H-B Shen | PubMed Health

PubMed2006

Predicting secretory protein signal sequence cleavage sites by fusing the marks of global alignments.

A newly synthesized secretory protein in cells bears a special sequence, called signal peptide or sequence, which plays the role of "address tag" in guiding the protein to wherever it is needed. Such a unique function of signal sequences has stimulated novel strategies for drug design or reprogramming cells for gene therapy. To realize these new ideas and plans, however, it is important to develop an automated method for fast and accurately identifying the signal sequences or their cleavage sites. In this paper, a new method is developed for predicting the signal sequence of a query secretory protein by fusing the results from a series of global alignments through a voting system. The very high success rates thus obtained suggest that the novel approach is very promising, and that the new method may become a useful vehicle in identifying signal sequence, or at least serve as a complementary tool to the existing algorithms of this field.

Algorithms↗

PubMed2006

Using ensemble classifier to identify membrane protein types.

Predicting membrane protein type is both an important and challenging topic in current molecular and cellular biology. This is because knowledge of membrane protein type often provides useful clues for determining, or sheds light upon, the function of an uncharacterized membrane protein. With the explosion of newly-found protein sequences in the post-genomic era, it is in a great demand to develop a computational method for fast and reliably identifying the types of membrane proteins according to their primary sequences. In this paper, a novel classifier, the so-called "ensemble classifier", was introduced. It is formed by fusing a set of nearest neighbor (NN) classifiers, each of which is defined in a different pseudo amino acid composition space. The type for a query protein is determined by the outcome of voting among these constituent individual classifiers. It was demonstrated through the self-consistency test, jackknife test, and independent dataset test that the ensemble classifier outperformed other existing classifiers widely used in biological literatures. It is anticipated that the idea of ensemble classifier can also be used to improve the prediction quality in classifying other attributes of proteins according to their sequences.

Algorithms↗