Automated design of energy functions for protein structure prediction by means of genetic programming and improved structure similarity assessment

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{Widera:thesis,
  author =       "Pawel Widera",
  title =        "Automated design of energy functions for protein
                 structure prediction by means of genetic programming
                 and improved structure similarity assessment",
  school =       "University of Nottingham",
  year =         "2010",
  address =      "UK",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  bibsource =    "OAI-PMH server at etheses.nottingham.ac.uk",
  oai =          "oai:etheses.nottingham.ac.uk:1394",
  URL =          "http://etheses.nottingham.ac.uk/1394/1/thesis.pdf",
  URL =          "http://etheses.nottingham.ac.uk/1394/",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=56&uin=uk.bl.ethos.519682",
  size =         "144 pages",
  abstract =     "The process of protein structure prediction is a
                 crucial part of understanding the function of the
                 building blocks of life. It is based on the
                 approximation of a protein free energy that is used to
                 guide the search through the space of protein
                 structures towards the thermodynamic equilibrium of the
                 native state. A function that gives a good
                 approximation of the protein free energy should be able
                 to estimate the structural distance of the evaluated
                 candidate structure to the protein native state. This
                 correlation between the energy and the similarity to
                 the native is the key to high quality
                 predictions.

                 State-of-the-art protein structure prediction methods
                 use very simple techniques to design such energy
                 functions. The individual components of the energy
                 functions are created by human experts with the use of
                 statistical analysis of common structural patterns that
                 occurs in the known native structures. The energy
                 function itself is then defined as a simple weighted
                 sum of these components. Exact values of the weights
                 are set in the process of maximisation of the
                 correlation between the energy and the similarity to
                 the native measured by a root mean square deviation
                 between coordinates of the protein backbone.

                 In this dissertation I argue that this process is
                 oversimplified and could be improved on at least two
                 levels. Firstly, a more complex functional combination
                 of the energy components might be able to reflect the
                 similarity more accurately and thus improve the
                 prediction quality. Secondly, a more robust similarity
                 measure that combines different notions of the protein
                 structural similarity might provide a much more
                 realistic baseline for the energy function
                 optimisation.

                 To test these two hypotheses I have proposed a novel
                 approach to the design of energy functions for protein
                 structure prediction using a genetic programming
                 algorithm to evolve the energy functions and a
                 structural similarity consensus to provide a reference
                 similarity measure. The best evolved energy functions
                 were found to reflect the similarity to the native
                 better than the optimised weighted sum of terms, and
                 therefore opening a new interesting area of research
                 for the machine learning techniques.",
  notes =        "Winner 2010 HUMIES GECCO 2010
                 http://www.genetic-programming.org/combined.php
                 uk.bl.ethos.519682",
}

Genetic Programming entries for Pawel Widera

Citations