NDLI: Protein Fold Recognition by Mixed Environment-Specific Amino Acid Substitution Mapping Using Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Jiang, N. Wu, W.X. Mitchell, I.
Copyright Year	2005
Description	Author affiliation: Sch. of Comput. Sci., Middllesex Univ., London (Jiang, N.; Wu, W.X.; Mitchell, I.)
Abstract	Protein fold recognition programs align a probe amino acid sequence onto a library of representative folds of known structure to identify structural similarities. Substitution matrix is the key to detect the aligned protein similarities. In this paper, a new mixed environment-specific substitution mapping (MESSM) is designed for fold recognition. It has two features: first, with amino acid residue level environmental description, structurally-derived substitution scores are generated using neural networks. The substitution probability of each pair of amino acids at any chosen structural environment can be instantly generated; second, the structurally-derived substitution score is linearly combined with sequence profile from traditional sequences substitution matrices to obtain a positive consensus for fold recognition. By fitting a single parameter in the combined substitution score, benchmark problems have been carried out to test the ability of the MESSM model. The results show that the new fold recognition model with mixed substitution mapping has a better performance than the one with either structure or sequence profile only. Moreover, it is comparable with those more computational intensive, energy potential based fold recognition models
Sponsorship	IEEE Comput. Soc. TCDP and TCPP Fukuoka Inst. of Technol. (FIT)
Starting Page	341
Ending Page	345
File Size	197991
Page Count	5
File Format	PDF
ISBN	0769522815
ISSN	15219097
DOI	10.1109/ICPADS.2005.244
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-20
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Amino acids Neural networks Matrices Probes Libraries Benchmark testing Potential energy Solvents Computer networks
Content Type	Text
Resource Type	Article
Subject	Hardware and Architecture

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