| Title |
A new fingerprint to predict nonribosomal peptides activity
|
|---|---|
| Published in |
Perspectives in Drug Discovery and Design, September 2012
|
| DOI | 10.1007/s10822-012-9608-4 |
| Pubmed ID | |
| Authors |
Ammar Abdo, Ségolène Caboche, Valérie Leclère, Philippe Jacques, Maude Pupin |
| Abstract |
Bacteria and fungi use a set of enzymes called nonribosomal peptide synthetases to provide a wide range of natural peptides displaying structural and biological diversity. So, nonribosomal peptides (NRPs) are the basis for some efficient drugs. While discovering new NRPs is very desirable, the process of identifying their biological activity to be used as drugs is a challenge. In this paper, we present a novel peptide fingerprint based on monomer composition (MCFP) of NRPs. MCFP is a novel method for obtaining a representative description of NRP structures from their monomer composition in fingerprint form. Experiments with Norine NRPs database and MCFP show high prediction accuracy (>93 %). Also a high recall rate (>82 %) is obtained when MCFP is used for screening NRPs database. From this study it appears that our fingerprint, built from monomer composition, allows an effective screening and prediction of biological activities of NRPs database. |
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 % |
|---|---|---|
| Unknown | 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Denmark | 1 | 2% |
| Romania | 1 | 2% |
| Unknown | 44 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 24% |
| Researcher | 8 | 17% |
| Student > Master | 7 | 15% |
| Student > Bachelor | 6 | 13% |
| Professor > Associate Professor | 3 | 7% |
| Other | 8 | 17% |
| Unknown | 3 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 16 | 35% |
| Computer Science | 9 | 20% |
| Biochemistry, Genetics and Molecular Biology | 7 | 15% |
| Chemistry | 5 | 11% |
| Engineering | 2 | 4% |
| Other | 2 | 4% |
| Unknown | 5 | 11% |
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 12 October 2012.
All research outputs
#22,986,241
of 25,628,260 outputs
Outputs from Perspectives in Drug Discovery and Design
#872
of 952 outputs
Outputs of similar age
#172,290
of 191,872 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#6
of 11 outputs
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