NDLI: Real Value Solvent Accessibility Prediction using Adaptive Support Vector Regression

Content Provider	IEEE Xplore Digital Library
Author	Gubbi, J. Shilton, A. Palaniswami, M. Parker, M.
Copyright Year	2007
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic. (Gubbi, J.; Shilton, A.; Palaniswami, M.)
Abstract	Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in prediction of fold, and eventually the tertiary structure of the protein. This paper deals with prediction of relative solvent accessibility, given only the amino-acid sequence. In this paper, we use an improved support vector regression (SVR) and new kernels for real valued prediction of solvent accessibility. In this regard, two main issues are addressed. First we address the problem of e selection, which we found to be somewhat problematic in our earlier work (e is a parameter with significant influence on noise insensitivity and generalization of SVRs). In particular, rather than employ the standard trial and error based approach, we used an improved tube shrinking method to find e. Secondly, a novel kernel combining solvation model, electrostatic charge model and evolutionary information in the form of position specific scoring matrix (PSSM) is given. A new dataset of 472 proteins with less than 20% sequence identity is curated and used to evaluate the result. To make a more objective comparison with earlier methods, we use a standard dataset and show that the proposed scheme is better than the ones normally used in literature. We also report a lowest mean absolute error (MAE) so far of 0.12 on the standard dataset.
Starting Page	395
Ending Page	401
File Size	8205241
Page Count	7
File Format	PDF
ISBN	1424407109
DOI	10.1109/CIBCB.2007.4221249
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Solvents Proteins Support vector machines Support vector machine classification Neural networks Sequences Kernel Feedforward systems Multi-layer neural network Feedforward neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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