ArticleBriefings in bioinformatics2021
APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed, 52 citations in OpenAlex.
- Cyclic Peptides as Modulators of Protein-Protein Interactions: A Survival Guide from Discovery Platforms to AI-Driven Design.International journal of molecular sciences · 2026Review
- Comparative Analysis of Deep Learning-Based Algorithms for Peptide Structure Prediction.Proteins · 2026Article
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- Molecular Modelling in Bioactive Peptide Discovery and Characterisation.Biomolecules · 2025Review
- Advances of deep Neural Networks (DNNs) in the development of peptide drugs.Future medicinal chemistry · 2025Review
- Structural information in therapeutic peptides: Emerging applications in biomedicine.FEBS open bio · 2025Review
- Insight into Protein Engineering: FromCurrent pharmaceutical design · 2025Review
- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- Evaluating the Antimicrobial Efficacy of a Designed Synthetic peptide against Pathogenic Bacteria.Journal of microbiology and biotechnology · 2024Article
- Antibacterial and Anti-Inflammatory Activity of Branched Peptides Derived from Natural Host Defense Sequences.Journal of medicinal chemistry · 2024Article
- Exploring protein functions from structural flexibility using CABS-flex modeling.Protein science : a publication of the Protein Society · 2024Review
- Conjugation with the Carrier Helped to Reveal acidification-Induced Structural Shift in the Peptide from Phospholipase Domain of Parvovirus B19.The protein journal · 2024Article
- Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives.Amino acids · 2024Review
- Machine learning for antimicrobial peptide identification and design.Nature reviews bioengineering · 2024Article
- Structure prediction of linear and cyclic peptides using CABS-flex.Briefings in bioinformatics · 2024Article
- A novel opsonic eCIRP inhibitor for lethal sepsis.Journal of leukocyte biology · 2024Article
- Structural Shifts of the Parvovirus B19 Capsid Receptor-binding Domain: A Peptide Study.Protein and peptide letters · 2024Article
- Article
- PEP-FOLD4: a pH-dependent force field for peptide structure prediction in aqueous solution.Nucleic acids research · 2023Article
- Comparative Evaluation of Existing and Rationally Designed Novel Antimicrobial Peptides for Treatment of Skin and Soft Tissue Infections.Antibiotics (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Good knowledge of a peptide's tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational protocol that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5-40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9Å from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and PepLook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest, facilitating in silico study and design of peptides by the wider research community.
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.