Intrinsically disordered regions that drive phase separation form a robustly distinct protein class

Ibrahim, Ayyam Y; Khaodeuanepheng, Nathan P.; Amarasekara, Dhanush L.; Correia, John J.; Lewis, Karen A.; Fitzkee, Nicholas C.; Hough, Loren E.; Whitten, Steven T.

doi:10.1016/j.jbc.2022.102801

Cited by 44 publications

(64 citation statements)

References 111 publications

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“…Our results were consistent with that idea (Paiz et al, 2021). However, we also found robust property differences between folded, ID, and PS ID protein regions (Ibrahim et al, 2023). In ParSe 2.0, an optimal set of property scales allows facile predictions of domain-level structure and provides a simple, quantitative metric for the sequence-calculated phase separation potential.…”

supporting

confidence: 90%

“…Previously, we found that ParSe 2.0 is at least as accurate in identifying proteins and regions within proteins that drive phase separation compared to other published phase separation predictors and using publicly available datasets (Ibrahim et al, 2023). To demonstrate such predictor comparisons here, we used ParSe 2.0 to generate three separate sequence sets derived from the human proteome.…”

Section: Comparing Parse 20 To Other Sequence-based Predictorsmentioning

confidence: 99%

“…The algorithm is computationally simple and fast enough to scan tens of thousands of sequences in minutes using a single computer. Moreover, its algorithmic simplicity does not diminish its accuracy; we have found ParSe 2.0 to be as, or more, accurate than other published predictors in identifying proteins and regions within proteins that drive phase separation (Ibrahim et al, 2023). We have created a web application that enables researchers to utilize ParSe 2.0 for proteome-scale searches for sequences that drive protein phase separation.…”

mentioning

confidence: 99%

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ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level

Wilson,

Lewis,

Fitzkee

et al. 2023

Protein Science

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We have developed an algorithm, ParSe, that accurately identifies from the primary sequence those protein regions likely to exhibit physiological phase separation behavior. Originally, ParSe was designed to test the hypothesis that, for flexible proteins, phase separation potential is correlated to hydrodynamic size. While our results were consistent with that idea, we also found that many different descriptors could successfully differentiate between three classes of protein regions: folded, intrinsically disordered, and phase‐separating intrinsically disordered. Consequently, numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. Built from that finding, ParSe 2.0 uses an optimal set of property scales to predict domain‐level organization and compute a sequence‐based prediction of phase separation potential. The algorithm is fast enough to scan the whole of the human proteome in minutes on a single computer and is equally or more accurate than other published predictors in identifying proteins and regions within proteins that drive phase separation. Here, we describe a web application for ParSe 2.0 that may be accessed through a browser by visiting https://stevewhitten.github.io/Parse_v2_FASTA to quickly identify phase‐separating proteins within large sequence sets, or by visiting https://stevewhitten.github.io/Parse_v2_web to evaluate individual protein sequences.This article is protected by copyright. All rights reserved.

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supporting

confidence: 90%

Section: Comparing Parse 20 To Other Sequence-based Predictorsmentioning

confidence: 99%

mentioning

confidence: 99%

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ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level

Wilson,

Lewis,

Fitzkee

et al. 2023

Protein Science

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“…It should be noted that structure prediction methods such as AlphaFold v2.0 [ 141 , 142 ] can be used to distinguish folded and disordered regions. In addition, sequence-based algorithms can be extended to predict other factors such as prion-like domains [ 143 , 144 ], liquid–liquid phase separation [ 145 , 146 , 147 ], protein aggregation [ 148 , 149 ], and mutual synergistic protein folding [ 150 ], with an increasing number of experimental measurements serving as the training data set. A clear advantage of sequence-based predictors is their ease of use.…”

Section: Theoretical and Computational Biophysical Techniquesmentioning

confidence: 99%

Biophysical and Integrative Characterization of Protein Intrinsic Disorder as a Prime Target for Drug Discovery

et al. 2023

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Protein intrinsic disorder is increasingly recognized for its biological and disease-driven functions. However, it represents significant challenges for biophysical studies due to its high conformational flexibility. In addressing these challenges, we highlight the complementary and distinct capabilities of a range of experimental and computational methods and further describe integrative strategies available for combining these techniques. Integrative biophysics methods provide valuable insights into the sequence–structure–function relationship of disordered proteins, setting the stage for protein intrinsic disorder to become a promising target for drug discovery. Finally, we briefly summarize recent advances in the development of new small molecule inhibitors targeting the disordered N-terminal domains of three vital transcription factors.

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“…Moreover, 30% to 50% of eukaryotic proteins have at least one long intrinsically disordered region (IDR; ≥30 consecutive amino acids (Ward et al., 2004; Xue et al., 2012; Yan et al., 2013). IDPs are crucial for many diverse cellular functions (Xie et al., 2007), such as transcription and translation (Liu et al., 2006; Peng et al., 2012, 2014; Staby et al., 2017; Toth‐Petroczy et al., 2008), protein‐protein interactions (Fuxreiter et al., 2014; Hu et al., 2017; Uversky, 2015c; Vacic et al., 2007; Yan et al., 2016), protein‐nucleic acids interactions (Varadi et al., 2015; Wang et al., 2016; Zhao, Katuwawala, Oldfield, Hu, et al., 2021), cell signaling (Bondos et al., 2022; Mitrea & Kriwacki, 2013; Uversky et al., 2005), and phase separation (Ibrahim et al., 2023; Uversky, 2017). Disordered proteins also underly dark proteomes, which are collections of proteins that are not amenable to experimental structure determination (Hu et al., 2018; Kulkarni & Uversky, 2018; Uversky, 2018).…”

Section: Introductionmentioning

confidence: 99%

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

Uversky

Kurgan

2023

Current Protocols

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There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid-level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence-based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions.

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Intrinsically disordered regions that drive phase separation form a robustly distinct protein class

Cited by 44 publications

References 111 publications

ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level

ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level

Biophysical and Integrative Characterization of Protein Intrinsic Disorder as a Prime Target for Drug Discovery

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

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