| Title |
SPIDR: small-molecule peptide-influenced drug repurposing
|
|---|---|
| Published in |
BMC Bioinformatics, April 2018
|
| DOI | 10.1186/s12859-018-2153-y |
| Pubmed ID | |
| Authors |
Matthew D. King, Thomas Long, Daniel L. Pfalmer, Timothy L. Andersen, Owen M. McDougal |
| Abstract |
Conventional de novo drug design is costly and time consuming, making it accessible to only the best resourced research organizations. An emergent approach to new drug development is drug repurposing, in which compounds that have already gone through some level of clinical testing are examined for efficacy against diseases divergent than their original application. Repurposing of existing drugs circumvents the time and considerable cost of early stages of drug development, and can be accelerated by using software to screen existing chemical databases to identify suitable drug candidates. Small-molecule Peptide-Influenced Drug Repurposing (SPIDR) was developed to identify small molecule drugs that target a specific receptor by exploring the conformational binding space of peptide ligands. SPIDR was tested using the potent and selective 16-amino acid peptide α-conotoxin MII ligand and the α3β2-nicotinic acetylcholine receptor (nAChR) isoform. SPIDR incorporates a genetic algorithm-based, heuristic search procedure, which was used to explore the ligand binding domain of the α3β2-nAChR isoform using a library consisting of 640,000 α-conotoxin MII peptide analogs. The peptides that exhibited the highest affinity for α3β2-nAChR were used as models for a small-molecule structure similarity search of the PubChem Compound database. SPIDR incorporates the SimSearcher utility, which generates shape distribution signatures of molecules and employs multi-level K-means clustering to insure fast database queries. SPIDR identified non-peptide drugs with estimated binding affinities nearly double that of the native α-conotoxin MII peptide. SPIDR has been generalized and integrated into DockoMatic v 2.1. This software contains an intuitive graphical interface for peptide mutant screening workflow and facilitates mapping, clustering, and searching of local molecular databases, making DockoMatic a valuable tool for researchers in drug design and repurposing. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 40% |
| United Kingdom | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 21% |
| Student > Bachelor | 3 | 10% |
| Professor | 3 | 10% |
| Researcher | 3 | 10% |
| Student > Ph. D. Student | 3 | 10% |
| Other | 5 | 17% |
| Unknown | 6 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 21% |
| Chemistry | 4 | 14% |
| Agricultural and Biological Sciences | 3 | 10% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 7% |
| Engineering | 2 | 7% |
| Other | 5 | 17% |
| Unknown | 7 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 05 December 2019.
All research outputs
#14,672,427
of 25,483,400 outputs
Outputs from BMC Bioinformatics
#4,039
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Outputs of similar age
#162,042
of 324,427 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 106 outputs
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