| Title |
Topology independent protein structural alignment
|
|---|---|
| Published in |
BMC Bioinformatics, October 2007
|
| DOI | 10.1186/1471-2105-8-388 |
| Pubmed ID | |
| Authors |
Joe Dundas, TA Binkowski, Bhaskar DasGupta, Jie Liang |
| Abstract |
Identifying structurally similar proteins with different chain topologies can aid studies in homology modeling, protein folding, protein design, and protein evolution. These include circular permuted protein structures, and the more general cases of non-cyclic permutations between similar structures, which are related by non-topological rearrangement beyond circular permutation. We present a method based on an approximation algorithm that finds sequence-order independent structural alignments that are close to optimal. We formulate the structural alignment problem as a special case of the maximum-weight independent set problem, and solve this computationally intensive problem approximately by iteratively solving relaxations of a corresponding integer programming problem. The resulting structural alignment is sequence order independent. Our method is also insensitive to insertions, deletions, and gaps. |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 6% |
| United States | 2 | 6% |
| Mexico | 1 | 3% |
| Unknown | 29 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 15% |
| Researcher | 5 | 15% |
| Professor | 4 | 12% |
| Student > Bachelor | 4 | 12% |
| Professor > Associate Professor | 4 | 12% |
| Other | 10 | 29% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 26% |
| Biochemistry, Genetics and Molecular Biology | 8 | 24% |
| Computer Science | 7 | 21% |
| Engineering | 3 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 4 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 January 2008.
All research outputs
#15,240,835
of 22,660,862 outputs
Outputs from BMC Bioinformatics
#5,353
of 7,241 outputs
Outputs of similar age
#61,789
of 72,330 outputs
Outputs of similar age from BMC Bioinformatics
#39
of 51 outputs
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