Protein structure prediction
Proteins are large macromolecules
that are essential for the working of organisms and contribute
to processes as diverse as the catalysis of reactions, immune response,
structural support, transportation and messaging. The three-dimensional
shape (tertiary structure) of proteins is crucial to allow them to
do their specific jobs. Computational protein
structure prediction aims to
develop methods that can reliably predict the tertiary structure
of a protein from its molecular composition, and is a long-standing
challenge in computational biology.
My research on computational protein structure prediction
is centered around a number of different questions:
- Knowledge-based energy functions for protein structure
prediction are typically linear combinations of a number of
different weighted energy terms. Are current weight settings
in these functions close to optimal and / or do such optimal
weight settings even exist? If they do not exist, can we
benefit from using multiobjective or interactive approaches
that avoid the priori selection of a single fixed weight
setting? Our two papers in PPSN 2008 (right) explore the effect that
the decomposition of an energy function (or of any other
objective) has on the performance of a simple search
- How can we evaluate the performance of a given (single
or multiobjective) energy function? What problems might we
encounter if we compare energy functions on decoy sets of protein
structures? Our recent Bioinformatics paper (right) addresses some of
Fragment-assembly techniques currently present the state-of-the-art for
de novo structure prediction, but they do not scale to large proteins or
those with high contact order. Is this due to limitations of the energy functions, the optimization
methods, the quality of the fragment libraries or a combination
of these three factors? Answering these questions will require a better
understanding of the working mechanisms of fragment-assembly
methods, and is crucial to facilitate further improvement of
these techniques. My recent work (see below) has analyzed the
effects of fragment and insertion size on search space size and
Julia Handl, Joshua Knowles, Robert Vernon, David Baker
and Simon Lovell (2012). The dual role of fragments
in fragment-assembly methods for de novo protein
structure prediction. Proteins 80(2,): 490-504
Julia Handl, Joshua Knowles and Simon Lovell (2009).
Artefacts and biases affecting the evaluation of
scoring function on decoy sets for protein structure
prediction. Bioinformatics 25(10):1271-1279
Julia Handl, Simon C. Lovell and Joshua Knowles. (2008) Multiobjectivization by decomposition of scalar cost functions. Proceedings of the Tenth International Conference on Parallel Problem Solving from Nature (PPSN X).
Julia Handl, Simon C. Lovell and Joshua Knowles. (2008) Investigations into the effect of multiobjectivization in protein structure prediction. Proceedings of the Tenth International Conference on Parallel Problem Solving from Nature (PPSN X).
Links and downloads:
- The R project for Statistical Computing.
The specific functions used were funtion cor(..,method="spearman") to compute Spearman
rank correlations, function cor.test(..,method="spearman") to test the statistical significance
of a given correlation and function parcoord(..) from the MASS library to generate parallel
- The Rosetta method for protein structure
prediction, version 2.3.
- The TINKER molecular modelling software, which implements
the Amber99 all-atom energy function.