ArticlePLoS computational biology2017
Amino acid composition predicts prion activity.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 37 citations in OpenAlex.
- Assessment of Aggregation- and Condensation-Prone Regions of Proteins Involved in Neurodegenerative, Neurological and Mental-State Diseases.Biomolecules · 2026Article
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- Detecting anomalous proteins using deep representations.NAR genomics and bioinformatics · 2024Article
- Exploring cryptic amyloidogenic regions in prion-like proteins from plants.Frontiers in plant science · 2022Article
- Prion-like proteins: from computational approaches to proteome-wide analysis.FEBS open bio · 2021Review
- Generalizable Compositional Features Influencing the Proteostatic Fates of Polar Low-Complexity Domains.International journal of molecular sciences · 2021Article
- The Hunt for Ancient Prions: Archaeal Prion-Like Domains Form Amyloid-Based Epigenetic Elements.Molecular biology and evolution · 2021Article
- From Prions to Stress Granules: Defining the Compositional Features of Prion-Like Domains That Promote Different Types of Assemblies.International journal of molecular sciences · 2021Review
- ISLAND: in-silico proteins binding affinity prediction using sequence information.BioData mining · 2020Article
- Protein assembly systems in natural and synthetic biology.BMC biology · 2020Review
- Composition-based prediction and rational manipulation of prion-like domain recruitment to stress granules.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Using protein engineering to understand and modulate aggregation.Current opinion in structural biology · 2020Review
- Natural and pathogenic protein sequence variation affecting prion-like domains within and across human proteomes.BMC genomics · 2020Article
- HLMethy: a machine learning-based model to identify the hidden labels of mPlant molecular biology · 2019Article
- The molecular language of membraneless organelles.The Journal of biological chemistry · 2019Review
- AMYCO: evaluation of mutational impact on prion-like proteins aggregation propensity.BMC bioinformatics · 2019Article
- Article
- The Amino Acid Composition of Quadruplex Binding Proteins Reveals a Shared Motif and Predicts New Potential Quadruplex Interactors.Molecules (Basel, Switzerland) · 2018Article
- An integrative approach to predicting the functional effects of small indels in non-coding regions of the human genome.BMC bioinformatics · 2017Article
- Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Many prion-forming proteins contain glutamine/asparagine (Q/N) rich domains, and there are conflicting opinions as to the role of primary sequence in their conversion to the prion form: is this phenomenon driven primarily by amino acid composition, or, as a recent computational analysis suggested, dependent on the presence of short sequence elements with high amyloid-forming potential. The argument for the importance of short sequence elements hinged on the relatively-high accuracy obtained using a method that utilizes a collection of length-six sequence elements with known amyloid-forming potential. We weigh in on this question and demonstrate that when those sequence elements are permuted, even higher accuracy is obtained; we also propose a novel multiple-instance machine learning method that uses sequence composition alone, and achieves better accuracy than all existing prion prediction approaches. While we expect there to be elements of primary sequence that affect the process, our experiments suggest that sequence composition alone is sufficient for predicting protein sequences that are likely to form prions. A web-server for the proposed method is available at http://faculty.pieas.edu.pk/fayyaz/prank.html, and the code for reproducing our experiments is available at http://doi.org/10.5281/zenodo.167136.
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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.