Design and modeling of a protein based nanoGripper

Gaurav Sharma, Kaushal Rege, David Budil, Martin Yarmush, Constantinos Mavroidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The design hypothesis, architectures, and computational modeling of a novel peptide based nanoGripper are presented in this paper. We engineered the α-helical coiled coil portion of the yeast transcriptional activator peptide called GCN4 to obtain an environmentally-responsive nanoGripper. The dimeric coiled-coil peptide consists of two identical ∼4.5nm long and ~3nm wide polypeptide chains. The actuation mechanism depends on the modification of electrostatic charges along the peptide by varying the pH of the solution resulting in the reversible movement of helices and therefore, creating the motion of a gripper. Using molecular dynamics simulations we showed that pH changes led to a reversible opening of up-to 1.5nm which is approximately 150 % of the initial separation of the nanoGripper. We also investigated the forces generated by the nanoGripper upon pH actuation. Using a new method based on a modified steered molecular dynamics technique we were able to show that the force output of the nanoGripper is comparable to that generated by ATP-based molecular motors such as myosin and kinesin even though our molecular tweezer is smaller in size to these molecular motors.

Original languageEnglish (US)
Title of host publication2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
Pages476-481
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS - Nice, France
Duration: Sep 22 2008Sep 26 2008

Publication series

Name2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

Other

Other2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
Country/TerritoryFrance
CityNice
Period9/22/089/26/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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