title: Structure prediction for the helical skeletons detected from the low resolution protein density map.
abstract: BACKGROUND
  The current advances in electron cryo-microscopy technique have made it possible to obtain protein density maps at about 6-10 A resolution. Although it is hard to derive the protein chain directly from such a low resolution map, the location of the secondary structures such as helices and strands can be computationally detected. It has been demonstrated that such low-resolution map can be used during the protein structure prediction process to enhance the structure prediction.
  
  
  RESULTS
  We have developed an approach to predict the 3-dimensional structure for the helical skeletons that can be detected from the low resolution protein density map. This approach does not require the construction of the entire chain and distinguishes the structures based on the conformation of the helices. A test with 35 low resolution density maps shows that the highest ranked structure with the correct topology can be found within the top 1% of the list ranked by the effective energy formed by the helices.
  
  
  CONCLUSIONS
  The results in this paper suggest that it is possible to eliminate the great majority of the bad conformations of the helices even without the construction of the entire chain of the protein. For many proteins, the effective contact energy formed by the secondary structures alone can distinguish a small set of likely structures from the pool.
authors.affiliation: Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA. kal@cs.odu.edu
authors.forename: Kamal
authors.initials: K
authors.lastname: Al Nasr
authors.forename: Weitao
authors.initials: W
authors.lastname: Sun
authors.forename: Jing
authors.initials: J
authors.lastname: He
mesh_terms: D020285:Cryoelectron Microscopy / Q000379:methods; D030562:Databases, Protein; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins