  Predicting the final folded structure of protein molecules and simulating
their folding pathways is of crucial importance for designing viral drugs and
studying diseases such as Alzheimer's at the molecular level. To this end, this
paper investigates the problem of protein conformation prediction under the
constraint of avoiding high-entropy-loss routes during folding. Using the
well-established kinetostatic compliance (KCM)-based nonlinear dynamics of a
protein molecule, this paper formulates the protein conformation prediction as
a pointwise optimal control synthesis problem cast as a quadratic program (QP).
It is shown that the KCM torques in the protein folding literature can be
utilized for defining a reference vector field for the QP-based control
generation problem. The resulting kinetostatic control torque inputs will be
close to the KCM-based reference vector field and guaranteed to be constrained
by a predetermined bound; hence, high-entropy-loss routes during folding are
avoided while the energy of the molecule is decreased.
