In silico generation of peptides by replica exchange monte carlo: Docking-based optimization of maltose-binding-protein ligands
dc.creator | Russo, A | |
dc.creator | Scognamiglio, PL | |
dc.creator | Enriquez, RPH | |
dc.creator | Santambrogio, C | |
dc.creator | Grandori, R | |
dc.creator | Marasco, D | |
dc.creator | Giordano, A | |
dc.creator | Scoles, G | |
dc.creator | Fortuna, S | |
dc.date.accessioned | 2021-01-29T21:21:58Z | |
dc.date.available | 2021-01-29T21:21:58Z | |
dc.date.issued | 2015-08-07 | |
dc.identifier.issn | 1932-6203 | |
dc.identifier.issn | 1932-6203 | |
dc.identifier.doi | http://dx.doi.org/10.34944/dspace/5193 | |
dc.identifier.other | 26252476 (pubmed) | |
dc.identifier.uri | http://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.extent | e0133571-e0133571 | |
dc.language.iso | en | |
dc.relation.haspart | PLoS ONE | |
dc.relation.isreferencedby | Public Library of Science (PLoS) | |
dc.rights | CC BY | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Algorithms | |
dc.subject | Amino Acid Sequence | |
dc.subject | Fluorescence | |
dc.subject | Immobilized Proteins | |
dc.subject | Kinetics | |
dc.subject | Ligands | |
dc.subject | Maltose-Binding Proteins | |
dc.subject | Molecular Docking Simulation | |
dc.subject | Molecular Sequence Data | |
dc.subject | Monte Carlo Method | |
dc.subject | Peptides | |
dc.subject | Protein Binding | |
dc.subject | Spectrometry, Mass, Electrospray Ionization | |
dc.subject | Surface Plasmon Resonance | |
dc.subject | Thermodynamics | |
dc.subject | Tryptophan | |
dc.title | In silico generation of peptides by replica exchange monte carlo: Docking-based optimization of maltose-binding-protein ligands | |
dc.type | Article | |
dc.type.genre | Journal Article | |
dc.relation.doi | 10.1371/journal.pone.0133571 | |
dc.ada.note | For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu | |
dc.creator.orcid | Giordano, Antonio|0000-0002-5959-016X | |
dc.date.updated | 2021-01-29T21:21:54Z | |
refterms.dateFOA | 2021-01-29T21:21:59Z |