Atomic Four - Body Statistical Potential for Macromolecular Structure Analysis

Over recent years, exponential growth of the Protein Data Bank (PDB) has facilitated selection of larger, nonredundant subsets of experimentally solved macromolecular structures at higher resolutions, which in turn have provided the data used in developing more effective knowledge-based statistical potentials for improved structure prediction. In contrast to physics-based energy functions, statistical potentials generally perform better and are more computationally efficient at identifying the native structure as a global minimum. Distance-dependent statistical potentials often focus on pairwise atomic contacts within macromolecular structures; however, such energy functions fail to consider important higher-order contributions based on multibody interactions. In the present work, we develop an all-atom four-body statistical potential and illustrate its applicability with a constructive example.