Energy Landscapes
The concept of energy landscapes has proven to be of fundamental relevance
in investigations of complex disordered systems, from simple spin glass models to
biopolymer folding. In this picture, energy is viewed as an explicit function E(S) of underlying
conformational degrees of freedom S. The topological structure of the conformation
space is determined in terms of the elementary moves that underly the dynamical behavior.
Examples are single spin flips in spin glasses, the formation or breaking of a base pair in RNA
folding models, or rotation around a bond in a protein folding model.
The geometric properties and topological details of the energy landscape, such as number
of local optima, the saddle points separating them, as well as the size distributions of the
basins of attraction, therefore directly influence the dynamics of the underlying system. A
thorough understanding of these aspects of geometricall andscape structure is thus of wide interest.
The Energy Landscape Library - a Platform for Generic Algorithms
The ELL-library provides a platform for generic algorithms to study kinetics
and structure of energy landscapes with discrete states. These algorithms
need an abstract representation of these states to be applied to a multitude
of state instances and their corresponding energy or fitness landscapes.
Click here for further details
Coarse Energy Landscape Representations for RNA Molecules
Based on the energy landscape library, a set of tools to investigate the
structure and topology of RNA energy landscapes has been implemented. These
programs allow for:
- Minima sampling
- Exact calculation of barrier trees or saddle networks
- Approximation of barrier trees
- Barrier estimation using a heuristic by Morgan and Higgs
Click here for further details
Energy Landscapes and Kinetics of Lattice Proteins
For this research topic, we analyze the energy landscape of
three-dimensional model proteins. We plan to investigate new techniques for
the construction of energy landscape representations and the computation of
protein kinetics. We are furthermore interested in the analysis of landscapes
of even more complex protein models.
Current status
We could show how protein structure prediction helps in the construction of
barrier-trees, which represent local minima and energy barriers of the
energy landscape. Starting from predicted global optima and structures on
the first raised energy levels, we can cover the structure space up to a
defined degree.
Software
- CPSP-tools
- Optimal structure prediction etc. in 3D HP models
- LatPack
Global and sequential (vectorial) folding simulation in arbitrary
3D lattice protein models
- LatFit
High resolution fitting of 3D full atom protein data (PDB) onto lattices
Contributing group members
Main Publications
- Michael T. Wolfinger,
Sebastian Will, Ivo L. Hofacker, Rolf Backofen, and Peter F. Stadler.
Exploring the lower part of discrete polymer model energy
landscapes.
Europhysics Letters, 2006.
- Martin Mann, Sebastian Will and Rolf Backofen.
CPSP-tools - Exact and Complete Algorithms for High-throughput 3D Lattice Protein Studies.
In BMC Bioinformatics, 9, 230, 2008.
- Martin Mann, Daniel Maticzka, Rhodri Saunders, and Rolf Backofen.
Classifying protein-like sequences in arbitrary lattice protein models using LatPack.
In HFSP Journal, 2 no. 6 pp. 396, 2008.
Special issue on protein folding: experimental and theoretical approaches.
Supplementary data can be obtained HERE .
Cooperations
- Ivo Hofacker, TBI,
University of Vienna, Austria
- Rhodri Saunders,
University of Oxford, UK
- Peter Stadler,
University of Leipzig, Germany
- Michael Wolfinger, TBI,
University of Vienna, Austria
Funding
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