ArticleProtein science : a publication of the Protein Society2017
Usage of a dataset of NMR resolved protein structures to test aggregation versus solubility prediction algorithms.
Article in Protein science : a publication of the Protein Society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Identification of New FG-Repeat Nucleoporins with Amyloid Properties.International journal of molecular sciences · 2023Article
- NOS1AP Interacts with α-Synuclein and Aggregates in Yeast and Mammalian Cells.International journal of molecular sciences · 2022Article
- The Human NUP58 Nucleoporin Can Form Amyloids In Vitro and In Vivo.Biomedicines · 2021Article
- Solubility and Aggregation of Selected Proteins Interpreted on the Basis of Hydrophobicity Distribution.International journal of molecular sciences · 2021Article
- Aggregation and Prion-Inducing Properties of the G-Protein Gamma Subunit Ste18 are Regulated by Membrane Association.International journal of molecular sciences · 2020Article
- WALTZ-DB 2.0: an updated database containing structural information of experimentally determined amyloid-forming peptides.Nucleic acids research · 2020Article
- Solubility of proteins.ADMET & DMPK · 2020Article
- Design of a New [Frontiers in molecular neuroscience · 2019Article
- Overproduction of Sch9 leads to its aggregation and cell elongation in Saccharomyces cerevisiae.PloS one · 2018Article
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Authors and funding
3 authors.
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Abstract
There has been an increased interest in computational methods for amyloid and (or) aggregate prediction, due to the prevalence of these aggregates in numerous diseases and their recently discovered functional importance. To evaluate these methods, several datasets have been compiled. Typically, aggregation-prone regions of proteins, which form aggregates or amyloids in vivo, are more than 15 residues long and intrinsically disordered. However, the number of such experimentally established amyloid forming and non-forming sequences are limited, not exceeding one hundred entries in existing databases. In this work, we parsed all available NMR-resolved protein structures from the PDB and assembled a new, sevenfold larger, dataset of unfolded sequences, soluble at high concentrations. We proposed to use these sequences as a negative set for evaluating methods for predicting aggregation in vivo. We also present the results of benchmarking cutting edge tools for the prediction of aggregation versus solubility propensity.
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