Name
Abstract | ||
|---|---|---|
Motivation: Amyloidogenic regions in polypeptide chains are very important because such regions are responsible for amyloid formation and aggregation. It is useful to be able to predict positions of amyloidogenic regions in protein chains. Results: Two characteristics (expected probability of hydrogen bonds formation and expected packing density of residues) have been introduced by us to detect amyloidogenic regions in a protein sequence. We demonstrate that regions with high expected probability of the formation of backbone-backbone hydrogen bonds as well as regions with high expected packing density are mostly responsible for the formation of amyloid fibrils. Our method (FoldAmyloid) has been tested on a dataset of 407 peptides (144 amyloidogenic and 263 non-amyloidogenic peptides) and has shown good performance in predicting a peptide status: amyloidogenic or non-amyloidogenic. The prediction based on the expected packing density classified correctly 75% of amyloidogenic peptides and 74% of non-amyloidogenic ones. Two variants (averaging by donors and by acceptors) of prediction based on the probability of formation of backbone-backbone hydrogen bonds gave a comparable efficiency. With a hybrid-scale constructed by merging the above three scales, our method is correct for 80% of amyloidogenic peptides and for 72% of non-amyloidogenic ones. Prediction of amyloidogenic regions in proteins where positions of amyloidogenic regions are known from experimental data has also been done. In the proteins, our method correctly finds 10 out of 11 amyloidogenic regions. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1093/bioinformatics/btp691 | BIOINFORMATICS |
Keywords | Field | DocType |
protein sequence | Protein sequencing,Computer science,Amyloid,Peptide,Bioinformatics,Fibril,Hydrogen bond,Merge (version control) | Journal |
Volume | Issue | ISSN |
26 | 3 | 1367-4803 |
Citations | PageRank | References |
14 | 1.07 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergiy O. Garbuzynskiy | 1 | 95 | 12.37 |
| Michail Yu. Lobanov | 2 | 79 | 10.18 |
| Oxana V. Galzitskaya | 3 | 125 | 20.15 |