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A heuristic approach to predicting the tertiary structure of bovine somatotropin
Authors:L Carlacci  K C Chou  G M Maggiora
Institution:Computational Chemistry, Upjohn Laboratories, Kalamazoo, Michigan 49001.
Abstract:A combination of a heuristic approach and energy minimization was used to predict the three-dimensional structure of bovine somatotropin (bSt), also known as bovine growth hormone, a protein of 191 amino acids. The starting points for energy minimizations were generated from the following two types of inputs: (a) the amino acid sequence and (b) the heuristic inputs, which were derived according to physical, chemical, and biological principles by piecing together all useful information available. The predicted 3-D structure of the bSt molecule has all the features observed in four-helix bundle proteins. The four alpha-helices in bSt are intimately packed to form an assembly with an approximately square cross section. All the adjacent alpha-helices are antiparallel, with a somewhat tilted angle between each of the adjacent pairs so that the assembly of the four helices looks like a left-handed twisted bundle. There are two disulfide bonds in the bSt structure: one "hooking" the middle of a long loop with helix 4 so as to pull the long loop onto the surface of the helix bundle and the other "hooking" the C-terminal segment with the same helix so as to force the C-terminal segment to bend toward the helix bundle. As a consequence, a considerable part of the surface of the four-helix bundle is closely packed or intimately embraced by the loop segments. The predicted bSt structure has a hydrophobic core and a hydrophilic exterior surface. The energetic analysis of the predicted bSt structure indicates that the interaction between helices and loops plays a dominant role in stabilizing the four-helix bundle structure from the viewpoint of both electrostatic and nonbonded interactions. A technique called FOLD was meanwhile developed, by which one can fold a polypeptide chain into any shape as desired. This tool proved to be very useful during the heuristic model-building process.
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