Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure

Emenecker, Ryan; Griffith, Daniel; Holehouse, Alex S.

doi:10.1016/j.bpj.2021.08.039

Cited by 125 publications

(133 citation statements)

References 61 publications

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“…1A ). To assess this, we performed a systematic analysis of sequence conservation and predicted disorder across the S. cerevisiae proteome (see Methods ) ( 26, 27 ). This analysis revealed pervasive disorder and confirmed an expected anti-correlation between conservation and disorder ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Sequence- and chemical specificity define the functional landscape of intrinsically disordered regions

Langstein-Skora¹,

Schmid²,

Emenecker

et al. 2022

Preprint

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Intrinsically disordered protein regions (IDRs) pervasively engage in essential molecular functions, yet they are often poorly conserved as assessed by sequence alignment. To understand the seeming paradox of how sequence variability is compatible with function, we examined the functional determinants for a poorly conserved but essential IDR. We show that IDR function depends on two distinct but related properties: sequence- and chemical specificity. While sequence-specificity works via linear binding motifs, chemical-specificity reflects the sequence-encoded chemistry of multivalent interactions through amino acids across an IDR. Unexpectedly, an apparently essential binding motif can be removed if compensatory changes to the sequence chemistry are made, highlighting the orthogonality and interoperability of both properties. Our results provide a general framework to understand the functional constraints on IDR sequence evolution.

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Section: Resultsmentioning

confidence: 99%

Sequence- and chemical specificity define the functional landscape of intrinsically disordered regions

Langstein-Skora¹,

Schmid²,

Emenecker

et al. 2022

Preprint

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“…This is comparable to other established ID predictors. For example, using metapredict ( 94 ), selected because it can quickly process large numbers of sequences, we found 99.5% of sequences to have average disorder scores less than 0.5 (indicating folded). Of the proteins in the SCOPe database with average disorder score greater than 0.5 by metapredict, only half were also identified as not folded by ParSe, indicating that combining an established ID predictor with our classification scheme could improve its fidelity.…”

Section: Resultsmentioning

confidence: 99%

“…Metapredict score ( 94 ), which predicts the presence of ID in a sequence, was calculated by computer algorithm using the Python script available at http://metapredict.net .…”

Section: Methodsmentioning

confidence: 99%

Beta turn propensity and a model polymer scaling exponent identify intrinsically disordered phase-separating proteins

Paiz

Allen

Correia

et al. 2021

Journal of Biological Chemistry

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…For the analysis of sequence-based predictors of disorder in Figure 2A , we used the software packages metapredict, SPOT-Disorder, DISOPRED3, and IUPred2A (Emenecker et al 2021; Hanson et al 2017; Jones & Cozzetto 2015; Mészáros et al 2018). SPOT-Disorder was run as noted above.…”

Section: Methodsmentioning

confidence: 99%

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

Alderson

Pritišanac

Moses

et al. 2022

Preprint

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Deep learning-based approaches to protein structure prediction, such as AlphaFold2 and RoseTTAFold, can now define many protein structures with atomic-level accuracy. The AlphaFold Protein Structure Database (AFDB) contains a predicted structure for nearly every protein in the human proteome, including proteins that have intrinsically disordered regions (IDRs), which do not adopt a stable structure and rapidly interconvert between conformations. Although it is generally assumed that IDRs have very low AlphaFold2 confidence scores that reflect low-confidence structural predictions, we show here that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. The amino-acid sequences of IDRs with high-confidence structures do not show significant similarity to the Protein Data Bank; instead, these IDR sequences exhibit a higher degree of positional amino-acid sequence conservation and are more enriched in charged and hydrophobic residues than IDRs with low-confidence structures. We compared the AlphaFold2 predictions to experimental NMR data for a subset of IDRs known to fold under specific conditions, finding that AlphaFold2 tends to capture the folded state structure. We note, however, that these AlphaFold2 predictions cannot detect functionally relevant structural plasticity within IDRs and cannot offer an ensemble representation of IDRs. Nevertheless, AlphaFold2 assigns high-confidence scores to about 60% of a set of 350 IDRs that have been reported to conditionally fold, suggesting that AlphaFold2 has learned to identify conditionally folded IDRs, which is unexpected, since IDRs were minimally represented in the training data. Leveraging this ability to discover IDRs that conditionally fold, we find that up to 80% of IDRs in archaea and bacteria are predicted to conditionally fold, but less than 20% of eukaryotic IDRs. Our results suggest that a large majority of IDRs in the proteomes of human and other eukaryotes would be expected to function in the absence of conditional folding.

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Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure

Cited by 125 publications

References 61 publications

Sequence- and chemical specificity define the functional landscape of intrinsically disordered regions

Sequence- and chemical specificity define the functional landscape of intrinsically disordered regions

Beta turn propensity and a model polymer scaling exponent identify intrinsically disordered phase-separating proteins

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

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