Design of a Protein Potential Energy Landscape by Parameter Optimization

TL;DR

This paper introduces an automated parameter optimization protocol for protein energy functions, improving the native-like conformation energy landscape and demonstrating transferability across different proteins.

Contribution

The authors develop a novel automated protocol for optimizing protein energy function parameters, enhancing native conformation accuracy and transferability.

Findings

Optimized parameters better distinguish native-like from non-native conformations.

Protocol outperforms previous methods by considering degrees of native-likeness.

Transferability of parameters demonstrated through jackknife tests on new proteins.

Abstract

We propose an automated protocol for designing the energy landscape of a protein energy function by optimizing its parameters. The parameters are optimized so that not only the global minimum energy conformation becomes native-like, but also the conformations distinct from the native structure have higher energies than those close to the native one. We successfully apply our protocol to the parameter optimization of the UNRES potential energy, using the training set of betanova, 1fsd, the 36-residue subdomain of chicken villin headpiece (PDB ID 1vii), and the 10-55 residue fragment of staphylococcal protein A (PDB ID 1bdd). The new protocol of the parameter optimization shows better performance than earlier methods where only the difference between the lowest energies of native-like and non-native conformations was adjusted without considering various degrees of native-likeness of the conformations. We also perform jackknife tests on other proteins not included in the training set and obtain promising results. The results suggest that the parameters we obtained using the training set of the four proteins are transferable to other proteins to some extent.