Name
Abstract | ||
|---|---|---|
Theoretical models of protein folding often make simplifying assumptions that allow analysis, yielding interesting theoretical results. In this article, we study models where folding dynamics is primarily driven by local topological features in an iterative manner. We illustrate the merit of the proposed approach through its ability to simulate realistic protein folding processes even when the sequence content information is reduced to just hydrophobic and polar. We then analyze our models and show that under our simple assumptions, certain structures are inherently unstable, and that determining whether structures can be stable is an NP-hard problem. Interestingly, we find that when our model has only two amino acids, the problem becomes solvable in polynomial time. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1089/cmb.2017.0034 | JOURNAL OF COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
complexity,HP model,inverse folding,minimalist model,off-lattice,protein folding | Protein folding,Stable states,Pure mathematics,Theoretical models,Artificial intelligence,Geometry,Inverse folding,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
24.0 | 9 | 1066-5277 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Dai | 1 | 0 | 0.34 |
| David Becerra | 2 | 0 | 0.68 |
| Jérôme Waldispühl | 3 | 111 | 16.24 |