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The spatial and temporal organization of interactions between proteins underlie the regulation of most cellular processes. The requirement for such interactions to be specific predisposes a view that protein–protein interactions are relatively static and are formed through the stable complementarity of the interacting partners. A growing body of reports indicate, however, that many interactions lead to fuzzy complexes with an ensemble of conformations in dynamic exchange accounting for the observed binding. Here, we discuss how NMR has facilitated the characterization of these discrete, dynamic complexes and how such characterization has aided the understanding of dynamic, condensed phases of phase-separating proteins with exchanging multivalent interactions.

The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

Tubulinopathies and neurodevelopmental ciliopathies are two groups of genetic disorders characterized by abnormal brain development resulting in structural brain malformations. Tubulinopathies are caused by dominant missense mutations in genes encoding for tubulins, the building blocks of microtubules. Neurodevelopmental ciliopathies are mostly recessive disorders caused by defects in the function of the primary cilium, a sensory organelle that modulates signaling pathways important for brain development. Though more than 40 genes have been associated with neurodevelopmental ciliopathies, many patients still do not have an identified genetic etiology. Herein, we present a novelde novoheterozygous missense variant in Tubulin Beta Class I (TUBB) identified through whole-genome sequencing analysis in a patient with both ciliopathy and tubulinopathy brain features. While microtubules are fundamental to primary cilia formation and function, no association between mutations in tubulin genes and neurodevelopmental ciliopathies has been found to date. Using patient-derived cells and gene-edited isogenic cell lines, we show that the identified variant impairs the early stages of cilia formation by altering microtubule dynamics and structure. Furthermore, we demonstrate that the disease mechanism is not haploinsufficiency and that other patient mutations inTUBBaffect cilia formationin vitro, putting forward defective ciliogenesis as a contributing pathogenic factor in a subset of tubulinopathy patients.

Algorithms for computational protein design usually begin with a 3D structure and design an amino acid sequence that will fold into that structure. Since intrinsically disordered protein regions lack stable 3D structures, strategies for their design have been limited. Here we describe and validate a general computational strategy for design of intrinsically disordered regions (IDRs). Our algorithm designs synthetic IDRs by minimizing the distance of an initially random amino acid sequence to a known IDR in a high-dimensional space of molecular properties, such as repeat content, charge and hydrophobicity. We tested a handful of IDRs designed to target proteins to mitochondria and heat-induced condensates, and several showed the expected patterns in cells.One sentence summaryWe design synthetic mitochondrial targeting signals and condensate forming intrinsically disordered regions

Supplementary Methods from Synuclein-γ Targeting Peptide Inhibitor that Enhances Sensitivity of Breast Cancer Cells to Antimicrotubule Drugs

Supplementary Figures 1-4 from Synuclein-γ Targeting Peptide Inhibitor that Enhances Sensitivity of Breast Cancer Cells to Antimicrotubule Drugs

Electrostatic interactions and charge balance are important for the formation of biomolecular condensates involving proteins and nucleic acids. However, a detailed, atomistic picture of the charge distribution around proteins during the phase-separation process is lacking. Here, we use solution NMR spectroscopy to measure residue-specific near-surface electrostatic potentials ( ϕ ENS ) of the positively charged carboxyl-terminal intrinsically disordered 103 residues of CAPRIN1, an RNA-binding protein localized to membraneless organelles playing an important role in messenger RNA (mRNA) storage and translation. Measured ϕ ENS values have been mapped along the adenosine triphosphate (ATP)–induced phase-separation trajectory. In the absence of ATP, ϕ ENS values for the mixed state of CAPRIN1 are positive and large and progressively decrease as ATP is added. This is coupled to increasing interchain interactions, particularly between aromatic-rich and arginine-rich regions of the protein. Upon phase separation, CAPRIN1 molecules in the condensed phase are neutral ( ϕ ENS ≈ 0 mV), with ∼five molecules of ATP associated with each CAPRIN1 chain. Increasing the ATP concentration further inverts the CAPRIN1 electrostatic potential, so that molecules become negatively charged, especially in aromatic-rich regions, leading to re-entrance into a mixed phase. Our results collectively show that a subtle balance between electrostatic repulsion and interchain attractive interactions regulates CAPRIN1 phase separation and provides insight into how nucleotides, such as ATP, can induce formation of and subsequently dissolve protein condensates.

We consider a generic representation problem of internal coordinates (bond lengths, valence angles, and dihedral angles) and their transformation to 3-dimensional Cartesian coordinates of a biomolecule. We show that the internal-to-Cartesian process relies on correctly predicting chemically subtle correlations among the internal coordinates themselves, and learning these correlations increases the fidelity of the Cartesian representation. This general problem has been solved with machine learning for proteins, but with appropriately formulated data is extensible to any type of chain biomolecule including RNA, DNA, and lipids. We show that the internal-to-Cartesian process relies on correctly predicting chemically subtle correlations among the internal coordinates themselves, and learning these correlations increases the fidelity of the Cartesian representation. We developed a machine learning algorithm, Int2Cart, to predict bond lengths and bond angles from backbone torsion angles and residue types of a protein, and allows reconstruction of protein structures better than using fixed bond lengths and bond angles, or a static library method that relies on backbone torsion angles and residue types on a single residue. The Int2Cart algorithm has been implemented as an individual python package at https://github.com/THGLab/int2cart.

Exciting recent work has highlighted that numerous cellular compartments lack encapsulating lipid bilayers (often called “membraneless organelles”), and that their structure and function are central to the regulation of key biological processes, including transcription, RNA splicing, translation, and more. These structures have been described as “biomolecular condensates” to underscore that biomolecules can be significantly concentrated in them. Many condensates, including RNA granules and processing bodies, are enriched in proteins and nucleic acids. Biomolecular condensates exhibit a range of material states from liquid- to gel-like, with the physical process of liquid–liquid phase separation implicated in driving or contributing to their formation. To date, in vitro studies of phase separation have provided mechanistic insights into the formation and function of condensates. However, the link between the often micron-sized in vitro condensates with nanometer-sized cellular correlates has not been well established. Consequently, questions have arisen as to whether cellular structures below the optical resolution limit can be considered biomolecular condensates. Similarly, the distinction between condensates and discrete dynamic hub complexes is debated. Here we discuss the key features that define biomolecular condensates to help understand behaviors of structures containing and generating RNA.

The precise regulation of gene expression is fundamental to neurodevelopment, plasticity and cognitive function. Although several studies have profiled transcription in the developing human brain, there is a gap in understanding of accompanying translational regulation. In this study, we performed ribosome profiling on 73 human prenatal and adult cortex samples. We characterized the translational regulation of annotated open reading frames (ORFs) and identified thousands of previously unknown translation events, including small ORFs that give rise to human-specific and/or brain-specific microproteins, many of which we independently verified using proteomics. Ribosome profiling in stem-cell-derived human neuronal cultures corroborated these findings and revealed that several neuronal activity-induced non-coding RNAs encode previously undescribed microproteins. Physicochemical analysis of brain microproteins identified a class of proteins that contain arginine-glycine-glycine (RGG) repeats and, thus, may be regulators of RNA metabolism. This resource expands the known translational landscape of the human brain and illuminates previously unknown brain-specific protein products.

Many membraneless organelles are thought to be biomolecular condensates formed by phase separation of proteins and other biopolymers. Post-translational modifications (PTMs) can impact protein phase separation behavior, although for many PTMs this aspect of their function is unknown. O-linked β-D-N-acetylglucosaminylation (O-GlcNAcylation) is an abundant form of intracellular glycosylation whose roles in regulating biomolecular condensate assembly and dynamics have not been delineated. Using an in vitro approach, we found that O-GlcNAcylation reduces the phase separation propensity of the EWS N-terminal low complexity region (LCRN) under different conditions, including in the presence of the arginine- and glycine-rich RNA-binding domains (RBD). O-GlcNAcylation enhances fluorescence recovery after photobleaching (FRAP) within EWS LCRN condensates and causes the droplets to exhibit more liquid-like relaxation following fusion. Following extended incubation times, EWS LCRN+RBD condensates exhibit diminished FRAP, indicating a loss of fluidity, while condensates containing the O-GlcNAcylated LCRN do not. In HeLa cells, EWS is less O-GlcNAcylated following OGT knockdown, which correlates with its increased accumulation in a filter retardation assay. Relative to the human proteome, O-GlcNAcylated proteins are enriched with regions that are predicted to phase separate, suggesting a general role of O-GlcNAcylation in regulation of biomolecular condensates.

O-GlcNAc transferase (OGT) is an essential glycosylating enzyme that catalyzes the addition of N-acetylglucosamine to serine or threonine residues of nuclear and cytoplasmic proteins. The enzyme glycosylates a broad range of peptide sequences and prediction of glycosylation sites has proven challenging. The lack of an experimentally verified set of polypeptide sequences that are not glycosylated by OGT has made prediction of legitimate glycosylation sites more difficult. Here, we tested a number of intrinsically disordered protein regions as substrates of OGT to establish a set of sequences that are not glycosylated by OGT. The negative data set suggests an amino acid compositional bias for OGT targets. This compositional bias was validated by modifying the amino acid composition of the protein Fused in sarcoma (FUS) to enhance glycosylation. NMR experiments demonstrate that the tetratricopeptide repeat (TPR) region of OGT can bind FUS and that glycosylation-promoting mutations enhance binding. These results provide evidence that the TPR recognizes disordered segments of substrates with particular compositions to promote glycosylation, providing insight into the broad specificity of OGT.

The structural characterization of proteins with a disorder requires a computational approach backed by experiments to model their diverse and dynamic structural ensembles. The selection of conformational ensembles consistent with solution experiments of disordered proteins highly depends on the initial pool of conformers, with currently available tools limited by conformational sampling. We have developed a Generative Recurrent Neural Network (GRNN) that uses supervised learning to bias the probability distributions of torsions to take advantage of experimental data types such as nuclear magnetic resonance J-couplings, nuclear Overhauser effects, and paramagnetic resonance enhancements. We show that updating the generative model parameters according to the reward feedback on the basis of the agreement between experimental data and probabilistic selection of torsions from learned distributions provides an alternative to existing approaches that simply reweight conformers of a static structural pool for disordered proteins. Instead, the biased GRNN, DynamICE, learns to physically change the conformations of the underlying pool of the disordered protein to those that better agree with experiments.

The power of structural information for informing biological mechanism is clear for stable folded macromolecules, but similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here we present IDPConformerGenerator, a flexible, modular open source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank, and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal to Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

Intrinsically disordered proteins and unfolded proteins have fluctuating conformational ensembles that are fundamental to their biological function and impact protein folding, stability and misfolding. Despite the importance of protein dynamics and conformational sampling, time-dependent data types are not fully exploited when defining and refining disordered protein ensembles. Here we introduce a computational framework using an elastic network model and normal mode displacements to generate a dynamic disordered ensemble consistent with NMR-derived dynamics parameters, including transverse [Formula: see text] relaxation rates and Lipari-Szabo order parameters ([Formula: see text] values). We illustrate our approach using the unfolded state of the drkN SH3 domain to show that the dynamical ensembles give better agreement than a static ensemble for a wide range of experimental validation data including NMR chemical shifts, J-couplings, nuclear Overhauser effects, paramagnetic relaxation enhancements, residual dipolar couplings, hydrodynamic radii, single-molecule fluorescence Förster resonance energy transfer, and small-angle X-ray scattering.

The AlphaFold Protein Structure Database contains predicted structures for millions of proteins. For the majority of human proteins that contain intrinsically disordered regions (IDRs), which do not adopt a stable structure, it is generally assumed these regions have low AlphaFold2 confidence scores that reflect low-confidence structural predictions. Here, we show that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. By comparison to experimental NMR data for a subset of IDRs that are known to conditionally fold (i.e., upon binding or under other specific conditions), we find that AlphaFold2 often predicts the structure of the conditionally folded state. Based on databases of IDRs that are known to conditionally fold, we estimate that AlphaFold2 can identify conditionally folding IDRs at a precision as high as 88% at a 10% false positive rate, which is remarkable considering that conditionally folded IDR structures were minimally represented in its training data. We find that human disease mutations are nearly 5-fold enriched in conditionally folded IDRs over IDRs in general, and that up to 80% of IDRs in prokaryotes are predicted to conditionally fold, compared to less than 20% of eukaryotic IDRs. These results indicate that a large majority of IDRs in the proteomes of human and other eukaryotes function in the absence of conditional folding, but the regions that do acquire folds are more sensitive to mutations. We emphasize that the AlphaFold2 predictions do not reveal functionally relevant structural plasticity within IDRs and cannot offer realistic ensemble representations of conditionally folded IDRs.Significance StatementAlphaFold2 and other machine learning-based methods can accurately predict the structures of most proteins. However, nearly two-thirds of human proteins contain segments that are highly flexible and do not autonomously fold, otherwise known as intrinsically disordered regions (IDRs). In general, IDRs interconvert rapidly between a large number of different conformations, posing a significant problem for protein structure prediction methods that define one or a small number of stable conformations. Here, we found that AlphaFold2 can readily identify structures for a subset of IDRs that fold under certain conditions (conditional folding). We leverage AlphaFold2’s predictions of conditionally folded IDRs to quantify the extent of conditional folding across the tree of life, and to rationalize disease-causing mutations in IDRs.Classifications: Biological Sciences; Biophysics and Computational Biology

Intrinsically disordered proteins (IDPs) play critical roles in regulatory protein interactions, but detailed structural/dynamics characterization of their ensembles remain challenging, both in isolation and they form dynamic ‘fuzzy’ complexes. Such is the case for mRNA cap-dependent translation initiation, which is regulated by the interaction of the predominantly folded eukaryotic initiation factor 4E (eIF4E) with the intrinsically disordered eIF4E binding proteins (4E-BPs) in a phosphorylation-dependent manner. Single-molecule Förster resonance energy transfer showed that the conformational changes of 4E-BP2 induced by binding to eIF4E are non-uniform along the sequence; while a central region containing both motifs that bind to eIF4E expands and becomes stiffer, the C-terminal region is less affected. Fluorescence anisotropy decay revealed a nonuniform segmental flexibility around six different labelling sites along the chain. Dynamic quenching of these fluorescent probes by intrinsic aromatic residues measured via fluorescence correlation spectroscopy report on transient intra- and inter-molecular contacts on ns-μs timescales. Upon hyperphosphorylation, which induces folding of ~40 residues in 4E-BP2, the quenching rates decreased at most labelling sites. The chain dynamics around sites in the C-terminal region far away from the two binding motifs significantly increased upon binding to eIF4E, suggesting that this region is also involved in the highly dynamic 4E-BP2:eIF4E complex. Our time-resolved fluorescence data paint a sequence-level rigidity map of three states of 4E-BP2 differing in phosphorylation or binding status and distinguish regions that form contacts with eIF4E. This study adds complementary structural and dynamics information to recent studies of 4E-BP2, and it constitutes an important step towards a mechanistic understanding of this important IDP via integrative modelling.

Biomolecular condensates concentrate proteins, nucleic acids, and small molecules and play an essential role in many biological processes. Their formation is tuned by a balance between energetically favorable and unfavorable contacts, with charge-charge interactions playing a central role in some systems. The positively charged intrinsically disordered carboxy-terminal region of the RNA-binding protein CAPRIN1 is one such example, phase separating upon addition of negatively charged ATP or high concentrations of sodium chloride (NaCl). Using solution NMR spectroscopy, we measured residue-specific near-surface electrostatic potentials (ϕ

Autism Spectrum Disorder (ASD) is genetically complex with ~100 copy number variants and genes involved. To try to establish more definitive genotype and phenotype correlations in ASD, we searched genome sequence data, and the literature, for recurrent predicted damaging sequence-level variants affecting single genes. We identified 18 individuals from 16 unrelated families carrying a heterozygous guanine duplication (c.3679dup; p.Ala1227Glyfs*69) occurring within a string of 8 guanines (genomic location [hg38]g.50,721,512dup) affecting SHANK3, a prototypical ASD gene (0.08% of ASD-affected individuals carried the predicted p.Ala1227Glyfs*69 frameshift variant). Most probands carried de novo mutations, but five individuals in three families inherited it through somatic mosaicism. We scrutinized the phenotype of p.Ala1227Glyfs*69 carriers, and while everyone (17/17) formally tested for ASD carried a diagnosis, there was the variable expression of core ASD features both within and between families. Defining such recurrent mutational mechanisms underlying an ASD outcome is important for genetic counseling and early intervention.

The precise regulation of gene expression is fundamental to neurodevelopment, plasticity, and cognitive function. While several studies have deeply profiled mRNA dynamics in the developing human brain, there is a fundamental gap in our understanding of accompanying translational regulation. We perform ribosome profiling from more than 70 human prenatal and adult cortex samples across ontogeny and into adulthood, mapping translation events at nucleotide resolution. In addition to characterizing the translational regulation of annotated open reading frames (ORFs), we identify thousands of previously unknown translation events, including small open reading frames (sORFs) that give rise to human- and/or brain-specific microproteins, many of which we independently verify using size-selected proteomics. Ribosome profiling in stem cell-derived human neuronal cultures further corroborates these findings and shows that several neuronal activity-induced long non-coding RNAs (lncRNAs), including LINC00473, a primate-specific lncRNA implicated in depression, encode previously undescribed microproteins. Physicochemical analysis of these brain microproteinss identifies a large class harboring arginine-glycine-glycine (RGG) repeats as strong candidates for regulating RNA metabolism. Moreover, we find that, collectively, these previously unknown human brain sORFs are enriched for variants associated with schizophrenia. In addition to significantly expanding the translational landscape of the developing brain, this atlas will serve as a rich resource for the annotation and functional interrogation of thousands of previously unknown brain-specific protein products.

International audience; Macroautophagy is a highly conserved eukaryotic cellular pathway involving the engulfment of macromolecules, organelles, and invading microbes by a double-membrane compartment and subsequent lysosomal degradation. The mechanisms that regulate macroautophagy, and the interaction of its components with other cellular pathways, have remained unclear. Here, we performed proximity-dependent biotin identification (BioID) on 39 core human macroautophagy proteins, identifying over 700 unique high confidence proximity interactors with new putative connections between macroautophagic and essential cellular processes. Of note, we identify members of the OSBPL (oxysterol binding protein like) family as Atg8-family protein interactors. We subsequently conducted comprehensive screens of the OSBPL family for LC3B-binding and roles in xenophagy and aggrephagy. OSBPL7 and OSBPL11 emerged as novel lipid transfer proteins required for macroautophagy of selective cargo. Altogether, our proximity interaction map provides a valuable resource for the study of autophagy and highlights the critical role of membrane contact site proteins in the pathway.

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 that mediate functions of these regions, such as short motifs, amino acid repeats and physicochemical properties. Here, we introduce a proteome-scale feature discovery approach for IDRs. Our approach, which we call “reverse homology”, exploits the principle that important functional features are conserved over evolution. We use this as a contrastive learning signal for deep learning: given a set of homologous IDRs, the neural network has to correctly choose a held-out homologue from another set of IDRs sampled randomly from the proteome. We pair reverse homology with a simple architecture and standard interpretation techniques, and show that the network learns conserved features of IDRs that can be interpreted as motifs, repeats, or bulk features like charge or amino acid propensities. We also show that our model can be used to produce visualizations of what residues and regions are most important to IDR function, generating hypotheses for uncharacterized IDRs. Our results suggest that feature discovery using unsupervised neural networks is a promising avenue to gain systematic insight into poorly understood protein sequences.

Cytoplasmic mislocalization of FUS-ALS mutants determines aberrant FMRP condensates and protein synthesis repression., FUsed in Sarcoma (FUS) is a multifunctional RNA binding protein (RBP). FUS mutations lead to its cytoplasmic mislocalization and cause the neurodegenerative disease amyotrophic lateral sclerosis (ALS). Here, we use mouse and human models with endogenous ALS-associated mutations to study the early consequences of increased cytoplasmic FUS. We show that in axons, mutant FUS condensates sequester and promote the phase separation of fragile X mental retardation protein (FMRP), another RBP associated with neurodegeneration. This leads to repression of translation in mouse and human FUS-ALS motor neurons and is corroborated in vitro, where FUS and FMRP copartition and repress translation. Last, we show that translation of FMRP-bound RNAs is reduced in vivo in FUS-ALS motor neurons. Our results unravel new pathomechanisms of FUS-ALS and identify a novel paradigm by which mutations in one RBP favor the formation of condensates sequestering other RBPs, affecting crucial biological functions, such as protein translation.

Rationale: SLC6A14 mediated L-arginine transport has been shown to augment the residual anion channel activity of the major mutant, F508del-CFTR, in the murine gastrointestinal tract. It is not yet...

Stress granules and P-bodies are cytosolic biomolecular condensates that dynamically form by the phase separation of RNAs and proteins. They participate in translational control and buffer the proteome. Upon stress, global translation halts and mRNAs bound to the translational machinery and other proteins coalesce to form stress granules (SGs). Similarly, translationally stalled mRNAs devoid of translation initiation factors shuttle to P-bodies (PBs). Here, we review the cumulative progress made in defining the protein components that associate with mammalian SGs and PBs. We discuss the composition of SG and PB proteomes, supported by a new user-friendly database (http://rnagranuledb.lunenfeld.ca/) that curates current literature evidence for genes or proteins associated with SGs or PBs. As previously observed, the SG and PB proteomes are biased toward intrinsically disordered regions and have a high propensity to contain primary sequence features favoring phase separation. We also provide an outlook on how the various components of SGs and PBs may cooperate to organize and form membraneless organelles.

Keeping RNA processing contained Biomolecular condensates that assemble by phase separation are involved in RNA processing. Posttranslational modifications in intrinsically disordered regions of proteins can regulate RNA processing by regulating phase separation. Kim et al. used nuclear magnetic resonance spectroscopy to characterize phase separation in a model system comprising the C-terminal disordered regions of the translational regulators FMRP and CAPRIN1 that repress translation by deadenylating mRNA. Interactions between the proteins involve specific sequences in FMRP and CAPRIN1, and the propensity for mRNA to partition into the condensates depends on the phosphorylation patterns in the disordered regions. This mechanism integrates signaling pathways with the regulation of RNA processing. Science , this issue p. 825

Activity-dependent translation requires the transport of mRNAs within membraneless protein assemblies known as neuronal granules from the cell body toward synaptic regions. Translation of mRNA is inhibited in these granules during transport but quickly activated in response to neuronal stimuli at the synapse. This raises an important question: how does synaptic activity trigger translation of once-silenced mRNAs? Here, we demonstrate a strong connection between phase separation, the process underlying the formation of many different types of cellular granules, and in vitro inhibition of translation. By using the Fragile X Mental Retardation Protein (FMRP), an abundant neuronal granule component and translational repressor, we show that FMRP phase separates in vitro with RNA into liquid droplets mediated by its C-terminal low-complexity disordered region (i.e., FMRP(LCR)). FMRP(LCR) posttranslational modifications by phosphorylation and methylation have opposing effects on in vitro translational regulation, which corroborates well with their critical concentrations for phase separation. Our results, combined with bioinformatics evidence, are supportive of phase separation as a general mechanism controlling activity-dependent translation.

The role of biomolecular condensates in regulating biological function and the importance of dynamic interactions involving intrinsically disordered protein regions (IDRs) in their assembly are increasingly appreciated. While computational and theoretical approaches have provided significant insights into IDR phase behavior, establishing the critical interactions that govern condensation with atomic resolution through experiment is more difficult, given the lack of applicability of standard structural biological tools to study these highly dynamic large-scale associated states. NMR can be a valuable method, but the dynamic and viscous nature of condensed IDRs presents challenges. Using the C-terminal IDR (607 to 709) of CAPRIN1, an RNA-binding protein found in stress granules, P bodies, and messenger RNA transport granules, we have developed and applied a variety of NMR methods for studies of condensed IDR states to provide insights into interactions driving and modulating phase separation. We identify ATP interactions with CAPRIN1 that can enhance or reduce phase separation. We also quantify specific side-chain and backbone interactions within condensed CAPRIN1 that define critical sequences for phase separation and that are reduced by O-GlcNAcylation known to occur during cell cycle and stress. This expanded NMR toolkit that has been developed for characterizing IDR condensates has generated detailed interaction information relevant for understanding CAPRIN1 biology and informing general models of phase separation, with significant potential future applications to illuminate dynamic structure-function relationships in other biological condensates.

Many pairwise additive force fields are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), some of which modify energetic terms to improve the description of IDPs/IDRs but are largely in disagreement with solution experiments for the disordered states. This work considers a new direction—the connection to configurational entropy—and how it might change the nature of our understanding of protein force field development to equally well encompass globular proteins, IDRs/IDPs, and disorder-to-order transitions. We have evaluated representative pairwise and many-body protein and water force fields against experimental data on representative IDPs and IDRs, a peptide that undergoes a disorder-to-order transition, for seven globular proteins ranging in size from 130 to 266 amino acids. We find that force fields with the largest statistical fluctuations consistent with the radius of gyration and universal Lindemann values for folded states simultaneously better describe IDPs and IDRs and disorder-to-order transitions. Hence, the crux of what a force field should exhibit to well describe IDRs/IDPs is not just the balance between protein and water energetics but the balance between energetic effects and configurational entropy of folded states of globular proteins.

The Protein Ensemble Database (PED) (https://proteinensemble.org), which holds structural ensembles of intrinsically disordered proteins (IDPs), has been significantly updated and upgraded since its last release in 2016. The new version, PED 4.0, has been completely redesigned and reimplemented with cutting-edge technology and now holds about six times more data (162 versus 24 entries and 242 versus 60 structural ensembles) and a broader representation of state of the art ensemble generation methods than the previous version. The database has a completely renewed graphical interface with an interactive feature viewer for region-based annotations, and provides a series of descriptors of the qualitative and quantitative properties of the ensembles. High quality of the data is guaranteed by a new submission process, which combines both automatic and manual evaluation steps. A team of biocurators integrate structured metadata describing the ensemble generation methodology, experimental constraints and conditions. A new search engine allows the user to build advanced queries and search all entry fields including cross-references to IDP-related resources such as DisProt, MobiDB, BMRB and SASBDB. We expect that the renewed PED will be useful for researchers interested in the atomic-level understanding of IDP function, and promote the rational, structure-based design of IDP-targeting drugs.

Nephrotic syndrome (NS) is a leading cause of chronic kidney disease. We found recessive NOS1AP variants in two families with early-onset NS by exome sequencing. Overexpression of wild-type (WT) NOS1AP, but not cDNA constructs bearing patient variants, increased active CDC42 and promoted filopodia and podosome formation. Pharmacologic inhibition of CDC42 or its effectors, formin proteins, reduced NOS1AP-induced filopodia formation. NOS1AP knockdown reduced podocyte migration rate (PMR), which was rescued by overexpression of WT Nos1ap but not by constructs bearing patient variants. PMR in NOS1AP knockdown podocytes was also rescued by constitutively active CDC42Q61L or the formin DIAPH3 Modeling a NOS1AP patient variant in knock-in human kidney organoids revealed malformed glomeruli with increased apoptosis. Nos1apEx3-/Ex3- mice recapitulated the human phenotype, exhibiting proteinuria, foot process effacement, and glomerulosclerosis. These findings demonstrate that recessive NOS1AP variants impair CDC42/DIAPH-dependent actin remodeling, cause aberrant organoid glomerulogenesis, and lead to a glomerulopathy in humans and mice.