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dc.creatorRusso, A
dc.creatorScognamiglio, PL
dc.creatorEnriquez, RPH
dc.creatorSantambrogio, C
dc.creatorGrandori, R
dc.creatorMarasco, D
dc.creatorGiordano, A
dc.creatorScoles, G
dc.creatorFortuna, S
dc.date.accessioned2021-01-29T21:21:58Z
dc.date.available2021-01-29T21:21:58Z
dc.date.issued2015-08-07
dc.identifier.issn1932-6203
dc.identifier.issn1932-6203
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5193
dc.identifier.other26252476 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5211
dc.description.abstract© 2015 Russo et al. Short peptides can be designed in silico and synthesized through automated techniques, making them advantageous and versatile protein binders. A number of docking-based algorithms allow for a computational screening of peptides as binders. Here we developed ex-novo peptides targeting the maltose site of the Maltose Binding Protein, the prototypical system for the study of protein ligand recognition.We used a Monte Carlo based protocol, to computationally evolve a set of octapeptides starting from a polialanine sequence. We screened in silico the candidate peptides and characterized their binding abilities by surface plasmon resonance, fluorescence and electrospray ionization mass spectrometry assays. These experiments showed the designed binders to recognize their target with micromolar affinity. We finally discuss the obtained results in the light of further improvement in the exnovo optimization of peptide based binders. Copyright:
dc.format.extente0133571-e0133571
dc.language.isoen
dc.relation.haspartPLoS ONE
dc.relation.isreferencedbyPublic Library of Science (PLoS)
dc.rightsCC BY
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAlgorithms
dc.subjectAmino Acid Sequence
dc.subjectFluorescence
dc.subjectImmobilized Proteins
dc.subjectKinetics
dc.subjectLigands
dc.subjectMaltose-Binding Proteins
dc.subjectMolecular Docking Simulation
dc.subjectMolecular Sequence Data
dc.subjectMonte Carlo Method
dc.subjectPeptides
dc.subjectProtein Binding
dc.subjectSpectrometry, Mass, Electrospray Ionization
dc.subjectSurface Plasmon Resonance
dc.subjectThermodynamics
dc.subjectTryptophan
dc.titleIn silico generation of peptides by replica exchange monte carlo: Docking-based optimization of maltose-binding-protein ligands
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1371/journal.pone.0133571
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidGiordano, Antonio|0000-0002-5959-016X
dc.date.updated2021-01-29T21:21:54Z
refterms.dateFOA2021-01-29T21:21:59Z


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