Predicting Protein Conformational Transitions by Trajectory Planning Through Torsion Angle Propensity Maps

Abstract

The function of a protein macromolecule often requires conformational transitions between two native configurations. Understanding these transitions is essential to the understanding of how proteins function, as well as to the ability to design and manipulate proteinbased nano-mechanical systems. It is widely accepted that the pathway connecting two native protein conformations in nature should satisfy a minimum energy criterion. The premise of this paper is that such a pathway can be found by using dihedral angle combinations that have been shown to have a high probability of occurrence in naturally observed proteins. In order to quantify this probability, we are proposing statistical propensity torque maps for tuples of dihedral angles. These maps are constructed in the angle space, similar to the Ramachandran Charts, but are based on data obtained from more than 38,600 proteins from Protein Data Bank (PDB) so that each map contains the experimentally observed pairs of dihedral angles (ϕ _i, ψ _i), (ϕ _i, ψ _i + 1), (ϕ _i, ϕ _i + 1) and (ψ _i, ψ _i + 1).