Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 14, Issue 3, Pages 578-586Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2016.2543721
Keywords
Cryo-electron microscopy; dynamic programming; graph; image; protein; secondary structure
Categories
Funding
- NSF [DBI-1356621]
- NIH [R01-GM062968]
- Modeling & Simulation grant from Old Dominion University
- FP3 fund from Old Dominion University
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1356621] Funding Source: National Science Foundation
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A key idea in de novo modeling of a medium-resolution density image obtained from cryo-electron microscopy is to compute the optimal mapping between the secondary structure traces observed in the density image and those predicted on the protein sequence. When secondary structures are not determined precisely, either from the image or from the amino acid sequence of the protein, the computational problem becomes more complex. We present an efficient method that addresses the secondary structure placement problem in presence of multiple secondary structure predictions and computes the optimal mapping. We tested the method using 12 simulated images from alpha-proteins and two Cryo-EM images of alpha-beta proteins. We observed that the rank of the true topologies is consistently improved by using multiple secondary structure predictions instead of a single prediction. The results show that the algorithm is robust and works well even when errors/ misses in the predicted secondary structures are present in the image or the sequence. The results also show that the algorithm is efficient and is able to handle proteins with as many as 33 helices.
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