Abstract of Soeren Brunak
An integrated computational approach is needed to face the challenge of the
functional assignment of thousands of new gene products derived from
different sequencing projects. Standard functional assignment by homology
using proteins of known function is very powerful, but still leaves
unassigned proteins belonging to families without known function (orphan
families), or isolated sequences (orphan sequences). The number of orphan
families and sequences will increase over time since experimental
functional analysis is highly demanding in time and effort.
Function is a multilevel, complex phenomenon, where different levels are
interwoven (chemical, biochemical, cellular, organismal and
developmental). We present an indirect approach where predicted structural
features, putative protein modifications, sorting signals, and gene expression
data from DNA array experiments are integrated and used to infer the functiona class.
References:
Identification of prokaryotic and eukaryotic signal peptides and prediction of
their cleavage sites, H. Nielsen, J. Engelbrecht, S. Brunak and G. von Heijne,
Protein Eng., 10, 1-6, 1997.
Machine learning approaches to the prediction of signal peptides and other
protein sorting signals, H. Nielsen, S. Brunak and G. von Heijne, Protein Eng.,11, 3-9, 1999.
Prediction of mucin type O-glycosylation sites based on sequence context andsurface accessibility, J.E. Hansen, O. Lund, N. Tolstrup, K. Rapacki and S. Brunak, Glycoconjugate J., 15:115-130, 1998.
Sequence and structure-based prediction of eukaryotic protein phosphorylation
sites, N. Blom, S. Gammeltoft, and S. Brunak, J. Mol. Biol., 294, 1351-1362,
1999.
Bioinformatics: The Machine Learning Approach, P. Baldi and S. Brunak, MIT
Press, Cambridge, Mass. 351 p., 1998.