The genetic landscape for amyloid beta fibril nucleation accurately discriminates familial Alzheimer's disease mutations

Fibrinaloid Microclots and Atrial Fibrillation

Atrial fibrillation (AF) is a comorbidity of a variety of other chronic, inflammatory diseases for which fibrinaloid microclots are a known accompaniment (and in some cases, a cause, with a mechanistic basis). Clots are, of course, a well-known consequence of atrial fibrillation. We here ask the question whether the fibrinaloid microclots seen in plasma or serum may in fact also be a cause of (or contributor to) the development of AF. We consider known 'risk factors' for AF, and in particular, exogenous stimuli such as infection and air pollution by particulates, both of which are known to cause AF. The external accompaniments of both bacterial (lipopolysaccharide and lipoteichoic acids) and viral (SARS-CoV-2 spike protein) infections are known to stimulate fibrinaloid microclots when added in vitro, and fibrinaloid microclots, as with other amyloid proteins, can be cytotoxic, both by inducing hypoxia/reperfusion and by other means. Strokes and thromboembolisms are also common consequences of AF. Consequently, taking a systems approach, we review the considerable evidence in detail, which leads us to suggest that it is likely that microclots may well have an aetiological role in the development of AF. This has significant mechanistic and therapeutic implications.

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.

The Proof-of-Concept of MBA121, a Tacrine-Ferulic Acid Hybrid, for Alzheimer's Disease Therapy

Great effort has been devoted to the synthesis of novel multi-target directed tacrine derivatives in the search of new treatments for Alzheimer's disease (AD). Herein we describe the proof of concept of MBA121, a compound designed as a tacrine-ferulic acid hybrid, and its potential use in the therapy of AD. MBA121 shows good β-amyloid (Aβ) anti-aggregation properties, selective inhibition of human butyrylcholinesterase, good neuroprotection against toxic insults, such as Aβ1-40, Aβ1-42, and H2O2, and promising ADMET properties that support translational developments. A passive avoidance task in mice with experimentally induced amnesia was carried out, MBA121 being able to significantly decrease scopolamine-induced learning deficits. In addition, MBA121 reduced the Aβ plaque burden in the cerebral cortex and hippocampus in APPswe/PS1ΔE9 transgenic male mice. Our in vivo results relate its bioavailability with the therapeutic response, demonstrating that MBA121 is a promising agent to treat the cognitive decline and neurodegeneration underlying AD.

The landscape of tolerated genetic variation in humans and primates

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.

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 .

The landscape of tolerated genetic variation in humans and primates

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole genome sequencing data for 809 individuals from 233 primate species, and identified 4.3 million common protein-altering variants with orthologs in human. We show that these variants can be inferred to have non-deleterious effects in human based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.

One Sentence Summary

Deep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.

TRPM2 Channel Inhibition Attenuates Amyloid β42-Induced Apoptosis and Oxidative Stress in the Hippocampus of Mice

Alzheimer's disease (AD) is characterized by the increase of hippocampal Ca²⁺ influx-induced apoptosis and mitochondrial oxidative stress (OS). The OS is a stimulator of TRPM2, although N-(p-amylcinnamoyl)anthranilic acid (ACA), 2-aminoethyl diphenylborinate (2/APB), and glutathione (GSH) are non-specific antagonists of TRPM2. In the present study, we investigated the protective roles of GSH and TRPM2 antagonist treatments on the amyloid β42 peptide (Aβ)-caused oxidative neurotoxicity and apoptosis in the hippocampus of mice with AD model. After the isolation of hippocampal neurons from the newborn mice, they were divided into five incubation groups as follows: control, ACA, Aβ, Aβ+ACA, and Aβ+GSH. The levels of apoptosis, hippocampus death, cytosolic ROS, cytosolic Zn²⁺, mitochondrial ROS, caspase-3, caspase-9, lipid peroxidation, and cytosolic Ca²⁺ were increased in the primary hippocampus cultures by treatments of Aβ, although their levels were decreased in the neurons by the treatments of GSH, PARP-1 inhibitors (PJ34 and DPQ), and TRPM2 blockers (ACA and 2/APB). The Aβ-induced decreases of cell viability, cytosolic GSH, reduced GSH, and GSH peroxidase levels were also increased in the groups of Aβ+ACA and Aβ+GSH by the treatments of ACA and GSH. However, the Aβ-caused changes were not observed in the hippocampus of TRPM2-knockout mice. In conclusion, the present data demonstrate that maintaining the activation of TRPM2 is not only important for the quenching OS and neurotoxicity in the hippocampal neurons of mice with experimental AD but also equally critical to the modulation of Aβ-induced apoptosis. The possible positive effects of GSH and TRPM2 antagonist treatments on the amyloid-beta (Aβ)-induced oxidative toxicity in the hippocampus of mice. The ADP-ribose (ADPR) is produced via the stimulation of PARP-1 in the nucleus of neurons. The NUT9 in the C terminus of TRPM2 channel acts as a key role for the activation of TRPM2. The antagonists of TRPM2 are glutathione (GSH), ACA, and 2/APB in the hippocampus. The Aβ incubation-mediated TRPM2 stimulation increases the concentration of cytosolic-free Ca²⁺ and Zn²⁺ in the hippocampus. In turn, the increased concentration causes the increase of mitochondrial membrane potential (ΔΨm), which causes the excessive generations of mitochondria ROS and the decrease of cytosolic GSH and GSH peroxidase (GSH-Px). The ROS production and GSH depletion are two main causes in the neurobiology of Alzheimer's disease. However, the effect of Aβ was not shown in the hippocampus of TRPM2-knockout mice. The Aβ and TRPM2 stimulation-caused overload Ca²⁺ entry cause apoptosis and cell death via the activations of caspase-3 (Casp/3) and caspase-9 (Casp/9) in the hippocampus. The actions of Aβ-induced oxidative toxicity were modulated in the primary hippocampus by the incubations of ACA, GSH, 2/APB, and PARP-1 inhibitors (PJ34 and DPQ). (↑) Increase. (↓) Decrease.

Towards mechanistic models of mutational effects: Deep Learning on Alzheimer’s Aβ peptide

Deep Mutational Scanning (DMS) has enabled multiplexed measurement of mutational effects on protein properties, including kinematics and self-organization, with unprecedented resolution. However, potential bottlenecks of DMS characterization include experimental design, data quality, and depth of mutational coverage. Here, we apply deep learning to comprehensively model the mutational effect of the Alzheimer's Disease associated peptide Aβ₄₂ on aggregation-related biochemical traits from DMS measurements. Among tested neural network architectures, Convolutional Neural Networks and Recurrent Neural Networks are found to be the most cost-effective models with high performance even under insufficiently-sampled DMS studies. While sequence features are essential for satisfactory prediction from neural networks, geometric-structural features further enhance the prediction performance. Notably, we demonstrate how mechanistic insights into phenotype may be extracted from the neural networks themselves suitably designed. This methodological benefit is particularly relevant for biochemical systems displaying a strong coupling between structure and phenotype such as the conformation of Aβ₄₂ aggregate and nucleation, as shown here using a Graph Convolutional Neural Network (GCN) developed from the protein atomic structure input. In addition to accurate imputation of missing values (which here ranged up to 55% of all phenotype values at key residues), the mutationally-defined nucleation phenotype generated from a GCN shows improved resolution for identifying known disease-causing mutations relative to the original DMS phenotype. Our study suggests that neural network derived sequence-phenotype mapping can be exploited not only to provide direct support for protein engineering or genome editing but also to facilitate therapeutic design with the gained perspectives from biological modeling.

Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes

Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.

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.

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.

Insights into Cerebral Amyloid Angiopathy Type 1 and Type 2 from Comparisons of the Fibrillar Assembly and Stability of the Aβ40-Iowa and Aβ40-Dutch Peptides

Two distinct diseases are associated with the deposition of fibrillar amyloid-β (Aβ) peptides in the human brain in an age-dependent fashion. Alzheimer's disease is primarily associated with parenchymal plaque deposition of Aβ42, while cerebral amyloid angiopathy (CAA) is associated with amyloid formation of predominantly Aβ40 in the cerebral vasculature. In addition, familial mutations at positions 22 and 23 of the Aβ sequence can enhance vascular deposition in the two major subtypes of CAA. The E22Q (Dutch) mutation is associated with CAA type 2, while the D23N (Iowa) mutation is associated with CAA type 1. Here we investigate differences in the formation and structure of fibrils of these mutant Aβ peptides in vitro to gain insights into their biochemical and physiological differences in the brain. Using Fourier transform infrared and nuclear magnetic resonance spectroscopy, we measure the relative propensities of Aβ40-Dutch and Aβ40-Iowa to form antiparallel structure and compare these propensities to those of the wild-type Aβ40 and Aβ42 isoforms. We find that both Aβ40-Dutch and Aβ40-Iowa have strong propensities to form antiparallel β-hairpins in the first step of the fibrillization process. However, there is a marked difference in the ability of these peptides to form elongated antiparallel structures. Importantly, we find marked differences in the stability of the protofibril or fibril states formed by the four Aβ peptides. We discuss these differences with respect to the mechanisms of Aβ fibril formation in CAA.

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect

Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general strategy for inferring quantitative models of genotype-phenotype maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning genotype-phenotype maps—including biophysically interpretable models—from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise.

Reduced mitochondria membrane potential and lysosomal acidification are associated with decreased oligomeric Aβ degradation induced by hyperglycemia: A study of mixed glia cultures

Diabetes is a risk factor for Alzheimer’s disease (AD), a chronic neurodegenerative disease. We and others have shown prediabetes, including hyperglycemia and obesity induced by high fat and high sucrose diets, is associated with exacerbated amyloid beta (Aβ) accumulation and cognitive impairment in AD transgenic mice. However, whether hyperglycemia reduce glial clearance of oligomeric amyloid-β (oAβ), the most neurotoxic Aβ aggregate, remains unclear. Mixed glial cultures simulating the coexistence of astrocytes and microglia in the neural microenvironment were established to investigate glial clearance of oAβ under normoglycemia and chronic hyperglycemia. Ramified microglia and low IL-1β release were observed in mixed glia cultures. In contrast, amoeboid-like microglia and higher IL-1β release were observed in primary microglia cultures. APPswe/PS1dE9 transgenic mice are a commonly used AD mouse model. Microglia close to senile plaques in APPswe/PS1dE9 transgenic mice exposed to normoglycemia or chronic hyperglycemia exhibited an amoeboid-like morphology; other microglia were ramified. Therefore, mixed glia cultures reproduce the in vivo ramified microglial morphology. To investigate the impact of sustained high-glucose conditions on glial oAβ clearance, mixed glia were cultured in media containing 5.5 mM glucose (normal glucose, NG) or 25 mM glucose (high glucose, HG) for 16 days. Compared to NG, HG reduced the steady-state level of oAβ puncta internalized by microglia and astrocytes and decreased oAβ degradation kinetics. Furthermore, the lysosomal acidification and lysosomal hydrolysis activity of microglia and astrocytes were lower in HG with and without oAβ treatment than NG. Moreover, HG reduced mitochondrial membrane potential and ATP levels in mixed glia, which can lead to reduced lysosomal function. Overall, continuous high glucose reduces microglial and astrocytic ATP production and lysosome activity which may lead to decreased glial oAβ degradation. Our study reveals diabetes-induced hyperglycemia hinders glial oAβ clearance and contributes to oAβ accumulation in AD pathogenesis.

Impact of the Mutational Landscape of the Sodium/Iodide Symporter in Congenital Hypothyroidism

Background: Iodide transport defect is an uncommon cause of dyshormonogenic congenital hypothyroidism due to homozygous or compound heterozygous pathogenic variants in the SLC5A5 gene, which encodes the sodium/iodide symporter (NIS), causing deficient iodide accumulation in thyroid follicular cells, thus impairing thyroid hormonogenesis. Methods:SLC5A5 gene variants were compiled from public databases and research articles exploring the molecular bases of congenital hypothyroidism. Using a dataset of 198 missense NIS variants classified as either benign or pathogenic, we developed and validated a machine learning-based NIS-specific variant classifier to predict the impact of missense NIS variants. Results: We generated a manually curated dataset containing 7793 unique SLC5A5 variants. As most databases compiled exome sequencing data, variant mapping revealed an increased density of variants in SLC5A5 coding exons. Based on allele frequency (AF) analysis, we established an AF threshold of 1:10,000 above which a variant should be considered benign. Most pathogenic NIS variants were located in the protein-coding region, as most patients were genetically diagnosed by using a candidate gene strategy limited to this region. Significantly, we evidenced that 94.5% of missense NIS variants were classified as of uncertain significance. Therefore, we developed an NIS-specific variant classifier to improve the prediction of pathogenicity of missense variants. Our classifier predicted the clinical outcome of missense variants with high accuracy (90%), outperforming state-of-the-art pathogenicity predictors, such as REVEL, PolyPhen-2, and SIFT. Based on the excellent performance of our classifier, we predicted the mutational landscape of NIS. The analysis of the mutational landscape revealed that most missense variants located in transmembrane segments are frequently pathogenic. Moreover, we predicted that ∼28% of all single-nucleotide variants that could cause missense NIS variants are pathogenic, thus putatively leading to congenital hypothyroidism if present in homozygous or compound heterozygous state. Conclusions: We reported the first NIS-specific variant classifier aiming at improving the interpretation of missense NIS variants in clinical practice. Deciphering the mutational landscape for every protein involved in thyroid hormonogenesis is a relevant task for a deep understanding of the molecular mechanisms causing dyshormonogenic congenital hypothyroidism.

Amyloid-β40 E22K fibril in familial Alzheimer's disease is more thermostable and susceptible to seeding

Alzheimer's disease (AD) is the most prevalent and devastating neurodegenerative disease occurred in the elderly. One of the pathogenic hallmarks is senile plaques composed of amyloid-β (Aβ) fibrils. Single mutations resided in Aβ were found in familial AD (FAD) patients that have early onset of the disease. The molecular details and properties of each FAD Aβ variants are still elusive. Here, we employed collective spectroscopic techniques to examine the properties of various Aβ40 fibrils. We generated fibrils of wild type (WT) and three FAD mutants on residue E22 including E22G, E22K, and E22Q. We monitored fibril formation by thioflavin T (ThT) assay, examined secondary structure by Fourier transform infrared and far-UV circular dichroism spectroscopy, imaged fibril morphology by transmission electron microscopy, and evaluated ThT-binding kinetics. In the thermal experiments, we found E22K fibrils resisted to high temperature and retained significant β-sheet content than the others. E22K fibril seeds after high-temperature treatment still possess the seeding property, whereas WT fibril seeds are disturbed after the treatment. Therefore, in this study we demonstrated the mutation at E22K increases the thermal stability and seeding function of amyloid fibrils.

Linking genome variants to disease: scalable approaches to test the functional impact of human mutations

The application of genomics to medicine has accelerated the discovery of mutations underlying disease and has enhanced our knowledge of the molecular underpinnings of diverse pathologies. As the amount of human genetic material queried via sequencing has grown exponentially in recent years, so too has the number of rare variants observed. Despite progress, our ability to distinguish which rare variants have clinical significance remains limited. Over the last decade, however, powerful experimental approaches have emerged to characterize variant effects orders of magnitude faster than before. Fueled by improved DNA synthesis and sequencing and, more recently, by CRISPR/Cas9 genome editing, multiplex functional assays provide a means of generating variant effect data in wide-ranging experimental systems. Here, I review recent applications of multiplex assays that link human variants to disease phenotypes and I describe emerging strategies that will enhance their clinical utility in coming years.

Protein structure and aggregation: a marriage of necessity ruled by aggregation gatekeepers

Protein aggregation propensity is a pervasive and seemingly inescapable property of proteomes. Strikingly, a significant fraction of the proteome is supersaturated, meaning that, for these proteins, their native conformation is less stable than the aggregated state. Maintaining the integrity of a proteome under such conditions is precarious and requires energy-consuming proteostatic regulation. Why then is aggregation propensity maintained at such high levels over long evolutionary timescales? Here, we argue that the conformational stability of the native and aggregated states are correlated thermodynamically and that codon usage strengthens this correlation. As a result, the folding of stable proteins requires kinetic control to avoid aggregation, provided by aggregation gatekeepers. These unique residues are evolutionarily selected to kinetically favor native folding, either on their own or by coopting chaperones.

PARROT is a flexible recurrent neural network framework for analysis of large protein datasets

The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex nonlinear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.

On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.

Exploring absent protein function in yeast: assaying post translational modification and human genetic variation

Yeast is a valuable eukaryotic model organism that has evolved many processes conserved up to humans, yet many protein functions, including certain DNA and protein modifications, are absent. It is this absence of protein function that is fundamental to approaches using yeast as an in vivo test system to investigate human proteins. Functionality of the heterologous expressed proteins is connected to a quantitative, selectable phenotype, enabling the systematic analyses of mechanisms and specificity of DNA modification, post-translational protein modifications as well as the impact of annotated cancer mutations and coding variation on protein activity and interaction. Through continuous improvements of yeast screening systems, this is increasingly carried out on a global scale using deep mutational scanning approaches. Here we discuss the applicability of yeast systems to investigate absent human protein function with a specific focus on the impact of protein variation on protein-protein interaction modulation.

Exploring the origins of nucleation

An approach called deep mutational scanning is improving our understanding of amyloid beta aggregation.

The genetic landscape for amyloid beta fibril nucleation accurately discriminates familial Alzheimer's disease mutations

Plaques of the amyloid beta (Aß) peptide are a pathological hallmark of Alzheimer's disease (AD), the most common form of dementia. Mutations in Aß also cause familial forms of AD (fAD). Here, we use deep mutational scanning to quantify the effects of >14,000 mutations on the aggregation of Aß. The resulting genetic landscape reveals mechanistic insights into fibril nucleation, including the importance of charge and gatekeeper residues in the disordered region outside of the amyloid core in preventing nucleation. Strikingly, unlike computational predictors and previous measurements, the empirical nucleation scores accurately identify all known dominant fAD mutations in Aß, genetically validating that the mechanism of nucleation in a cell-based assay is likely to be very similar to the mechanism that causes the human disease. These results provide the first comprehensive atlas of how mutations alter the formation of any amyloid fibril and a resource for the interpretation of genetic variation in Aß.