One-dimensional short-range interaction models for specific-sequence copolymers of amino acids have been developed in this series of papers. In this paper, a general method for predicting protein conformation (that is based on a one-dimensional short-range interaction model, and eliminates the need for the empirical rules introduced in papers III and IV) is described. The present method involves the use of conformational (or conformational-sequence) probabilities of higher order than the first- or second-order probabilities used in papers IV and V, i.e., it treats a sequence of any number of residues; it thus alters the predictive methods that involved empirical rules in papers III and IV, and low-order (first- or second-order) probabilities in papers IV and V. The general method is applied here to the prediction of the backbone conformations of proteins, using the three-state model [helical (h), extended (epilson), and other coil (c) states] proposed in the theoretical formulation of paper II. The statistical weights in the three-state model are evaluated from the atomic coordinates of the x-ray structures of 26 proteins. The conformational-sequence probabilities (taken for three consecutive residues for numerical computation in this paper) are calculated for all possible triads (i.e., for all possible combinations of the three states, h, epilson, and c for each residue) for bovine pancreatic trypsin inhibitor and clostridial flavodoxin, in order to select the most probable conformations of these proteins. The predicted results for these proteins are compared to those predicted in paper III and to those observed experimentally. The method is applied further to the prediction of the backbone structures of homologous neurotoxin proteins whose amino acid sequences are known but whose x-ray structures are not. The effects of variation in the amino acid sequence on the conformations of the backbones are discussed from the point of view of the homologies in the amino acid sequences of 19 neurotoxins. Application of the present general predictive method to a four- and a multistate model is also described.