title: A position-specific distance-dependent statistical potential for protein structure and functional study.
abstract: Although studied extensively, designing highly accurate protein energy potential is still challenging. A lot of knowledge-based statistical potentials are derived from the inverse of the Boltzmann law and consist of two major components: observed atomic interacting probability and reference state. These potentials mainly distinguish themselves in the reference state and use a similar simple counting method to estimate the observed probability, which is usually assumed to correlate with only atom types. This article takes a rather different view on the observed probability and parameterizes it by the protein sequence profile context of the atoms and the radius of the gyration, in addition to atom types. Experiments confirm that our position-specific statistical potential outperforms currently the popular ones in several decoy discrimination tests. Our results imply that, in addition to reference state, the observed probability also makes energy potentials different and evolutionary information greatly boost performance of energy potentials.
authors.affiliation: Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.
authors.forename: Feng
authors.initials: F
authors.lastname: Zhao
authors.forename: Jinbo
authors.initials: J
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D016013:Likelihood Functions; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D012984:Software; D013816:Thermodynamics
chemical_list: D011506:Proteins