Biased Monte Carlo optimization of protein sequences

We demonstrate the application of a biased Monte Carlo method for the optimization of protein sequences. The concept of configurational-biased Monte Carlo has been used, but applied to sequence/composition rather than coordinates. Sequences of two-dimensional lattice proteins were optimized with the new approach and results compared with conventional Monte Carlo and a self-consistent mean-field (SCMF) method. Biased Monte Carlo(MC) was far more efficient than conventional MC, especially on more complex systems and with faster cooling rates. Biased MC did not converge as quickly as SCMF, but often found better sequences

The identification of similarities between biological networks: Application to the metabolome and interactome

14 page(s

The automatic discovery of structural principles describing protein fold space

12 page(s

Untangling biological networks using bioinformatics

26 page(s

Automated Protein Design and Sequence 0ptimisation: Scoring Functions and the Search Problem

Advances in molecular biology may mean that almost any protein sequence can be synthesised, but perhaps this has served to highlight the inadequacy of theoretical work. For a given protein fold, it is probably not possible to reliably predict an "ideal" sequence. We identify and survey several aspects of the problem. Firstly, it is not clear what is the best way to score a sequence-structure pair. Secondly, there is no consensus as to what the score function should represent (free energy or some abstract measure of sequence-structure compatibility). Finally, the number of possible sequences is astronomical and searching this space poses a daunting optimisation problem. These problems are discussed in the light of recent experimental successes

Integration of protein Interaction and Metabolic Data for Subcellular Localization Prediction

1 page(s