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
Modifying a Protein-Protein Interaction Identifier with a Topology and Sequence-Order Independent Structural Comparison Method
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Using computational methods to identify protein-protein interactions (PPIs) supports experimental techniques by using less time and less resources. Identifying PPIs can be made through a template-based approach that describes how unstudied proteins interact by aligning a common structural template that exists in both interacting proteins. A pipeline that uses this is InterPred, that combines homology modelling and massive template comparison to construct coarse interaction models. These models are reviewed by a machine learning classifier that classifies models that shows traits of being true, which can be further refined with a docking technique. However, InterPred is dependent on using complex structural information, that might not be available from unstudied proteins, while it is suggested that PPIs are dependent of the shape and interface of proteins. A method that aligns structures based on the interface attributes is InterComp, which uses topological and sequence-order independent structural comparison. Implementing this method into InterPred will lead to restricting structural information to the interface of proteins, which could lead to discovery of undetected PPI models. The result showed that the modified pipeline was not comparable based on the receiver operating characteristic (ROC) performance. However, the modified pipeline could identify new potential PPIs that were undetected by InterPred.

Place, publisher, year, edition, pages
2018. , p. 44
Keywords [en]
PPI, Protein-protein interaction, Machine learning, Protein modelling, Structural bioinformatics, Structural alignment, Sequence-order independent
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:liu:diva-147777ISRN: LITH-IFM-A-EX—18/3497—SEOAI: oai:DiVA.org:liu-147777DiVA, id: diva2:1205572
Subject / course
Biotechnology
Presentation
2018-04-12, Bikupan, Linköping, 10:00 (English)
Supervisors
Examiners
Available from: 2018-09-25 Created: 2018-05-14 Last updated: 2018-09-25Bibliographically approved

Open Access in DiVA

fulltext(2061 kB)28 downloads
File information
File name FULLTEXT01.pdfFile size 2061 kBChecksum SHA-512
592f83a592d5f12e7476fff4a9d6413b40a7587def8f82db35d293dd6fcebc927b605b750660d9e2be86f70cf3478564443191d954bd03a6e609bdfffd77f292
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Johansson, Joakim
By organisation
Bioinformatics
Bioinformatics and Systems Biology

Search outside of DiVA

GoogleGoogle Scholar
Total: 28 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: 187 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