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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Computational Design of Ligand Binding Proteins
|
|---|---|
| Chapter number | 8 |
| Book title |
Computational Design of Ligand Binding Proteins
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3569-7_8 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3567-3, 978-1-4939-3569-7
|
| Authors |
Wang, Meng, Zhao, Huimin, Meng Wang, Huimin Zhao |
| Editors |
Barry L. Stoddard |
| Abstract |
The advantages of computational design and directed evolution are complementary, and only through combined and iterative use of both approaches, a daunting task such as protein-ligand interaction design, can be achieved efficiently. Here, we describe a systematic strategy to combine structure-guided computational design, iterative site saturation mutagenesis, and yeast two-hybrid system (Y2H)-based phenotypic screening to engineer novel and orthogonal interactions between synthetic ligands and human estrogen receptor α (hERα) for the development of novel gene switches. |
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 % |
|---|---|---|
| France | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 6 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 33% |
| Student > Bachelor | 1 | 17% |
| Researcher | 1 | 17% |
| Other | 1 | 17% |
| Unknown | 1 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 50% |
| Neuroscience | 1 | 17% |
| Engineering | 1 | 17% |
| Unknown | 1 | 17% |
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 21 April 2016.
All research outputs
#20,322,106
of 22,865,319 outputs
Outputs from Methods in molecular biology
#9,916
of 13,127 outputs
Outputs of similar age
#330,679
of 393,645 outputs
Outputs of similar age from Methods in molecular biology
#1,053
of 1,470 outputs
Altmetric has tracked 22,865,319 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,127 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% 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 393,645 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,470 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.