Name
Title | ||
|---|---|---|
PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides |
Abstract | ||
|---|---|---|
Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1186/s12859-015-0633-x | BMC Bioinformatics |
Keywords | Field | DocType |
Machine learning, SVM, Tri-disulfide peptide toxins, Sequential tri-disulfide peptides (STPs), Inhibitory cytine knot (ICKs), Cylotides, Nonknotted STPs, Insecticidal peptides, Antimicrobial peptides | Biology,Disulfide bond,Support vector machine,Peptide,Antimicrobial peptides,Bioinformatics,Genetics,DNA microarray,Cystine | Journal |
Volume | Issue | ISSN |
16 | 1 | 1471-2105 |
Citations | PageRank | References |
2 | 0.38 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Islam | 1 | 2 | 0.38 |
| Tanvir Sajed | 2 | 116 | 5.96 |
| Christopher Kearney | 3 | 2 | 1.39 |
| Erich J Baker | 4 | 53 | 6.94 |