Name
Abstract | ||
|---|---|---|
An important unsolved problem in structural bioinformatics is that of protein structure prediction (PSP), the reconstruction of a biologically plausible three-dimensional structure for a given protein given only its amino acid sequence. The PSP problem is of enormous interest, because the function of proteins is a direct consequence of their three-dimensional structure. Approaches to solve the PSP use protein models that range from very realistic (all-atom) to very simple (on a lattice). Finer representations usually generate better candidate structures, but are computationally more costly than the simpler on-lattice ones. In this work we propose a combined approach that makes use of a simple and fast lattice protein structure prediction algorithm, REMC-HPPFP, to compute a number of coarse candidate structures. These are later refined by 3Distill, an off-lattice, residue-level protein structure predictor. We prove that the lattice algorithm is able to bootstrap 3Distill, which consequently converges much faster, allowing for shorter execution times without noticeably degrading the quality of the predictions. This novel method allows us to generate a large set of decoys of quality comparable to those computed by the off-lattice method alone, but using a fraction of the computations. As a result, our method could be used to build large databases of predicted decoys for analysis, or for selecting the best candidate structures through reranking techniques. Furthermore our method is generic, in that it can be applied to other algorithms than 3Distill. |
Year | Venue | Keywords |
|---|---|---|
2010 | PRIB | protein structure prediction,fast lattice protein structure,psp use protein model,novel method,coarse candidate structure,better candidate structure,contact map,residue-level protein structure predictor,biologically plausible three-dimensional structure,best candidate structure,three-dimensional structure,lattice approach,structural bioinformatics,amino acid sequence,simulated annealing,protein structure |
Field | DocType | Volume |
Global distance test,Simulated annealing,Structural bioinformatics,Protein structure prediction,Lattice (order),Computer science,Lattice protein,Artificial intelligence,Bioinformatics,Machine learning,Protein structure,Computation | Conference | 6282 |
ISSN | ISBN | Citations |
0302-9743 | 3-642-16000-X | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| stefano teso | 1 | 38 | 14.21 |
| Cristina Di Risio | 2 | 0 | 0.34 |
| Andrea Passerini | 3 | 569 | 46.88 |
| Roberto Battiti | 4 | 1937 | 262.40 |