Computational Protein Design: trying an Answer Set Programming approach to solve the problem

Proteins are macromolecules made of a chain of amino-acids. The combinatorial nature of the space of possible protein conformations makes computer-aided protein study a major research field in bioinformatics. The problem of \textit{computational protein design} aims at finding the best protein conformation to perform a given task. This problem can be reduced to an optimization problem, looking for the minimum of an energy function depending on the amino-acid interactions in the protein. We have designed a model based on Answer Set Programming. The CPD problem may be easily modeled as an ASP program but a practical implementation able to work on real-sized instances has never been published. We have raised the main source of difficulty for current ASP solvers and ran a series of benchmarks highlighting the importance of finding a good upper bound estimation of the target minimum energy to reduce the amount of combinatorial search. Our solution clearly outperforms a direct ASP implementation without this estimation and has comparable performances with respect to SAT-based approaches. It remains less efficient that the recent approach by cost function networks of D. Allouche & al., showing there exists still some place for improving the optimization component in ASP with more dynamical strategies.