On the Potential of Machine Learning to Examine the Relationship Between Sequence, Structure, Dynamics and Function of Intrinsically Disordered Proteins

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Valence and patterning of aromatic residues determine the phase behavior of prion-like domains

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Conformations of intrinsically disordered proteins are influenced by linear sequence distributions of oppositely charged residues

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Pi-Pi contacts are an overlooked protein feature relevant to phase separation

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Correlated mutations and residue contacts in proteins

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Evaluating Protein Transfer Learning with TAPE

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Phosphorylation of the FUS low‐complexity domain disrupts phase separation, aggregation, and toxicity

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SHIFTX2: significantly improved protein chemical shift prediction

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Critical assessment of methods of protein structure prediction (CASP) — round x

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Phase Separation of FUS Is Suppressed by Its Nuclear Import Receptor and Arginine Methylation

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Extreme disorder in an ultrahigh-affinity protein complex

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Sequence determinants of protein phase behavior from a coarse-grained model

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Modeling aspects of the language of life through transfer-learning protein sequences

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Deciphering how naturally occurring sequence features impact the phase behaviors of disordered prion-like domains

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Relation between single-molecule properties and phase behavior of intrinsically disordered proteins

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Distance-based protein folding powered by deep learning

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Cancer Mutations of the Tumor Suppressor SPOP Disrupt the Formation of Active, Phase-Separated Compartments

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Friend or foe—Post-translational modifications as regulators of phase separation and RNP granule dynamics

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Variant interpretation: functional assays to the rescue

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

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Systematic Discovery of New Recognition Peptides Mediating Protein Interaction Networks

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The language of proteins: NLP, machine learning & protein sequences

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Critical assessment of protein intrinsic disorder prediction

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The Eukaryotic Proteome Is Shaped by E3 Ubiquitin Ligases Targeting C-Terminal Degrons

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Deep learning methods in protein structure prediction

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Unfoldomics of human diseases: linking protein intrinsic disorder with diseases

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Phase Separation and Single-Chain Compactness of Charged Disordered Proteins Are Strongly Correlated

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Decoding molecular mechanisms for loss-of-function variants in the human proteomePreprint

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Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins Using Deep Learning

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A Deep Learning Method for Predicting Interactions for Intrinsically Disordered Regions of ProteinsPreprint

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amyloid-predict and LLPS-predict: Predicting phase separation propensities in the intrinsically disordered proteome

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Invisible but not inaccessible—Revealing transient oligomers formed by intrinsically disordered proteins with solution NMR and complementary methods

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GeoSeq-EGNN: A geometry-sequence fused equivariant graph neural network for predicting short linear motifs within intrinsically disordered regions

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Bioinformatics Approaches to the Structure and Function of Intrinsically Disordered Proteins

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Identification of intrinsic disorder in complexes from Protein Data Bank

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Predicting Conformational Ensembles of Intrinsically Disordered Proteins: From Molecular Dynamics to Machine Learning

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