ArticleBioMed research international2016
SABinder: A Web Service for Predicting Streptavidin-Binding Peptides.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Predicting Antidiabetic Peptide Activity: A Machine Learning Perspective on Type 1 and Type 2 Diabetes.International journal of molecular sciences · 2024Article
- CD47Binder: Identify CD47 Binding Peptides by Combining Next-Generation Phage Display Data and Multiple Peptide Descriptors.Interdisciplinary sciences, computational life sciences · 2023Article
- AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains.Antibody therapeutics · 2023Article
- SSH2.0: A Better Tool for Predicting the Hydrophobic Interaction Risk of Monoclonal Antibody.Frontiers in genetics · 2022Article
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- BBPpredict: A Web Service for Identifying Blood-Brain Barrier Penetrating Peptides.Frontiers in genetics · 2022Article
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- White-matter functional topology: a neuromarker for classification and prediction in unmedicated depression.Translational psychiatry · 2020Article
- ncPro-ML: An integrated computational tool for identifying non-coding RNA promoters in multiple species.Computational and structural biotechnology journal · 2020Article
- SSH: A Tool for Predicting Hydrophobic Interaction of Monoclonal Antibodies Using Sequences.BioMed research international · 2020Article
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- SAROTUP: a suite of tools for finding potential target-unrelated peptides from phage display data.International journal of biological sciences · 2019Article
- Compositional Bias in Naïve and Chemically-modified Phage-Displayed Libraries uncovered by Paired-end Deep Sequencing.Scientific reports · 2018Article
- Biopanning data bank 2018: hugging next generation phage display.Database : the journal of biological databases and curation · 2018Article
- Special issue on Computational Resources and Methods in Biological Sciences.International journal of biological sciences · 2018Article
- HBPred: a tool to identify growth hormone-binding proteins.International journal of biological sciences · 2018Article
- Recent Advances in Conotoxin Classification by Using Machine Learning Methods.Molecules (Basel, Switzerland) · 2017Review
- PSBinder: A Web Service for Predicting Polystyrene Surface-Binding Peptides.BioMed research international · 2017Article
- A novel peptide specifically binding to VEGF receptor suppresses angiogenesisSignal transduction and targeted therapyArticle
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Streptavidin is sometimes used as the intended target to screen phage-displayed combinatorial peptide libraries for streptavidin-binding peptides (SBPs). More often in the biopanning system, however, streptavidin is just a commonly used anchoring molecule that can efficiently capture the biotinylated target. In this case, SBPs creeping into the biopanning results are not desired binders but target-unrelated peptides (TUP). Taking them as intended binders may mislead subsequent studies. Therefore, it is important to find if a peptide is likely to be an SBP when streptavidin is either the intended target or just the anchoring molecule. In this paper, we describe an SVM-based ensemble predictor called SABinder. It is the first predictor for SBP. The model was built with the feature of optimized dipeptide composition. It was observed that 89.20% (MCC = 0.78; AUC = 0.93; permutation test, p < 0.001) of peptides were correctly classified. As a web server, SABinder is freely accessible. The tool provides a highly efficient way to exclude potential SBP when they are TUP or to facilitate identification of possibly new SBP when they are the desired binders. In either case, it will be helpful and can benefit related scientific community.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.