Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure

Garg, Aarti ; Kaur, Harpreet ; Raghava, G. P. S. (2005) Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure Proteins: Structure, Function, and Bioinformatics, 61 (2). pp. 318-324. ISSN 0887-3585

PDF - Publisher Version

Official URL:

Related URL:


The present study is an attempt to develop a neural network-based method for predicting the real value of solvent accessibility from the sequence using evolutionary information in the form of multiple sequence alignment. In this method, two feed-forward networks with a single hidden layer have been trained with standard back-propagation as a learning algorithm. The Pearson's correlation coefficient increases from 0.53 to 0.63, and mean absolute error decreases from 18.2 to 16% when multiple-sequence alignment obtained from PSI-BLAST is used as input instead of a single sequence. The performance of the method further improves from a correlation coefficient of 0.63 to 0.67 when secondary structure information predicted by PSIPRED is incorporated in the prediction. The final network yields a mean absolute error value of 15.2% between the experimental and predicted values, when tested on two different nonhomologous and nonredundant datasets of varying sizes. The method consists of two steps: (1) in the first step, a sequence-to-structure network is trained with the multiple alignment profiles in the form of PSI-BLAST-generated position-specific scoring matrices, and (2) in the second step, the output obtained from the first network and PSIPRED-predicted secondary structure information is used as an input to the second structure-to-structure network. Based on the present study, a server SARpred ( has been developed that predicts the real value of solvent accessibility of residues for a given protein sequence. We have also evaluated the performance of SARpred on 47 proteins used in CASP6 and achieved a correlation coefficient of 0.68 and a MAE of 15.9% between predicted and observed values.

Item Type:Article
Source:Copyright of this article belongs to John Wiley and Sons.
Keywords:Solvent Accessibility; Prediction; Real Value; Neural Network; Multiple Alignment; Secondary Structure
ID Code:37636
Deposited On:25 Apr 2011 13:06
Last Modified:17 May 2016 20:31

Repository Staff Only: item control page