Pareto optimization in computational protein design with multiple objectives.

The optimization for function in computational design requires the treatment of, often competing, multiple objectives. Current algorithms reduce the problem to a single objective optimization problem, with the consequent loss of relevant solutions. We present a procedure, based on a variant of a Pareto algorithm, to optimize various competing objectives in protein design that allows reducing in several orders of magnitude the search of the solution space. Our methodology maintains the diversity of solutions and provides an iterative way to incorporate automatic design methods in the design of functional proteins. We have applied our systematic procedure to design enzymes optimized for both catalysis and stability. However, this methodology can be applied to any computational chemistry application requiring multi-objective combinatorial optimization techniques.