Combining sequence and structural profiles for protein solvent accessibility prediction

Rajkumar Bondugula, Dong Xu*

Digital Biology Laboratory, 201 Engineering Building West, University of Missouri, Columbia, MO 65211, USA. xudong@missouri.edu

Proc LSS Comput Syst Bioinform Conf. August, 2008. Vol. 7, p. 195-202. Full-Text PDF

*To whom correspondence should be addressed.

Solvent accessibility is an important structural feature for a protein. We propose a new method for solvent accessibility prediction that uses known structure and sequence information more efficiently. We first estimate the relative solvent accessibility of the query protein using fuzzy mean operator from the solvent accessibilities of known structure fragments that have similar sequences to the query protein. We then integrate the estimated solvent accessibility and the position specific scoring matrix of the query protein using a neural network. We tested our method on a large data set consisting of 3386 non-redundant proteins. The comparison with other methods show slightly improved prediction accuracies with our method. The resulting system does need not be re-trained when new data is available. We incorporated our method into the MUPRED system, which is available as a web server at http://digbio.missouri.edu/mupred.