A combinatorial distance-constraint approach to predicting protein tertiary models from known secondary structure

Abstract

Distance geometry methods allow protein structures to be constructed using a large number of distance constraints, which can be elucidated by experimental techniques such as NMR. New methods for gleaning tertiary structural information from multiple sequence alignments make it possible for distance constraints to be predicted from sequence information alone. The basic distance geometry method can thus be applied using these empirically derived distance constraints. Such an approach, which incorporates a novel combinatoric procedure, is reported here. Given the correct sheet topology and disulfide formations, the fully automated procedure is generally able to construct native-like Calpha models for eight small beta-protein structures. When the sheet topology was unknown but disulfide connectivities were included, all sheet topologies were explored by the combinatorial procedure. Using a simple geometric evaluation scheme, models with the correct sheet topology were ranked first in four of the eight example cases, second in three examples and third in one example. If neither the sheet topology nor the disulfide connectivities were given a priori, all combinations of sheet topologies and disulfides were explored by the combinatorial procedure. The evaluation scheme ranked the correct topology within the top five folds for half the example cases. The combinatorial procedure is a useful technique for identifying a limited number of low-resolution candidate folds for small, disulfide-rich, beta-protein structures. Better results are obtained, however, if correct disulfide connectivities are known in advance. Combinatorial distance constraints can be applied whenever there are a sufficiently small number of finite connectivities.