In Silico Generation of Peptides by Replica Exchange Monte Carlo: Docking-Based Optimization of Maltose-Binding-Protein Ligands

PLoS One. 2015 Aug 7;10(8):e0133571. doi: 10.1371/journal.pone.0133571. eCollection 2015.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amino Acid Sequence
  • Fluorescence
  • Immobilized Proteins / metabolism
  • Kinetics
  • Ligands
  • Maltose-Binding Proteins / chemistry
  • Maltose-Binding Proteins / metabolism*
  • Molecular Docking Simulation*
  • Molecular Sequence Data
  • Monte Carlo Method*
  • Peptides / chemistry
  • Peptides / metabolism*
  • Protein Binding
  • Spectrometry, Mass, Electrospray Ionization
  • Surface Plasmon Resonance
  • Thermodynamics
  • Tryptophan / metabolism

Substances

  • Immobilized Proteins
  • Ligands
  • Maltose-Binding Proteins
  • Peptides
  • Tryptophan

Grants and funding

This work has been funded by “AIRC 5 per mille Special program 2011” (www.airc.it) with the project “Application of advanced Nanotechnology in the development of Cancer Diagnostics tools”. Further, we would like to gratefully aknowledge partial finacial support by the ERC Ideas Program (http://erc.europa.eu/) through a senior grant to GS entitled: MOlecular NAnotechnology for LIfe Science Applications: QUantitative Interactomics for Diagnostics, PROteomics and QUantitative Oncology (MONALISA QUIDPROQUO) Grant agreement no.: 269051 (2011–2016). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.