A reduced representation model, which has been described in previous reports, was used to predict the folded structures of proteins from their primary sequences and random starting conformations. The molecular structure of each protein has been reduced to its backbone atoms (with ideal fixed bond lengths and valence angles) and each side chain approximated by a single virtual united-atom. The coordinate variables were the backbone dihedral angles I$ and 4. A statistical potential function, which included local and nonlocal interactions and was computed from known protein structures, was used in the structure minimization. A novel approach, employing the concepts of genetic algorithms, has been developed to simultaneously optimize a population of conformations. With the information of primary sequence and the radius of gyration of the crystal structure only, and starting from randomly generated initial conformations, I have been able to fold melittin, a protein of 26 residues, with high computational convergence. The computed structures have a root mean square error of 1.66 A (distance matrix error = 0.99 A) on average to the crystal structure. Similar results for avian pancreatic polypeptide inhibitor, a protein of 36 residues, are obtained. Application of the method to apamin, an 18-residue polypeptide with two disulfide bonds, shows that it folds apamin to native-like conformations with the correct disulfide bonds formed.Keywords: conformation population; conformation prediction; genetic algorithms; reduced representation; statistical potentialThe basic question of whether it is possible to compute the native conformation of a protein according to certain physical principles with the known physicochemical properties of its constituent amino acids has not yet been answered. Enough evidence has been collected that one can conclude that the primary sequence of a protein determines its structure and function. Furthermore, it has been demonstrated experimentally that the thermodynamic hypothesis that the native structure of a protein is at its global minimum (or deep local minima) of the thermodynamic potential (free energy) of the protein (Anfinsen, 1973) is a valid principle that governs the protein conformational search in both the small perturbation from the The computational difficulties that have hindered progress in protein structure prediction and protein folding come from two related aspects of the problem: first, proteins are highly heterogeneous and have many degrees of freedom, and second, the number of minima in the free energy landscape of the system depends exponentially on the total number of degrees of freedom of the system. Molecular mechanics and molecular dynamics (McCammon & Harvey, 1987; Brooks et al., 1988) with full atomic empirical potential functions have been successfully used in protein studies such as crystallographic structure refinement, normal mode analysis, and free energy simulation, in which the conformations of proteins are near their native states. However, these method...
We present two methods for designing amino acid sequences of proteins that will fold to have good hydrophobic cores. Given the coordinates of the desired target protein or polymer structure, the methods generate sequences of hydrophobic (H) and polar (P) monomers that are intended to fold to these structures. One method designs hydrophobic inside, polar outside; the other minimizes an energy function in a sequence evolution process. The sequences generated by these methods agree at the level of 60-80% of the sequence positions in 20 proteins in the Protein Data Bank. A major challenge in protein design is to create sequences that can fold uniquely, i.e. to a single conformation rather than to many. While an earlier lattice-based sequence evolution method was shown not to design unique folders, our method generates unique folders in lattice model tests. These methods may also be useful in designing other types of foldable polymer not based on amino acids.
We describe an algorithm to predict tertiary structures of small proteins. In contrast to most current folding algorithms, it uses very few energy parameters. Given the secondary structural elements in the sequence--alpha-helices and beta-strands--the algorithm searches the remaining conformational space of a simplified real-space representation of chains to find a minimum energy of an exceedingly simple potential function. The potential is based only on a single type of favorable interaction between hydrophobic residues, an unfavorable excluded volume term of spatial overlaps and, for sheet proteins, an interstrand hydrogen bond interaction. Where appropriate, the known disulfide bonds are constrained by a square-law potential. Conformations are searched by a genetic algorithm. The model predicts reasonably well the known tertiary folds of seven out of the 10 small proteins we consider. We draw two conclusions. First, for the proteins we tested, this exceedingly simple potential function is no worse than others having hundreds of energy parameters in finding the right general tertiary structures. Second, despite its simplicity, the potential function is not the weak link in this algorithm. Differences between our predicted structures and the correct targets can be ascribed to shortcomings in our search strategy. This potential function may be useful for testing other conformational search strategies.
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