Multi-objective evolutionary algorithm for discovering peptide binding motifs

Menaka Rajapakse, Bertil Schmidt, Vladimir Brusic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Multi-Objective Evolutionary Algorithms (MOEA) use Genetic Algorithms (GA) to find a set of potential solutions, which are reached by compromising trade-offs between the multiple objectives. This paper presents a novel approach using MOEA to search for a motif which can unravel rules governing peptide binding to medically important receptors with applications to drugs and vaccines target discovery. However, the degeneracy of motifs due to the varying physicochemical properties at the binding sites across large number of active peptides poses a challenge for the detection of motifs of specific molecules such as MHC Class II molecule I-Ag7 of the non-obese diabetic (NOD) mouse. Several motifs have been experimentally derived for I-Ag7 molecule, but they differ from each other significantly. We have formulated the problem of finding a consensus motif for I-Ag7 by using MOEA as an outcome that satisfies two objectives: extract prior information by minimizing the distance between the experimentally derived motifs and the resulting matrix by MOEA; minimize the overall number of false positives and negatives resulting by using the putative MOEA-derived motif. The MOEA results in a Pareto optimal set of motifs from which the best motif is chosen by the Area under the Receiver Operator Characteristics (AROC) performance on an independent test dataset. We compared the MOEA-derived motif with the experimentally derived motifs and motifs derived by computational techniques such as MEME, RANKPEP, and Gibbs Motif Sampler. The overall predictive performance of the MOEA derived motif is comparable or better than the experimentally derived motifs and is better than the computationally derived motifs.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages149-158
Number of pages10
Volume3907 LNCS
DOIs
Publication statusPublished - 2006
Externally publishedYes
EventEvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC - Budapest, Hungary
Duration: Apr 10 2006Apr 12 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3907 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherEvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC
CountryHungary
CityBudapest
Period4/10/064/12/06

Fingerprint

Multi-objective Evolutionary Algorithm
Evolutionary algorithms
Peptides
Molecules
Vaccines
Vaccine
Computational Techniques
Multiple Objectives
Inbred NOD Mouse
Binding sites
Prior Information
False Positive
Degeneracy
Receptor
Mouse
Drugs
Receiver
Genetic algorithms
Trade-offs
Binding Sites

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Rajapakse, M., Schmidt, B., & Brusic, V. (2006). Multi-objective evolutionary algorithm for discovering peptide binding motifs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3907 LNCS, pp. 149-158). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3907 LNCS). https://doi.org/10.1007/11732242_14

Multi-objective evolutionary algorithm for discovering peptide binding motifs. / Rajapakse, Menaka; Schmidt, Bertil; Brusic, Vladimir.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3907 LNCS 2006. p. 149-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3907 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Rajapakse, M, Schmidt, B & Brusic, V 2006, Multi-objective evolutionary algorithm for discovering peptide binding motifs. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3907 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3907 LNCS, pp. 149-158, EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, Budapest, Hungary, 4/10/06. https://doi.org/10.1007/11732242_14
Rajapakse M, Schmidt B, Brusic V. Multi-objective evolutionary algorithm for discovering peptide binding motifs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3907 LNCS. 2006. p. 149-158. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11732242_14
Rajapakse, Menaka ; Schmidt, Bertil ; Brusic, Vladimir. / Multi-objective evolutionary algorithm for discovering peptide binding motifs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3907 LNCS 2006. pp. 149-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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