Systematic Comparison of Amber and Rosetta Energy Functions for Protein Structure Evaluation

An
accurate energy function is an essential component of biomolecular
structural modeling and design. The comparison of differently derived
energy functions enables analysis of the strengths and weaknesses
of each energy function and provides independent benchmarks for evaluating
improvements within a given energy function. We compared the molecular
mechanics Amber empirical energy function to two versions of the Rosetta
energy function (talaris2014 and REF2015) in decoy discrimination
and loop modeling tests. In decoy discrimination tests, both Rosetta
and Amber (ff14SBonlySC) energy functions performed well in scoring
the native state as the lowest energy conformation in many cases,
but several false minima were found in with both talaris2014 and Amber
ff14SBonlySC scoring functions. The current default version of the
Rosetta energy function, REF2015, which is parametrized on both small
molecule and macromolecular benchmark sets to improve decoy discrimination,
performs significantly better than talaris2014, highlighting the improvements
made to the Rosetta scoring approach. There are no cases in Rosetta
REF2015, and 8/140 cases in Amber, where a false minimum is found
that is absent in the alternative landscape. In loop modeling tests,
Amber ff14SBonlySC and REF2015 perform equivalently, although false
minima are detected in several cases for both. The balance between
dihedral, electrostatic, solvation and hydrogen bonding scores contribute
to the existence of false minima. To take advantage of the semi-orthogonal
nature of the Rosetta and Amber energy functions, we developed a technique
that combines Amber and Rosetta conformational rankings to predict
the most near-native model for a given protein. This algorithm improves
upon predictions from either energy function in isolation and should
aid in model selection for structure evaluation and loop modeling
tasks.