Name
Abstract | ||
|---|---|---|
The determination of factors that influence protein conformational changes is very important for the identification of potentially amyloidogenic and disordered regions in polypeptide chains. In our work we introduce a new parameter, mean packing density, to detect both amyloidogenic and disordered regions in a protein sequence. It has been shown that regions with strong expected packing density are responsible for amyloid formation. Our predictions are consistent with known disease-related amyloidogenic regions for eight of 12 amyloid-forming proteins and peptides in which the positions of amyloidogenic regions have been revealed experimentally. Our findings support the concept that the mechanism of amyloid fibril formation is similar for different peptides and proteins. Moreover, we have demonstrated that regions with weak expected packing density are responsible for the appearance of disordered regions. Our method has been tested on datasets of globular proteins and long disordered protein segments, and it shows improved performance over other widely used methods. Thus, we demonstrate that the expected packing density is a useful value with which one can predict both intrinsically disordered and amyloidogenic regions of a protein based on sequence alone. Our results are important for understanding the structural characteristics of protein folding and misfolding. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1371/journal.pcbi.0020177 | PLOS COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
protein conformation,amyloid,protein folding,computational biology,amino acids,protein sequence | Protein folding,Protein sequencing,Biology,Globular protein,Amyloid,Amino acid,Bioinformatics,Fibril,Protein structure | Journal |
Volume | Issue | ISSN |
2 | 12 | 1553-7358 |
Citations | PageRank | References |
30 | 4.08 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oxana V. Galzitskaya | 1 | 125 | 20.15 |
| Sergiy O. Garbuzynskiy | 2 | 95 | 12.37 |
| Lobanov Michail Yurievich | 3 | 30 | 4.08 |