Identifying interacting residues using Boolean Learning and Support Vector Machines: Case study on mRFP and DsRedproteins

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dc.contributor.authorLoo, Bernard L. W.ko
dc.contributor.authorDubey, Anshulko
dc.contributor.authorRealff, Matthew J.ko
dc.contributor.authorLee, Jay H.ko
dc.contributor.authorBommarius, Andreas S.ko
dc.identifier.citationBIOTECHNOLOGY JOURNAL, v.3, no.1, pp.63 - 73-
dc.description.abstractIn a protein, interactions exist between amino acid residues that influence the proteins structural integrity or stability and thus affect its catalytic function. The loss of this interaction due to mutations in these amino acids usually leads to a non-functional protein. Probing the sequence space of a protein through mutations or recombinations, as performed in directed evolution to search for an improved variant, frequently results in such inactive sequences. In this work, we demonstrate the use of machine learning to identify such interacting residues and the use of template engineering strategies to increase the fraction of active variants in a library. We show that using the sequences from recombination of monomeric red fluorescent protein (mRFP) and Discosoma red fluorescent protein (DsRed), we were able to identify a pair of interacting residues using an algorithm based on Boolean Learning and Support Vector Machines. The interaction between the identified residues was verified through point mutations on the mRFP and DsRed genes. We also show that it is possible to use such results to alter the parental genes such that the probability of disrupting the important interactions is minimized. This will result in a larger fraction of active variants in the recombinant library and allow us to access more functional space. We demonstrate this effect by comparing the recombinant library of wild-type (WT) DsRed, mRFP and an altered sequence of DsRed with mRFP WT genes. © 2008 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.-
dc.publisherWiley-VCH Verlag-
dc.titleIdentifying interacting residues using Boolean Learning and Support Vector Machines: Case study on mRFP and DsRedproteins-
dc.citation.publicationnameBIOTECHNOLOGY JOURNAL-
dc.contributor.localauthorLee, Jay H.-
dc.contributor.nonIdAuthorLoo, Bernard L. W.-
dc.contributor.nonIdAuthorDubey, Anshul-
dc.contributor.nonIdAuthorRealff, Matthew J.-
dc.contributor.nonIdAuthorBommarius, Andreas S.-
dc.subject.keywordAuthorBoolean Learning Support Vector Machines-
dc.subject.keywordAuthorFluorescent proteins-
dc.subject.keywordAuthorInteracting position-
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CBE-Journal Papers(저널논문)
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