Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Improving detection of promising unrefined protein docking complexes
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Understanding protein-protein interaction (PPI) is important in order to understand cellular processes. X-ray crystallography and mutagenesis, expensive methods both in time and resources, are the most reliable methods for detecting PPI. Computational approaches could, therefore, reduce resources and time spent on detecting PPIs. During this master thesis a method, cProQPred, was created for scoring how realistic coarse PPI models are. cProQPred use the machine learning method Random Forest trained on previously calculated features from the programs ProQDock and InterPred. By combining some of ProQDock’s features and the InterPred score from InterPred the cProQPred method generated a higher performance than both ProQDock and InterPred.

This work also tried to predict the quality of the PPI model after refinement and the chance for a coarse PPI model to succeed at refinement. The result illustrated that the predicted quality of a coarse PPI model also was a relatively good prediction of the quality the coarse PPI model would get after refinement. Prediction of the chance for a coarse PPI model to succeed at refinement was, however, without success.

Place, publisher, year, edition, pages
2016. , p. 35
Keywords [en]
protein-protein interaction, Random Forest
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-133633ISRN: LITH-IFM-A-EX--16/3280—SEOAI: oai:DiVA.org:liu-133633DiVA, id: diva2:1061777
Subject / course
Biomedical Laboratory Science
Presentation
2016-01-16, Bikupan, Linköping, 13:15 (English)
Supervisors
Examiners
Available from: 2017-01-04 Created: 2017-01-03 Last updated: 2017-01-04Bibliographically approved

Open Access in DiVA

fulltext(1714 kB)123 downloads
File information
File name FULLTEXT01.pdfFile size 1714 kBChecksum SHA-512
a135eda9eb19fb3a0aabd3c8a4d418e7e3ae1660441266830db2f3188c4ed64f42dc3f3402464cf3a614c761427c49f2c57937c22328683e203fb92c33498645
Type fulltextMimetype application/pdf

By organisation
Bioinformatics
Natural Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 123 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

urn-nbn

Altmetric score

urn-nbn
Total: 241 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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