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| Title |
The identification of protein domains that mediate functional interactions between Rab-GTPases and RabGAPs using 3D protein modeling
|
|---|---|
| Published in |
Advances and Applications in Bioinformatics and Chemistry : AABC, April 2017
|
| DOI | 10.2147/aabc.s121245 |
| Pubmed ID | |
| Authors |
Jeremiah J Davie, Silviu L Faitar |
| Abstract |
Currently, time-consuming serial in vitro experimentation involving immunocytochemistry or radiolabeled materials is required to identify which of the numerous Rab-GTPases (Rab) and Rab-GTPase activating proteins (RabGAP) are capable of functional interactions. These interactions are essential for numerous cellular functions, and in silico methods of reducing in vitro trial and error would accelerate the pace of research in cell biology. We have utilized a combination of three-dimensional protein modeling and protein bioinformatics to identify domains present in Rab proteins that are predictive of their functional interaction with a specific RabGAP. The RabF2 and RabSF1 domains appear to play functional roles in mediating the interaction between Rabs and RabGAPs. Moreover, the RabSF1 domain can be used to make in silico predictions of functional Rab/RabGAP pairs. This method is expected to be a broadly applicable tool for predicting protein-protein interactions where existing crystal structures for homologs of the proteins of interest are available. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 33% |
| Student > Ph. D. Student | 2 | 22% |
| Student > Bachelor | 2 | 22% |
| Professor > Associate Professor | 1 | 11% |
| Unknown | 1 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 33% |
| Engineering | 2 | 22% |
| Neuroscience | 1 | 11% |
| Computer Science | 1 | 11% |
| Unknown | 2 | 22% |
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 17 April 2017.
All research outputs
#20,660,571
of 25,382,440 outputs
Outputs from Advances and Applications in Bioinformatics and Chemistry : AABC
#34
of 55 outputs
Outputs of similar age
#249,416
of 323,961 outputs
Outputs of similar age from Advances and Applications in Bioinformatics and Chemistry : AABC
#2
of 3 outputs
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So far Altmetric has tracked 55 research outputs from this source. They receive a mean Attention Score of 2.5. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.