2014
DOI: 10.1093/bioinformatics/btu310
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PLAAC: a web and command-line application to identify proteins with prion-like amino acid composition

Abstract: http://plaac.wi.mit.edu/. The Ruby-based web framework and the command-line software (implemented in Java, with visualization routines in R) are available at http://github.com/whitehead/plaac under the MIT license. All software can be run under OS X, Windows and Unix.

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Cited by 494 publications
(569 citation statements)
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“…PLAAC scores (Lancaster et al , 2014) indicating the degree of prion‐like character in WT (black) and phosphomimetic (12E, red) full‐length FUS show that phosphomimetic mutations reduce the prion‐like character of the low‐complexity domain. 12E refers to FUS containing 12 phosphomimetic (E, glutamate) substitutions at the S/TQ DNA‐PK consensus phosphorylation sites in the LC domain, indicated by a gray line.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…PLAAC scores (Lancaster et al , 2014) indicating the degree of prion‐like character in WT (black) and phosphomimetic (12E, red) full‐length FUS show that phosphomimetic mutations reduce the prion‐like character of the low‐complexity domain. 12E refers to FUS containing 12 phosphomimetic (E, glutamate) substitutions at the S/TQ DNA‐PK consensus phosphorylation sites in the LC domain, indicated by a gray line.…”
Section: Resultsmentioning
confidence: 99%
“…One measure of the self‐assembly/aggregation propensity of disordered domains is a quantitative assessment of their “prion‐like” character, a name given to sequences enriched in polar residues and lacking charged and aliphatic residues as found in yeast prion proteins (Cascarina & Ross, 2014). Mimicking phosphorylation at S/TQ positions by substitution of serine or threonine with the negatively charged residue glutamic acid (glutamate) results in a marked decrease in the prion‐like propensity as measured by the PLAAC algorithm (Fig 1A; Lancaster et al , 2014), which identifies probable prion‐like protein segments. This result is consistent with changing the sequence composition character of the domain from nearly uncharged (only two negatively charged residues; no positively charged lysine, arginine, or histidine) to polyanionic and hence soluble and self‐repulsive by like‐charge interactions.…”
Section: Resultsmentioning
confidence: 99%
“…We previously developed a computational algorithm, PLAAC, to identify prion domains. The algorithm uses a hidden Markov model (HMM) to compare the similarity of a given sequence to the known sequence characteristics of previously identified yeast prions (18). We scored the entire Arabidopsis proteome using this algorithm and identified 474 Arabidopsis proteins that contained PrDs (Fig.…”
Section: Identification and Functional Classification Of Arabidopsismentioning
confidence: 99%
“…However, no prions have been identified in plants. Using a computational algorithm developed to find yeast prions (5,18), we identified ∼500 Arabidopsis proteins that have distinct prionlike domains (PrDs). These proteins showed a functional enrichment for involvement in flowering.…”
mentioning
confidence: 99%
“…These results are at the core of several algorithms for prion domain prediction, all relying on the analysis of amino acid sequences. 17,[21][22][23][24] These programs are constructed on 2 alternative models for amyloid formation by prion-like domains ( Figure 1): (1) The compositional model relying on the establishment of a large number of weak interactions 17 and (2) our model, which proposes 'classical' nucleation by short amyloidogenic stretches, whose amyloid propensity is modulated by the structural context. 24 Despite the mechanistic difference between algorithms, the advent of accurate computational tools to detect yeast prion domains opens new and exciting possibilities, allowing the exploration of proteomes for the discovery of novel and hitherto unexpected Q/N-enriched domains that may drive conformational conversion in novel prion proteins.…”
mentioning
confidence: 99%