Protein Folding and Unfolding Simulations: A New Challenge for Data Mining

Abstract

One of the unsolved paradigms in molecular biology is the protein folding problem. In recent years, with the identification of several diseases as protein folding disorders and with the explosion of genome information and the need for efficient ways to predict protein structure, protein folding became a central issue in molecular sciences research. Using molecular dynamics unfolding simulations of an amyloidogenic protein--transthyretin--as an example, we put forward a series of ideas on how simulations of this type may be used to infer rules and unfolding behavior in amyloidogenic proteins, and to extrapolate rules for protein folding in different structural classes of proteins. These, in turn, could help in the development of protein structure prediction methods. The need to analyse different proteins and to run multiple simulations creates a huge amount of data which has to be stored, managed, analyzed and shared (database and Grid technology; data mining). Once the data is captured, the next challenge is to find meaningful patterns (associations, correlations, clusters, rules, relationships) among molecular properties, or their relative importance at different stages of the folding or unfolding processes. This clearly puts new and interesting challenges to the bioinformatics community.

A Closer Look on Protein Unfolding Simulations through Hierarchical Clustering

“…Short range non-bonded interactions were calculated with a 12Å cut-off, and long range electrostatic interactions were treated using the particle mesh Ewald summation (PME) algorithm. More details on the simulations can be found elsewhere ( [2], [5]). In Fig.…”

“…These MD simulations are computationally expensive and generate a huge amount of data, making the comparison of different trajectories a difficult task [5]. Data mining techniques might provide the technology required to contrast, compare and characterize the variation of molecular properties associated with each simulation.…”

Probing Protein Folding Dynamics Using Multivariate Statistical Techniques

“…As such, uncovering the mysteries of proteins requires an interdisciplinary approach, enlisting not only biologists and medical professionals but also engineers, mathematicians and computer scientists. Recent studies include bioinformatics approaches that explore data mining (Brito, Dubitzky et al 2004) and evolutionary algorithms (Pal, Bandyopadhyay et al 2006), in addition to structure prediction problems (Krogh, Larsson et al 2001;Floudas 2007) and computational techniques focusing on optimization (Krogh, Larsson et al 2001;Greenberg, Hart et al 2004). …”

On Mining Protein Unfolding Simulation Data with Inductive Logic Programming

“…The simulations [1] include 5 runs using the wilde type (WT) and 5 others using the amyloidogenic type (L55P). In each simulation run we collected, for each residue and instant of time, information concerning the secondary structure it belongs to and its Solvent Accessible Surface Area (SASA) value.…”

“…Thus, the detailed study of folding and unfolding events in proteins is not only important to the characterization of the mechanisms associated with several amyloid diseases but also is becoming central to the development of rational therapeutic strategies against these diseases. In this context, computer simulations based on molecular dynamics have been succesfully applied to explore and analyse the folding and unfolding events in proteins [5,4,1].…”