Reference : A Novel Multi-objectivisation Approach for Optimising the Protein Inverse Folding Problem
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/25029
A Novel Multi-objectivisation Approach for Optimising the Protein Inverse Folding Problem
English
Nielsen, Sune Steinbjorn mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Danoy, Grégoire mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Jurkowski, Wiktor mailto [TGAC, Norwich Research Park, Norwich, UK]
Jimenez Laredo, Juan Luis mailto [LITIS, Universite du Havre, Le Havre, France]
Schneider, Reinhard mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Talbi, El-Ghazali mailto []
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
2015
Applications of Evolutionary Computation: 18th European Conference, EvoApplications 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings
Yes
International
18th European Conference on the Applications of Evolutionary Computation
from 08-04-2015 to 10-04-2015
Copenhagen
Denmark
[en] Inverse Folding Problem ; Genetic Algorithm ; Multi-objectivisation
[en] In biology, the subject of protein structure prediction is of continued interest, not only to chart the molecular map of the living cell, but also to design proteins of new functions. The Inverse Folding Problem (IFP) is in itself an important research problem, but also at the heart of most rational protein design approaches. In brief, the IFP consists in finding sequences that will fold into a given structure, rather than determining the structure for a given sequence - as in conventional structure prediction. In this work we present a Multi Objective Genetic Algorithm (MOGA) using the diversity-as-objective (DAO) variant of multi-objectivisation, to optimise secondary structure similarity and sequence diversity at the same time, hence pushing the search farther into wide-spread areas of the sequence solution-space. To control the high diversity generated by the DAO approach, we add a novel Quantile Constraint (QC) mechanism to discard an adjustable worst quantile of the population. This DAO-QC approach can efficiently emphasise exploitation rather than exploration to a selectable degree achieving a trade-off producing both better and more diverse sequences than the standard Genetic Algorithm (GA). To validate the final results, a subset of the best sequences was selected for tertiary structure prediction. The super-positioning with the original protein structure demonstrated that meaningful sequences are generated underlining the potential of this work.
University of Luxembourg: High Performance Computing - ULHPC
Fonds National de la Recherche - FnR
http://hdl.handle.net/10993/25029

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Limited access
chp%3A10.1007%2F978-3-319-16549-3_2.pdfPublisher postprint913.08 kBRequest a copy

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.