Change search
ReferencesLink to record
Permanent link

Direct link
Prediction of Cell-Penetrating Peptides
Stockholm University, Faculty of Science, Department of Neurochemistry.
2015 (English)In: Cell-Penetrating Peptides: Methods and Protocols / [ed] Ülo Langel, Springer-Verlag New York, 2015, 39-58 p.Chapter in book (Refereed)
Abstract [en]

The in silico methods for the prediction of the cell-penetrating peptides are reviewed. Those include the multivariate statistical methods, machine-learning methods such as the artificial neural networks and support vector machines, and molecular modeling techniques including molecular docking and molecular dynamics.

The applicability of the methods is demonstrated on the basis of the exemplary cases from the literature.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2015. 39-58 p.
, Methods in Molecular Biology, ISSN 1064-3745 ; 1324
Keyword [en]
Cell-penetrating peptides, Multivariate statistics, Artificial neural networks, Support vector machines, Molecular docking, Molecular dynamics
National Category
Biochemistry and Molecular Biology
URN: urn:nbn:se:su:diva-125165DOI: 10.1007/978-1-4939-2806-4_3ISI: 000376037700004ISBN: 978-1-4939-2805-7ISBN: 978-1-4939-2806-4OAI: diva2:892023
Available from: 2016-01-08 Created: 2016-01-08 Last updated: 2016-06-20Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Hällbrink, Mattias
By organisation
Department of Neurochemistry
Biochemistry and Molecular Biology

Search outside of DiVA

GoogleGoogle Scholar
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

Altmetric score

Total: 8 hits
ReferencesLink to record
Permanent link

Direct link