Using Data Mining Techniques to Probe the Role of Hydrophobic Residues in Protein Folding and Unfolding Simulations - Evolving Application Domains of Data Warehousing and Mining - page 258

Chapter 12
Using Data Mining
Techniques to Probe the Role
of Hydrophobic Residues
in Protein Folding and
Unfolding Simulations
Cândida G. Silva
University of Coimbra, Portugal
Pedro Gabriel Ferreira
Center for Genomic Regulation, Spain
Paulo J. Azevedo
University of Minho, Portugal
Rui M. M. Brito
University of Coimbra, Portugal
ABStrAct
The protein folding problem, i.e. the identification of the rules that determine the acquisition of the native,
functional, three-dimensional structure of a protein from its linear sequence of amino-acids, still is a major
challenge in structural molecular biology. Moreover, the identification of a series of neurodegenerative
diseases as protein unfolding/misfolding disorders highlights the importance of a detailed characterisation
of the molecular events driving the unfolding and misfolding processes in proteins. One way of exploring
these processes is through the use of molecular dynamics simulations. The analysis and comparison of the
enormous amount of data generated by multiple protein folding or unfolding simulations is not a trivial
task, presenting many interesting challenges to the data mining community. Considering the central role
of the hydrophobic effect in protein folding, we show here the application of two data mining methods -
hierarchical clustering and association rules - for the analysis and comparison of the solvent accessible
surface area (SASA) variation profiles of each one of the 127 amino-acid residues in the amyloidogenic
protein Transthyretin, across multiple molecular dynamics protein unfolding simulations.