PhaSepDB: a database of liquid–liquid phase separation related proteins

You, Kaiqiang; Huang, Qi; Yu, Chunyu; Shen, Boyan; Sevilla, Cristoffer; Shi, Minglei; Hermjakob, Henning; Yang, Chen; Li, Tingting

doi:10.1093/nar/gkz847

Cited by 169 publications

(156 citation statements)

References 40 publications

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“…We have analysed three public datasets of proteins reported to undergo liquid-liquid phase separation (see Methods). The first is the PhaSepDB dataset (http:/db.phasep.pro) (29), which assembles data from three resources (Methods , Table S1): (1) proteins from the literature with in vivo and in vitro experimental data on liquid-liquid phase separation (REV, 351 proteins, Methods, Table S1), (2) proteins from UniProt associated with known organelles (UNI, 378 proteins, Methods, Table S1), and…”

Section: Properties Of the Proteins That Can Form The Droplet Statementioning

confidence: 99%

“…Proteins in the REV and PSP datasets exhibit disordered binding modes, which are comparable to droplet-driver proteins, so they likely phase separate spontaneously. Proteins associated with known membraneless organelles (UNI), or identified by high-throughput experiments (HTS) (29) have significantly lower conformational entropy in both free and bound states, thus likely have components, which form droplets via partner-interactions. Comparison of spontaneously phase-separating and non-phase separating proteins ( Figures 3C,D) indicates that a high conformational entropy is a characteristic of the droplet state.…”

Section: Analysis Of the Conformational Entropy Of Droplet-driving Anmentioning

confidence: 99%

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Widespread occurrence of the droplet state of proteins in the human proteome

Hardenberg

Horváth

Ambrus

et al. 2020

Preprint

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A wide range of proteins have been reported to condensate into a dense liquid phase, forming a reversible droplet state. Failure in the control of the droplet state can lead to the formation of the more stable amyloid state, which is often disease-related. These observations prompt the question of how many proteins can undergo liquid-liquid phase separation. Here, in order to address this problem, we discuss the biophysical principles underlying the droplet state of proteins by analyzing current evidence for droplet-driver and droplet-client proteins. Based on the concept that the droplet state is stabilized by the large conformational entropy associated with non-specific side-chain interactions, we develop the FuzDrop method to predict droplet-promoting regions and proteins, which can spontaneously phase separate. We use this approach to carry out a proteome-level study to rank proteins according to their propensity to form the droplet state, spontaneously or via partner interactions. Our results lead to the conclusion that the droplet state could be, at least transiently, accessible to most proteins under conditions found in the cellular environment.

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Section: Properties Of the Proteins That Can Form The Droplet Statementioning

confidence: 99%

Section: Analysis Of the Conformational Entropy Of Droplet-driving Anmentioning

confidence: 99%

Widespread occurrence of the droplet state of proteins in the human proteome

Hardenberg

Horváth

Ambrus

et al. 2020

Preprint

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“…The combination of these strategies enabled us to construct a final model that showed both high specificity and sensitivity (AUC-ROC -0.85) in identifying LLPS-prone sequences from external data constructed through an exhaustive literature search. 32 These results shed light onto the physicochemical factors modulating protein condensate formation, and provide a platform rooted in molecular principles for the prediction of protein phase behaviour.…”

Section: Introductionmentioning

confidence: 85%

“…Having established the high cross-validation performance of the models within our generated datasets, we set out to test the model on external test data. To this effect, we used the PhaSepDB 32 Table S5).…”

Section: Performance Of the Models On An External Datasetmentioning

confidence: 99%

Machine learning models for predicting protein condensate formation from sequence determinants and embeddings

Saar

Morgunov

et al. 2020

Preprint

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Intracellular phase separation of proteins into biomolecular condensates is increasingly recognised as an important phenomenon for cellular compartmentalisation and regulation of biological function. Different hypotheses about the parameters that determine the tendency of proteins to form condensates have been proposed with some of them probed experimentally through the use of constructs generated by sequence alterations. To broaden the scope of these observations, here, we established an in silico strategy for understanding on a global level the associations between protein sequence and condensate formation, and used this information to construct machine learning classifiers for predicting liquid-liquid phase separation (LLPS) from protein sequence. Our analysis highlighted that LLPS-prone sequences are more disordered, hydrophobic and of lower Shannon entropy than sequences in the Protein Data Bank or the Swiss-Prot database, and have their disordered regions enriched in polar, aromatic and charged residues. Using these determining features together with neural network based word2vec sequence embeddings, we developed machine learning classifiers for predicting protein condensate formation. Our model, trained to distinguish LLPS-prone sequences from structured proteins, achieved high accuracy (93%; 25-fold cross-validation) and identified condensate forming sequences from external independent test data at 97% sensitivity. Moreover, in combination with a classifier that had developed a nuanced insight into the features governing protein phase behaviour by learning to distinguish between sequences of varying LLPS propensity, the sensitivity was supplemented with high specificity (approximated ROC-AUC of 0.85). These results provide a platform rooted in molecular principles for understanding protein phase behaviour. The predictor is accessible from https://deephase.ch.cam.ac.uk/ .

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“…We noticed that all the reviewed demixing proteins were characterized by an internal disorder content greater than 0.15; thus, we focused on those PPI having a disordered content above that threshold, defining a subset made of 88 members. We compared them to entries in PhaSepDB (56), which aggregates a wide range of direct and indirect evidence of proteins phase separation (e.g. fully demonstrated or suggested by high-throughput data), reaching a final set of 49 likely demixing interactors (Table S1 in the Supporting Material).…”

Section: Figure 1 Distribution Of the Disorder Content Of The Ape1 Imentioning

confidence: 99%

Is there a role of phase partitioning in coordinating DNA damage response?

Antoniali

Dalla

et al. 2020

Preprint

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DNA repair pathways are critical processes that need both spatial and temporal fine regulation. Liquid-liquid phase separation (LLPS) is a way to concentrate biochemical reactions, while excluding non-interacting components. Protein’s disordered domains, as well as RNA, favor condensation to modulate this process. Recent insights about phase-separation mechanisms pointed to new fascinating models that could explain how cells could cope with DNA damage responses. In this context, it is emerging that RNA-processing pathways and PARylation events, through the addition of an ADP-ribose moiety to both proteins and DNA, participate in different aspects of the DNA Damage Response (DDR). Remarkably, defects in these regulatory connections are associated with genomic instability and human pathologies. In addition, it has been recently noticed that several DNA repair enzymes, such as 53BP1 and APE1, are endowed with RNA binding abilities. APE1 is a multifunctional protein belonging to the Base Excision Repair (BER) pathway of non-distorting DNA lesions, bearing additional ‘non-canonical’ DNA-repair functions associated with processes coping with RNA metabolism. In this work, after reviewing the recent literature supporting a role of LLPS in DDR, we analyze, as a proof of principle, the interactome of APE1 using a bioinformatics approach to look for clues of LLPS in BER. Some of the APE1 interactors are associated with cellular processes in which LLPS has been either proved or proposed and are involved in several tumorigenic and amyloidogenic events. This work represents a paradigmatical pipeline for evaluating the relevance of LLPS in DDR.Statement of significanceIn this work, we aimed to test the hypothesis of an involvement of phase-separation in regulating the molecular mechanisms of the multifunctional enzyme APE1 starting from the analysis of its recently-characterized protein-protein interactome (PPI). We compared APE1-PPI to phase-separation databases and we performed functional enrichment analysis, uncovering links between APE1 and already known demixing factors, establishing an association with liquidliquid phase separation. This analysis could represent a starting point for implementing downstream experimental validations, using in vitro and in vivo approaches, to assess actual demixing.

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PhaSepDB: a database of liquid–liquid phase separation related proteins

Cited by 169 publications

References 40 publications

Widespread occurrence of the droplet state of proteins in the human proteome

Widespread occurrence of the droplet state of proteins in the human proteome

Machine learning models for predicting protein condensate formation from sequence determinants and embeddings

Is there a role of phase partitioning in coordinating DNA damage response?

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