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Cooperativity among short amyloid stretches in long amyloidogenic sequences.


ABSTRACT: Amyloid fibrillar aggregates of polypeptides are associated with many neurodegenerative diseases. Short peptide segments in protein sequences may trigger aggregation. Identifying these stretches and examining their behavior in longer protein segments is critical for understanding these diseases and obtaining potential therapies. In this study, we combined machine learning and structure-based energy evaluation to examine and predict amyloidogenic segments. Our feature selection method discovered that windows consisting of long amino acid segments of ~30 residues, instead of the commonly used short hexapeptides, provided the highest accuracy. Weighted contributions of an amino acid at each position in a 27 residue window revealed three cooperative regions of short stretch, resemble the ?-strand-turn-?-strand motif in A-?peptide amyloid and ?-solenoid structure of HET-s(218-289) prion (C). Using an in-house energy evaluation algorithm, the interaction energy between two short stretches in long segment is computed and incorporated as an additional feature. The algorithm successfully predicted and classified amyloid segments with an overall accuracy of 75%. Our study revealed that genome-wide amyloid segments are not only dependent on short high propensity stretches, but also on nearby residues.

SUBMITTER: Hu L 

PROVIDER: S-EPMC3382238 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Cooperativity among short amyloid stretches in long amyloidogenic sequences.

Hu Lele L   Cui Weiren W   He Zhisong Z   Shi Xiaohe X   Feng Kaiyan K   Ma Buyong B   Cai Yu-Dong YD  

PloS one 20120622 6


Amyloid fibrillar aggregates of polypeptides are associated with many neurodegenerative diseases. Short peptide segments in protein sequences may trigger aggregation. Identifying these stretches and examining their behavior in longer protein segments is critical for understanding these diseases and obtaining potential therapies. In this study, we combined machine learning and structure-based energy evaluation to examine and predict amyloidogenic segments. Our feature selection method discovered  ...[more]

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