improved sensitivity of profile searches through the use of sequence weights and gap excision , 1994, Comput. Appl. Biosci..

[18]  W. Taylor,et al.  Identification of protein sequence homology by consensus template alignment. , 1986, Journal of molecular biology.

[19]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[20]  Martin Vingron,et al.  A fast and sensitive multiple sequence alignment algorithm , 1989, Comput. Appl. Biosci..