The mutational landscape of a prion-like domain

catGRANULE 2.0: accurate predictions of liquid-liquid phase separating proteins at single amino acid resolution

Liquid-liquid phase separation (LLPS) enables the formation of membraneless organelles, essential for cellular organization and implicated in diseases. We introduce catGRANULE 2.0 ROBOT, an algorithm integrating physicochemical properties and AlphaFold-derived structural features to predict LLPS at single-amino-acid resolution. The method achieves high performance and reliably evaluates mutation effects on LLPS propensity, providing detailed predictions of how specific mutations enhance or inhibit phase separation. Supported by experimental validations, including microscopy data, it predicts LLPS across diverse organisms and cellular compartments, offering valuable insights into LLPS mechanisms and mutational impacts. The tool is freely available at https://tools.tartaglialab.com/catgranule2 and https://doi.org/10.5281/zenodo.14205831 .

A DNA language model based on multispecies alignment predicts the effects of genome-wide variants

Protein language models have demonstrated remarkable performance in predicting the effects of missense variants but DNA language models have not yet shown a competitive edge for complex genomes such as that of humans. This limitation is particularly evident when dealing with the vast complexity of noncoding regions that comprise approximately 98% of the human genome. To tackle this challenge, we introduce GPN-MSA (genomic pretrained network with multiple-sequence alignment), a framework that leverages whole-genome alignments across multiple species while taking only a few hours to train. Across several benchmarks on clinical databases (ClinVar, COSMIC and OMIM), experimental functional assays (deep mutational scanning and DepMap) and population genomic data (gnomAD), our model for the human genome achieves outstanding performance on deleteriousness prediction for both coding and noncoding variants. We provide precomputed scores for all ~9 billion possible single-nucleotide variants in the human genome. We anticipate that our advances in genome-wide variant effect prediction will enable more accurate rare disease diagnosis and improve rare variant burden testing.

Site-saturation mutagenesis of 500 human protein domains

Missense variants that change the amino acid sequences of proteins cause one-third of human genetic diseases1. Tens of millions of missense variants exist in the current human population, and the vast majority of these have unknown functional consequences. Here we present a large-scale experimental analysis of human missense variants across many different proteins. Using DNA synthesis and cellular selection experiments we quantify the effect of more than 500,000 variants on the abundance of more than 500 human protein domains. This dataset reveals that 60% of pathogenic missense variants reduce protein stability. The contribution of stability to protein fitness varies across proteins and diseases and is particularly important in recessive disorders. We combine stability measurements with protein language models to annotate functional sites across proteins. Mutational effects on stability are largely conserved in homologous domains, enabling accurate stability prediction across entire protein families using energy models. Our data demonstrate the feasibility of assaying human protein variants at scale and provides a large consistent reference dataset for clinical variant interpretation and training and benchmarking of computational methods.

A model of inborn metabolism errors associated with adenine amyloid-like fiber formation reduces TDP-43 aggregation and toxicity in yeast

TDP-43 is linked to human diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). Expression of TDP-43 in yeast is known to be toxic, cause cells to elongate, form liquid-like aggregates, and inhibit autophagy and TOROID formation. Here, we used the apt1Δ aah1Δ yeast model of disorders of inborn errors of metabolism, previously shown to lead to intracellular adenine accumulation and adenine amyloid-like fiber formation, to explore interactions with TDP-43. Results show that the double deletion shifts the TDP-43 aggregates from a liquid-like, toward a more amyloid-like, state. At the same time the deletions reduce TDP-43's effects on toxicity, cell morphology, autophagy, and TOROID formation without affecting the level of TDP-43. This suggests that the liquid-like and not amyloid-like TDP-43 aggregates are responsible for the deleterious effects in yeast. How the apt1Δ aah1Δ deletions alter TDP-43 aggregate formation is not clear. Possibly, it results from adenine/TDP-43 fiber interactions as seen for other heterologous fibers. The work offers new insights into the potential interactions between metabolite-based amyloids and pathological protein aggregates, with broad implications for understanding protein misfolding diseases.

Membraneless organelles in health and disease: exploring the molecular basis, physiological roles and pathological implications

Once considered unconventional cellular structures, membraneless organelles (MLOs), cellular substructures involved in biological processes or pathways under physiological conditions, have emerged as central players in cellular dynamics and function. MLOs can be formed through liquid-liquid phase separation (LLPS), resulting in the creation of condensates. From neurodegenerative disorders, cardiovascular diseases, aging, and metabolism to cancer, the influence of MLOs on human health and disease extends widely. This review discusses the underlying mechanisms of LLPS, the biophysical properties that drive MLO formation, and their implications for cellular function. We highlight recent advances in understanding how the physicochemical environment, molecular interactions, and post-translational modifications regulate LLPS and MLO dynamics. This review offers an overview of the discovery and current understanding of MLOs and biomolecular condensate in physiological conditions and diseases. This article aims to deliver the latest insights on MLOs and LLPS by analyzing current research, highlighting their critical role in cellular organization. The discussion also covers the role of membrane-associated condensates in cell signaling, including those involving T-cell receptors, stress granules linked to lysosomes, and biomolecular condensates within the Golgi apparatus. Additionally, the potential of targeting LLPS in clinical settings is explored, highlighting promising avenues for future research and therapeutic interventions.

TDP43 aggregation at ER-exit sites impairs ER-to-Golgi transport

Protein aggregation plays key roles in age-related degenerative diseases, but how different proteins coalesce to form inclusions that vary in composition, morphology, molecular dynamics and confer physiological consequences is poorly understood. Here we employ a general reporter based on mutant Hsp104 to identify proteins forming aggregates in human cells under common proteotoxic stress. We identify over 300 proteins that form different inclusions containing subsets of aggregating proteins. In particular, TDP43, implicated in Amyotrophic Lateral Sclerosis (ALS), partitions dynamically between two distinct types of aggregates: stress granule and a previously unknown non-dynamic (solid-like) inclusion at the ER exit sites (ERES). TDP43-ERES co-aggregation is induced by diverse proteotoxic stresses and observed in the motor neurons of ALS patients. Such aggregation causes retention of secretory cargos at ERES and therefore delays ER-to-Golgi transport, providing a link between TDP43 aggregation and compromised cellular function in ALS patients.

Potential role of solid lipid curcumin particle (SLCP) as estrogen replacement therapy in mitigating TDP-43-related neuropathy in the mouse model of ALS disease

BACKGROUND

Amyotrophic lateral sclerosis (ALS) was first identified in 1869, but it wasn't until the 2014 Ice Bucket Challenge that widespread attention was drawn to the disease. Since then, substantial research has been dedicated to developing treatments for ALS. Despite this, only three drugs - riluzole, edaravone and AMX0035, have been approved for clinical use, and they can only temporarily alleviate mild symptoms without significant disease modification or cure. Therefore, there remains a critical unmet need to identify disease modifying or curative therapies for ALS. The higher incidence and more severe progression of ALS and FTLD (frontotemporal lobar degeneration) observed in men and postmenopausal woman compared to young women suggests that sex hormones may significantly influence disease onset and progression. In both animal models and human clinical studies, 17β estradiol (E2) has been shown to delay and improve the outcomes of many neurodegenerative diseases. Here, we examined the role of TDP-43 in the regulation of estrogen-related enzymes, CYP19A1 and CYP3A4. In addition, we examined the impact of curcumin on the regulation of estrogen E2 levels and TDP-43-associated neuropathy as a potential therapeutic strategy for the treatment of FTLD and ALS.

METHODS

Prp-TDP-43A315T mice was used as a model of ALS/FTLD to examine the expression patterns of E2 and its biosynthesis and degradation enzymes, CYP19A1 and CYP3A4. Moreover, the molecular mechanisms and the potency of solid lipid curcumin particles (SLCP) as an E2 replacement therapy for TDP-43 associated neuropathy was analyzed. We further examined the survival rates and the pathological TDP43 patterns in female and male Prp-TDP-43A315T mice administrated with or without SLCP. In addition, the changed expression levels of enzymes corresponding to E2 biosynthesis and degradation in the spinal cord of female and male Prp-TDP-43A315T mice with or without SLCP were determined.

RESULTS

We found that in addition to E2, the expression patterns of CYP19A1 and CYP3A4 proteins differed between Prp-TDP-43A315T mice compared to wild-type control, suggesting that toxic phosphorylated TDP43 oligomers may disrupt the balance between CYP19A1 and CYP3A4 expression, leading to reduced estrogen biosynthesis and accelerated degradation. In addition, we found that oral administration of SLCP prolonged the survival rates in female Prp-TDP-43A315T mice and significantly reduced the pathological insoluble phosphorylated TDP-43 species. Furthermore, SLCP attenuated disease progression associated with TDP-43-related neuropathies through modulating estrogen biosynthesis and the activity of CYP450 enzymes.

CONCLUSIONS

Our results showed that Prp-TDP-43A315T mice exhibit altered estradiol levels. Moreover, we demonstrated the efficacy of SLCP as an estrogen replacement therapy in mitigating TDP-43-associated disease progression and pathogenesis. These findings suggest that SLCP could be a promising strategy to induce E2 expression for the treatment of ALS and FTLD.

Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.

Hetero-oligomerization of TDP-43 carboxy-terminal fragments with cellular proteins contributes to proteotoxicity

Carboxy terminal fragments (CTFs) of TDP-43 contain an intrinsically disordered region (IDR) and form cytoplasmic condensates containing amyloid fibrils. Such condensates are toxic and associated with pathogenicity in amyotrophic lateral sclerosis. However, the molecular details of how the domain of TDP-43 CTFs leads to condensation and cytotoxicity remain elusive. Here, we show that truncated RNA/DNA-recognition motif (RRM) at the N-terminus of TDP-43 CTFs leads to the structural transition of the IDR, whereas the IDR itself of TDP-43 CTFs is difficult to assemble even if they are proximate intermolecularly. Hetero-oligomers of TDP-43 CTFs that have recruited other proteins are more toxic than homo-oligomers, implicating loss-of-function of the endogenous proteins by such oligomers is associated with cytotoxicity. Furthermore, such toxicity of TDP-43 CTFs was cell-nonautonomously affected in the nematodes. Therefore, misfolding and oligomeric characteristics of the truncated RRM at the N-terminus of TDP-43 CTFs define their condensation properties and toxicity.

Expression of Wild-Type and Mutant Human TDP-43 in Yeast Inhibits TOROID (TORC1 Organized in Inhibited Domain) Formation and Autophagy Proportionally to the Levels of TDP-43 Toxicity

TDP-43 forms aggregates in the neurons of patients with several neurodegenerative diseases. Human TDP-43 also aggregates and is toxic in yeast. Here, we used a yeast model to investigate (1) the nature of TDP-43 aggregates and (2) the mechanism of TDP-43 toxicity. Thioflavin T, which stains amyloid but not wild-type TDP-43 aggregates, also did not stain mutant TDP-43 aggregates made from TDP-43 with intragenic mutations that increase or decrease its toxicity. However, 1,6-hexanediol, which dissolves liquid droplets, dissolved wild-type or mutant TDP-43 aggregates. To investigate the mechanism of TDP-43 toxicity, the effects of TDP-43 mutations on the autophagy of the GFP-ATG8 reporter were examined. Mutations in TDP-43 that enhance its toxicity, but not mutations that reduce its toxicity, caused a larger reduction in autophagy. TOROID formation, which enhances autophagy, was scored as GFP-TOR1 aggregation. TDP-43 inhibited TOROID formation. TORC1 bound to both toxic and non-toxic TDP-43, and to TDP-43, with reduced toxicity due to pbp1Δ. However, extragenic modifiers and TDP-43 mutants that reduced TDP-43 toxicity, but not TDP-43 mutants that enhanced toxicity, restored TOROID formation. This is consistent with the hypothesis that TDP-43 is toxic in yeast because it reduces TOROID formation, causing the inhibition of autophagy. Whether TDP-43 exerts a similar effect in higher cells remains to be determined.

Adding intrinsically disordered proteins to biological ageing clocks

Research into how the young and old differ, and which biomarkers reflect the diverse biological processes underlying ageing, is a current and fast-growing field. Biological clocks provide a means to evaluate whether a molecule, cell, tissue or even an entire organism is old or young. Here we summarize established and emerging molecular clocks as timepieces. We emphasize that intrinsically disordered proteins (IDPs) tend to transform into a β-sheet-rich aggregated state and accumulate in non-dividing or slowly dividing cells as they age. We hypothesize that understanding these protein-based molecular ageing mechanisms might provide a conceptual pathway to determining a cell's health age by probing the aggregation state of IDPs, which we term the IDP clock.

Toxicity of the model protein 3×GFP arises from degradation overload, not from aggregate formation

Although protein aggregation can cause cytotoxicity, such aggregates can also form to mitigate cytotoxicity from misfolded proteins, although the nature of these contrasting aggregates remains unclear. We previously found that overproduction (op) of a three green fluorescent protein-linked protein (3×GFP) induces giant aggregates and is detrimental to growth. Here, we investigated the mechanism of growth inhibition by 3×GFP-op using non-aggregative 3×MOX-op as a control in Saccharomyces cerevisiae. The 3×GFP aggregates were induced by misfolding, and 3×GFP-op had higher cytotoxicity than 3×MOX-op because it perturbed the ubiquitin-proteasome system. Static aggregates formed by 3×GFP-op dynamically trapped Hsp70 family proteins (Ssa1 and Ssa2 in yeast), causing the heat-shock response. Systematic analysis of mutants deficient in the protein quality control suggested that 3×GFP-op did not cause a critical Hsp70 depletion and aggregation functioned in the direction of mitigating toxicity. Artificial trapping of essential cell cycle regulators into 3×GFP aggregates caused abnormalities in the cell cycle. In conclusion, the formation of the giant 3×GFP aggregates itself is not cytotoxic, as it does not entrap and deplete essential proteins. Rather, it is productive, inducing the heat-shock response while preventing an overload to the degradation system.

Amyloidogenic regions in beta-strands II and III modulate the aggregation and toxicity of SOD1 in living cells

Mutations in the protein superoxide dismutase-1 (SOD1) promote its misfolding and aggregation, ultimately causing familial forms of the debilitating neurodegenerative disease amyotrophic lateral sclerosis (ALS). Currently, over 220 (mostly missense) ALS-causing mutations in the SOD1 protein have been identified, indicating that common structural features are responsible for aggregation and toxicity. Using in silico tools, we predicted amyloidogenic regions in the ALS-associated SOD1-G85R mutant, finding seven regions throughout the structure. Introduction of proline residues into β-strands II (I18P) or III (I35P) reduced the aggregation propensity and toxicity of SOD1-G85R in cells, significantly more so than proline mutations in other amyloidogenic regions. The I18P and I35P mutations also reduced the capability of SOD1-G85R to template onto previously formed non-proline mutant SOD1 aggregates as measured by fluorescence recovery after photobleaching. Finally, we found that, while the I18P and I35P mutants are less structurally stable than SOD1-G85R, the proline mutants are less aggregation-prone during proteasome inhibition, and less toxic to cells overall. Our research highlights the importance of a previously underappreciated SOD1 amyloidogenic region in β-strand II (15QGIINF20) to the aggregation and toxicity of SOD1 in ALS mutants, and suggests that β-strands II and III may be good targets for the development of SOD1-associated ALS therapies.

A complete map of specificity encoding for a partially fuzzy protein interaction

Thousands of human proteins function by binding short linear motifs embedded in intrinsically disordered regions. How affinity and specificity are encoded in these binding domains and the motifs themselves is not well understood. The evolvability of binding specificity - how rapidly and extensively it can change upon mutation - is also largely unexplored, as is the contribution of 'fuzzy' dynamic residues to affinity and specificity in protein-protein interactions. Here we report the first complete map of specificity encoding for a globular protein domain. Quantifying >200,000 energetic interactions between a PDZ domain and its ligand identifies 20 major energetically coupled pairs of sites that control specificity. These are organized into six modules, with most mutations in each module reprogramming specificity for a single position in the ligand. Nine of the major energetic couplings controlling specificity are between structural contacts and 11 have an allosteric mechanism of action. The dynamic tail of the ligand is more robust to mutation than the structured residues but contributes additively to binding affinity and communicates with structured residues to enable changes in specificity. Our results quantify the binding specificities of >1,800 globular proteins to reveal how specificity is encoded and provide a direct comparison of the encoding of affinity and specificity in structured and dynamic molecular recognition.

Alternative low-populated conformations prompt phase transitions in polyalanine repeat expansions

Abnormal trinucleotide repeat expansions alter protein conformation causing malfunction and contribute to a significant number of incurable human diseases. Scarce structural insights available on disease-related homorepeat expansions hinder the design of effective therapeutics. Here, we present the dynamic structure of human PHOX2B C-terminal fragment, which contains the longest polyalanine segment known in mammals. The major α-helical conformation of the polyalanine tract is solely extended by polyalanine expansions in PHOX2B, which are responsible for most congenital central hypoventilation syndrome cases. However, polyalanine expansions in PHOX2B additionally promote nascent homorepeat conformations that trigger length-dependent phase transitions into solid condensates that capture wild-type PHOX2B. Remarkably, HSP70 and HSP90 chaperones specifically seize PHOX2B alternative conformations preventing phase transitions. The precise observation of emerging polymorphs in expanded PHOX2B postulates unbalanced phase transitions as distinct pathophysiological mechanisms in homorepeat expansion diseases, paving the way towards the search of therapeutics modulating biomolecular condensates in central hypoventilation syndrome.

Inference of annealed protein fitness landscapes with AnnealDCA

The design of proteins with specific tasks is a major challenge in molecular biology with important diagnostic and therapeutic applications. High-throughput screening methods have been developed to systematically evaluate protein activity, but only a small fraction of possible protein variants can be tested using these techniques. Computational models that explore the sequence space in-silico to identify the fittest molecules for a given function are needed to overcome this limitation. In this article, we propose AnnealDCA, a machine-learning framework to learn the protein fitness landscape from sequencing data derived from a broad range of experiments that use selection and sequencing to quantify protein activity. We demonstrate the effectiveness of our method by applying it to antibody Rep-Seq data of immunized mice and screening experiments, assessing the quality of the fitness landscape reconstructions. Our method can be applied to several experimental cases where a population of protein variants undergoes various rounds of selection and sequencing, without relying on the computation of variants enrichment ratios, and thus can be used even in cases of disjoint sequence samples.

Nuclear-import receptors as gatekeepers of pathological phase transitions in ALS/FTD

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are fatal neurodegenerative disorders on a disease spectrum that are characterized by the cytoplasmic mislocalization and aberrant phase transitions of prion-like RNA-binding proteins (RBPs). The common accumulation of TAR DNA-binding protein-43 (TDP-43), fused in sarcoma (FUS), and other nuclear RBPs in detergent-insoluble aggregates in the cytoplasm of degenerating neurons in ALS/FTD is connected to nuclear pore dysfunction and other defects in the nucleocytoplasmic transport machinery. Recent advances suggest that beyond their canonical role in the nuclear import of protein cargoes, nuclear-import receptors (NIRs) can prevent and reverse aberrant phase transitions of TDP-43, FUS, and related prion-like RBPs and restore their nuclear localization and function. Here, we showcase the NIR family and how they recognize cargo, drive nuclear import, and chaperone prion-like RBPs linked to ALS/FTD. We also discuss the promise of enhancing NIR levels and developing potentiated NIR variants as therapeutic strategies for ALS/FTD and related neurodegenerative proteinopathies.

Analysis of Phase Separation of EARLY FLOWERING 3

Phase separation is an important mechanism for regulating various cellular functions. The EARLY FLOWERING 3 (ELF3) protein, an essential element of the EVENING COMPLEX (EC) involved in circadian clock regulation, has been shown to undergo phase separation. ELF3 is known to significantly influence elongation growth and flowering time regulation, and this is postulated to be due to whether the protein is in the dilute or phase-separated state. Here, we present a brief overview of methods for analyzing ELF3 phase separation in vitro, including the generation of phase diagrams as a function of pH and salt versus protein concentrations, optical microscopy, fluorescence recovery after photobleaching (FRAP), and turbidity assays.

ACIDES: on-line monitoring of forward genetic screens for protein engineering

Forward genetic screens of mutated variants are a versatile strategy for protein engineering and investigation, which has been successfully applied to various studies like directed evolution (DE) and deep mutational scanning (DMS). While next-generation sequencing can track millions of variants during the screening rounds, the vast and noisy nature of the sequencing data impedes the estimation of the performance of individual variants. Here, we propose ACIDES that combines statistical inference and in-silico simulations to improve performance estimation in the library selection process by attributing accurate statistical scores to individual variants. We tested ACIDES first on a random-peptide-insertion experiment and then on multiple public datasets from DE and DMS studies. ACIDES allows experimentalists to reliably estimate variant performance on the fly and can aid protein engineering and research pipelines in a range of applications, including gene therapy.

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction

Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.

Probing the drivers of Staphylococcus aureus biofilm protein amyloidogenesis and disrupting biofilms with engineered protein disaggregases

Phenol-soluble modulins (PSMs) are the primary proteinaceous component of Staphylococcus aureus biofilms. Residence in the protective environment of biofilms allows bacteria to rapidly evolve and acquire antimicrobial resistance, which can lead to persistent infections such as those caused by methicillin-resistant S. aureus (MRSA). In their soluble form, PSMs hinder the immune response of the host and can increase the virulence potential of MRSA. PSMs also self-assemble into insoluble functional amyloids that contribute to the structural scaffold of biofilms. The specific roles of PSM peptides in biofilms remain poorly understood. Here, we report the development of a genetically tractable yeast model system for studying the properties of PSMα peptides. Expression of PSMα peptides in yeast drives the formation of toxic insoluble aggregates that adopt vesicle-like structures. Using this system, we probed the molecular drivers of PSMα aggregation to delineate key similarities and differences among the PSMs and identified a crucial residue that drives PSM features. Biofilms are a major public health threat; thus, biofilm disruption is a key goal. To solubilize aggregates comprised of a diverse range of amyloid and amyloid-like species, we have developed engineered variants of Hsp104, a hexameric AAA+ protein disaggregase from yeast. Here, we demonstrate that potentiated Hsp104 variants counter the toxicity and aggregation of PSMα peptides. Further, we demonstrate that a potentiated Hsp104 variant can drive the disassembly of preformed S. aureus biofilms. We suggest that this new yeast model can be a powerful platform for screening for agents that disrupt PSM aggregation and that Hsp104 disaggregases could be a promising tool for the safe enzymatic disruption of biofilms. IMPORTANCE Biofilms are complex mixtures secreted by bacteria that form a material in which the bacteria can become embedded. This process transforms the properties of the bacteria, and they become more resistant to removal, which can give rise to multidrug-resistant strains, such as methicillin-resistant Staphylococcus aureus (MRSA). Here, we study phenol-soluble modulins (PSMs), which are amyloidogenic proteins secreted by S. aureus, that become incorporated into biofilms. Biofilms are challenging to study, so we have developed a new genetically tractable yeast model to study the PSMs. We used our system to learn about several key features of the PSMs. We also demonstrate that variants of an amyloid disaggregase, Hsp104, can disrupt the PSMs and, more importantly, dissolve preformed S. aureus biofilms. We propose that our system can be a powerful screening tool and that Hsp104 disaggregases may be a new avenue to explore for biofilm disruption agents.

Chaotic aging: intrinsically disordered proteins in aging-related processes

The development of aging is associated with the disruption of key cellular processes manifested as well-established hallmarks of aging. Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) have no stable tertiary structure that provide them a power to be configurable hubs in signaling cascades and regulate many processes, potentially including those related to aging. There is a need to clarify the roles of IDPs/IDRs in aging. The dataset of 1702 aging-related proteins was collected from established aging databases and experimental studies. There is a noticeable presence of IDPs/IDRs, accounting for about 36% of the aging-related dataset, which is however less than the disorder content of the whole human proteome (about 40%). A Gene Ontology analysis of the used here aging proteome reveals an abundance of IDPs/IDRs in one-third of aging-associated processes, especially in genome regulation. Signaling pathways associated with aging also contain IDPs/IDRs on different hierarchical levels, revealing the importance of "structure-function continuum" in aging. Protein-protein interaction network analysis showed that IDPs present in different clusters associated with different aging hallmarks. Protein cluster with IDPs enrichment has simultaneously high liquid-liquid phase separation (LLPS) probability, "nuclear" localization and DNA-associated functions, related to aging hallmarks: genomic instability, telomere attrition, epigenetic alterations, and stem cells exhaustion. Intrinsic disorder, LLPS, and aggregation propensity should be considered as features that could be markers of pathogenic proteins. Overall, our analyses indicate that IDPs/IDRs play significant roles in aging-associated processes, particularly in the regulation of DNA functioning. IDP aggregation, which can lead to loss of function and toxicity, could be critically harmful to the cell. A structure-based analysis of aging and the identification of proteins that are particularly susceptible to disturbances can enhance our understanding of the molecular mechanisms of aging and open up new avenues for slowing it down.

Monomerization of TDP-43 is a key determinant for inducing TDP-43 pathology in amyotrophic lateral sclerosis

The cytoplasmic aggregation of TAR DNA binding protein-43 (TDP-43), also known as TDP-43 pathology, is the pathological hallmark of amyotrophic lateral sclerosis (ALS). However, the mechanism underlying TDP-43 cytoplasmic mislocalization and subsequent aggregation remains unclear. Here, we show that TDP-43 dimerization/multimerization is impaired in the postmortem brains and spinal cords of patients with sporadic ALS and that N-terminal dimerization-deficient TDP-43 consists of pathological inclusion bodies in ALS motor neurons. Expression of N-terminal dimerization-deficient mutant TDP-43 in Neuro2a cells and induced pluripotent stem cell-derived motor neurons recapitulates TDP-43 pathology, such as Nxf1-dependent cytoplasmic mislocalization and aggregate formation, which induces seeding effects. Furthermore, TDP-DiLuc, a bimolecular luminescence complementation reporter assay, could detect decreased N-terminal dimerization of TDP-43 before TDP-43 pathological changes caused by the transcription inhibition linked to aberrant RNA metabolism in ALS. These findings identified TDP-43 monomerization as a critical determinant inducing TDP-43 pathology in ALS.

Missense variant interaction scanning reveals a critical role of the FERM domain for tumor suppressor protein NF2 conformation and function

NF2 (moesin-ezrin-radixin-like [MERLIN] tumor suppressor) is frequently inactivated in cancer, where its NF2 tumor suppressor functionality is tightly coupled to protein conformation. How NF2 conformation is regulated and how NF2 conformation influences tumor suppressor activity is a largely open question. Here, we systematically characterized three NF2 conformation-dependent protein interactions utilizing deep mutational scanning interaction perturbation analyses. We identified two regions in NF2 with clustered mutations which affected conformation-dependent protein interactions. NF2 variants in the F2-F3 subdomain and the α3H helix region substantially modulated NF2 conformation and homomerization. Mutations in the F2-F3 subdomain altered proliferation in three cell lines and matched patterns of disease mutations in NF2 related-schwannomatosis. This study highlights the power of systematic mutational interaction perturbation analysis to identify missense variants impacting NF2 conformation and provides insight into NF2 tumor suppressor function.

CovET: A covariation-evolutionary trace method that identifies protein structure-function modules

Measuring the relative effect that any two sequence positions have on each other may improve protein design or help better interpret coding variants. Current approaches use statistics and machine learning but rarely consider phylogenetic divergences which, as shown by Evolutionary Trace studies, provide insight into the functional impact of sequence perturbations. Here, we reframe covariation analyses in the Evolutionary Trace framework to measure the relative tolerance to perturbation of each residue pair during evolution. This approach (CovET) systematically accounts for phylogenetic divergences: at each divergence event, we penalize covariation patterns that belie evolutionary coupling. We find that while CovET approximates the performance of existing methods to predict individual structural contacts, it performs significantly better at finding structural clusters of coupled residues and ligand binding sites. For example, CovET found more functionally critical residues when we examined the RNA recognition motif and WW domains. It correlates better with large-scale epistasis screen data. In the dopamine D2 receptor, top CovET residue pairs recovered accurately the allosteric activation pathway characterized for Class A G protein-coupled receptors. These data suggest that CovET ranks highest the sequence position pairs that play critical functional roles through epistatic and allosteric interactions in evolutionarily relevant structure-function motifs. CovET complements current methods and may shed light on fundamental molecular mechanisms of protein structure and function.

Scanning mutagenesis of the voltage-gated sodium channel NaV1.2 using base editing

It is challenging to apply traditional mutational scanning to voltage-gated sodium channels (NaVs) and functionally annotate the large number of coding variants in these genes. Using a cytosine base editor and a pooled viability assay, we screen a library of 368 guide RNAs (gRNAs) tiling NaV1.2 to identify more than 100 gRNAs that change NaV1.2 function. We sequence base edits made by a subset of these gRNAs to confirm specific variants that drive changes in channel function. Electrophysiological characterization of these channel variants validates the screen results and provides functional mechanisms of channel perturbation. Most of the changes caused by these gRNAs are classifiable as loss of function along with two missense mutations that lead to gain of function in NaV1.2 channels. This two-tiered strategy to functionally characterize ion channel protein variants at scale identifies a large set of loss-of-function mutations in NaV1.2.

Thermodynamic and kinetic approaches for drug discovery to target protein misfolding and aggregation

INTRODUCTION

Protein misfolding diseases, including Alzheimer's and Parkinson's diseases, are characterized by the aberrant aggregation of proteins. These conditions are still largely untreatable, despite having a major impact on our healthcare systems and societies.

AREAS COVERED

We describe drug discovery strategies to target protein misfolding and aggregation. We compare thermodynamic approaches, which are based on the stabilization of the native states of proteins, with kinetic approaches, which are based on the slowing down of the aggregation process. This comparison is carried out in terms of the current knowledge of the process of protein misfolding and aggregation, the mechanisms of disease and the therapeutic targets.

EXPERT OPINION

There is an unmet need for disease-modifying treatments that target protein misfolding and aggregation for the over 50 human disorders known to be associated with this phenomenon. With the approval of the first drugs that can prevent misfolding or inhibit aggregation, future efforts will be focused on the discovery of effective compounds with these mechanisms of action for a wide range of conditions.

C-terminal frameshift variant of TDP-43 with pronounced aggregation-propensity causes rimmed vacuole myopathy but not ALS/FTD

Neuronal TDP-43-positive inclusions are neuropathological hallmark lesions in frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). Pathogenic missense variants in TARDBP, the gene encoding TDP-43, can cause ALS and cluster in the C-terminal prion-like domain (PrLD), where they modulate the liquid condensation and aggregation properties of the protein. TDP-43-positive inclusions are also found in rimmed vacuole myopathies, including sporadic inclusion body myositis, but myopathy-causing TDP-43 variants have not been reported. Using genome-wide linkage analysis and whole exome sequencing in an extended five-generation family with an autosomal dominant rimmed vacuole myopathy, we identified a conclusively linked frameshift mutation in TDP-43 producing a C-terminally altered PrLD (TDP-43p.Trp385IlefsTer10) (maximum multipoint LOD-score 3.61). Patient-derived muscle biopsies showed TDP-43-positive sarcoplasmic inclusions, accumulation of autophagosomes and transcriptomes with abnormally spliced sarcomeric genes (including TTN and NEB) and increased expression of muscle regeneration genes. In vitro phase separation assays demonstrated that TDP-43Trp385IlefsTer10 does not form liquid-like condensates and readily forms solid-like fibrils indicating increased aggregation propensity compared to wild-type TDP-43. In Drosophila TDP-43p.Trp385IlefsTer10 behaved as a partial loss-of-function allele as it was able to rescue the TBPH (fly ortholog of TARDBP) neurodevelopmental lethal null phenotype while showing strongly reduced toxic gain-of-function properties upon overexpression. Accordingly, TDP-43p.Trp385IlefsTer10 showed reduced toxicity in a primary rat neuron disease model. Together, these genetic, pathological, in vitro and in vivo results demonstrate that TDP-43p.Trp385IlefsTer10 is an aggregation-prone partial loss-of-function variant that causes autosomal dominant vacuolar myopathy but not ALS/FTD. Our study genetically links TDP-43 proteinopathy to myodegeneration, and reveals a tissue-specific role of the PrLD in directing pathology.

mutscan-a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data

Multiplexed assays of variant effect (MAVE) experimentally measure the effect of large numbers of sequence variants by selective enrichment of sequences with desirable properties followed by quantification by sequencing. mutscan is an R package for flexible analysis of such experiments, covering the entire workflow from raw reads up to statistical analysis and visualization. The core components are implemented in C++ for efficiency. Various experimental designs are supported, including single or paired reads with optional unique molecular identifiers. To find variants with changed relative abundance, mutscan employs established statistical models provided in the edgeR and limma packages. mutscan is available from https://github.com/fmicompbio/mutscan .

Accuracy of a machine learning method based on structural and locational information from AlphaFold2 for predicting the pathogenicity of TARDBP and FUS gene variants in ALS

BACKGROUND

In the sporadic form of amyotrophic lateral sclerosis (ALS), the pathogenicity of rare variants in the causative genes characterizing the familial form remains largely unknown. To predict the pathogenicity of such variants, in silico analysis is commonly used. In some ALS causative genes, the pathogenic variants are concentrated in specific regions, and the resulting alterations in protein structure are thought to significantly affect pathogenicity. However, existing methods have not taken this issue into account. To address this, we have developed a technique termed MOVA (method for evaluating the pathogenicity of missense variants using AlphaFold2), which applies positional information for structural variants predicted by AlphaFold2. Here we examined the utility of MOVA for analysis of several causative genes of ALS.

METHODS

We analyzed variants of 12 ALS-related genes (TARDBP, FUS, SETX, TBK1, OPTN, SOD1, VCP, SQSTM1, ANG, UBQLN2, DCTN1, and CCNF) and classified them as pathogenic or neutral. For each gene, the features of the variants, consisting of their positions in the 3D structure predicted by AlphaFold2, pLDDT score, and BLOSUM62 were trained into a random forest and evaluated by the stratified fivefold cross validation method. We compared how accurately MOVA predicted mutant pathogenicity with other in silico prediction methods and evaluated the prediction accuracy at TARDBP and FUS hotspots. We also examined which of the MOVA features had the greatest impact on pathogenicity discrimination.

RESULTS

MOVA yielded useful results (AUC ≥ 0.70) for TARDBP, FUS, SOD1, VCP, and UBQLN2 of 12 ALS causative genes. In addition, when comparing the prediction accuracy with other in silico prediction methods, MOVA obtained the best results among those compared for TARDBP, VCP, UBQLN2, and CCNF. MOVA demonstrated superior predictive accuracy for the pathogenicity of mutations at hotspots of TARDBP and FUS. Moreover, higher accuracy was achieved by combining MOVA with REVEL or CADD. Among the features of MOVA, the x, y, and z coordinates performed the best and were highly correlated with MOVA.

CONCLUSIONS

MOVA is useful for predicting the virulence of rare variants in which they are concentrated at specific structural sites, and for use in combination with other prediction methods.

Towards sequence-based principles for protein phase separation predictions

The phenomenon of protein phase separation, which underlies the formation of biomolecular condensates, has been associated with numerous cellular functions. Recent studies indicate that the amino acid sequences of most proteins may harbour not only the code for folding into the native state but also for condensing into the liquid-like droplet state and the solid-like amyloid state. Here we review the current understanding of the principles for sequence-based methods for predicting the propensity of proteins for phase separation. A guiding concept is that entropic contributions are generally more important to stabilise the droplet state than they are for the native and amyloid states. Although estimating these entropic contributions has proven difficult, we describe some progress that has been recently made in this direction. To conclude, we discuss the challenges ahead to extend sequence-based prediction methods of protein phase separation to include quantitative in vivo characterisations of this process.

TDP-43 Proteinopathy Specific Biomarker Development

TDP-43 is the primary or secondary pathological hallmark of neurodegenerative diseases, such as amyotrophic lateral sclerosis, half of frontotemporal dementia cases, and limbic age-related TDP-43 encephalopathy, which clinically resembles Alzheimer's dementia. In such diseases, a biomarker that can detect TDP-43 proteinopathy in life would help to stratify patients according to their definite diagnosis of pathology, rather than in clinical subgroups of uncertain pathology. For therapies developed to target pathological proteins that cause the disease a biomarker to detect and track the underlying pathology would greatly enhance such undertakings. This article reviews the latest developments and outlooks of deriving TDP-43-specific biomarkers from the pathophysiological processes involved in the development of TDP-43 proteinopathy and studies using biosamples from clinical entities associated with TDP-43 pathology to investigate biomarker candidates.

SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering

Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (< 50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.

Sequence determinants and solution conditions underlying liquid to solid phase transition

Life consists of numberless functional biomolecules that exist in various states. Besides well-dissolved phases, biomolecules especially proteins and nucleic acids can form liquid droplets through liquid-liquid phase separation (LLPS). Stronger interactions promote a solid-like state of biomolecular condensates, which are also formerly referred to as detergent-insoluble aggregates. Solid-like condensates exist in vivo physiologically and pathologically, and their formation has not been fully understood. Recently, more and more research has proven that liquid to solid phase transition (LST) is an essential way to form solid condensates. In this review, we summarized the regions in the sequence that have different impacts on phase transition and emphasized that the LST is affected by its sequence characteristics. Moreover, increasing evidence unveiled that LST is affected by various solution conditions. We discussed solution conditions like protein concentration, pH, ATP, ions, and small molecules in a solution. Methods have been established to study these solid phase components. Here, we summarized low-throughput experimental techniques and high-throughput omics methods in the study of the LST.

Metamorphism in TDP-43 prion-like domain determines chaperone recognition

The RNA binding protein TDP-43 forms cytoplasmic inclusions via its C-terminal prion-like domain in several neurodegenerative diseases. Aberrant TDP-43 aggregation arises upon phase de-mixing and transitions from liquid to solid states, following still unknown structural conversions which are primed by oxidative stress and chaperone inhibition. Despite the well-established protective roles for molecular chaperones against protein aggregation pathologies, knowledge on the determinants of chaperone recognition in disease-related prions is scarce. Here we show that chaperones and co-chaperones primarily recognize the structured elements in TDP-43´s prion-like domain. Significantly, while HSP70 and HSP90 chaperones promote TDP-43 phase separation, co-chaperones from the three classes of the large human HSP40 family (namely DNAJA2, DNAJB1, DNAJB4 and DNAJC7) show strikingly different effects on TDP-43 de-mixing. Dismantling of the second helical element in TDP-43 prion-like domain by methionine sulfoxidation impacts phase separation and amyloid formation, abrogates chaperone recognition and alters phosphorylation by casein kinase-1δ. Our results show that metamorphism in the post-translationally modified TDP-43 prion-like domain encodes determinants that command mechanisms with major relevance in disease.

PolyQ-Expanded Mutant Huntingtin Forms Inclusion Body Following Transient Cold Shock in a Two-Step Aggregation Mechanism

Age-dependent formation of insoluble protein aggregates is a hallmark of many neurodegenerative diseases. We are interested in the cell chemistry that drives the aggregation of polyQ-expanded mutant Huntingtin (mHtt) protein into insoluble inclusion bodies (IBs). Using an inducible cell model of Huntington's disease, we show that a transient cold shock (CS) at 4 °C followed by recovery incubation at temperatures of 25-37 °C strongly and rapidly induces the compaction of diffuse polyQ-expanded HuntingtinExon1-enhanced green fluorescent protein chimera protein (mHtt) into round, micron size, cytosolic IBs. This transient CS-induced mHtt IB formation is independent of microtubule integrity or de novo protein synthesis. The addition of millimolar concentrations of sodium chloride accelerates, whereas urea suppresses this transient CS-induced mHtt IB formation. These results suggest that the low temperature of CS constrains the conformation dynamics of the intrinsically disordered mHtt into labile intermediate structures to facilitate de-solvation and hydrophobic interaction for IB formation at the higher recovery temperature. This work, along with our previous observation of the effects of heat shock protein chaperones and osmolytes in driving mHtt IB formation, underscores the primacy of mHtt structuring and rigidification for H-bond-mediated cross-linking in a two-step mechanism of mHtt IB formation in living cells.

A Zn-dependent structural transition of SOD1 modulates its ability to undergo phase separation

The misfolding and mutation of Cu/Zn superoxide dismutase (SOD1) is commonly associated with amyotrophic lateral sclerosis (ALS). SOD1 can accumulate within stress granules (SGs), a type of membraneless organelle, which is believed to form via liquid-liquid phase separation (LLPS). Using wild-type, metal-deficient, and different ALS disease mutants of SOD1 and computer simulations, we report here that the absence of Zn leads to structural disorder within two loop regions of SOD1, triggering SOD1 LLPS and amyloid formation. The addition of exogenous Zn to either metal-free SOD1 or to the severe ALS mutation I113T leads to the stabilization of the loops and impairs SOD1 LLPS and aggregation. Moreover, partial Zn-mediated inhibition of LLPS was observed for another severe ALS mutant, G85R, which shows perturbed Zn-binding. By contrast, the ALS mutant G37R, which shows reduced Cu-binding, does not undergo LLPS. In addition, SOD1 condensates induced by Zn-depletion exhibit greater cellular toxicity than aggregates formed by prolonged incubation under aggregating conditions. Overall, our work establishes a role for Zn-dependent modulation of SOD1 conformation and LLPS properties that may contribute to amyloid formation.

Molecular Investigations of Protein Aggregation in the Pathogenesis of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a devastating progressive neurodegenerative disorder characterized by selective loss of lower and upper motor neurons (MNs) in the brain and spinal cord, resulting in paralysis and eventually death due to respiratory insufficiency. Although the fundamental physiological mechanisms underlying ALS are not completely understood, the key neuropathological hallmarks of ALS pathology are the aggregation and accumulation of ubiquitinated protein inclusions within the cytoplasm of degenerating MNs. Herein, we discuss recent insights into the molecular mechanisms that lead to the accumulation of protein aggregates in ALS. This will contribute to a better understanding of the pathophysiology of the disease and may open novel avenues for the development of therapeutic strategies.

Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants

BACKGROUND

Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors.

RESULTS

In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins.

CONCLUSIONS

We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.

Scalable Functional Assays for the Interpretation of Human Genetic Variation

Scalable sequence-function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.

An atlas of amyloid aggregation: the impact of substitutions, insertions, deletions and truncations on amyloid beta fibril nucleation

Multiplexed assays of variant effects (MAVEs) guide clinical variant interpretation and reveal disease mechanisms. To date, MAVEs have focussed on a single mutation type-amino acid (AA) substitutions-despite the diversity of coding variants that cause disease. Here we use Deep Indel Mutagenesis (DIM) to generate a comprehensive atlas of diverse variant effects for a disease protein, the amyloid beta (Aβ) peptide that aggregates in Alzheimer's disease (AD) and is mutated in familial AD (fAD). The atlas identifies known fAD mutations and reveals that many variants beyond substitutions accelerate Aβ aggregation and are likely to be pathogenic. Truncations, substitutions, insertions, single- and internal multi-AA deletions differ in their propensity to enhance or impair aggregation, but likely pathogenic variants from all classes are highly enriched in the polar N-terminal region of Aβ. This comparative atlas highlights the importance of including diverse mutation types in MAVEs and provides important mechanistic insights into amyloid nucleation.

Intragenic compensation through the lens of deep mutational scanning

A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01005-w.

How Functional Genomics Can Keep Pace With VUS Identification

Over the last two decades, an exponentially expanding number of genetic variants have been identified associated with inherited cardiac conditions. These tremendous gains also present challenges in deciphering the clinical relevance of unclassified variants or variants of uncertain significance (VUS). This review provides an overview of the advancements (and challenges) in functional and computational approaches to characterize variants and help keep pace with VUS identification related to inherited heart diseases.

Understanding and evolving prions by yeast multiplexed assays

Yeast genetics made it possible to derive the first fundamental insights into prion composition, conformation, and propagation. Fast-forward 30 years and the same model organism is now proving an extremely powerful tool to comprehensively explore the impact of mutations in prion sequences on their function, toxicity, and physical properties. Here, we provide an overview of novel multiplexed strategies where deep mutagenesis is combined to a range of tailored selection assays in yeast, which are particularly amenable for investigating prions and prion-like sequences. By mimicking evolution in a flask, these multiplexed approaches are revealing mechanistic insights on the consequences of prion self-assembly, while also reporting on the structure prion sequences adopt in vivo.

Humanized yeast to model human biology, disease and evolution

For decades, budding yeast, a single-cellular eukaryote, has provided remarkable insights into human biology. Yeast and humans share several thousand genes despite morphological and cellular differences and over a billion years of separate evolution. These genes encode critical cellular processes, the failure of which in humans results in disease. Although recent developments in genome engineering of mammalian cells permit genetic assays in human cell lines, there is still a need to develop biological reagents to study human disease variants in a high-throughput manner. Many protein-coding human genes can successfully substitute for their yeast equivalents and sustain yeast growth, thus opening up doors for developing direct assays of human gene function in a tractable system referred to as 'humanized yeast'. Humanized yeast permits the discovery of new human biology by measuring human protein activity in a simplified organismal context. This Review summarizes recent developments showing how humanized yeast can directly assay human gene function and explore variant effects at scale. Thus, by extending the 'awesome power of yeast genetics' to study human biology, humanizing yeast reinforces the high relevance of evolutionarily distant model organisms to explore human gene evolution, function and disease.

From systems to structure - using genetic data to model protein structures

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.