ab_top_dk94

Fitting 1D predictions into 3D structures

Rost, Burkhard

In: Bohr,H. & Brunak,S. (eds.) "Protein folds. A distance-based approach", Boca Raton:CRC Press, 1995, pp. 132-151.

Abstract

The experimental determination of protein structure cannot keep track with the rapid generation of new sequence information. Can theory contribute? The most successful prediction method - and the only one for prediction of 3D structure - is homology modelling. It is applicable for about one quarter of the proteins. For the rest, the prediction task has to be simplified. An extreme simplification is to project 3D structure onto 1D strings of secondary structure or solvent accessibility. For these 1D aspects of 3D structure, prediction accuracy has been improved significantly by using evolutionary information as input to neural network systems. The gain in accuracy bases on the conservation of secondary structure and relative solvent accessibility within sequence families. Secondary structure and accessibility are conserved, as well, between remote homologues. This fact can be used by fitting 1D predictions into 3D structures to detect such remote homologues. In comparison to other threading approaches, 1D threading is rather flexible. However, two factors decrease detection accuracy. First, the loss of information by projecting 3D structure onto 1D strings (in particular the loss of distances between secondary structure segments). And second, the inaccuracy of predicting 1D structure. A preliminary result is that every fifth remote homologue is detected correctly.