High-throughput analysis of in vivo protein stability

Facile Method for High-throughput Identification of Stabilizing Mutations

Characterizing the effects of mutations on stability is critical for understanding the function and evolution of proteins and improving their biophysical properties. High throughput folding and abundance assays have been successfully used to characterize missense mutations associated with reduced stability. However, screening for increased thermodynamic stability is more challenging since such mutations are rarer and their impact on assay readout is more subtle. Here, a multiplex assay for high throughput screening of protein folding was developed by combining deep mutational scanning, fluorescence-activated cell sorting, and deep sequencing. By analyzing a library of 2000 variants of Adenylate kinase we demonstrate that the readout of the method correlates with stability and that mutants with up to 13 °C increase in thermal melting temperature could be identified with low false positive rate. The discovery of many stabilizing mutations also enabled the analysis of general substitution patterns associated with increased stability in Adenylate kinase. This high throughput method to identify stabilizing mutations can be combined with functional screens to identify mutations that improve both stability and activity.

Intra-FCY1: a novel system to identify mutations that cause protein misfolding

Protein misfolding is a common intracellular occurrence. Most mutations to coding sequences increase the propensity of the encoded protein to misfold. These misfolded molecules can have devastating effects on cells. Despite the importance of protein misfolding in human disease and protein evolution, there are fundamental questions that remain unanswered, such as, which mutations cause the most misfolding? These questions are difficult to answer partially because we lack high-throughput methods to compare the destabilizing effects of different mutations. Commonly used systems to assess the stability of mutant proteins in vivo often rely upon essential proteins as sensors, but misfolded proteins can disrupt the function of the essential protein enough to kill the cell. This makes it difficult to identify and compare mutations that cause protein misfolding using these systems. Here, we present a novel in vivo system named Intra-FCY1 that we use to identify mutations that cause misfolding of a model protein [yellow fluorescent protein (YFP)] in Saccharomyces cerevisiae. The Intra-FCY1 system utilizes two complementary fragments of the yeast cytosine deaminase Fcy1, a toxic protein, into which YFP is inserted. When YFP folds, the Fcy1 fragments associate together to reconstitute their function, conferring toxicity in media containing 5-fluorocytosine and hindering growth. But mutations that make YFP misfold abrogate Fcy1 toxicity, thus strains possessing misfolded YFP variants rise to high frequency in growth competition experiments. This makes such strains easier to study. The Intra-FCY1 system cancels localization of the protein of interest, thus can be applied to study the relative stability of mutant versions of diverse cellular proteins. Here, we confirm this method can identify novel mutations that cause misfolding, highlighting the potential for Intra-FCY1 to illuminate the relationship between protein sequence and stability.

Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers

Protein-protein interactions (PPIs) regulate many cellular processes, and engineered PPIs have cell and gene therapy applications. Here we introduce massively parallel protein-protein interaction measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast-two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs, and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Finally, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics simulation energy terms to predict MP3-seq values. We find that AF-M and AF-M complex prediction-based models could be valuable for pre-screening interactions, but that measuring interactions experimentally remains necessary to rank their strengths quantitatively.

Protein engineering via sequence-performance mapping

Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies-experimental and computational-enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance-e.g., binding, catalytic efficiency, biological activity, and developability-thereby advancing fundamental protein science and facilitating protein discovery and evolution.

An Atlas of Variant Effects to understand the genome at nucleotide resolution

Sequencing has revealed hundreds of millions of human genetic variants, and continued efforts will only add to this variant avalanche. Insufficient information exists to interpret the effects of most variants, limiting opportunities for precision medicine and comprehension of genome function. A solution lies in experimental assessment of the functional effect of variants, which can reveal their biological and clinical impact. However, variant effect assays have generally been undertaken reactively for individual variants only after and, in most cases long after, their first observation. Now, multiplexed assays of variant effect can characterise massive numbers of variants simultaneously, yielding variant effect maps that reveal the function of every possible single nucleotide change in a gene or regulatory element. Generating maps for every protein encoding gene and regulatory element in the human genome would create an 'Atlas' of variant effect maps and transform our understanding of genetics and usher in a new era of nucleotide-resolution functional knowledge of the genome. An Atlas would reveal the fundamental biology of the human genome, inform human evolution, empower the development and use of therapeutics and maximize the utility of genomics for diagnosing and treating disease. The Atlas of Variant Effects Alliance is an international collaborative group comprising hundreds of researchers, technologists and clinicians dedicated to realising an Atlas of Variant Effects to help deliver on the promise of genomics.

yGPS-P: A Yeast-Based Peptidome Screen for Studying Quality Control-Associated Proteolysis

Quality control-associated proteolysis (QCAP) is a fundamental mechanism that maintains cellular homeostasis by eliminating improperly folded proteins. In QCAP, the exposure of normally hidden cis-acting protein sequences, termed degrons, triggers misfolded protein ubiquitination, resulting in their elimination by the proteasome. To identify the landscape of QCAP degrons and learn about their unique features we have developed an unbiased screening method in yeast, termed yGPS-P, which facilitates the determination of thousands of proteome-derived sequences that enhance proteolysis. Here we describe the fundamental features of the yGPS-P method and provide a detailed protocol for its use as a tool for degron search. This includes the cloning of a synthetic peptidome library in a fluorescence-based reporter system, and data acquisition, which entails the combination of Fluorescence-Activated Cell Sorting (FACS) and Next-Generation Sequencing (NGS). We also provide guidelines for data extraction and analysis and for the application of a machine-learning algorithm that established the evolutionarily conserved amino acid preferences and secondary structure propensities of QCAP degrons.

Elements of the ERAD ubiquitin ligase Doa10 regulating sequential poly-ubiquitylation of its targets

In ER-associated degradation (ERAD), misfolded ER proteins are degraded by the proteasome after undergoing ubiquitylation. Yeast Doa10 (human MARCHF6/TEB4) is a membrane-embedded E3 ubiquitin ligase that functions with E2s Ubc6 and Ubc7. Ubc6 attaches a single ubiquitin to substrates, which is extended by Ubc7 to form a polyubiquitin chain. We show the conserved C-terminal element (CTE) of Doa10 promotes E3-mediated Ubc6 activity. Doa10 substrates undergoing an alternative ubiquitylation mechanism are still degraded in CTE-mutant cells. Structure prediction by AlphaFold2 suggests the CTE binds near the catalytic RING-CH domain, implying a direct role in substrate ubiquitylation, and we confirm this interaction using intragenic suppression. Truncation analysis defines a minimal E2-binding region of Doa10; structural predictions suggest that Doa10 forms a retrotranslocation channel and that E2s bind within the cofactor-binding region defined here. These results provide mechanistic insight into how Doa10, and potentially other ligases, interact with their cofactors and mediate ERAD.

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The conserved Doa10 C-terminus promotes E3-mediated activity of Ubc6

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The minimal E2-binding region of Doa10 includes TMs 1–9

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The N- and C-terminus of Doa10 interact, likely forming an ERAD protein channel

Mutant libraries reveal negative design shielding proteins from supramolecular self-assembly and relocalization in cells

Genetic mutations fuel organismal evolution but can also cause disease. As proteins are the cell’s workhorses, the ways in which mutations can disrupt their structure, stability, function, and interactions have been studied extensively. However, proteins evolve and function in a cellular context, and our ability to relate changes in protein sequence to cell-level phenotypes remains limited. In particular, the molecular mechanism underlying most disease-associated mutations is unknown. Here, we show that mutations changing a protein’s surface chemistry can dramatically impact its supramolecular self-assembly and localization in the cell. These results highlight the complex nature of genotype–phenotype relationships with a simple system.

Understanding the molecular consequences of mutations in proteins is essential to map genotypes to phenotypes and interpret the increasing wealth of genomic data. While mutations are known to disrupt protein structure and function, their potential to create new structures and localization phenotypes has not yet been mapped to a sequence space. To map this relationship, we employed two homo-oligomeric protein complexes in which the internal symmetry exacerbates the impact of mutations. We mutagenized three surface residues of each complex and monitored the mutations’ effect on localization and assembly phenotypes in yeast cells. While surface mutations are classically viewed as benign, our analysis of several hundred mutants revealed they often trigger three main phenotypes in these proteins: nuclear localization, the formation of puncta, and fibers. Strikingly, more than 50% of random mutants induced one of these phenotypes in both complexes. Analyzing the mutant’s sequences showed that surface stickiness and net charge are two key physicochemical properties associated with these changes. In one complex, more than 60% of mutants self-assembled into fibers. Such a high frequency is explained by negative design: charged residues shield the complex from self-interacting with copies of itself, and the sole removal of the charges induces its supramolecular self-assembly. A subsequent analysis of several other complexes targeted with alanine mutations suggested that such negative design is common. These results highlight that minimal perturbations in protein surfaces’ physicochemical properties can frequently drive assembly and localization changes in a cellular context.

Ubiquitin Ligase Redundancy and Nuclear-Cytoplasmic Localization in Yeast Protein Quality Control

The diverse functions of proteins depend on their proper three-dimensional folding and assembly. Misfolded cellular proteins can potentially harm cells by forming aggregates in their resident compartments that can interfere with vital cellular processes or sequester important factors. Protein quality control (PQC) pathways are responsible for the repair or destruction of these abnormal proteins. Most commonly, the ubiquitin-proteasome system (UPS) is employed to recognize and degrade those proteins that cannot be refolded by molecular chaperones. Misfolded substrates are ubiquitylated by a subset of ubiquitin ligases (also called E3s) that operate in different cellular compartments. Recent research in Saccharomyces cerevisiae has shown that the most prominent ligases mediating cytoplasmic and nuclear PQC have overlapping yet distinct substrate specificities. Many substrates have been characterized that can be targeted by more than one ubiquitin ligase depending on their localization, and cytoplasmic PQC substrates can be directed to the nucleus for ubiquitylation and degradation. Here, we review some of the major yeast PQC ubiquitin ligases operating in the nucleus and cytoplasm, as well as current evidence indicating how these ligases can often function redundantly toward substrates in these compartments.

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.

Protein quality control degron-containing substrates are differentially targeted in the cytoplasm and nucleus by ubiquitin ligases

Intracellular proteolysis by the ubiquitin-proteasome system regulates numerous processes and contributes to protein quality control (PQC) in all eukaryotes. Covalent attachment of ubiquitin to other proteins is specified by the many ubiquitin ligases (E3s) expressed in cells. Here we determine the E3s in Saccharomyces cerevisiae that function in degradation of proteins bearing various PQC degradation signals (degrons). The E3 Ubr1 can function redundantly with several E3s, including nuclear-localized San1, endoplasmic reticulum/nuclear membrane-embedded Doa10, and chromatin-associated Slx5/Slx8. Notably, multiple degrons are targeted by more ubiquitylation pathways if directed to the nucleus. Degrons initially assigned as exclusive substrates of Doa10 were targeted by Doa10, San1, and Ubr1 when directed to the nucleus. By contrast, very short hydrophobic degrons-typical targets of San1-are shown here to be targeted by Ubr1 and/or San1, but not Doa10. Thus, distinct types of PQC substrates are differentially recognized by the ubiquitin system in a compartment-specific manner. In human cells, a representative short hydrophobic degron appended to the C-terminus of GFP-reduced protein levels compared with GFP alone, consistent with a recent study that found numerous natural hydrophobic C-termini of human proteins can act as degrons. We also report results of bioinformatic analyses of potential human C-terminal degrons, which reveal that most peptide substrates of Cullin-RING ligases (CRLs) are of low hydrophobicity, consistent with previous data showing CRLs target degrons with specific sequences. These studies expand our understanding of PQC in yeast and human cells, including the distinct but overlapping PQC E3 substrate specificity of the cytoplasm and nucleus.

Functional Characterization of PALB2 Variants of Uncertain Significance: Toward Cancer Risk and Therapy Response Prediction

In recent years it has become clear that pathogenic variants in PALB2 are associated with a high risk for breast, ovarian and pancreatic cancer. However, the clinical relevance of variants of uncertain significance (VUS) in PALB2, which are increasingly identified through clinical genetic testing, is unclear. Here we review recent advances in the functional characterization of VUS in PALB2. A combination of assays has been used to assess the impact of PALB2 VUS on its function in DNA repair by homologous recombination, cell cycle regulation and the control of cellular levels of reactive oxygen species (ROS). We discuss the outcome of this comprehensive analysis of PALB2 VUS, which showed that VUS in PALB2’s Coiled-Coil (CC) domain can impair the interaction with BRCA1, whereas VUS in its WD40 domain affect PALB2 protein stability. Accordingly, the CC and WD40 domains of PALB2 represent hotspots for variants that impair PALB2 protein function. We also provide a future perspective on the high-throughput analysis of VUS in PALB2, as well as the functional characterization of variants that affect PALB2 RNA splicing. Finally, we discuss how results from these functional assays can be valuable for predicting cancer risk and responsiveness to cancer therapy, such as treatment with PARP inhibitor- or platinum-based chemotherapy.

Co-Chaperones in Targeting and Delivery of Misfolded Proteins to the 26S Proteasome

Protein homeostasis (proteostasis) is essential for the cell and is maintained by a highly conserved protein quality control (PQC) system, which triages newly synthesized, mislocalized and misfolded proteins. The ubiquitin-proteasome system (UPS), molecular chaperones, and co-chaperones are vital PQC elements that work together to facilitate degradation of misfolded and toxic protein species through the 26S proteasome. However, the underlying mechanisms are complex and remain partly unclear. Here, we provide an overview of the current knowledge on the co-chaperones that directly take part in targeting and delivery of PQC substrates for degradation. While J-domain proteins (JDPs) target substrates for the heat shock protein 70 (HSP70) chaperones, nucleotide-exchange factors (NEFs) deliver HSP70-bound substrates to the proteasome. So far, three NEFs have been established in proteasomal delivery: HSP110 and the ubiquitin-like (UBL) domain proteins BAG-1 and BAG-6, the latter acting as a chaperone itself and carrying its substrates directly to the proteasome. A better understanding of the individual delivery pathways will improve our ability to regulate the triage, and thus regulate the fate of aberrant proteins involved in cell stress and disease, examples of which are given throughout the review.

Current and Emerging Methods for the Synthesis of Single-Stranded DNA

Methods for synthesizing arbitrary single-strand DNA (ssDNA) fragments are rapidly becoming fundamental tools for gene editing, DNA origami, DNA storage, and other applications. To meet the rising application requirements, numerous methods have been developed to produce ssDNA. Some approaches allow the synthesis of freely chosen user-defined ssDNA sequences to overcome the restrictions and limitations of different length, purity, and yield. In this perspective, we provide an overview of the representative ssDNA production strategies and their most significant challenges to enable the readers to make informed choices of synthesis methods and enhance the availability of increasingly inexpensive synthetic ssDNA. We also aim to stimulate a broader interest in the continued development of efficient ssDNA synthesis techniques and improve their applications in future research.

Computational and cellular studies reveal structural destabilization and degradation of MLH1 variants in Lynch syndrome

Defective mismatch repair leads to increased mutation rates, and germline loss-of-function variants in the repair component MLH1 cause the hereditary cancer predisposition disorder known as Lynch syndrome. Early diagnosis is important, but complicated by many variants being of unknown significance. Here we show that a majority of the disease-linked MLH1 variants we studied are present at reduced cellular levels. We show that destabilized MLH1 variants are targeted for chaperone-assisted proteasomal degradation, resulting also in degradation of co-factors PMS1 and PMS2. In silico saturation mutagenesis and computational predictions of thermodynamic stability of MLH1 missense variants revealed a correlation between structural destabilization, reduced steady-state levels and loss-of-function. Thus, we suggest that loss of stability and cellular degradation is an important mechanism underlying many MLH1 variants in Lynch syndrome. Combined with analyses of conservation, the thermodynamic stability predictions separate disease-linked from benign MLH1 variants, and therefore hold potential for Lynch syndrome diagnostics.

MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

Recent insights into the genotype-phenotype relationship from massively parallel genetic assays

With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

A molecular toolbox for studying protein degradation in mammalian cells

Protein degradation is a crucial regulatory process in maintaining cellular proteostasis. The selective degradation of intracellular proteins controls diverse cellular and biochemical processes in all kingdoms of life. Targeted protein degradation is implicated in controlling the levels of regulatory proteins as well as eliminating misfolded and any otherwise abnormal proteins. Deregulation of protein degradation is concomitant with the progression of various neurodegenerative disorders such as Parkinson's and Alzheimer's diseases. Thus, methods of measuring metabolic half-lives of proteins greatly influence our understanding of the diverse functions of proteins in mammalian cells including neuronal cells. Historically, protein degradation rates have been studied via exploiting methods that estimate overall protein degradation or focus on few individual proteins. Notably, with the recent technical advances and developments in proteomic and imaging techniques, it is now possible to measure degradation rates of a large repertoire of defined proteins and analyze the degradation profile in a detailed spatio-temporal manner, with the aim of determining proteome-wide protein stabilities upon different physiological conditions. Herein, we discuss some of the classical and novel methods for determining protein degradation rates highlighting the crucial role of some state of art approaches in deciphering the global impact of dynamic nature of targeted degradation of cellular proteins. This article is part of the Special Issue "Proteomics".

Determining protein structures using deep mutagenesis

Determining the three-dimensional structures of macromolecules is a major goal of biological research, because of the close relationship between structure and function; however, thousands of protein domains still have unknown structures. Structure determination usually relies on physical techniques including X-ray crystallography, NMR spectroscopy and cryo-electron microscopy. Here we present a method that allows the high-resolution three-dimensional backbone structure of a biological macromolecule to be determined only from measurements of the activity of mutant variants of the molecule. This genetic approach to structure determination relies on the quantification of genetic interactions (epistasis) between mutations and the discrimination of direct from indirect interactions. This provides an alternative experimental strategy for structure determination, with the potential to reveal functional and in vivo structures.

The Hunt for Degrons of the 26S Proteasome

Since the discovery of ubiquitin conjugation as a cellular mechanism that triggers proteasomal degradation, the mode of substrate recognition by the ubiquitin-ligation system has been the holy grail of research in the field. This entails the discovery of recognition determinants within protein substrates, which are part of a degron, and explicit E3 ubiquitin (Ub)-protein ligases that trigger their degradation. Indeed, many protein substrates and their cognate E3′s have been discovered in the past 40 years. In the course of these studies, various degrons have been randomly identified, most of which are acquired through post-translational modification, typically, but not exclusively, protein phosphorylation. Nevertheless, acquired degrons cannot account for the vast diversity in cellular protein half-life times. Obviously, regulation of the proteome is largely determined by inherent degrons, that is, determinants integral to the protein structure. Inherent degrons are difficult to predict since they consist of diverse sequence and secondary structure features. Therefore, unbiased methods have been employed for their discovery. This review describes the history of degron discovery methods, including the development of high throughput screening methods, state of the art data acquisition and data analysis. Additionally, it summarizes major discoveries that led to the identification of cognate E3 ligases and hitherto unrecognized complexities of degron function. Finally, we discuss future perspectives and what still needs to be accomplished towards achieving the goal of understanding how the eukaryotic proteome is regulated via coordinated action of components of the ubiquitin-proteasome system.

Does functional specialization of ribosomes really exist?

It has recently become clear that ribosomes are much more heterogeneous than previously thought, with diversity arising from rRNA sequence and modifications, ribosomal protein (RP) content and posttranslational modifications (PTMs), as well as bound nonribosomal proteins. In some cases, the existence of these diverse ribosome populations has been verified by biochemical or structural methods. Furthermore, knockout or knockdown of RPs can diversify ribosome populations, while also affecting the translation of some mRNAs (but not others) with biological consequences. However, the effects on translation arising from depletion of diverse proteins can be highly similar, suggesting that there may be a more general defect in ribosome function or stability, perhaps arising from reduced ribosome numbers. Consistently, overall reduced ribosome numbers can differentially affect subclasses of mRNAs, necessitating controls for specificity. Moreover, in order to study the functional consequences of ribosome diversity, perturbations including affinity tags and knockouts are introduced, which can also affect the outcome of the experiment. Here we review the available literature to carefully evaluate whether the published data support functional diversification, defined as diverse ribosome populations differentially affecting translation of distinct mRNA (classes). Based on these observations and the commonly observed cellular responses to perturbations in the system, we suggest a set of important controls to validate functional diversity, which should include gain-of-function assays and the demonstration of inducibility under physiological conditions.

Functional assays for transcription mechanisms in high-throughput

Dramatic increases in the scale of programmed synthesis of nucleic acid libraries coupled with deep sequencing have powered advances in understanding nucleic acid and protein biology. Biological systems centering on nucleic acids or encoded proteins greatly benefit from such high-throughput studies, given that large DNA variant pools can be synthesized and DNA, or RNA products of transcription, can be easily analyzed by deep sequencing. Here we review the scope of various high-throughput functional assays for studies of nucleic acids and proteins in general, followed by discussion of how these types of study have yielded insights into the RNA Polymerase II (Pol II) active site as an example. We discuss methodological considerations in the design and execution of these experiments that should be valuable to studies in any system.

Mapping Degradation Signals and Pathways in a Eukaryotic N-terminome

Most eukaryotic proteins are N-terminally acetylated. This modification can be recognized as a signal for selective protein degradation (degron) by the N-end rule pathways. However, the prevalence and specificity of such degrons in the proteome are unclear. Here, by systematically examining how protein turnover is affected by N-terminal sequences, we perform a comprehensive survey of degrons in the yeast N-terminome. We find that approximately 26% of nascent protein N termini encode cryptic degrons. These degrons exhibit high hydrophobicity and are frequently recognized by the E3 ubiquitin ligase Doa10, suggesting a role in protein quality control. In contrast, N-terminal acetylation rarely functions as a degron. Surprisingly, we identify two pathways where N-terminal acetylation has the opposite function and blocks protein degradation through the E3 ubiquitin ligase Ubr1. Our analysis highlights the complexity of N-terminal degrons and argues that hydrophobicity, not N-terminal acetylation, is the predominant feature of N-terminal degrons in nascent proteins.

N-terminal methionine excision of proteins creates tertiary destabilizing N-degrons of the Arg/N-end rule pathway

All organisms begin protein synthesis with methionine (Met). The resulting initiator Met of nascent proteins is irreversibly processed by Met aminopeptidases (MetAPs). N-terminal (Nt) Met excision (NME) is an evolutionarily conserved and essential process operating on up to two-thirds of proteins. However, the universal function of NME remains largely unknown. MetAPs have a well-known processing preference for Nt-Met with Ala, Ser, Gly, Thr, Cys, Pro, or Val at position 2, but using CHX-chase assays to assess protein degradation in yeast cells, as well as protein-binding and RT-qPCR assays, we demonstrate here that NME also occurs on nascent proteins bearing Met-Asn or Met-Gln at their N termini. We found that the NME at these termini exposes the tertiary destabilizing Nt residues (Asn or Gln) of the Arg/N-end rule pathway, which degrades proteins according to the composition of their Nt residues. We also identified a yeast DNA repair protein, MQ-Rad16, bearing a Met-Gln N terminus, as well as a human tropomyosin-receptor kinase-fused gene (TFG) protein, MN-TFG, bearing a Met-Asn N terminus as physiological, MetAP-processed Arg/N-end rule substrates. Furthermore, we show that the loss of the components of the Arg/N-end rule pathway substantially suppresses the growth defects of naa20Δ yeast cells lacking the catalytic subunit of NatB Nt acetylase at 37 °C. Collectively, the results of our study reveal that NME is a key upstream step for the creation of the Arg/N-end rule substrates bearing tertiary destabilizing residues in vivo.

Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations

Biophysical interactions between proteins and peptides are key determinants of molecular recognition specificity landscapes. However, an understanding of how molecular structure and residue-level energetics at protein-peptide interfaces shape these landscapes remains elusive. We combine information from yeast-based library screening, next-generation sequencing, and structure-based modeling in a supervised machine learning approach to report the comprehensive sequence-energetics-function mapping of the specificity landscape of the hepatitis C virus (HCV) NS3/4A protease, whose function-site-specific cleavages of the viral polyprotein-is a key determinant of viral fitness. We screened a library of substrates in which five residue positions were randomized and measured cleavability of ∼30,000 substrates (∼1% of the library) using yeast display and fluorescence-activated cell sorting followed by deep sequencing. Structure-based models of a subset of experimentally derived sequences were used in a supervised learning procedure to train a support vector machine to predict the cleavability of 3.2 million substrate variants by the HCV protease. The resulting landscape allows identification of previously unidentified HCV protease substrates, and graph-theoretic analyses reveal extensive clustering of cleavable and uncleavable motifs in sequence space. Specificity landscapes of known drug-resistant variants are similarly clustered. The described approach should enable the elucidation and redesign of specificity landscapes of a wide variety of proteases, including human-origin enzymes. Our results also suggest a possible role for residue-level energetics in shaping plateau-like functional landscapes predicted from viral quasispecies theory.

Protein stability and degradation in health and disease

The cellular proteome performs highly varied functions to sustain life. Since most of these functions require proteins to fold properly, they can be impaired by mutations that affect protein structure, leading to diseases such as Alzheimer's disease, cystic fibrosis, and Lynch syndrome. The cell has evolved an intricate protein quality control (PQC) system that includes degradation pathways and a multitude of molecular chaperones and co-chaperones, all working together to catalyze the refolding or removal of aberrant proteins. Thus, the PQC system limits the harmful consequences of dysfunctional proteins, including those arising from disease-causing mutations. This complex system is still not fully understood. In particular the structural and sequence motifs that, when exposed, trigger degradation of misfolded proteins are currently under investigation. Moreover, several attempts are being made to activate or inhibit parts of the PQC system as a treatment for diseases. Here, we briefly review the present knowledge on the PQC system and list current strategies that are employed to exploit the system in disease treatment.

Ubiquitin-dependent protein degradation at the endoplasmic reticulum and nuclear envelope

Numerous nascent proteins undergo folding and maturation within the luminal and membrane compartments of the endoplasmic reticulum (ER). Despite the presence of various factors in the ER that promote protein folding, many proteins fail to properly fold and assemble and are subsequently degraded. Regulatory proteins in the ER also undergo degradation in a way that is responsive to stimuli or the changing needs of the cell. As in most cellular compartments, the ubiquitin-proteasome system (UPS) is responsible for the majority of the degradation at the ER-in a process termed ER-associated degradation (ERAD). Autophagic processes utilizing ubiquitin-like protein-conjugating systems also play roles in protein degradation at the ER. The ER is continuous with the nuclear envelope (NE), which consists of the outer nuclear membrane (ONM) and inner nuclear membrane (INM). While ERAD is known also to occur at the NE, only some of the ERAD ubiquitin-ligation pathways function at the INM. Protein degradation machineries in the ER/NE target a wide variety of substrates in multiple cellular compartments, including the cytoplasm, nucleoplasm, ER lumen, ER membrane, and the NE. Here, we review the protein degradation machineries of the ER and NE and the underlying mechanisms dictating recognition and processing of substrates by these machineries.

Biophysical Inference of Epistasis and the Effects of Mutations on Protein Stability and Function

Understanding the relationship between protein sequence, function, and stability is a fundamental problem in biology. The essential function of many proteins that fold into a specific structure is their ability to bind to a ligand, which can be assayed for thousands of mutated variants. However, binding assays do not distinguish whether mutations affect the stability of the binding interface or the overall fold. Here, we introduce a statistical method to infer a detailed energy landscape of how a protein folds and binds to a ligand by combining information from many mutated variants. We fit a thermodynamic model describing the bound, unbound, and unfolded states to high quality data of protein G domain B1 binding to IgG-Fc. We infer distinct folding and binding energies for each mutation providing a detailed view of how mutations affect binding and stability across the protein. We accurately infer the folding energy of each variant in physical units, validated by independent data, whereas previous high-throughput methods could only measure indirect changes in stability. While we assume an additive sequence-energy relationship, the binding fraction is epistatic due its nonlinear relation to energy. Despite having no epistasis in energy, our model explains much of the observed epistasis in binding fraction, with the remaining epistasis identifying conformationally dynamic regions.

Multiplexed assays of variant effects contribute to a growing genotype-phenotype atlas

Given the constantly improving cost and speed of genome sequencing, it is reasonable to expect that personal genomes will soon be known for many millions of humans. This stands in stark contrast with our limited ability to interpret the sequence variants which we find. Although it is, perhaps, easiest to interpret variants in coding regions, knowledge of functional impact is unknown for the vast majority of missense variants. While many computational approaches can predict the impact of coding variants, they are given a little weight in the current guidelines for interpreting clinical variants. Laboratory assays produce comparatively more trustworthy results, but until recently did not scale to the space of all possible mutations. The development of deep mutational scanning and other multiplexed assays of variant effect has now brought feasibility of this endeavour within view. Here, we review progress in this field over the last decade, break down the different approaches into their components, and compare methodological differences.

Multiplex assessment of protein variant abundance by massively parallel sequencing

Determining the pathogenicity of genetic variants is a critical challenge, and functional assessment is often the only option. Experimentally characterizing millions of possible missense variants in thousands of clinically important genes requires generalizable, scalable assays. We describe variant abundance by massively parallel sequencing (VAMP-seq), which measures the effects of thousands of missense variants of a protein on intracellular abundance simultaneously. We apply VAMP-seq to quantify the abundance of 7,801 single-amino-acid variants of PTEN and TPMT, proteins in which functional variants are clinically actionable. We identify 1,138 PTEN and 777 TPMT variants that result in low protein abundance, and may be pathogenic or alter drug metabolism, respectively. We observe selection for low-abundance PTEN variants in cancer, and show that p.Pro38Ser, which accounts for ~10% of PTEN missense variants in melanoma, functions via a dominant-negative mechanism. Finally, we demonstrate that VAMP-seq is applicable to other genes, highlighting its generalizability.

A reference-based protein degradation assay without global translation inhibitors

Although it is widely appreciated that the use of global translation inhibitors, such as cycloheximide, in protein degradation assays may result in artefacts, these inhibitors continue to be employed, owing to the absence of robust alternatives. We describe here the promoter reference technique (PRT), an assay for protein degradation with two advantageous features: a reference protein and a gene-specific inhibition of translation. In PRT assays, one measures, during a chase, the ratio of a test protein to a long-lived reference protein, a dihydrofolate reductase (DHFR). The test protein and DHFR are coexpressed, in the yeast Saccharomyces cerevisiae, on a low-copy plasmid from two identical P _TDH3 promoters containing additional, previously developed DNA elements. Once transcribed, these elements form 5'-RNA aptamers that bind to the added tetracycline, which represses translation of aptamer-containing mRNAs. The selectivity of repression avoids a global inhibition of translation. This selectivity is particularly important if a component of a relevant proteolytic pathway (e.g. a specific ubiquitin ligase) is itself short-lived. We applied PRT to the Pro/N-end rule pathway, whose substrates include the short-lived Mdh2 malate dehydrogenase. Mdh2 is targeted for degradation by the Gid4 subunit of the GID ubiquitin ligase. Gid4 is also a metabolically unstable protein. Through analyses of short-lived Mdh2 as a target of short-lived Gid4, we illustrate the advantages of PRT over degradation assays that lack a reference and/or involve cycloheximide. In sum, PRT avoids the use of global translation inhibitors during a chase and also provides a "built-in" reference protein.

Variant Interpretation: Functional Assays to the Rescue

Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of "lookup tables" of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.

A statistical framework for analyzing deep mutational scanning data

Deep mutational scanning is a widely used method for multiplex measurement of functional consequences of protein variants. We developed a new deep mutational scanning statistical model that generates error estimates for each measurement, capturing both sampling error and consistency between replicates. We apply our model to one novel and five published datasets comprising 243,732 variants and demonstrate its superiority in removing noisy variants and conducting hypothesis testing. Simulations show our model applies to scans based on cell growth or binding and handles common experimental errors. We implemented our model in Enrich2, software that can empower researchers analyzing deep mutational scanning data.

High-Resolution Phenotypic Landscape of the RNA Polymerase II Trigger Loop

The active sites of multisubunit RNA polymerases have a “trigger loop” (TL) that multitasks in substrate selection, catalysis, and translocation. To dissect the Saccharomyces cerevisiae RNA polymerase II TL at individual-residue resolution, we quantitatively phenotyped nearly all TL single variants en masse. Three mutant classes, revealed by phenotypes linked to transcription defects or various stresses, have distinct distributions among TL residues. We find that mutations disrupting an intra-TL hydrophobic pocket, proposed to provide a mechanism for substrate-triggered TL folding through destabilization of a catalytically inactive TL state, confer phenotypes consistent with pocket disruption and increased catalysis. Furthermore, allele-specific genetic interactions among TL and TL-proximal domain residues support the contribution of the funnel and bridge helices (BH) to TL dynamics. Our structural genetics approach incorporates structural and phenotypic data for high-resolution dissection of transcription mechanisms and their evolution, and is readily applicable to other essential yeast proteins.

Proper regulation of Pol II transcription, the first step of gene expression, is essential for life. Extensive evidence has revealed a widely conserved and dynamic polymerase active site component, termed the Trigger Loop (TL), in balancing transcription rate and fidelity while possibly allowing control of transcription elongation. Coupling high-throughput sequencing with our previously established genetic system, we are able to assess the in vivo phenotypes for almost all possible single substitution Pol II TL mutants in the budding yeast Saccharomyces cerevisiae. We show that mutants in the TL nucleotide interacting and linker regions widely confer dominant and severe growth defects. Clustering of TL mutants’ transcription-related and general stress phenotypes reveals three main classes of TL mutants, including previously identified fast and slow elongating mutants. Comprehensive analyses of the distribution of fast and slow elongation mutants in light of existing Pol II crystal structures reveal critical regions contributing to proper TL dynamics and function. Evidence is presented linking a previously observed hydrophobic pocket to NTP substrate-induced TL closing, the mechanism critical for correct substrates selection and transcription fidelity. Finally, we assess the functional interplay between TL and its proximal domains, and their presumptive roles in the function and evolution of the TL. Utilizing the Pol II TL as a case study, we present a structural genetics approach that reveals insights into a complex, multi-functional, and essential domain in yeast.

Mapping the Landscape of a Eukaryotic Degronome

The ubiquitin-proteasome system (UPS) for protein degradation has been under intensive study, and yet, we have only partial understanding of mechanisms by which proteins are selected to be targeted for proteolysis. One of the obstacles in studying these recognition pathways is the limited repertoire of known degradation signals (degrons). To better understand what determines the susceptibility of intracellular proteins to degradation by the UPS, we developed an unbiased method for large-scale identification of eukaryotic degrons. Using a reporter-based high-throughput competition assay, followed by deep sequencing, we measured a degradation potency index for thousands of native polypeptides in a single experiment. We further used this method to identify protein quality control (PQC)-specific and compartment-specific degrons. Our method provides an unprecedented insight into the yeast degronome, and it can readily be modified to study protein degradation signals and pathways in other organisms and in various settings.

The power of multiplexed functional analysis of genetic variants

New technologies have recently enabled saturation mutagenesis and functional analysis of nearly all possible variants of regulatory elements or proteins of interest in single experiments. Here we discuss the past, present, and future of such multiplexed (functional) assays for variant effects (MAVEs). MAVEs provide detailed insight into sequence-function relationships, and they may prove critical for the prospective clinical interpretation of genetic variants.

Molecular Determinants of Mutant Phenotypes, Inferred from Saturation Mutagenesis Data

Understanding how mutations affect protein activity and organismal fitness is a major challenge. We used saturation mutagenesis combined with deep sequencing to determine mutational sensitivity scores for 1,664 single-site mutants of the 101 residue Escherichia coli cytotoxin, CcdB at seven different expression levels. Active-site residues could be distinguished from buried ones, based on their differential tolerance to aliphatic and charged amino acid substitutions. At nonactive-site positions, the average mutational tolerance correlated better with depth from the protein surface than with accessibility. Remarkably, similar results were observed for two other small proteins, PDZ domain (PSD95^pdz3) and IgG-binding domain of protein G (GB1). Mutational sensitivity data obtained with CcdB were used to derive a procedure for predicting functional effects of mutations. Results compared favorably with those of two widely used computational predictors. In vitro characterization of 80 single, nonactive-site mutants of CcdB showed that activity in vivo correlates moderately with thermal stability and solubility. The inability to refold reversibly, as well as a decreased folding rate in vitro, is associated with decreased activity in vivo. Upon probing the effect of modulating expression of various proteases and chaperones on mutant phenotypes, most deleterious mutants showed an increased in vivo activity and solubility only upon over-expression of either Trigger factor or SecB ATP-independent chaperones. Collectively, these data suggest that folding kinetics rather than protein stability is the primary determinant of activity in vivo. This study enhances our understanding of how mutations affect phenotype, as well as the ability to predict fitness effects of point mutations.

A Statistical Guide to the Design of Deep Mutational Scanning Experiments

The characterization of the distribution of mutational effects is a key goal in evolutionary biology. Recently developed deep-sequencing approaches allow for accurate and simultaneous estimation of the fitness effects of hundreds of engineered mutations by monitoring their relative abundance across time points in a single bulk competition. Naturally, the achievable resolution of the estimated fitness effects depends on the specific experimental setup, the organism and type of mutations studied, and the sequencing technology utilized, among other factors. By means of analytical approximations and simulations, we provide guidelines for optimizing time-sampled deep-sequencing bulk competition experiments, focusing on the number of mutants, the sequencing depth, and the number of sampled time points. Our analytical results show that sampling more time points together with extending the duration of the experiment improves the achievable precision disproportionately compared with increasing the sequencing depth or reducing the number of competing mutants. Even if the duration of the experiment is fixed, sampling more time points and clustering these at the beginning and the end of the experiment increase experimental power and allow for efficient and precise assessment of the entire range of selection coefficients. Finally, we provide a formula for calculating the 95%-confidence interval for the measurement error estimate, which we implement as an interactive web tool. This allows for quantification of the maximum expected a priori precision of the experimental setup, as well as for a statistical threshold for determining deviations from neutrality for specific selection coefficient estimates.

Degradation elements coincide with cofactor binding sites in a short-lived transcription factor

Elaborate control of gene expression by transcription factors is common to all kingdoms of life. In eukaryotes, transcription factor abundance and activity are often regulated by targeted proteolysis via the ubiquitin-proteasome system (UPS). The yeast MATα2 (α2) cell type regulator has long served as a model for UPS-dependent transcription factor degradation. Proteolysis of α2 is complex: it involves at least 2 ubiquitylation pathways and multiple regions of α2 affect its degradation. Such complexity also exists for the degradation of other UPS substrates. Here I review α2 degradation, most notably our recent identification of 2 novel degradation elements within α2 that overlap corepressor binding sites. I discuss possible implications of these findings and consider how principles of α2 proteolysis may be relevant to the degradation of other UPS substrates.

N-Terminal Acetylation-Targeted N-End Rule Proteolytic System: The Ac/N-End Rule Pathway

Although Nα-terminal acetylation (Nt-acetylation) is a pervasive protein modification in eukaryotes, its general functions in a majority of proteins are poorly understood. In 2010, it was discovered that Nt-acetylation creates a specific protein degradation signal that is targeted by a new class of the N-end rule proteolytic system, called the Ac/N-end rule pathway. Here, we review recent advances in our understanding of the mechanism and biological functions of the Ac/N-end rule pathway, and its crosstalk with the Arg/N-end rule pathway (the classical N-end rule pathway).

STUbL-mediated degradation of the transcription factor MATα2 requires degradation elements that coincide with corepressor binding sites

The yeast cell type regulator MATα (α2) is degraded through two ubiquitylation pathways, one of which has been minimally characterized. We identify two regions in α2 important for this pathway and show that these regions overlap specific binding sites for α2 corepressors, suggesting that α2 degradation is coordinated with its functional status.

The yeast transcription factor MATα2 (α2) is a short-lived protein known to be ubiquitylated by two distinct pathways, one involving the ubiquitin-conjugating enzymes (E2s) Ubc6 and Ubc7 and the ubiquitin ligase (E3) Doa10 and the other operating with the E2 Ubc4 and the heterodimeric E3 Slx5/Slx8. Although Slx5/Slx8 is a small ubiquitin-like modifier (SUMO)-targeted ubiquitin ligase (STUbL), it does not require SUMO to target α2 but instead directly recognizes α2. Little is known about the α2 determinants required for its Ubc4- and STUbL-mediated degradation or how these determinants substitute for SUMO in recognition by the STUbL pathway. We describe two distinct degradation elements within α2, both of which are necessary for α2 recognition specifically by the Ubc4 pathway. Slx5/Slx8 can directly ubiquitylate a C-terminal fragment of α2, and mutating one of the degradation elements impairs this ubiquitylation. Surprisingly, both degradation elements identified here overlap specific interaction sites for α2 corepressors: the Mcm1 interaction site in the central α2 linker and the Ssn6 (Cyc8) binding site in the α2 homeodomain. We propose that competitive binding to α2 by the ubiquitylation machinery and α2 cofactors is balanced so that α2 can function in transcription repression yet be short lived enough to allow cell-type switching.

Deep Mutational Scanning: A Highly Parallel Method to Measure the Effects of Mutation on Protein Function

Deep mutational scanning is a method that makes use of next-generation sequencing technology to measure in a single experiment the activity of 10(5) or more unique variants of a protein. Because of this depth of mutational coverage, this strategy provides data that can be analyzed to reveal many protein properties. Deep mutational scanning approaches are particularly amenable to being performed in Saccharomyces cerevisiae, given the extensive toolkit of reagents and technologies available for this organism.

Functional Constraint Profiling of a Viral Protein Reveals Discordance of Evolutionary Conservation and Functionality

Viruses often encode proteins with multiple functions due to their compact genomes. Existing approaches to identify functional residues largely rely on sequence conservation analysis. Inferring functional residues from sequence conservation can produce false positives, in which the conserved residues are functionally silent, or false negatives, where functional residues are not identified since they are species-specific and therefore non-conserved. Furthermore, the tedious process of constructing and analyzing individual mutations limits the number of residues that can be examined in a single study. Here, we developed a systematic approach to identify the functional residues of a viral protein by coupling experimental fitness profiling with protein stability prediction using the influenza virus polymerase PA subunit as the target protein. We identified a significant number of functional residues that were influenza type-specific and were evolutionarily non-conserved among different influenza types. Our results indicate that type-specific functional residues are prevalent and may not otherwise be identified by sequence conservation analysis alone. More importantly, this technique can be adapted to any viral (and potentially non-viral) protein where structural information is available.

The analysis of sequence conservation is a common approach to identify functional residues within a protein. However, not all functional residues are conserved as natural evolution and species diversification permit continuous innovation of protein functionality through the retention of advantageous mutations. Non-conserved functional residues, which are often species-specific, may not be identified by conventional analysis of sequence conservation despite being biologically important. Here we described a novel approach to identify functional residues within a protein by coupling a high-throughput experimental fitness profiling approach with computational protein modeling. Our methodology is independent of sequence conservation and is applicable to any protein where structural information is available. In this study, we systematically mapped the functional residues on the influenza A PA protein and revealed that non-conserved functional residues are prevalent. Our results not only have significant implication on how functionality evolves during natural evolution, but also highlight the caveats when applying conservation-based approaches to identify functional residues within a protein.

A method for multiplex gene synthesis employing error correction based on expression

Our ability to engineer organisms with new biosynthetic pathways and genetic circuits is limited by the availability of protein characterization data and the cost of synthetic DNA. With new tools for reading and writing DNA, there are opportunities for scalable assays that more efficiently and cost effectively mine for biochemical protein characteristics. To that end, we have developed the Multiplex Library Synthesis and Expression Correction (MuLSEC) method for rapid assembly, error correction, and expression characterization of many genes as a pooled library. This methodology enables gene synthesis from microarray-synthesized oligonucleotide pools with a one-pot technique, eliminating the need for robotic liquid handling. Post assembly, the gene library is subjected to an ampicillin based quality control selection, which serves as both an error correction step and a selection for proteins that are properly expressed and folded in E. coli. Next generation sequencing of post selection DNA enables quantitative analysis of gene expression characteristics. We demonstrate the feasibility of this approach by building and testing over 90 genes for empirical evidence of soluble expression. This technique reduces the problem of part characterization to multiplex oligonucleotide synthesis and deep sequencing, two technologies under extensive development with projected cost reduction.

CHOPER filters enable rare mutation detection in complex mutagenesis populations by next-generation sequencing

Next-generation sequencing (NGS) has revolutionized genetics and enabled the accurate identification of many genetic variants across many genomes. However, detection of biologically important low-frequency variants within genetically heterogeneous populations remains challenging, because they are difficult to distinguish from intrinsic NGS sequencing error rates. Approaches to overcome these limitations are essential to detect rare mutations in large cohorts, virus or microbial populations, mitochondria heteroplasmy, and other heterogeneous mixtures such as tumors. Modifications in library preparation can overcome some of these limitations, but are experimentally challenging and restricted to skilled biologists. This paper describes a novel quality filtering and base pruning pipeline, called Complex Heterogeneous Overlapped Paired-End Reads (CHOPER), designed to detect sequence variants in a complex population with high sequence similarity derived from All-Codon-Scanning (ACS) mutagenesis. A novel fast alignment algorithm, designed for the specified application, has O(n) time complexity. CHOPER was applied to a p53 cancer mutant reactivation study derived from ACS mutagenesis. Relative to error filtering based on Phred quality scores, CHOPER improved accuracy by about 13% while discarding only half as many bases. These results are a step toward extending the power of NGS to the analysis of genetically heterogeneous populations.

Analysis of genetic variation and potential applications in genome-scale metabolic modeling

Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes.

Deep mutational scanning: a new style of protein science

Mutagenesis provides insight into proteins, but only recently have assays that couple genotype to phenotype been used to assess the activities of as many as 1 million mutant versions of a protein in a single experiment. This approach-'deep mutational scanning'-yields large-scale data sets that can reveal intrinsic protein properties, protein behavior within cells and the consequences of human genetic variation. Deep mutational scanning is transforming the study of proteins, but many challenges must be tackled for it to fulfill its promise.

Ubiquitin-dependent protein degradation at the yeast endoplasmic reticulum and nuclear envelope

The endoplasmic reticulum (ER) is the primary organelle in eukaryotic cells where membrane and secreted proteins are inserted into or across cell membranes. Its membrane bilayer and luminal compartments provide a favorable environment for the folding and assembly of thousands of newly synthesized proteins. However, protein folding is intrinsically error-prone, and various stress conditions can further increase levels of protein misfolding and damage, particularly in the ER, which can lead to cellular dysfunction and disease. The ubiquitin-proteasome system (UPS) is responsible for the selective destruction of a vast array of protein substrates, either for protein quality control or to allow rapid changes in the levels of specific regulatory proteins. In this review, we will focus on the components and mechanisms of ER-associated protein degradation (ERAD), an important branch of the UPS. ER membranes extend from subcortical regions of the cell to the nuclear envelope, with its continuous outer and inner membranes; the nuclear envelope is a specialized subdomain of the ER. ERAD presents additional challenges to the UPS beyond those faced with soluble substrates of the cytoplasm and nucleus. These include recognition of sugar modifications that occur in the ER, retrotranslocation of proteins across the membrane bilayer, and transfer of substrates from the ER extraction machinery to the proteasome. Here, we review characteristics of ERAD substrate degradation signals (degrons), mechanisms underlying substrate recognition and processing by the ERAD machinery, and ideas on the still unresolved problem of how substrate proteins are moved across and extracted from the ER membrane.

Measuring the activity of protein variants on a large scale using deep mutational scanning

Deep mutational scanning marries selection for protein function to high-throughput DNA sequencing in order to quantify the activity of variants of a protein on a massive scale. First, an appropriate selection system for the protein function of interest is identified and validated. Second, a library of variants is created, introduced into the selection system and subjected to selection. Third, library DNA is recovered throughout the selection and deep-sequenced. Finally, a functional score for each variant is calculated on the basis of the change in the frequency of the variant during the selection. This protocol describes the steps that must be carried out to generate a large-scale mutagenesis data set consisting of functional scores for up to hundreds of thousands of variants of a protein of interest. Establishing an assay, generating a library of variants and carrying out a selection and its accompanying sequencing takes on the order of 4-6 weeks; the initial data analysis can be completed in 1 week.

Large-scale de novo DNA synthesis: technologies and applications

For over 60 years, the synthetic production of new DNA sequences has helped researchers understand and engineer biology. Here we summarize methods and caveats for the de novo synthesis of DNA, with particular emphasis on recent technologies that allow for large-scale and low-cost production. In addition, we discuss emerging applications enabled by large-scale de novo DNA constructs, as well as the challenges and opportunities that lie ahead.