Refinement of Protein Structure Predicted Models Using Minimum Spanning Tree
Bayumy A. Youssef.
The protein structure prediction is of three categories: homology modeling, fold recognition
and ab initio modeling, and this division into categories depends on whether similar protein structures
were previously determined using X-ray crystallography or NMR or not. Protein structure models
predicted by the free modeling (ab initio modeling) are considered as low-resolution models. Progress
has recently been made in refining low-resolution models (ab initio modeling) closer to the native
ones; this can be done by minimizing the energy funnel of physics-based force fields. In this paper, we
present a new refinement method based on applying minimum spanning tree to produce a connected
graph from the atoms forming the protein. This connected graph represents the minimum van der Waals energy path. The
new refinement method causes supplementary execution time (about 55.83486 sec. in the average for a protein sequence
of length 166 amino acids) but enhance the predicted model of low resolution. We used a small set of 18 different targets
got from CASP10. The results show the improvement in about (83.333%) of the cases. We compare our results with stateof-
art algorithms results.
Keywords: Ab initio modeling, minimum spanning tree, drug design, structure prediction.
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