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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
AGGRESCAN3D: Toward the Prediction of the Aggregation Propensities of Protein Structures
|
|---|---|
| Chapter number | 21 |
| Book title |
Computational Drug Discovery and Design
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7756-7_21 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7755-0, 978-1-4939-7756-7
|
| Authors |
Jordi Pujols, Samuel Peña-Díaz, Salvador Ventura |
| Abstract |
Protein aggregation is responsible for the onset and spread of many human diseases, ranging from neurodegenerative disorders to cancer and diabetes. Moreover, it is one of the major bottlenecks for the production of protein-based therapeutics such as antibodies or enzymes. AGGRESCAN3D (A3D) is a web server aimed to identify and evaluate structural aggregation prone regions, overcoming the limitations of sequence-based algorithms in the prediction of the aggregation propensity of globular proteins. A3D allows the redesign of protein solubility by predicting in silico the impact of mutations and protein conformational fluctuations on the aggregation of native polypeptides. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 21 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 4 | 19% |
| Student > Ph. D. Student | 4 | 19% |
| Student > Bachelor | 2 | 10% |
| Researcher | 2 | 10% |
| Student > Master | 2 | 10% |
| Other | 1 | 5% |
| Unknown | 6 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 33% |
| Agricultural and Biological Sciences | 2 | 10% |
| Mathematics | 1 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 5% |
| Computer Science | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 8 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 03 April 2018.
All research outputs
#13,895,132
of 23,031,582 outputs
Outputs from Methods in molecular biology
#3,896
of 13,177 outputs
Outputs of similar age
#227,676
of 442,391 outputs
Outputs of similar age from Methods in molecular biology
#385
of 1,499 outputs
Altmetric has tracked 23,031,582 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,177 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 69% 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 442,391 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.