Lattices for ab initio protein structure prediction.

In the study of the protein folding problem with ab initio methods, the protein backbone can be built on some periodic lattices. Any vertex of these lattices can be occupied by a "ball," which can represent the mass center of an amino acid in a simplified coarse-grained model of the protein. The backbone, at a coarse-grained level, can be constituted of a No Reverse Self Avoiding Walk, which cannot intersect itself and cannot go back on itself. There is still much debate between those who use lattices to simplify the study of the protein folding problem and those preferring to work by using an off-lattice approach. Lattices can help to identify the protein tertiary structure in a computational less-expensive way, than off-lattice approaches that have to consider a potentially infinite number of possible structures. However, the use of a lattice, constituted of insufficiently accurate direction vectors, constrains the predictive ability of the model. The aim of this study is to perform a systematic screening of 7 known classic and 11 newly proposed lattices in terms of predictive power. The crystal structures of 42 different proteins (14 mainly alpha helical, 14 mainly beta sheet and 14 mixed structure proteins) were compared to the most accurate simulated models for each lattice. This strategy defines a scale of fitness for all the analyzed lattices and demonstrates that an increase in the coordination number and in the degrees of freedom is necessary but not sufficient to reach the best result. Instead, the introduction of a good set of direction vectors, as developed and tested in this study, strongly increases the lattice performance. (c) 2008 Wiley-Liss, Inc.