You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
The Usage of ACCLUSTER for Peptide Binding Site Prediction
|
|---|---|
| Chapter number | 1 |
| Book title |
Modeling Peptide-Protein Interactions
|
| Published in |
Methods in molecular biology, February 2017
|
| DOI | 10.1007/978-1-4939-6798-8_1 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6796-4, 978-1-4939-6798-8
|
| Authors |
Chengfei Yan, Xianjin Xu, Xiaoqin Zou |
| Editors |
Ora Schueler-Furman, Nir London |
| Abstract |
Peptides mediate up to 40 % of protein-protein interactions in a variety of cellular processes and are also attractive drug candidates. Thus, predicting peptide binding sites on the given protein structure is of great importance for mechanistic investigation of protein-peptide interactions and peptide therapeutics development. In this chapter, we describe the usage of our web server, referred to as ACCLUSTER, for peptide binding site prediction for a given protein structure. ACCLUSTER is freely available for users without registration at http://zougrouptoolkit.missouri.edu/accluster . |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 27% |
| Other | 1 | 9% |
| Student > Ph. D. Student | 1 | 9% |
| Professor | 1 | 9% |
| Student > Master | 1 | 9% |
| Other | 1 | 9% |
| Unknown | 3 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 3 | 27% |
| Computer Science | 2 | 18% |
| Biochemistry, Genetics and Molecular Biology | 1 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 9% |
| Neuroscience | 1 | 9% |
| Other | 1 | 9% |
| Unknown | 2 | 18% |
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 02 March 2017.
All research outputs
#15,448,846
of 22,958,253 outputs
Outputs from Methods in molecular biology
#5,372
of 13,137 outputs
Outputs of similar age
#198,244
of 311,787 outputs
Outputs of similar age from Methods in molecular biology
#97
of 266 outputs
Altmetric has tracked 22,958,253 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,137 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 311,787 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 266 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.