Phase Separation as a Missing Mechanism for Interpretation of Disease Mutations

Tsang, Brian; Pritišanac, Iva; Scherer, Stephen W.; Moses, Alan M.; Forman‐Kay, Julie D.

doi:10.1016/j.cell.2020.11.050

Cited by 161 publications

(141 citation statements)

References 137 publications

(155 reference statements)

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“…Perhaps more exciting, our analysis of individual regions (such as in p27 shown above) indicate that unsupervised deep learning approaches like reverse homology, paired with appropriate interpretation methods, will lead to highly specific predictions of functional residues and regions within IDRs. This would represent an urgently needed advance, especially for IDRs that are mutated in disease, for which we have few mechanistic hypotheses about function (Vacic et al, 2012;Pritišanac et al, 2019;Tsang et al, 2020;Lindorff-Larsen and Kragelund, 2021).…”

Section: Discussionmentioning

confidence: 99%

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning

Pritišanac

et al. 2021

Preprint

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A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features, such as short motifs, amino acid repeats and physicochemical properties that mediate the functions of these regions. Here, we introduce a proteome-scale feature discovery method for IDRs. Our method, which we call "reverse homology", exploits the principle that important functional features are conserved over evolution as a contrastive learning signal for deep learning: given a set of homologous IDRs, the neural network has to correctly choose a randomly held-out homologue from another set of IDRs sampled randomly from the proteome. We pair reverse homology with a simple architecture and interpretation techniques, and show that the network learns conserved features of IDRs that can be interpreted as motifs, repeats, and other features. We also show that our model can be used to produce specific predictions of what residues and regions are most important to the function, providing a computational strategy for designing mutagenesis experiments in uncharacterized IDRs. Our results suggest that feature discovery using neural networks is a promising avenue to gain systematic insight into poorly understood protein sequences.

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Section: Discussionmentioning

confidence: 99%

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning

Pritišanac

et al. 2021

Preprint

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“…Condensate formation and LLPS is a direct result of multivalency, either acting through intermolecular interactions between the same protein (homotypic) or between different proteins (heterotypic) [77][78][79] (Figure 1D). Condensates form in a concentration dependent manner and is driven by low-affinity interactions between several sites, often of low sequence complexity [80][81][82].…”

Section: K Av ¼ [P Bound ]=([P Free ][L])mentioning

confidence: 99%

On the specificity of protein–protein interactions in the context of disorder

Teilum

Olsen

Kragelund

2021

Biochemical Journal

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With the increased focus on intrinsically disordered proteins (IDPs) and their large interactomes, the question about their specificity — or more so on their multispecificity — arise. Here we recapitulate how specificity and multispecificity are quantified and address through examples if IDPs in this respect differ from globular proteins. The conclusion is that quantitatively, globular proteins and IDPs are similar when it comes to specificity. However, compared with globular proteins, IDPs have larger interactome sizes, a phenomenon that is further enabled by their flexibility, repetitive binding motifs and propensity to adapt to different binding partners. For IDPs, this adaptability, interactome size and a higher degree of multivalency opens for new interaction mechanisms such as facilitated exchange through trimer formation and ultra-sensitivity via threshold effects and ensemble redistribution. IDPs and their interactions, thus, do not compromise the definition of specificity. Instead, it is the sheer size of their interactomes that complicates its calculation. More importantly, it is this size that challenges how we conceptually envision, interpret and speak about their specificity.

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“…Many autism spectrum disorder susceptibility genes encode proteins that function in RNA processing, activity-dependent translation and synaptic function (125). Furthermore, both phase separation and the partitioning of proteins in the cell can be J o u r n a l P r e -p r o o f disrupted by pathogenic mutations in these susceptibility genes (61). Given that frameshift mutations in MAGEL2 cause autism spectrum disorder, our study raises the possibility that an untapped reservoir of mutations in the intrinsically disordered N-terminus of MAGEL2 could contribute to some of the missing heritability in neurodevelopmental disorders.…”

Section: Discussionmentioning

confidence: 99%

The N-terminal domain of the Schaaf–Yang syndrome protein MAGEL2 likely has a role in RNA metabolism

Sanderson

Fahlman

Wevrick

2021

Journal of Biological Chemistry

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Phase Separation as a Missing Mechanism for Interpretation of Disease Mutations

Cited by 161 publications

References 137 publications

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning

On the specificity of protein–protein interactions in the context of disorder

The N-terminal domain of the Schaaf–Yang syndrome protein MAGEL2 likely has a role in RNA metabolism

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