his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Models for Protein Structure Prediction by Evolutionary Algorithms
University of Skövde, Department of Computer Science.
2001 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

Evolutionary algorithms (EAs) have been shown to be competent at solving complex, multimodal optimisation problems in applications where the search space is large and badly understood. EAs are therefore among the most promising classes of algorithms for solving the Protein Structure Prediction Problem (PSPP). The PSPP is how to derive the 3D-structure of a protein given only its sequence of amino acids. This dissertation defines, evaluates and shows limitations of simplified models for solving the PSPP. These simplified models are off-lattice extensions to the lattice HP model which has been proposed and is claimed to possess some of the properties of real protein folding such as the formation of a hydrophobic core. Lattice models usually model a protein at the amino acid level of detail, use simple energy calculations and are used mainly for search algorithm development. Off-lattice models usually model the protein at the atomic level of detail, use more complex energy calculations and may be used for comparison with real proteins. The idea is to combine the fast energy calculations of lattice models with the increased spatial possibilities of an off-lattice environment allowing for comparison with real protein structures. A hypothesis is presented which claims that a simplified off-lattice model which considers other amino acid properties apart from hydrophobicity will yield simulated structures with lower Root Mean Square Deviation (RMSD) to the native fold than a model only considering hydrophobicity. The hypothesis holds for four of five tested short proteins with a maximum of 46 residues. Best average RMSD for any model tested is above 6Å, i.e. too high for useful structure prediction and excludes significant resemblance between native and simulated structure. Hence, the tested models do not contain the necessary biological information to capture the complex interactions of real protein folding. It is also shown that the EA itself is competent and can produce near-native structures if given a suitable evaluation function. Hence, EAs are useful for eventually solving the PSPP.

Place, publisher, year, edition, pages
Skövde: Institutionen för datavetenskap , 2001. , 134 p.
Keyword [en]
Bioinformatics, Evolutionary Algorithms, Protein Structure Prediction
National Category
Information Science
Identifiers
URN: urn:nbn:se:his:diva-623OAI: oai:DiVA.org:his-623DiVA: diva2:3014
Presentation
(English)
Uppsok
Social and Behavioural Science, Law
Supervisors
Available from: 2008-01-30 Created: 2008-01-30 Last updated: 2009-09-25

Open Access in DiVA

fulltext(4073 kB)155 downloads
File information
File name FULLTEXT01.psFile size 4073 kBChecksum SHA-1
db2d8d9cc80ded962e0246435d6ba04e5eea73ddce5809ed55ea1061961f7be38ea44e24
Type fulltextMimetype application/postscript
fulltext(1431 kB)546 downloads
File information
File name FULLTEXT02.pdfFile size 1431 kBChecksum SHA-512
0663dca9cee3a8c10b76cec5e0db21086df756f60bd974b57a236985c9c8b15b1876e679baf43fd60515dd848f8c6b6eafab5a4781c94e248b223dcbbf9da026
Type fulltextMimetype application/pdf

By organisation
Department of Computer Science
Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 701 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 180 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf