CD-CODE: crowdsourcing condensate database and encyclopedia

PICNIC accurately predicts condensate-forming proteins regardless of their structural disorder across organisms

Biomolecular condensates are membraneless organelles that can concentrate hundreds of different proteins in cells to operate essential biological functions. However, accurate identification of their components remains challenging and biased towards proteins with high structural disorder content with focus on self-phase separating (driver) proteins. Here, we present a machine learning algorithm, PICNIC (Proteins Involved in CoNdensates In Cells) to classify proteins that localize to biomolecular condensates regardless of their role in condensate formation. PICNIC successfully predicts condensate members by learning amino acid patterns in the protein sequence and structure in addition to the intrinsic disorder. Extensive experimental validation of 24 positive predictions in cellulo shows an overall ~82% accuracy regardless of the structural disorder content of the tested proteins. While increasing disorder content is associated with organismal complexity, our analysis of 26 species reveals no correlation between predicted condensate proteome content and disorder content across organisms. Overall, we present a machine learning classifier to interrogate condensate components at whole-proteome levels across the tree of life.

Evolutionary analysis of ZAP and its cofactors identifies intrinsically disordered regions as central elements in host-pathogen interactions

The zinc-finger antiviral protein (ZAP) is an innate immunity sensor of non-self nucleic acids. Its antiviral activity is exerted through the physical interaction with different cofactors, including TRIM25, Riplet and KHNYN. Cellular proteins that interact with infectious agents are expected to be engaged in genetic conflicts that often result in their rapid evolution. To test this possibility and to identify the regions most strongly targeted by natural selection, we applied in silico molecular evolution tools to analyze the evolutionary history of ZAP and cofactors in four mammalian groups. We report evidence of positive selection in all genes and in most mammalian groups. On average, the intrinsically disordered regions (IDRs) embedded in the four proteins evolve significantly faster than folded domains and most positively selected sites fall within IDRs. In ZAP, the PARP domain also shows abundant signals of selection, and independent evolution in different mammalian groups suggests modulation of its ADP-ribose binding ability. Detailed analyses of the biophysical properties of IDRs revealed that chain compaction and conformational entropy are conserved across mammals. The IDRs in ZAP and KHNYN are particularly compact, indicating that they may promote phase separation (PS). In line with this hypothesis, we predicted several PS-promoting regions in ZAP and KHNYN, as well as in TRIM25. Positively selected sites are abundant in these regions, suggesting that PS may be important for the antiviral functions of these proteins and the evolutionary arms race with viruses. Our data shed light into the evolution of ZAP and cofactors and indicate that IDRs represent central elements in host-pathogen interactions.

The RNA Revolution in the Central Molecular Biology Dogma Evolution

Human genome projects in the 1990s identified about 20,000 protein-coding sequences. We are now in the RNA revolution, propelled by the realization that genes determine phenotype beyond the foundational central molecular biology dogma, stating that inherited linear pieces of DNA are transcribed to RNAs and translated into proteins. Crucially, over 95% of the genome, initially considered junk DNA between protein-coding genes, encodes essential, functionally diverse non-protein-coding RNAs, raising the gene count by at least one order of magnitude. Most inherited phenotype-determining changes in DNA are in regulatory areas that control RNA and regulatory sequences. RNAs can directly or indirectly determine phenotypes by regulating protein and RNA function, transferring information within and between organisms, and generating DNA. RNAs also exhibit high structural, functional, and biomolecular interaction plasticity and are modified via editing, methylation, glycosylation, and other mechanisms, which bestow them with diverse intra- and extracellular functions without altering the underlying DNA. RNA is, therefore, currently considered the primary determinant of cellular to populational functional diversity, disease-linked and biomolecular structural variations, and cell function regulation. As demonstrated by RNA-based coronavirus vaccines' success, RNA technology is transforming medicine, agriculture, and industry, as did the advent of recombinant DNA technology in the 1980s.

Functional characteristics and computational model of abundant hyperactive loci in the human genome

Enhancers and promoters are classically considered to be bound by a small set of transcription factors (TFs) in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected five distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates.

Aggregation of the amyloid-β peptide (Aβ40) within condensates generated through liquid-liquid phase separation

The deposition of the amyloid-β (Aβ) peptide into amyloid fibrils is a hallmark of Alzheimer's disease. Recently, it has been reported that some proteins can aggregate and form amyloids through an intermediate pathway involving a liquid-like condensed phase. These observations prompted us to investigate the phase space of Aβ. We thus explored the ability of Aβ to undergo liquid-liquid phase separation, and the subsequent liquid-to-solid transition that takes place within the resulting condensates. Through the use of microfluidic approaches, we observed that the 40-residue form of Αβ (Αβ40) can undergo liquid-liquid phase separation, and that accessing a liquid-like intermediate state enables Αβ40 to self-assemble and aggregate into amyloid fibrils through this pathway. These results prompt further studies to investigate the possible role of Αβ liquid-liquid phase separation and its subsequent aggregation in the context of Alzheimer's disease and more generally on neurodegenerative processes.

AlphaFold2-based prediction of the co-condensation propensity of proteins

The process of protein phase separation into liquid condensates has been implicated in the formation of membraneless organelles (MLOs), which selectively concentrate biomolecules to perform essential cellular functions. Although the importance of this process in health and disease is increasingly recognized, the experimental identification of proteins forming MLOs remains a complex challenge. In this study, we addressed this problem by harnessing the power of AlphaFold2 to perform computational predictions of the conformational properties of proteins from their amino acid sequences. We thus developed the CoDropleT (co-condensation into droplet transformer) method of predicting the propensity of co-condensation of protein pairs. The method was trained by combining experimental datasets of co-condensing proteins from the CD-CODE database with curated negative datasets of non-co-condensing proteins. To illustrate the performance of the method, we applied it to estimate the propensity of proteins to co-condense into MLOs. Our results suggest that CoDropleT could facilitate functional and therapeutic studies on protein condensation by predicting the composition of protein condensates.

Sequence, Structure, and Functional Space of Drosophila De Novo Proteins

During de novo emergence, new protein coding genes emerge from previously nongenic sequences. The de novo proteins they encode are dissimilar in composition and predicted biochemical properties to conserved proteins. However, functional de novo proteins indeed exist. Both identification of functional de novo proteins and their structural characterization are experimentally laborious. To identify functional and structured de novo proteins in silico, we applied recently developed machine learning based tools and found that most de novo proteins are indeed different from conserved proteins both in their structure and sequence. However, some de novo proteins are predicted to adopt known protein folds, participate in cellular reactions, and to form biomolecular condensates. Apart from broadening our understanding of de novo protein evolution, our study also provides a large set of testable hypotheses for focused experimental studies on structure and function of de novo proteins in Drosophila.

Preserving condensate structure and composition by lowering sequence complexity

Biomolecular condensates play a vital role in organizing cellular chemistry. They selectively partition biomolecules, preventing unwanted cross talk and buffering against chemical noise. Intrinsically disordered proteins (IDPs) serve as primary components of these condensates due to their flexibility and ability to engage in multivalent interactions, leading to spontaneous aggregation. Theoretical advancements are critical at connecting IDP sequences with condensate emergent properties to establish the so-called molecular grammar. We proposed an extension to the stickers and spacers model, incorporating heterogeneous, nonspecific pairwise interactions between spacers alongside specific interactions among stickers. Our investigation revealed that although spacer interactions contribute to phase separation and co-condensation, their nonspecific nature leads to disorganized condensates. Specific sticker-sticker interactions drive the formation of condensates with well-defined networked structures and molecular composition. We discussed how evolutionary pressures might emerge to affect these interactions, leading to the prevalence of low-complexity domains in IDP sequences. These domains suppress spurious interactions and facilitate the formation of biologically meaningful condensates.

Protein codes promote selective subcellular compartmentalization

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must efficiently assemble. Here, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. A protein language model, ProtGPS, was developed that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in targeted subcellular compartments. ProtGPS also identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code, but also a previously unrecognized code governing their distribution in specific cellular compartments.

MolPhase, an advanced prediction algorithm for protein phase separation

We introduce MolPhase, an advanced algorithm for predicting protein phase separation (PS) behavior that improves accuracy and reliability by utilizing diverse physicochemical features and extensive experimental datasets. MolPhase applies a user-friendly interface to compare distinct biophysical features side-by-side along protein sequences. By additional comparison with structural predictions, MolPhase enables efficient predictions of new phase-separating proteins and guides hypothesis generation and experimental design. Key contributing factors underlying MolPhase include electrostatic pi-interactions, disorder, and prion-like domains. As an example, MolPhase finds that phytobacterial type III effectors (T3Es) are highly prone to homotypic PS, which was experimentally validated in vitro biochemically and in vivo in plants, mimicking their injection and accumulation in the host during microbial infection. The physicochemical characteristics of T3Es dictate their patterns of association for multivalent interactions, influencing the material properties of phase-separating droplets based on the surrounding microenvironment in vivo or in vitro. Robust integration of MolPhase's effective prediction and experimental validation exhibit the potential to evaluate and explore how biomolecule PS functions in biological systems.

Glycogen phase separation drives macromolecular rearrangement and asymmetric division in E. coli

Bacteria often experience nutrient limitation in nature and the laboratory. While exponential and stationary growth phases are well characterized in the model bacterium Escherichia coli, little is known about what transpires inside individual cells during the transition between these two phases. Through quantitative cell imaging, we found that the position of nucleoids and cell division sites becomes increasingly asymmetric during transition phase. These asymmetries were coupled with spatial reorganization of proteins, ribosomes, and RNAs to nucleoid-centric localizations. Results from live-cell imaging experiments, complemented with genetic and 13C whole-cell nuclear magnetic resonance spectroscopy studies, show that preferential accumulation of the storage polymer glycogen at the old cell pole leads to the observed rearrangements and asymmetric divisions. In vitro experiments suggest that these phenotypes are likely due to the propensity of glycogen to phase separate in crowded environments, as glycogen condensates exclude fluorescent proteins under physiological crowding conditions. Glycogen-associated differences in cell sizes between strains and future daughter cells suggest that glycogen phase separation allows cells to store large glucose reserves without counting them as cytoplasmic space.

Conformational ensembles of the human intrinsically disordered proteome

Intrinsically disordered proteins and regions (collectively, IDRs) are pervasive across proteomes in all kingdoms of life, help to shape biological functions and are involved in numerous diseases. IDRs populate a diverse set of transiently formed structures and defy conventional sequence-structure-function relationships1. Developments in protein science have made it possible to predict the three-dimensional structures of folded proteins at the proteome scale2. By contrast, there is a lack of knowledge about the conformational properties of IDRs, partly because the sequences of disordered proteins are poorly conserved and also because only a few of these proteins have been characterized experimentally. The inability to predict structural properties of IDRs across the proteome has limited our understanding of the functional roles of IDRs and how evolution shapes them. As a supplement to previous structural studies of individual IDRs3, we developed an efficient molecular model to generate conformational ensembles of IDRs and thereby to predict their conformational properties from sequences4,5. Here we use this model to simulate nearly all of the IDRs in the human proteome. Examining conformational ensembles of 28,058 IDRs, we show how chain compaction is correlated with cellular function and localization. We provide insights into how sequence features relate to chain compaction and, using a machine-learning model trained on our simulation data, show the conservation of conformational properties across orthologues. Our results recapitulate observations from previous studies of individual protein systems and exemplify how to link-at the proteome scale-conformational ensembles with cellular function and localization, amino acid sequence, evolutionary conservation and disease variants. Our freely available database of conformational properties will encourage further experimental investigation and enable the generation of hypotheses about the biological roles and evolution of IDRs.

Preserving condensate structure and composition by lowering sequence complexity

Biological condensates play a vital role in organizing cellular chemistry. They selectively partition biomolecules, preventing unwanted cross-talk and buffering against chemical noise. Intrinsically disordered proteins (IDPs) serve as primary components of these condensates due to their flexibility and ability to engage in multivalent, non-specific interactions, leading to spontaneous aggregation. Theoretical advancements are critical at connecting IDP sequences with condensate emergent properties to establish the so-called molecular grammar. We proposed an extension to the stickers and spacers model, incorporating non-specific pairwise interactions between spacers alongside specific interactions among stickers. Our investigation revealed that while spacer interactions contribute to phase separation and co-condensation, their non-specific nature leads to disorganized condensates. Specific sticker-sticker interactions drive the formation of condensates with well-defined structures and molecular composition. We discussed how evolutionary pressures might emerge to affect these interactions, leading to the prevalence of low complexity domains in IDP sequences. These domains suppress spurious interactions and facilitate the formation of biologically meaningful condensates.

RNA granules: functional compartments or incidental condensates?

RNA granules are mesoscale assemblies that form in the absence of limiting membranes. RNA granules contain factors for RNA biogenesis and turnover and are often assumed to represent specialized compartments for RNA biochemistry. Recent evidence suggests that RNA granules assemble by phase separation of subsoluble ribonucleoprotein (RNP) complexes that partially demix from the cytoplasm or nucleoplasm. We explore the possibility that some RNA granules are nonessential condensation by-products that arise when RNP complexes exceed their solubility limit as a consequence of cellular activity, stress, or aging. We describe the use of evolutionary and mutational analyses and single-molecule techniques to distinguish functional RNA granules from "incidental condensates."