TY - GEN
T1 - Empirical probability functions derived from dihedral angles for protein structure prediction
AU - Dong, Qiwen
AU - Geng, Xin
AU - Zhou, Shuigeng
AU - Guan, Jihong
PY - 2009
Y1 - 2009
N2 - The development and evaluation of functions for protein energetics is an important part of current research aiming at understanding protein structures and functions. Knowledge-based 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 Boltzmann's 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.
AB - The development and evaluation of functions for protein energetics is an important part of current research aiming at understanding protein structures and functions. Knowledge-based 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 Boltzmann's 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.
KW - Conditional probability
KW - Joint probability
KW - Knowledge-based potential
KW - Statistical potential
UR - https://www.scopus.com/pages/publications/70449440582
U2 - 10.1109/BIBE.2009.55
DO - 10.1109/BIBE.2009.55
M3 - 会议稿件
AN - SCOPUS:70449440582
SN - 9780769536569
T3 - Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
SP - 146
EP - 152
BT - Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
T2 - 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
Y2 - 22 June 2009 through 24 June 2009
ER -