title: Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential.
abstract: BACKGROUND
  Experimental evidence suggests that certain short protein segments have stronger amyloidogenic propensities than others. Identification of the fibril-forming segments of proteins is crucial for understanding diseases associated with protein misfolding and for finding favorable targets for therapeutic strategies.
  
  
  RESULTS
  In this study, we used the microcrystal structure of the NNQQNY peptide from yeast prion protein and residue-based statistical potentials to establish an algorithm to identify the amyloid fibril-forming segment of proteins. Using the same sets of sequences, a comparable prediction performance was obtained from this study to that from 3D profile method based on the physical atomic-level potential ROSETTADESIGN. The predicted results are consistent with experiments for several representative proteins associated with amyloidosis, and also agree with the idea that peptides that can form fibrils may have strong sequence signatures. Application of the residue-based statistical potentials is computationally more efficient than using atomic-level potentials and can be applied in whole proteome analysis to investigate the evolutionary pressure effect or forecast other latent diseases related to amyloid deposits.
  
  
  BACKGROUND
  The fibril prediction program is available at ftp://mdl.ipc.pku.edu.cn/pub/software/pre-amyl/.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
authors.forename: Zhuqing
authors.initials: Z
authors.lastname: Zhang
authors.forename: Hao
authors.initials: H
authors.lastname: Chen
authors.forename: Luhua
authors.initials: L
authors.lastname: Lai
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D000682:Amyloid / Q000737:chemistry* / Q000648:ultrastructure*; D003627:Data Interpretation, Statistical; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D008969:Molecular Sequence Data; D011487:Protein Conformation; D017434:Protein Structure, Tertiary; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
chemical_list: D000596:Amino Acids; D000682:Amyloid