Bioinformatics Vol. 18 no. 6 2002
Pages 819-824
© 2002 Oxford University Press
NETASA: neural network based prediction of solvent accessibility
1 Institute of Multimedia and Software,
Universiti Putra Malaysia, Serdang, 43400, Selangor,
Malaysia
2 RIKEN Tsukuba Institute, 3-1-1 Koyadai,
Tsukuba, Ibaraki 305-0074, Japan
3 Department of Biosciences, Jamia Millia Islamia,
New Delhi, India
Received on August 24, 2001
; revised on November 2, 2001
; accepted on January 7, 2002
Motivation: Prediction of the tertiary structure of a protein from its amino acid sequence is one of the most important problems in molecular biology. The successful prediction of solvent accessibility will be very helpful to achieve this goal. In the present work, we have implemented a server, NETASA for predicting solvent accessibility of amino acids using our newly optimized neural network algorithm. Several new features in the neural network architecture and training method have been introduced, and the network learns faster to provide accuracy values, which are comparable or better than other methods of ASA prediction.
Results: Prediction in two and three state classification systems with several thresholds are provided. Our prediction method achieved the accuracy level upto 90% for training and 88% for test data sets. Three state prediction results provide a maximum 65% accuracy for training and 63% for the test data. Applicability of neural networks for ASA prediction has been confirmed with a larger data set and wider range of state thresholds. Salient differences between a linear and exponential network for ASA prediction have been analysed.
Availability: Online predictions are freely available at: http://www.netasa.org. Linux ix86 binaries of the program written for this work may be obtained by email from the corresponding author.
Contact: shandar{at}jamia.net
* To whom correspondence should be addressed at: RIKEN Tsukuba Institute, 3-1-1, Koyadai, Tsukuba 305 0074, Ibaraki, Japan.
Present address: Computational Biology
Research Center (CBRC), AIST 2-41-6
Aomi, Koto-ku, Tokyo 135-0064, Japan.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  K. Bajaj, P. Chakrabarti, and R. Varadarajan Mutagenesis-based definitions and probes of residue burial in proteins PNAS, November 8, 2005; 102(45): 16221 - 16226. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Kumar, M. Bhasin, N. K. Natt, and G. P. S. Raghava BhairPred: prediction of {beta}-hairpins in a protein from multiple alignment information using ANN and SVM techniques Nucleic Acids Res., July 1, 2005; 33(suppl_2): W154 - W159. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Sim, S.-Y. Kim, and J. Lee Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method Bioinformatics, June 15, 2005; 21(12): 2844 - 2849. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. M. Gromiha and M. Suwa A simple statistical method for discriminating outer membrane proteins with better accuracy Bioinformatics, April 1, 2005; 21(7): 961 - 968. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 B. Shen and M. Vihinen Conservation and covariance in PH domain sequences: physicochemical profile and information theoretical analysis of XLA-causing mutations in the Btk PH domain Protein Eng. Des. Sel., March 1, 2004; 17(3): 267 - 276. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. Gianese, F. Bossa, and S. Pascarella Improvement in prediction of solvent accessibility by probability profiles Protein Eng. Des. Sel., December 1, 2003; 16(12): 987 - 992. [Abstract] [Full Text] [PDF]
|
|