Projections for fast protein structure retrieval. Sourangshu Bhattacharya, Chiranjib Bhattacharyya and Nagasuma R. Chandra. BMC Bioinformatics. 2006 Dec 18;7 Suppl 5:S5.
Abstract:
Background:
In
recent times, there has been an exponential rise in the number of
protein structures in databases e.g. PDB. So, design of fast
algorithms capable of querying such databases is becoming an
increasingly important research issue. This paper reports an
algorithm, motivated from spectral graph matching techniques, for
retrieving protein structures similar to a query structure from a
large protein structure database. Each protein structure is specified
by the 3D coordinates of residues of the protein. The algorithm is
based on a novel characterization of the residues, called
projections, leading to a similarity measure between the residues of
the two proteins. This measure is exploited to efficiently compute
the optimal equivalences.
Results:
Experimental
results show that, the current algorithm outperforms the state of the
art on benchmark datasets in terms of speed without losing accuracy.
Search results on SCOP 95% nonredundant database, for fold similarity
with 5 proteins from different SCOP classes show that the current
method performs competitively with the standard algorithm CE. The
algorithm is also capable of detecting non-topological similarities
between two proteins which is not possible with most of the state of
the art tools like Dali.
Comparison of protein structures by growing neighborhood alignments. Sourangshu Bhattacharya, Chiranjib Bhattacharyya and Nagasuma R Chandra. BMC Bioinformatics. 2007 Mar 6;8:77.
Abstract:
Background:
Design
of protein structure comparison algorithm is an important research
issue, having far reaching implications. In this article, we describe
a protein structure comparison scheme, which is capable of detecting
correct alignments even in difficult cases, e.g. non-topological
similarities. The proposed method computes protein structure
alignments by comparing, small substructures, called neighborhoods.
Two different types of neighborhoods, sequence and structure, are
defined, and two algorithms arising out of the scheme are detailed. A
new method for computing equivalences having non-topological
similarities from pairwise similarity score is described. A novel and
fast technique for comparing sequence neighborhoods is also
developed.
Results:
The experimental
results show that the current programs show better performance on
Fischer and Novotny's benchmark datasets, than state of the art
programs, e.g. DALI, CE and SSM. Our programs were also found to
calculate correct alignments for proteins with huge amount of indels
and internal repeats. Finally, the sequence neighborhood based
program was used in extensive fold and non-topological similarity
detection experiments. The accuracy of the fold detection experiments
with the new measure of similarity was found to be similar or better
than that of the standard algorithm CE.
Conclusion:
A
new scheme, resulting in two algorithms, have been developed,
implemented and tested. The programs developed are accessible at
http://mllab.csa.iisc.ernet.in/mp2/runprog.html.
Structural Alignment based Kernels for Protein Structure Classification. Sourangshu Bhattacharya, Chiranjib Bhattacharyya and Nagasuma R Chandra. In Proceedings of 24th International Conference on Machine Learning (ICML), 2007.
Abstract
Structural alignments are the most widely used
tools for comparing proteins with low sequence similarity. The main
contribution of this paper is to derive various kernels on proteins
from structural alignments, which do not use sequence information.
Central to the kernels is a novel alignment algorithm which matches
substructures of fixed size using spectral graph matching techniques.
We derive positive semi-definite kernels which capture the notion of
similarity between substructures. Using these as base more
sophisticated kernels on protein structures are proposed. To
empirically evaluate the kernels we used a 40% sequence non-redundant
structures from 15 different SCOP superfamilies. The kernels when
used with SVMs show competitive performance with CE, a state of the
art structure comparison program.
Kernels on Attributed Pointsets with Applications. Mehul Parsana, Sourangshu Bhattacharya, Chiranjib Bhattacharyya and K. R. Ramakrishnan. In Proceedings of 21st Annual Conference on Neural Information Processing Systems (NIPS), 2007.
Abstract
This
paper introduces kernels on attributed pointsets, which are sets of
vectors embedded in an euclidean space. The embedding gives the
notion of neighborhood, which is used to define positive semidefinite
kernels on pointsets. Two novel kernels on neighborhoods are
proposed, one evaluating the attribute similarity and the other
evaluating shape similarity. Shape similarity function is motivated
from spectral graph matching techniques. The kernels are tested on
three real life applications: face recognition, photo album tagging,
and shot annotation in video sequences, with encouraging results.
Copyleft
2007
Sourangshu Bhattacharya
Feel free to
copy !! Why duplicate effort ?
Only give me some credit.
See
GNU GPL.