This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Madden, C. Articles by Ilies, H. T. Search for Related Content Social Bookmarking                         What's this?

Residue Level Three-dimensional Workspace Maps for Conformational Trajectory Planning of Proteins

Department of Mechanical Engineering, University of Connecticut, USA

Peter Bohnenkamp

Department of Mechanical Engineering, University of Connecticut, USA, peytah{at}gmail.com

Kazem Kazerounian

Department of Mechanical Engineering, University of Connecticut, USA, kazem{at}engr.uconn.edu

Horea T. Ilie

Department of Mechanical Engineering, University of Connecticut, USA, ilies{at}engr.uconn.edu

The function of a protein macromolecule often requires conformational transitions between two native conformations. Understanding these transitions is essential to the understanding of how proteins function, as well as to the ability to design and manipulate protein-based nanomechanical systems. In this paper we propose a set of 3D Cartesian workspace maps for exploring protein pathways. These 3D maps are constructed in the Euclidean space for triads of chain segments of protein molecules that have been shown to have a high probability of occurrence in naturally observed proteins based on data obtained from more than 38,600 proteins from the Protein Data Bank (PDB). We show that the proposed 3D propensity maps are more effective navigation tools than the propensity maps constructed in the angle space. We argue that the main reason for this improved efficiency is the fact that, although there is a one-to-one mapping between the 2D dihedral angle maps and the 3D Cartesian maps, the propensity distributions are significantly different in the two spaces. Hence, the 3D maps allow the pathway planning to be performed directly in the 3D Euclidean space based on propensities computed in the same space, which is where protein molecules change their conformations.

Key Words: protein • kinematic pathway • trajectory planning • conformation • transition • energy landscape.

The International Journal of Robotics Research, Vol. 28, No. 4, 450-463 (2009)
DOI: 10.1177/0278364908098092


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?