Computational Systems Bioinformatics: CSB2006 Conference by Peter Markstein, Ying Xu

By Peter Markstein, Ying Xu

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Extra info for Computational Systems Bioinformatics: CSB2006 Conference Proceedings Stanford CA, 14-18 August 2006 (Series on Advances in Bioinformatics and Computational Biology)

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1. INTRODUCTION Integral membrane proteins constitute a wide and important class of biological entities that are crucial for life, representing about 25% of the proteins encoded by several genomes1"3. They also play a key role in various cellular processes including signal and energy transduction, cell-cell interactions, and transport of solutes and macromolecules across membranes4. 5% of all solved structures5, compared to that of globular proteins deposited in the Protein Data Bank (PDB)6. In the absence of a high-resolution structure, an accurate structural model is important for the functional annotation of membrane proteins.

3% for the full average. These results suggest that, in the context of a larger search space, a hill-climbing ability is important, and that the hillclimbing abilities of HCC and HC/ are better than those of SA. 4. DISCUSSION AND CONCLUSIONS This paper presents two new techniques for optimizing scoring functions for protein structure predic- 28 tion. One of these approaches, HCC, using the scan technique, reaches better solutions than Simulated Annealing in comparable time. The performance of SA seems to saturate beyond a = 50, but HC/ will make use of an increased time allowance, finding the best solutions of all the examined algorithms.

The aim of predicting membrane protein structures is to identify the correct class of each residue. Since there are three classes for a protein sequence, we design a tertiary classifier, which consists of two binary classifiers in a hierarchical structure. An overview of the system architecture is shown in Fig. 1. STEP 1 : Helix Prediction STEP 2 : Topology i predicted TMH ' (from STEP 1) sliding window w\ identify non-helical segments q u e r y protein MVT L IALTPFVSRK peptide extraction l l l j j l l l l r • • TMH~|-rI I + ] Input features: 1.

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