Abstracts Keynote & Plenary

More documents

Recommendations

Info

discovered that the antimicrobial agents was adsorbed closely to the membrane after the 20ns’ simulation and frequently changed their structure to search for the conformation appropriate to insert the membrane. Key words: Biocidal guanides Polymers、molecular dynamics、cytoplasmic membrane. PO-034 Ab initio molecular dynamics study of solid β-HMX Jing Chang1,2, Dong-Qing Wei1*, Xiang-Rong Chen2,3 1. College of Life Science and Biotechnology, Shanghai Jiao tong University, Shanghai 200240, China 2. School of Physical Science and Technology, Sichuan University, Chengdu 610064, China; 3. International Centre for Materials Physics, Chinese Academy of Sciences, Shenyang 110016, China; The dynamical behavior of β-octahydro-1,3,5,7-tetranitro-1,3,5,7- tetrazocine (β-HMX) crystal has been studied using the Car-Parrinello ab initio molecular dynamics scheme, the optimized geometry of the molecule in the unit cell are shown to be in good agreement with the available experimental data[1] and other theoretical results[2,3]. The temperature effect on the bulk structure and properties has been investigated. Following the dynamics for a time scale of up to 50 ps and temperatures at about 3800K allows the construction of effective rate laws for typical products such as H2O, N2, CO2, and CO. In addition, the vibrational frequencies of a molecular system from molecular dynamics simulations is presented based on a localization criterion for the Fourier transformed velocity time-correlation functions of the effective modes. The temperature-induced frequency shifts of these modes are calculated and discussed in detail. Keywords: β-HMX, first-principles molecular dynamics, CPMD, vibrational frequencies [1] Choi C S. and Boutin H.P. Acta Crystallogr. B 1970, 26, 1235. [2] Lu L Y, Wei D Q, Chen X R, Lian, D, Ji, G F, Zhang, Q M and Gong, Z Z. Molecular Physics, 2008, 106, 2569. [3] Brand H V, Rabie R L,Funk D J, Diaz-Acosta I, Pulay P, Lippert T K. J. Phys. Chem. B, 2002, 106, 10594. PO-035 Molecular Dynamics Studies on phospholamban in membranes Peng Lian, Jingfang Wang, Dongqing Wei* Department of Bioinformatics and Biostatistics, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, P. R. China, 200240 * Correspondence: dqwei@sjtu.edu.cn Phospholamban (PLN) is a membrane protein with 52 residues which regulating the activity of sarcoplasmic reticulum Ca2+-ATPase. Different stable conformations, bellflower model and pinwheel model, were found on the surface of muscle cells. In order to explore the reasons it exhibits different conformation and the mechanism it realize its biological function, we have carried out molecular dynamics simulations on unphosphorylated and phosphorylated of both models in POPC membrane. It is found that the phosphate group plays an important role in regulating the activity of PLN through changing the flexibility of monomer helix. The different conformations of phosphorylated monomer helix become similar after long time simulation. This suggests that different
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.
Page 1 and 2: Abstracts Keynote & Plenary KN-001
Page 3 and 4: The nonlinear dynamical interaction
Page 5 and 6: Lim., Carmay Our research interests
Page 7 and 8: Email: jtus@phys.ualberta.caBackgro
Page 9 and 10: Formula drug or nutrient Any LMN is
Page 11 and 12: present study provide support to th
Page 13 and 14: Keywords: Amino acid index, non-cla
Page 15 and 16: mainly stabilized by the H-bonds fr
Page 17 and 18: generalized by Glazier and Graner [
Page 19 and 20: Profile-profile alignment may be th
Page 21 and 22: natural structure is coded in its a
Page 23 and 24: information will be presented. Assi
Page 25 and 26: Seasonal trade-off between water- a
Page 27 and 28: fluorescent protein ng, W.-D. Cheng
Page 29 and 30: 2. Email: xpgao@xtu.edu.cn Classifi
Page 31 and 32: * To whom correspondence should be
Page 33 and 34: analysis across different platforms
Page 35 and 36: 1.D. Q. Wei and G. N. Patey, Phys.
Page 37 and 38: 2. Göteborg University, Department
Page 39: Pushkal Samadhiya and S.K. Srivasta
Page 43 and 44: and imidopropyl with 1,2,3-benzotri

Abstracts Keynote & Plenary

Create successful ePaper yourself

Delete template?

Save as template?