Cost Function Networks to Solve Large Computational Protein Design Problems

Abstract

Proteins are chains of simple molecules called amino acids. The sequence of amino acids in the chain defines the three-dimensional shape of the protein and ultimately its biochemical function. Over millions of years, living organisms have evolved a large catalog of proteins. By exploring the space of possible amino acid sequences, protein engineering aims at similarly designing tailored proteins with specific desirable properties such as therapeutic properties in biomedical engineering for healthcare purposes. In computational protein design (CPD), the challenge of identifying a protein that performs a given task is defined as the combinatorial optimization of a complex energy function over amino acid sequences. First, we introduce the CPD problem and some of the main approaches that have been used by structural biologists to solve it. The CPD problem can be formulated as a cost function network (CFN). We present some of the most efficient techniques in CFN. Overall, the CFN approach shows the best efficiency on these problems, improving by several orders of magnitude against the previous exact CPD-dedicated approaches and also against integer programming approaches.