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 pa

SUBMITTER: Huang YM 

PROVIDER: S-EPMC3919130 | biostudies-literature | 2013 Sep-Oct

REPOSITORIES: biostudies-literature