Tertiary protein structure constraint methods

Approaches of de novo predictions, which try to calculate how the structural elements are folded into the 3D-stmcture (tertiary structure) of complete proteins are nowadays far away from reliable large-scale applications. On the other, hand this topic is under strong development indicated by recent successful results at the contest for structural prediction methods CASP4. With the fast growing number of experimentally solved 3D-stmctures of protein and new promising approaches like threading tools combined with experimental structural constraints, one can expect more reliable de novo predictions for 3D-protein structures in the future. 

The earliest neural network attempt for protein tertiary structure prediction was done by Bohr et al. (1990). They predicted the binary distance constraints for the C-a atoms in protein backbone using a standard three-layer back-propagation network and BIN20 sequence encoding method for 61-amino acid windows. The output layer had 33 units, three for the 3-state secondary structure prediction, and the remaining to measure the distance constraints between the central amino acid and the 30 preceding residues. 

As was the case with the solution structural studies on the mammalian proteins [154,219-222], there were no dipolar crmstraints between the two metal domains to permit their respective orientation. This was a limitation when working with MTs isolated from natural source at the time, which today could be overcome by the expression of recombinant MT with isotopic labeling and the use of residual dipolar coupling methods [227]. Another advancement in the 3D structural studies on this protein has arisen from the increased sensitivity of newer, higher field NMR instruments. NMR studies on MT at 800 MHz now provide a sufficient number of H- H NOE dipolar constraints to permit the calculation of the tertiary fold of the protein backbone in MT without the need for Cd substitution and the... 

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