Empirical Probability Functions Derived from Dihedral Angles for Protein Structure Prediction

The development and evaluation of functions for protein energetics is an important part of current research aiming at understanding protein structures and functions. Knowledgebase mean force potentials are derived from statistical analysis of interacting groups in experimentally determined protein structures. Current knowledge-based mean force potentials are based on the inverse Boltzmannpsilas law, which calculate the ratio of the observed probability with respect to the probability of the reference state. In this study, a general probability framework is presented with the aim to develop novel energy scores. A class of empirical probability functions is derived by decomposing the joint probability of backbone dihedral angles and amino acid sequences. The neighboring interactions are modeled by conditional probabilities. Such probability functions are based on the strict probability theory and some suitable suppositions for convenience of computation. Experiments are performed on several well-constructed decoy sets and the results show that the empirical probability functions presented here outperform previous statistical potentials based on dihedral angles. Such probability functions will be helpful for protein structure prediction,model quality evaluation, transcription factors identification and other challenging problems in computational biology.