Abstracts Keynote & Plenary

conformations may convert to each other via phosphorylation and dephosphrylation.
[1] Nathaniel J. Traaseth, Lei Shi, Raffaello Verardi et al. PNAS 2009 106 10165
[2] Becucci L, Cembran A, Karim CB et al. Biophys J. 2009 96 L60
[3] Kim T, Lee J. Im W Proteins 2009 76 86
PO-036
Support vector machines for identification of DNA-binding residues in proteins using
a hybrid feature
Yi Xiong1,2, Juan Liu1
and Dong-Qing Wei2
1School of Computer, Wuhan University, Wuhan
430079
2 College of Life Science and Biotechnology, Shanghai Jiaotong
University, Shanghai
200240
Protein–DNA
interactions are crucial for a variety of essential biological activities. An automatic and
reliable identification of DNA-binding residues in DNA-binding proteins is important for site-directed
mutagenesis and functional annotation. Toward this end, a pattern recognition method can be
developed using the distinguished features derived from the increasing number of determined
structures of protein–DNA complexes in Protein Data Bank. Here, we developed a series of classifiers
using support vector machines to identify DNA-binding residues in proteins with different
combinations of various features, which are comprised of evolutionary information of the amino acid
sequence, residue solvent accessibility, electrostatic potential and secondary structure. In addition, we
designed a heuristic undersampling scheme for preprocessing the training datasets and a novel
encoding strategy for the solvent accessibility feature. Our results indicate that the best classification
performance was obtained by a SVM classifier that utilizes a combination of evolutionary information,
residue solvent accessibility and second structure information. Finally, we applied our method in a
dataset of 62 protein–DNA complexes in comparison with other published studies. The observation
suggests that our method achieved a higher performance with an overall accuracy 84.5% on a 10-fold
cross validation test.
Keywords: Protein–DNA
Interaction; DNA-binding Residue; Support Vector Machine
PO-037
Inferring gene regulatory networks from ChIP-chip, TF knock-out and expression data
Lihua Jiang and Qi Liu
Department of Bioinformatics
and Biostatistics, College of Life Sciences and Biotechnology, Shanghai
Jiao Tong University, Shanghai, P. R. China, 200240
Uncovering the underlying regulatory mechanism remains
a challenge in bioinformatics
studies. With the availability of various kinds of high-throughput biological data,
researchers are trying to reconstruct the gene regulatory networks on a genomic scale.
Since each single data source provides only partial and complementary information of the
regulatory relationships, combining diverse data sources is expected to get more reliable
networks.
Here we present
a method to infer the regulatory networks by combining ChIP-chip, TF (transcription
factor) knock-out and expression data. Our method is the first approach to combine these three kinds of
data. ChIP-chip and TF knock-out data provide direct and complementary evidence on transcriptional
regulation. We use these two kinds of data to find the core regulatory module and then we refine the
module by the indirect evidence inferred from the expression data. The results are validated by the
YEASTRACT, high quality ChIP-chip datasets, literatures and Gene Ontology enrichment analysis.