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Title:In silico generation of peptides by replica exchange Monte Carlo: Docking-based optimization of maltose-binding-protein ligands
Authors:ID Russo, Anna (Author)
ID Scognamiglio, Pasqualina Liana (Author)
ID Hong Enriquez, Rolando Pablo (Author)
ID Santambrogio, Carlo (Author)
ID Grandori, Rita (Author)
ID Marasco, Daniela (Author)
ID Giordano, Antonio (Author)
ID Scoles, Giacinto (Author)
ID Fortuna, Sara (Author)
Files:.pdf 11_Russo_MBP.pdf (4,27 MB)
MD5: 885643A85EBD46544C3538AAAB074FB6
 
Language:English
Work type:Not categorized
Typology:1.01 - Original Scientific Article
Organization:UNG - University of Nova Gorica
Abstract: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 ex-novo optimization of peptide based binders.
Keywords:peptides, docking, optimisation, computation, maltose binding protein, probe, ligand
Year of publishing:2015
Number of pages:16
Numbering:10, 8
PID:20.500.12556/RUNG-2691 New window
COBISS.SI-ID:4536059 New window
DOI:10.1371/journal.pone.0133571 New window
NUK URN:URN:SI:UNG:REP:Z4VVUB3F
Publication date in RUNG:12.10.2016
Views:4084
Downloads:140
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Record is a part of a journal

Title:PLoS ONE
Publisher:Public Library of Science
Year of publishing:2015
ISSN:1932-6203

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:10.10.2016

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