Simulating the folding of small proteins by use of the local minimum energy and the free solvation energy yields native-like structures.
Assuming that the protein primary sequence contains all information required to fold a protein into its native tertiary structure, we propose a new computational approach to protein folding by distributing the total energy of the macromolecular system along the torsional axes. We further derive a new semiempirical equation to calculate the total energy of a macromolecular system including its free energy of solvation. The energy of solvation makes an important contribution to the stability of biological structures. The segregation of hydrophilic and hydrophobic domains is essential for the formation of micelles, lipid bilayers, and biological membranes, and it is also important for protein folding. The free energy of solvation consists of two components: one derived from interactions between the atoms of the protein, and the second resulting from interactions between the protein and the solvent. The latter component is expressed as a function of the fractional area of protein atoms accessible to the solvent. The protein-folding procedure described in this article consists of two successive steps: a theoretical transition from an ideal alpha helix to an ideal beta sheet is first imposed on the protein conformation, in order to calculate an initial secondary structure. The most stable secondary structure is built from a combination of the lowest energy structures calculated for each amino acid during this transition. An angular molecular dynamics step is then applied to this secondary structure. In this computational step, the total energy of the system consisting of the sum of the torsional energy, the van der Waals energy, the electrostatic energy, and the solvation energy is minimized. This process yields 3-D structures of minimal total energy that are considered to be the most probable native-like structures for the protein. This method therefore requires no prior hypothesis about either the secondary or the tertiary structure of the protein and restricts the input of data to its sequence. The validity of the results is tested by comparing the crystalline and computed structures of four proteins, i.e., the avian and bovine pancreatic polypeptide (36 residues each), uteroglobin (70 residues), and the calcium-binding protein (75 residues); the C alpha-C alpha maps show significant homologies and the position of secondary structure domains; that of the alpha helices is particularly close.
Brasseur
R
R
Centre de Biophysique Moléculaire Numérique, Faculté Universitaire de Gembloux, Belgium.
D000818:Animals; D002135:Calcium-Binding Proteins / Q000737:chemistry; D002417:Cattle; D003196:Computer Graphics; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D053771:Glicentin; D005934:Glucagon / Q000737:chemistry; D004763:Glucagon-Like Peptides; D010446:Peptide Fragments / Q000737:chemistry; D011487:Protein Conformation; D017510:Protein Folding; D011498:Protein Precursors / Q000737:chemistry; D017433:Protein Structure, Secondary; D012995:Solubility; D012997:Solvents; D013816:Thermodynamics; D014598:Uteroglobin / Q000737:chemistry
D002135:Calcium-Binding Proteins; D010446:Peptide Fragments; D011498:Protein Precursors; D012997:Solvents; D004763:Glucagon-Like Peptides; D053771:Glicentin; C031026:glicentin-related pancreatic peptide; D005934:Glucagon; D014598:Uteroglobin