Abstract for talk [I] at DE Shaw 10 August 2007

TITLE:

Computational Proteomics: Networks & Structures

Mark Gerstein

Yale University

Motions

An area of focus in the lab is analyzing small populations of structures in
terms of their detailed 3D-geometry and physical properties. Here, we try to
interpret macromolecular motions in terms of packing. We have set up a database
of macromolecular motions and coupled it with simulation tools to interpolate
between structural conformations; the database also has tools to predict likely
motions based on simple models, such as normal modes and localized hinges
connecting rigid domains. Part of this project involves devising a system for
characterizing motions in a highly standardized fashion. Our motions
classification scheme is motivated by the fact that protein interiors are packed
exceedingly tightly, and the tight packing can greatly constrains a protein's
mobility. We have developed tools for measuring and comparing the packing
efficiency at different interfaces (e.g. inter-domain, protein surface,
helix-helix, protein vs. RNA) using specialized geometric constructions (e.g.
Voronoi polyhedra).

http://molmovdb.org/

# The citation for the FlexOracle hinge predictor is SC Flores, MB Gerstein
(2007). BMC Bioinformatics 8: 215.

# Flores, Echols, Milburn, Hespenheide, Keating, Lu, Wells, Yu, Thorpe, Gerstein
(2006). Nucleic Acids Res. 34:D296-301.

SC Flores, LJ Lu, J Yang, N Carriero, MB Gerstein (2007). "Hinge Atlas: relating
protein sequence to sites of structural flexibility." BMC Bioinformatics 8: 167

http://papers.gersteinlab.org/papers/subject/motions/
http://papers.gersteinlab.org/papers/subject/volumes/

Networks

My talk will be concerned with topics in proteomics, in particular
predicting protein function on a genomic scale. We approach this
through the prediction and analysis of biological networks, focusing
on protein-protein interaction and transcription-factor-target ones. I
will describe how these networks can be determined through integration
of many genomic features and how they can be analyzed in terms of
various simple topological statistics. In particular, I will discuss a
number of specific analyses: (1) Integrating gene expression data with
the regulatory network illuminates transient hubs; (2) Integration of
the protein interaction network with 3D molecular structures reveals
different types of hubs, depending on the number of interfaces
involved in interactions (one or many); (3) Analysis of betweenness in
biological networks reveals that this quantity is more strongly
correlated with essentially than degree; (4) Analysis of structure of
the regulatory network shows that it has a hierarchiel layout with the
"middle-managers" acting as information bottlenecks. (5) Development
of a useful web-based tools for the analysis of networks, TopNet and
tYNA.

http://bioinfo.mbb.yale.edu
http://topnet.gersteinlab.org

http://papers.gersteinlab.org/papers/subject/interactions/

TopNet: a tool for comparing biological sub-networks, correlating
protein properties with topological statistics. H Yu, X Zhu, D
Greenbaum, J Karro, M Gerstein (2004) Nucleic Acids Res 32: 328-37.

Genomic analysis of regulatory network dynamics reveals large
topological changes. NM Luscombe, MM Babu, H Yu, M Snyder, SA
Teichmann, M Gerstein (2004) Nature 431: 308-12.

Annotation transfer between genomes: protein-protein interologs and
protein-DNA regulogs. H Yu, NM Luscombe, HX Lu, X Zhu, Y Xia, JD Han,
N Bertin, S Chung, M Vidal, M Gerstein (2004) Genome Res 14: 1107-18.

Integrated prediction of the helical membrane protein interactome in
yeast. Y Xia, LJ Lu, M Gerstein (2006) J Mol Biol 357: 339-49.

Relating three-dimensional structures to protein networks provides
evolutionary insights. PM Kim, LJ Lu, Y Xia, MB Gerstein (2006)
Science 314: 1938-41.

The tYNA platform for comparative interactomics: a web tool for
managing, comparing and mining multiple networks. KY Yip, H Yu, PM
Kim, M Schultz, M Gerstein (2006) Bioinformatics 22: 2968-70.

Positive Selection at the Protein Network Periphery: Evaluation in
Terms of Structural Constraints and Cellular Context. Philip M. Kim,
Jan O. Korbel and Mark B. Gerstein PNAS (in press)

The importance of bottlenecks in protein networks: correlation with
gene essentiality and expression dynamics. H Yu, PM Kim, E Sprecher,
V Trifonov, M Gerstein (2007) PLoS Comput Biol 3: e59.

Genomic analysis of the hierarchical structure of regulatory networks.
H Yu, M Gerstein (2006) Proc Natl Acad Sci U S A 103: 14724-31.