New statistical potential for quality assessment of protein models and a survey of energy functions

The performances of a number of publicly available scoring functions are compared with a statistical rigor, with an emphasis on knowledge-based potentials. We explored the effect on accuracy of alternative choices for representing interaction center types and other features of scoring functions, such as using information on solvent accessibility, on torsion angles, accounting for secondary structure preferences and side chain orientation. Partially based on the observations made, we present a novel residue based statistical potential, which employs a shuffled reference state definition and takes into account the mutual orientation of residue side chains. Atom- and residue-level statistical potentials and Linux executables to calculate the energy of a given protein proposed in this work can be downloaded from http://www.fiserlab.org/potentials webcite.Among the most influential terms we observed a critical role of a proper reference state definition and the benefits of including information about the microenvironment of interaction centers. Molecular mechanical potentials were also tested and found to be over-sensitive to small local imperfections in a structure, requiring unfeasible long energy relaxation before energy scores started to correlate with model quality.Statistical potentials are widely used tools for protein structure analysis, modeling and quality assessment. Many different aspects and properties of these potentials have been explored during the last few decades including the different theoretical foundations to derive them, the representation of interaction centers and types of interactions, and the various models for defining the reference state. Combinations of various types and flavors of potentials are often used together in order to boost their performance. Initially, statistical potentials were based on statistical mechanics [1-3], however knowledge-based potentials now employ many other ideas, including the use of conditional probabilities to o