title: Expanded explorations into the optimization of an energy function for protein design.
abstract: Nature possesses a secret formula for the energy as a function of the structure of a protein. In protein design, approximations are made to both the structural representation of the molecule and to the form of the energy equation, such that the existence of a general energy function for proteins is by no means guaranteed. Here, we present new insights toward the application of machine learning to the problem of finding a general energy function for protein design. Machine learning requires the definition of an objective function, which carries with it the implied definition of success in protein design. We explored four functions, consisting of two functional forms, each with two criteria for success. Optimization was carried out by a Monte Carlo search through the space of all variable parameters. Cross-validation of the optimized energy function against a test set gave significantly different results depending on the choice of objective function, pointing to relative correctness of the built-in assumptions. Novel energy cross terms correct for the observed nonadditivity of energy terms and an imbalance in the distribution of predicted amino acids. This paper expands on the work presented at the 2012 ACM-BCB.
authors.affiliation: University of California, San Francisco, San Francisco.
authors.forename: Yao-Ming
authors.initials: YM
authors.lastname: Huang
authors.affiliation: Rensselaer Polytechnic Institute, Troy.
authors.forename: Christopher
authors.initials: C
authors.lastname: Bystroff
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D015195:Drug Design; D004735:Energy Transfer; D008956:Models, Chemical; D008958:Models, Molecular; D015202:Protein Engineering / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*; D013816:Thermodynamics
chemical_list: D011506:Proteins