Evolutionary computation for the prediction of secondary protein structures
Pages 1082 - 1087
Abstract
We have developed an evolutionary computation approach to predict secondary structure motifs using some main amino acid physical-chemical properties. The prediction model will consist of rules that predict both the beginning and the end of the regions corresponding to a secondary structure state conformation (α-helix or β-strand). A study about propensities of each pair of amino acids in capping regions of α-helix and β-strand are also performed with a data set of 12,830 non-homologous and non-redundant protein sequences.
References
[1]
Complete list of pdb protein identifiers used in this article. http://www.upo.es/eps/marquez/proteins.txt.
[2]
Protein data bank advanced search. http://www.pdb.org/pdb/search/advSearch.do.
[3]
Protein data bank web. http://www.wwpdb.org.
[4]
J. Berg and L. Stryer. Biochemistry. Reverte, 2008.
[5]
J. Cheng and P. Baldi. Improved residue contact prediction using support vector machines and a large feature set. Bioinformatics, 8: 113, 2007.
[6]
P. Chou and G. Fasman. Prediction of protein conformation. Biochemistry, 13(2): 222--245, 1974.
[7]
Y. Cui, R. Chen, and W. Hung. Protein folding simulation with genetic algorithm and supersecondary structure constraints. Proteins: Structure, Function and Genetics, 31: 247--257, 1998.
[8]
A. Doig and B. R. L. N- and c-capping preferences for all 20 amino acids in alpha-helical peptides. Protein Science, 4(7): 1325--1336, 1995.
[9]
P. Fariselli and R. Casadio. A neural network based predictor of residue contacts in proteins. Protein Engineering, 12: 15--21, 1999.
[10]
F. FarzadFard, N. Gharaei, H. Pezeshk, and S. Marashi. Beta-sheet capping: Signals that initiate and terminate beta-sheet formation. J. Structural Biology, 161: 101--110, 2008.
[11]
N. Fonseca, R. Camacho, and A. Magalhaes. Amino acid pairing at the n- and c-termini of helical segments in proteins. Proteins, 70: 188--196, 2007.
[12]
D. Frishman and P. Argos. Incorporation of non-local interactions in protein secondary structure prediction from the amino acid sequence. Protein Engineering, 9: 133--142, 1996.
[13]
J. Garnier, D. Osguthorpe, and B. Robson. Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J. Mol. Biol., 120: 97--120, 1978.
[14]
R. Grantham. Amino acid difference formula to help explain protein evolution. J. J. Mol. Bio., 185: 862--864, 1974.
[15]
J. Gu and P. Bourne. Structural Bioinformatics (Methods of Biochemical Analysis). Wiley-Blackwell, 2003.
[16]
W. Kabsch and C. Sander. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 22(12): 2577--2637, 1983.
[17]
P. Klein, M. Kanehisa, and C. DeLisi. Prediction of protein function from sequence properties: Discriminant analysis of a data base. Biochim. Biophys., 787: 221--226, 1984.
[18]
J. Kyte and R. Doolittle. A simple method for displaying the hydropathic character of a protein. J. J. Mol. Bio., 157: 105--132, 1982.
[19]
V. Lim. Algorithms for prediction of a-helical and b-structural regions in globular proteins. J. Mol. Biol., 88: 857--872, 1974.
[20]
L. McGuffin, K. Bryson, and D. Jones. The psipred protein structure prediction server. Bioinformatics, 16: 404--405, 2000.
[21]
N. Qian and T. Sejnowski. Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol., 202: 865--884, 1988.
[22]
J. Richardson and D. Richardson. Amino acid preferences for specific locations at the ends of alpha helices. Science, 240: 1648--1652, 1998.
[23]
A. Salamov and V. Solovyev. Protein secondary structure prediction using local alignments. J. Mol. Biol., 268: 31--36, 1997.
[24]
R. Unger and J. Moult. Genetic algorithms for protein folding simulations. Biochim. Biophys., 231: 75--81, 1993.
[25]
J. Ward, L. McGuffin, B. Buxton, and D. Jone. Secondary structure prediction with support vector machines. Bioinformatics, 13: 1650--1655, 2003.
[26]
C. Wilson, P. Boardman, A. Doig, and S. Hubbard. Improved prediction for n-termini of alpha-helices using empirical information. Proteins, 57(2): 322--330, 2004.
[27]
C. Wilson, S. Hubbard, and A. Doig. A critical assessment of the secondary structure prediction of alpha-helices and their n-termini in proteins. Protein Eng., 15: 545--554, 2002.
Index Terms
- Evolutionary computation for the prediction of secondary protein structures
Recommendations
Protein secondary structures prediction based on evolutionary computation
In this paper we propose an approach based on evolutionary computation for the prediction of secondary protein structure motifs. The prediction model consists of a set of rules that predict both the beginning and the end of the regions corresponding to ...
Alpha helix prediction based on evolutionary computation
PRIB'10: Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformaticsMultiple approaches have been developed in order to predict the protein secondary structure. In this paper, we propose an approach to such a problem based on evolutionary computation. The proposed approach considers various amino acids properties in ...
Protein Secondary Structure Prediction Using Cascaded Feature Learning Model
AbstractThe protein secondary structure prediction (PSSP) is pivotal for predicting tertiary structure, which is proliferating in demand for drug design and development. Further, it can be used to learn different protein functions. Although ...
Highlights- Accepts PSSM, PSFM, solvent accessibility, and physicochemical properties features as input.
Comments
Information & Contributors
Information
Published In
March 2011
1868 pages
ISBN:9781450301138
DOI:10.1145/1982185
- Conference Chairs:
- William Chu,
- W. Eric Wong,
- Program Chairs:
- Mathew J. Palakal,
- Chih-Cheng Hung
Copyright © 2011 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 March 2011
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
SAC'11
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%
Upcoming Conference
SAC '25
- Sponsor:
- sigapp
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 98Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)1
Reflects downloads up to 23 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in