[IEEE-bhpjobs] Duke events Nov 19 - Dec 14

[esther L]

http://www.genome.duke.edu/pressevents/calendar


Nov 19: Genes and Screens: Cinematic Bodies and Machines
Griffith | The Island (dir. Michael Bay, 2005, 127 min, USA, in English,
Color, 35mm)  Showing at 7:00 pm and 9:30pm
Griffith Film Theater in the Bryan Center on Duke University's West
Campus or the Richard White Lecture Hall on East Campus
For more information about this or other events in the Genes & Screens
film series, please consult:
http://www.duke.edu/web/film/screensociety/Genes+Screens.html


Nov 20: Duke Genes and Screens: Cinematic Bodies and Machines


Nov 22: Duke Tuesday Tea, 3:00 pm
Outside of Conference Room 118, North Bldg.
cookies and beverages Presented by the IGSP's Center for Bioinformatics
& Computational Biology


Nov 28: Duke Genes and Screens: Cinematic Bodies and Machines


Nov 29: Duke Tuesday Seminar: Bill Sullivan


Nov 29: Duke Tuesday Tea, 3:00 pm
Outside of Conference Room 118, North Bldg.
cookies and beverages Presented by the IGSP's Center for Bioinformatics
& Computational Biology


Nov 30, 11AM: last seminar of semester
Duke Institute for Genome Sciences & Policy Center for Bioinformatics & 
Computational Biology, Jie Liang, PhD Associate Professor, Department of 
Bioengineering,  University of Illinois at Chicago
“Predicting Protein Functions through Evolutionary Models of
Structural Binding Surfaces”
Predicting biological roles of proteins and classifying them by their 
functions are challenging tasks, as global protein sequence and 
structure similarities are often unreliable for functional inference. 
Protein plays its role by interacting with other molecules, and local 
binding surfaces contain direct useful information. To identify locally 
similar binding surfaces and to assess their biological similarity, 
scoring matrix such as PAM and BLOSUM are not suitable, because residues 
on protein functional surfaces experience different selection pressure 
than residues in folding core. We develop methods for estimating 
replacement rates of residues based on a continuous time Markov model 
using Bayesian Markov chain Monte Carlo. Combined with geometrically 
computed libraries of millions of binding surfaces using alpha shape, we 
show our method can predict protein functions from structures with 
sensitivity and specificity. Our examples include proteins from 
structural genomics with unknown biological roles and with only 
hypothetical sequence homologs. We further discuss how to construct 
models to account for possible cross-reactivities of proteins to 
multiple substrates, and how to develop canonical models of binding 
surfaces for proteins of different functional classes.


http://www.genome.duke.edu/pressevents/calendar


-- 
-- Esther L.,  esther-L at mailsnare.net or esther-L at alumni.virginia.edu
Speaking only for myself.


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