ArticleBioMed research international2017
PSBinder: A Web Service for Predicting Polystyrene Surface-Binding Peptides.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Validation ofACS central science · 2026Article
- Discovering Plastic-Binding Peptides with Favorable Affinity, Water Solubility, and Binding Specificity Through Deep Learning and Biophysical Modeling.bioRxiv : the preprint server for biology · 2026Article
- AI-driven rational design of promiscuous and selective plastic-binding peptides.Chemical science · 2025Article
- Simple, sensitive and specific detection method for polystyrene nanoplastics in food matrix based on peptide-modified FeCurrent research in food science · 2025Article
- Predicting Antidiabetic Peptide Activity: A Machine Learning Perspective on Type 1 and Type 2 Diabetes.International journal of molecular sciences · 2024Article
- Current Development of Data Resources and Bioinformatics Tools for Anticoronavirus Peptide.Current medicinal chemistry · 2024Review
- In Silico Design and Analysis of Plastic-Binding Peptides.The journal of physical chemistry. B · 2023Article
- Modular Design for Proteins Assembling into Antifouling Coatings: Case of Gold Surfaces.Langmuir : the ACS journal of surfaces and colloids · 2023Article
- PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework.Bioinformatics (Oxford, England) · 2023Article
- CSM-peptides: A computational approach to rapid identification of therapeutic peptides.Protein science : a publication of the Protein Society · 2022Article
- Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics.Pharmaceutics · 2022Review
- Sensitive and specific capture of polystyrene and polypropylene microplastics using engineered peptide biosensors.RSC advances · 2022Article
- SSH2.0: A Better Tool for Predicting the Hydrophobic Interaction Risk of Monoclonal Antibody.Frontiers in genetics · 2022Article
- Anticancer Peptide PredictionFrontiers in genetics · 2022Article
- Article
- Peptide Specific Nanoplastic Detection Based on Sandwich Typed Localized Surface Plasmon Resonance.Nanomaterials (Basel, Switzerland) · 2021Article
- CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model.BMC bioinformatics · 2021Article
- Article
- 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
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polystyrene surface-binding peptides (PSBPs) are useful as affinity tags to build a highly effective ELISA system. However, they are also a quite common type of target-unrelated peptides (TUPs) in the panning of phage-displayed random peptide library. As TUP, PSBP will mislead the analysis of panning results if not identified. Therefore, it is necessary to find a way to quickly and easily foretell if a peptide is likely to be a PSBP or not. In this paper, we describe PSBinder, a predictor based on SVM. To our knowledge, it is the first web server for predicting PSBP. The SVM model was built with the feature of optimized dipeptide composition and 87.02% (MCC = 0.74; AUC = 0.91) of peptides were correctly classified by fivefold cross-validation. PSBinder can be used to exclude highly possible PSBP from biopanning results or to find novel candidates for polystyrene affinity tags. Either way, it is valuable for biotechnology 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.