fRMSDPred: Predicting local rmsd between structural fragments using sequence information
Huzefa Rangwala and George Karypis |
Computational Systems Biology (CSB), pp. 311-322, 2007 |
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Abstract The effectiveness of comparative modeling approaches for protein structure prediction can be substantially improved by incorporating predicted structural information in the initial sequence-structure alignment. Motivated by the approaches used to align protein structures, this paper focuses on developing machine learning approaches for estimating the RMSD value of a pair of protein fragments. These estimated fragment-level RMSD values can be used to construct the alignment, assess the quality of an alignment, and identify high-quality alignment segments.
We present algorithms to solve this fragment-level RMSD prediction problem using a |
Research topics: Bioinformatics | Classification | Data mining | Protein Structure |