Arun S. Konagurthu,
David Abramson, Peter J. Stuckey, Arthur M. Lesk
International Conference on Data Mining (ICDM 2013),
Dallas, Texas, USA, 7-10 December 2013
Abstract: Proteins are biomolecules of life.
They fold into a great variety of three-dimensional (3D) shapes.
Underlying these folding patterns are many recurrent structural fragments
or building blocks (analogous to "LEGO® bricks").
This paper reports an innovative statistical inference approach to
discover a comprehensive dictionary of protein structural building blocks from
a large corpus of experimentally determined protein structures.
Our approach is built on the Bayesian and information-theoretic criterion of
minimum message length [MML].
To the best of our knowledge, this work is the first systematic and
rigorous treatment of a very important data mining problem
that arises in the cross-disciplinary area of structural bioinformatics.
The quality of the dictionary we find is demonstrated by
its explanatory power - any protein within the corpus of
known 3D structures can be dissected into successive regions assigned
to fragments from this dictionary.
This induces a novel one-dimensional representation of
three-dimensional protein folding patterns, suitable for
application of the rich repertoire of character-string
processing algorithms, for rapid identification of folding patterns
of newly determined structures.
This paper presents the details of the methodology used to
infer the dictionary of building blocks, and is supported by
illustrative examples to demonstrate its effectiveness and utility.
- Also see
Arun S. Konagurthu, Arthur M. Lesk, David Abramson,
Peter J. Stuckey, Lloyd Allison,
Statistical Inference of a canonical dictionary of protein
substructural fragments, October 2013,