title: Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure.
abstract: 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 (http://www.imtech.res.in/raghava/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.
authors.affiliation: Institute of Microbial Technology, Sector-39A, Chandigarh, India.
authors.forename: Aarti
authors.initials: A
authors.lastname: Garg
authors.forename: Harpreet
authors.initials: H
authors.lastname: Kaur
authors.forename: G P S
authors.initials: GP
authors.lastname: Raghava
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012997:Solvents / Q000737:chemistry*; D013879:Thioredoxins / Q000737:chemistry
chemical_list: D000596:Amino Acids; D011506:Proteins; D012997:Solvents; D013879:Thioredoxins