Constraint Based Protein Structure Prediction Exploiting Secondary Structure Information

The protein structure prediction problem is one of the most studied problems in Computational Biology. It can be reasonably abstracted as a minimization problem. The function to be minimized depends on the distances between the various amino-acids composing the protein and on their types. Even with strong approximations, the problem is shown to be computationally intractable. However, the solution of the problem for an arbitrary input size is not needed. Solutions for proteins of length 100–200 would give a strong contribution to Biotechnology. In this paper, we tackle the problem with constraint-based methods, using additional constraints and heuristics coming from the secondary structure of a protein that can be quickly predicted with acceptable approximation. Our prototypic implementation is written using constraints over finite domains in the Mozart programming system. It improves over any previous constraint-based approach and shows the power and flexibility of the method. Especially, it is well suited for further extensions.