The ellipsoid algorithm as a method for the determination of polypeptide conformations from experimental distance constraints and energy minimization

A new method for constrained nonlinear optimization known as the ellipsoid algorithm is evaluated as a means of determining and refining the conformations of peptides. Advantages of the ellipsoid algorithm over conventional optimization methods include that it avoids many local minima that other methods would be trapped by, and that it is sometimes able to find optimum solutions in which the constraints are satisfied exactly. The dihedral angles about single bonds were used as variables to keep the dimensionality low (the rate of convergence decreases rapidly with increasing dimensionality of the problem). The method is evaluated on problems involving distance constraints, and for minimization of conformational energy functions. In an initial application, conformations consistent with an experimental set of NMR distance constraints were obtained in a problem involving 48 variable dihedral angles.