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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Predicting HLA Class I Non-Permissive Amino Acid Residues Substitutions
|
|---|---|
| Published in |
PLOS ONE, August 2012
|
| DOI | 10.1371/journal.pone.0041710 |
| Pubmed ID | |
| Authors |
T. Andrew Binkowski, Susana R. Marino, Andrzej Joachimiak |
| Abstract |
Prediction of peptide binding to human leukocyte antigen (HLA) molecules is essential to a wide range of clinical entities from vaccine design to stem cell transplant compatibility. Here we present a new structure-based methodology that applies robust computational tools to model peptide-HLA (p-HLA) binding interactions. The method leverages the structural conservation observed in p-HLA complexes to significantly reduce the search space and calculate the system's binding free energy. This approach is benchmarked against existing p-HLA complexes and the prediction performance is measured against a library of experimentally validated peptides. The effect on binding activity across a large set of high-affinity peptides is used to investigate amino acid mismatches reported as high-risk factors in hematopoietic stem cell transplantation. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 8% |
| Unknown | 36 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 28% |
| Student > Ph. D. Student | 10 | 26% |
| Student > Master | 8 | 21% |
| Other | 3 | 8% |
| Professor > Associate Professor | 3 | 8% |
| Other | 5 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 44% |
| Computer Science | 5 | 13% |
| Biochemistry, Genetics and Molecular Biology | 4 | 10% |
| Medicine and Dentistry | 4 | 10% |
| Immunology and Microbiology | 3 | 8% |
| Other | 6 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 09 April 2024.
All research outputs
#3,290,275
of 24,727,020 outputs
Outputs from PLOS ONE
#43,832
of 214,056 outputs
Outputs of similar age
#21,480
of 173,288 outputs
Outputs of similar age from PLOS ONE
#687
of 4,140 outputs
Altmetric has tracked 24,727,020 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 214,056 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.6. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 173,288 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 4,140 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.