ArticleBMC bioinformatics2009
Prediction of amyloid fibril-forming segments based on a support vector machine.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 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
44 citing papers in PubMed.
- Assessment of Aggregation- and Condensation-Prone Regions of Proteins Involved in Neurodegenerative, Neurological and Mental-State Diseases.Biomolecules · 2026Article
- Bioinformatic assessment of the potential amyloidogenicity of the human and evolutionarily more ancient proteomes.The Biochemical journal · 2026Article
- Experimentally validated deep learning control of protein aggregation.Communications chemistry · 2026Article
- Peptidomimetics Targeting the Amyloidogenicity of Nucleophosmin 1 Mutations in Acute Myeloid Leukemia.Chembiochem : a European journal of chemical biology · 2025Article
- Advanced computational approaches to understand protein aggregation.Biophysics reviews · 2024Review
- Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis.International journal of molecular sciences · 2023Review
- Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides.ACS omega · 2022Article
- ENTAIL: yEt aNoTher amyloid fIbrils cLassifier.BMC bioinformatics · 2022Article
- Insights into Network of Hot Spots of Aggregation in Nucleophosmin 1.International journal of molecular sciences · 2022Article
- Bioinformatics Methods in Predicting Amyloid Propensity of Peptides and Proteins.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Investigating the Disordered and Membrane-Active Peptide A-Cage-C Using Conformational Ensembles.Molecules (Basel, Switzerland) · 2021Article
- The Budapest Amyloid Predictor and Its Applications.Biomolecules · 2021Article
- Protein aggregation: in silico algorithms and applications.Biophysical reviews · 2021Review
- Using protein engineering to understand and modulate aggregation.Current opinion in structural biology · 2020Review
- Computational prediction of protein aggregation: Advances in proteomics, conformation-specific algorithms and biotechnological applications.Computational and structural biotechnology journal · 2020Review
- Ensembles of Hydrophobicity Scales as Potent Classifiers for Chimeric Virus-Like Particle Solubility - An Amino Acid Sequence-Based Machine Learning Approach.Frontiers in bioengineering and biotechnology · 2020Article
- Acid-denatured small heat shock protein HdeA fromThe Journal of biological chemistry · 2019Article
- A TLR4-derived non-cytotoxic, self-assembling peptide functions as a vaccine adjuvant in mice.The Journal of biological chemistry · 2018Article
- RFAmyloid: A Web Server for Predicting Amyloid Proteins.International journal of molecular sciences · 2018Article
- Theory of Sequence Effects in Amyloid Aggregation.The journal of physical chemistry. B · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAmyloid fibrillar aggregates of proteins or polypeptides are known to be associated with many human diseases. Recent studies suggest that short protein regions trigger this aggregation. Thus, identifying these short peptides is critical for understanding diseases and finding potential therapeutic targets.
resultsWe propose a method, named Pafig (Prediction of amyloid fibril-forming segments) based on support vector machines, to identify the hexpeptides associated with amyloid fibrillar aggregates. The features of Pafig were obtained by a two-round selection from AAindex. Using a 10-fold cross validation test on Hexpepset dataset, Pafig performed well with regards to overall accuracy of 81% and Matthews correlation coefficient of 0.63. Pafig was used to predict the potential fibril-forming hexpeptides in all of the 64,000,000 hexpeptides. As a result, approximately 5.08% of hexpeptides showed a high aggregation propensity. In the predicted fibril-forming hexpeptides, the amino acids--alanine, phenylalanine, isoleucine, leucine and valine occurred at the higher frequencies and the amino acids--aspartic acid, glutamic acid, histidine, lysine, arginine and praline, appeared with lower frequencies.
conclusionThe performance of Pafig indicates that it is a powerful tool for identifying the hexpeptides associated with fibrillar aggregates and will be useful for large-scale analysis of proteomic data.
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.