| Title |
NLDB: a database for 3D protein–ligand interactions in enzymatic reactions
|
|---|---|
| Published in |
Journal of Structural and Functional Genomics, August 2016
|
| DOI | 10.1007/s10969-016-9206-0 |
| Pubmed ID | |
| Authors |
Yoichi Murakami, Satoshi Omori, Kengo Kinoshita |
| Abstract |
NLDB (Natural Ligand DataBase; URL: http://nldb.hgc.jp ) is a database of automatically collected and predicted 3D protein-ligand interactions for the enzymatic reactions of metabolic pathways registered in KEGG. Structural information about these reactions is important for studying the molecular functions of enzymes, however a large number of the 3D interactions are still unknown. Therefore, in order to complement such missing information, we predicted protein-ligand complex structures, and constructed a database of the 3D interactions in reactions. NLDB provides three different types of data resources; the natural complexes are experimentally determined protein-ligand complex structures in PDB, the analog complexes are predicted based on known protein structures in a complex with a similar ligand, and the ab initio complexes are predicted by docking simulations. In addition, NLDB shows the known polymorphisms found in human genome on protein structures. The database has a flexible search function based on various types of keywords, and an enrichment analysis function based on a set of KEGG compound IDs. NLDB will be a valuable resource for experimental biologists studying protein-ligand interactions in specific reactions, and for theoretical researchers wishing to undertake more precise simulations of interactions. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 25% |
| Japan | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 50% |
| Members of the public | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 27 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 6 | 22% |
| Other | 3 | 11% |
| Student > Ph. D. Student | 3 | 11% |
| Student > Master | 3 | 11% |
| Professor | 2 | 7% |
| Other | 3 | 11% |
| Unknown | 7 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 26% |
| Agricultural and Biological Sciences | 4 | 15% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 7% |
| Chemistry | 2 | 7% |
| Chemical Engineering | 1 | 4% |
| Other | 4 | 15% |
| Unknown | 7 | 26% |
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 04 September 2016.
All research outputs
#13,766,415
of 23,344,526 outputs
Outputs from Journal of Structural and Functional Genomics
#78
of 108 outputs
Outputs of similar age
#169,515
of 314,934 outputs
Outputs of similar age from Journal of Structural and Functional Genomics
#1
of 2 outputs
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So far Altmetric has tracked 108 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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