|
|
|
Jeudi 8 décembre 2011 - Nicolas Bonnel (Université Bretagne Sud) |
|
|
Written by Pierre PETERLONGO
|
LNA: Fast Protein
Classification Using A Laplacian Characterization of Tertiary
Structure10h30 Salle Turing
In the last two decades, a lot of protein 3D shapes have been
discovered, characterized and made available thanks to the Protein
Data Bank (PDB), that is nevertheless growing very quickly. New
scalable methods are thus urgently required to search through the
PDB efficiently. We present in this paper an approach entitled LNA
(Laplacian Norm Alignment) that performs structural comparison of
two proteins with dynamic programming algorithms. This is achieved
by characterizing each residue in the protein with scalar features.
The feature values are calculated using a Laplacian operator applied
on the graph corresponding to the adjacency matrix of the residues.
The weighted Laplacian operator we use estimates at various scales
local deformations of the topology where each residue is located. On
some benchmarks widely shared by the community we obtain
qualitatively similar results compared to other competing
approaches, but with an algorithm one or two order of magnitudes
faster. 180,000 protein comparisons can be done within 1 seconds
with a single recent GPU, which makes our algorithm very scalable
and suitable for real-time database querying across the Web.
|
|
