AMYCO: evaluation of mutational impact on prion-like proteins aggregation propensity

Iglesias, Valentín; Conchillo-Solé, Òscar; Batlle, Cristina; Ventura, Salvador

doi:10.1186/s12859-019-2601-3

Cited by 24 publications

(18 citation statements)

References 23 publications

(32 reference statements)

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“…Other disease-associated mutations alter the propensity of PrLDs to undergo LLPS (26). The sequence features that promote transitions to amorphous aggregates and well-structured amyloids have been extensively studied, and various methods now exist to predict the aggregation propensity of PrLDs (27)(28)(29)(30)(31)(32). However, while a number of laboratories have made exciting progress in identifying specific sequence features that promote the formation of reversible, liquid-like assemblies in vivo (20,24,(33)(34)(35), these sequence features have not yet been comprehensively defined; consequently, our ability to predict the propensity of PrLDs to form these assemblies is more limited.…”

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confidence: 99%

Composition-based prediction and rational manipulation of prion-like domain recruitment to stress granules

Boncella

Shattuck

Cascarina

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Mutations in a number of stress granule-associated proteins have been linked to various neurodegenerative diseases. Several of these mutations are found in aggregation-prone prion-like domains (PrLDs) within these proteins. In this work, we examine the sequence features governing PrLD localization to stress granules upon stress. We demonstrate that many yeast PrLDs are sufficient for stress-induced assembly into microscopically visible foci that colocalize with stress granule markers. Additionally, compositional biases exist among PrLDs that assemble upon stress, and these biases are consistent across different stressors. Using these biases, we have developed a composition-based prediction method that accurately predicts PrLD assembly into foci upon heat shock. We show that compositional changes alter PrLD assembly behavior in a predictable manner, while scrambling primary sequence has little effect on PrLD assembly and recruitment to stress granules. Furthermore, we were able to design synthetic PrLDs that were efficiently recruited to stress granules, and found that aromatic amino acids, which have previously been linked to PrLD phase separation, were dispensable for this recruitment. These results highlight the flexible sequence requirements for stress granule recruitment and suggest that PrLD localization to stress granules is driven primarily by amino acid composition, rather than primary sequence.

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mentioning

confidence: 99%

Composition-based prediction and rational manipulation of prion-like domain recruitment to stress granules

Boncella

Shattuck

Cascarina

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…Somatic mutations in the PRNP gene in cancer patients were evaluated by PolyPhen-2 ( ), PANTHER ( ), PROVEAN ( ), and AMYCO ( ) [ 27 , 28 , 29 , 30 ]. The effects of sequence variation were evaluated on the basis of protein structure by PolyPhen-2.…”

Section: Methodsmentioning

confidence: 99%

“…Somatic mutations in the PRNP gene in cancer patients were evaluated by PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), PANTHER (http://www.pantherdb.org/), PROVEAN (http://provean.jcvi.org/index.php), and AMYCO (http://bioinf.uab.es/amycov04/) [27][28][29][30]. The effects of sequence variation were evaluated on the basis of protein structure by PolyPhen-2.…”

Section: In Silico Evaluation Of Somatic Mutations In the Prnp Gene Imentioning

confidence: 99%

“…In addition, we compared the information on the somatic mutations in the PRNP gene in cancer patients with that of previously reported pathogenic mutations associated with prion diseases and with the total cancer patient population. Furthermore, to assess whether the somatic mutations in the PRNP gene in cancer patients are deleterious, we performed in silico annotation using PolyPhen-2, PANTHER, PROVEAN, and AMYCO [27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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Identification of Prion Disease-Related Somatic Mutations in the Prion Protein Gene (PRNP) in Cancer Patients

Kim

Won

Jeong

2020

Cells

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Prion diseases are caused by misfolded prion protein (PrPSc) and are accompanied by spongiform vacuolation of brain lesions. Approximately three centuries have passed since prion diseases were first discovered around the world; however, the exact role of certain factors affecting the causative agent of prion diseases is still debatable. In recent studies, somatic mutations were assumed to be cause of several diseases. Thus, we postulated that genetically unstable cancer tissue may cause somatic mutations in the prion protein gene (PRNP), which could trigger the onset of prion diseases. To identify somatic mutations in the PRNP gene in cancer tissues, we analyzed somatic mutations in the PRNP gene in cancer patients using the Cancer Genome Atlas (TCGA) database. In addition, to evaluate whether the somatic mutations in the PRNP gene in cancer patients had a damaging effect, we performed in silico analysis using PolyPhen-2, PANTHER, PROVEAN, and AMYCO. We identified a total of 48 somatic mutations in the PRNP gene, including 8 somatic mutations that are known pathogenic mutations of prion diseases. We identified significantly different distributions among the types of cancer, the mutation counts, and the ages of diagnosis between the total cancer patient population and cancer patients carrying somatic mutations in the PRNP gene. Strikingly, although invasive breast carcinoma and glioblastoma accounted for a high percentage of the total cancer patient population (9.9% and 5.4%, respectively), somatic mutations in the PRNP gene have not been identified in these two cancer types. We suggested the possibility that somatic mutations of the PRNP gene in glioblastoma can be masked by a diagnosis of prion disease. In addition, we found four aggregation-prone somatic mutations, these being L125F, E146Q, R151C, and K204N. To the best of our knowledge, this is the first specific analysis of the somatic mutations in the PRNP gene in cancer patients.

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“…In some cases, increases in aggregation propensity due to single amino acid substitutions are accurately predicted by multiple aggregation prediction algorithms, including PAPA [33,35]. Furthermore, the effects of a broad range of mutations within PrLDs expressed in yeast can also be accurately predicted by PAPA and other prion prediction algorithms, and these predictions generally extend to multicellular eukaryotes, albeit with some exceptions [36,37]. The complementary strengths of PLAAC and PAPA are likely derived from their methods of development.…”

Section: Introductionmentioning

confidence: 99%

Natural and pathogenic protein sequence variation affecting prion-like domains within and across human proteomes

Cascarina

Ross

2020

BMC Genomics

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Background: Impaired proteostatic regulation of proteins with prion-like domains (PrLDs) is associated with a variety of human diseases including neurodegenerative disorders, myopathies, and certain forms of cancer. For many of these disorders, current models suggest a prion-like molecular mechanism of disease, whereby proteins aggregate and spread to neighboring cells in an infectious manner. The development of prion prediction algorithms has facilitated the large-scale identification of PrLDs among "reference" proteomes for various organisms. However, the degree to which intraspecies protein sequence diversity influences predicted prion propensity has not been systematically examined. Results: Here, we explore protein sequence variation introduced at genetic, post-transcriptional, and posttranslational levels, and its influence on predicted aggregation propensity for human PrLDs. We find that sequence variation is relatively common among PrLDs and in some cases can result in relatively large differences in predicted prion propensity. Sequence variation introduced at the post-transcriptional level (via alternative splicing) also commonly affects predicted aggregation propensity, often by direct inclusion or exclusion of a PrLD. Finally, analysis of a database of sequence variants associated with human disease reveals a number of mutations within PrLDs that are predicted to increase prion propensity. Conclusions: Our analyses expand the list of candidate human PrLDs, quantitatively estimate the effects of sequence variation on the aggregation propensity of PrLDs, and suggest the involvement of prion-like mechanisms in additional human diseases.

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AMYCO: evaluation of mutational impact on prion-like proteins aggregation propensity

Cited by 24 publications

References 23 publications

Composition-based prediction and rational manipulation of prion-like domain recruitment to stress granules

Composition-based prediction and rational manipulation of prion-like domain recruitment to stress granules

Identification of Prion Disease-Related Somatic Mutations in the Prion Protein Gene (PRNP) in Cancer Patients

Natural and pathogenic protein sequence variation affecting prion-like domains within and across human proteomes

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