  This paper proposes a sign gradient descent (SGD) algorithm for predicting
the three-dimensional folded protein molecule structures under the kinetostatic
compliance method (KCM). In the KCM framework, which can be used to simulate
the range of motion of peptide-based nanorobots/nanomachines, protein molecules
are modeled as a large number of rigid nano-linkages that form a kinematic
mechanism under motion constraints imposed by chemical bonds while folding
under the kinetostatic effect of nonlinear interatomic force fields. In a
departure from the conventional successive kinetostatic fold compliance
framework, the proposed SGD-based iterative algorithm in this paper results in
convergence to the local minima of the free energy of protein molecules
corresponding to their final folded conformations in a faster and more robust
manner. KCMbased folding dynamics simulations of the backbone chains of protein
molecules demonstrate the effectiveness of the proposed algorithm.
