ArticleiScience2019
In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences.
Article in iScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 16 citations in OpenAlex.
- Developing an artificial intelligence-generated peptide targeting platelet-type von Willebrand disease.Blood advances · 2026Article
- Artificial Intelligence Reveals Nature: Functional Parallels Between a Designed and a Natural Peptide.International journal of molecular sciences · 2025Article
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- Peptide-Drug Conjugates: A New Hope for Cancer.Journal of peptide science : an official publication of the European Peptide Society · 2025Review
- Future Perspective: Harnessing the Power of Artificial Intelligence in the Generation of New Peptide Drugs.Biomolecules · 2024Review
- Review
- Computational Protein Design for COVID-19 Research and Emerging Therapeutics.ACS central science · 2023Review
- A computational approach to rapidly design peptides that detect SARS-CoV-2 surface protein S.NAR genomics and bioinformatics · 2022Article
- Assessing sequence-based protein-protein interaction predictors for use in therapeutic peptide engineering.Scientific reports · 2022Article
- SYNBIP: synthetic binding proteins for research, diagnosis and therapy.Nucleic acids research · 2022Article
- Protein Binder (ProBi) as a New Class of Structurally Robust Non-Antibody Protein Scaffold for Directed Evolution.Viruses · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic proteins with high affinity and selectivity for a protein target can be used as research tools, biomarkers, and pharmacological agents, but few methods exist to design such proteins de novo. To this end, the In-Silico Protein Synthesizer (InSiPS) was developed to design synthetic binding proteins (SBPs) that bind pre-determined targets while minimizing off-target interactions. InSiPS is a genetic algorithm that refines a pool of random sequences over hundreds of generations of mutation and selection to produce SBPs with pre-specified binding characteristics. As a proof of concept, we design SBPs against three yeast proteins and demonstrate binding and functional inhibition of two of three targets in vivo. Peptide SPOT arrays confirm binding sites, and a permutation array demonstrates target specificity. Our foundational approach will support the field of de novo design of small binding polypeptide motifs and has robust applicability while offering potential advantages over the limited number of techniques currently available.
Indexed as
Identifiers
What OpenQuestion holds
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.