CRANKITE: A fast polypeptide backbone conformation sampler

In contrast to other programs relying on local Metropolis moves in the space of dihedral angles, our sampler utilizes local crankshaft rotations of rigid peptide bonds in Cartesian space.The sampler allows fast simulation and analysis of secondary structure formation and conformational changes for proteins of average length.One important challenge of structural protein modeling is an efficient sampling technique for rapid search through the enormous conformational space [1]. Monte Carlo (MC) simulations, along with molecular dynamics, are among the most commonly used methods of sampling conformational space [2]. Because peptide bonds are rigid and flat, MC simulations are often performed in the space of dihedral ？-ψ angles, which reduces the number of degrees of freedom and speeds up simulations [3]. In addition, the conformations of just a few amino acids are perturbed locally on each step, leaving the rest of the chain intact, which increases the acceptance probability of the attempted moves and the efficiency of Metropolis MC procedure [4].As an alternative to simulations in dihedral space, we modeled rigid peptide bonds explicitly and used local crankshaft rotations in Cartesian coordinates to displace them. An important feature of our model is the elasticity of the alpha carbon valence geometry. With flexible alpha carbon valence angles, it becomes possible to use crankshaft moves inspired by earlier Metropolis MC studies of large-scale DNA properties [5,6]. Our polypeptide model features all-atom representations of the polypeptide backbone as well as beta-carbon atoms. Other side-chain atoms are omitted from consideration. We also draw on parallel tempering (replica exchange) [7] to speed up equilibration of the system when necessary. A detailed description of the model is provided in our earlier publication [8].In our model, the primary descriptors of the polypeptide chain conformation are the orientations of the peptide bonds in the laboratory frame (Fig. 1A