A Bayesian-probability-based method for assigning protein backbone dihedral angles based on chemical shifts and local sequences |
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Authors: | Jun Wang Haiyan Liu |
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Institution: | (1) Hefei National Laboratory for Physical Sciences at the Microscale, and Key Laboratory of Structural Biology, School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, 230027, China |
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Abstract: | Chemical shifts contain substantial information about protein local conformations. We present a method to assign individual
protein backbone dihedral angles into specific regions on the Ramachandran map based on the amino acid sequences and the chemical
shifts of backbone atoms of tripeptide segments. The method uses a scoring function derived from the Bayesian probability
for the central residue of a query tripeptide segment to have a particular conformation. The Ramachandran map is partitioned
into representative regions at two levels of resolution. The lower resolution partitioning is equivalent to the conventional
definitions of different secondary structure regions on the map. At the higher resolution level, the α and β regions are further
divided into subregions. Predictions are attempted at both levels of resolution. We compared our method with TALOS using the
original TALOS database, and obtained comparable results. Although TALOS may produce the best results with currently available
databases which are much enlarged, the Bayesian-probability-based approach can provide a quantitative measure for the reliability
of predictions. |
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Keywords: | backbone dihedral angles Bayesian probability chemical shifts |
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