Development of an ab initio Protein Structure Prediction System ABLE

Takashi Ishida[1] (tak@bi.a.u-tokyo.ac.jp)
Takeshi Nishimura[1],[2] (takeshi@bi.a.u-tokyo.ac.jp)
Makoto Nozaki[1] (no@bi.a.u-tokyo.ac.jp)
Tsuyoshi Inoue[1] (ino@bi.a.u-tokyo.ac.jp)
Tohru Terada[1] (tterada@bi.a.u-tokyo.ac.jp)
Shugo Nakamura[1] (shugo@bi.a.u-tokyo.ac.jp)
Kentaro Shimizu[1] (shimizu@bi.a.u-tokyo.ac.jp)

[1]Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
[2]Media Center, Faculty of Letters, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan


Abstract

An ab initio protein structure prediction system called ABLE is described. It is based on the fragment assembly method, which consists of two steps: dividing a target sequence into overlapping subsequences (fragments) of short length and assigning a local structure to each fragment; and generating models by assembling the local structures and selecting the models with low potential energy. One of the most important problems in conventional fragment assembly methods is the difficulty of selecting native-like structures by energy minimization only. ABLE thus employs a structural clustering method to select the native-like models from among the generated models. By applying the unit-vector root mean square distance (URMS) as a measure of structure similarity, we achieve more robust, effective structural clustering. When no enough clusters of good quality are obtained, ABLE runs the energy minimization procedure again by incorporating structural restraint conditions obtained from the consensus substructures in the previously generated models. This approach is based on our observation that there is a high probability that the consensus substructures of the generated models have native-like structures. Another feature of ABLE is that in assigning local structures to fragments, it assigns mainchain dihedral angles (φ, ψ) to the central residue of each fragment according to a probability distribution map built from candidate sequences similar to each fragment. This enables the system to generate appropriate local structures that may not already exist in a protein structure database. We applied our system to 25 small proteins and obtain near-native folds for more than half of them. We also demonstrate the performance of our structural clustering method, which can be applied to other protein structure prediction systems.

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Japanese Society for Bioinformatics