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

Constitutively active Arabidopsis cryptochrome 2 alleles identified using yeast selection and deep mutational scanning

The Arabidopsis blue light photoreceptor cryptochrome 2 (CRY2) responds to blue light to initiate a variety of plant light-based behaviors and has been widely used for optogenetic engineering. Despite these important biological functions, the precise photoactivation mechanism of CRY2 remains incompletely understood. In light, CRY2 undergoes tetramerization and binds to partner proteins, including the transcription factor CIB1. Here we used yeast-two hybrid screening and deep mutational scanning to identify CRY2 amino acid changes that result in constitutive interaction with CIB1 in dark. The majority of CRY2 variants showing constitutive CIB1 interaction mapped to two regions, one near the FAD chromophore, and a second region located near the ATP binding site. Further testing of CRY2 variants from each region revealed three mapping near to the FAD binding pocket (D393S, D393A, and M378R) that also form constitutive CRY2-CRY2 homomers in dark, suggesting they adopt global conformational changes that mimic the photoactive state. Characterization of D393S in the homolog pCRY from Chlamydomonas reinhardtii using time-resolved UV-vis spectroscopy revealed that the FAD chromophore fails to form the neutral radical as signaling state upon illumination. Size exclusion chromatography of D393S shows the presence of homomers instead of a monomer in the dark, providing support for a hyperactive variant decoupled from the FAD. Our work provides new insight into photoactivation mechanisms of plant cryptochromes relevant for physiology and optogenetic application by revealing and localizing distinct activation pathways for light-driven CRY2-CIB1 and CRY2-CRY2 interactions.

Ancient biases in phenotype production drove the functional evolution of a protein family

Biological systems may be biased in the phenotypes they can access by mutation1-7, but the extent of these biases and their causal role in the evolution of extant phenotypic diversity remains unclear. There are three major challenges: it is difficult to isolate the effect of bias in the genotype-phenotype (GP) map from that of natural selection in producing natural diversity6,8-11, the universe of possible genotypes and phenotypes is so vast and complex that a direct characterization has been impossible, and most extant phenotypes evolved long ago in species whose GP maps cannot be recovered. Here we develop exhaustive multi-phenotype deep mutational scanning to experimentally characterize the complete GP maps of two reconstructed ancestral steroid receptor proteins, which existed during an ancient phylogenetic interval when a new phenotype-specific binding of a new DNA response element-evolved12. We measured all possible DNA specificity phenotypes encoded by all possible amino acid combinations at sites in the protein's DNA binding interface. We found that the ancestral GP maps are structured by strong global bias-unequal propensity to encode the various phenotypes-and extreme heterogeneity in the phenotypes accessible around each genotype, which strongly affect evolution on both long and short timescales. Distinct biases in the two ancestral maps steered evolution toward the lineage-specific functional phenotypes that evolved during history. Our findings establish that ancient biases in the GP relationship were causal factors in the evolutionary process that produced the present-day patterns of phenotypic conservation and diversity in this protein family.

Multiplexed Assays of Variant Effect and Automated Patch Clamping Improve KCNH2-LQTS Variant Classification and Cardiac Event Risk Stratification

BACKGROUND

Long QT syndrome is a lethal arrhythmia syndrome, frequently caused by rare loss-of-function variants in the potassium channel encoded by KCNH2. Variant classification is difficult, often because of lack of functional data. Moreover, variant-based risk stratification is also complicated by heterogenous clinical data and incomplete penetrance. Here we sought to test whether variant-specific information, primarily from high-throughput functional assays, could improve both classification and cardiac event risk stratification in a large, harmonized cohort of KCNH2 missense variant heterozygotes.

METHODS

We quantified cell-surface trafficking of 18 796 variants in KCNH2 using a multiplexed assay of variant effect (MAVE). We recorded KCNH2 current density for 533 variants by automated patch clamping. We calibrated the strength of evidence of MAVE data according to ClinGen guidelines. We deeply phenotyped 1458 patients with KCNH2 missense variants, including QTc, cardiac event history, and mortality. We correlated variant functional data and Bayesian long QT syndrome penetrance estimates with cohort phenotypes and assessed hazard ratios for cardiac events.

RESULTS

Variant MAVE trafficking scores and automated patch clamping peak tail currents were highly correlated (Spearman rank-order ρ=0.69; n=433). The MAVE data were found to provide up to pathogenic very strong evidence for severe loss-of-function variants. In the cohort, both functional assays and Bayesian long QT syndrome penetrance estimates were significantly predictive of cardiac events when independently modeled with patient sex and corrected QT interval (QTc); however, MAVE data became nonsignificant when peak tail current and penetrance estimates were also available. The area under the receiver operator characteristic curve for 20-year event outcomes based on patient-specific sex and QTc (area under the curve, 0.80 [0.76-0.83]) was improved with prospectively available penetrance scores conditioned on MAVE (area under the curve, 0.86 [0.83-0.89]) or attainable automated patch clamping peak tail current data (area under the curve, 0.84 [0.81-0.88]).

CONCLUSIONS

High-throughput KCNH2 variant MAVE data meaningfully contribute to variant classification at scale, whereas long QT syndrome penetrance estimates and automated patch clamping peak tail current measurements meaningfully contribute to risk stratification of cardiac events in patients with heterozygous KCNH2 missense variants.

Deep mutational scanning of a multi-domain signaling protein reveals mechanisms of regulation and pathogenicity

Multi-domain signaling enzymes are often regulated through extensive inter-domain interactions, and disruption of inter-domain interfaces by mutations can lead to aberrant signaling and diseases. For example, the tyrosine phosphatase SHP2 contains two phosphotyrosine recognition domains that auto-inhibit its catalytic domain. SHP2 is canonically activated by binding of these non-catalytic domains to phosphoproteins, which destabilizes the auto-inhibited state, but numerous mutations at the main auto-inhibitory interface have been shown to hyperactivate SHP2 in cancers and developmental disorders. Hundreds of clinically observed mutations in SHP2 have not been characterized, but their locations suggest alternative modes of dysregulation. We performed deep mutational scanning on full-length SHP2 and the isolated phosphatase domain to dissect mechanisms of SHP2 dysregulation. Our analysis revealed mechanistically diverse mutational effects and identified key intra- and inter-domain interactions that contribute to SHP2 activity, dynamics, and regulation. Our datasets also provide insights into the potential pathogenicity of previously uncharacterized clinical variants.

Saturation mutagenesis-reinforced functional assays for disease-related genes

Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods are obstacles for achieving genome-wide resolution of variants in disease-related genes. Our framework, saturation mutagenesis-reinforced functional assays (SMuRF), offers simple and cost-effective saturation mutagenesis paired with streamlined functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes FKRP and LARGE1, we generated functional scores for all possible coding single-nucleotide variants, which aid in resolving clinically reported variants of uncertain significance. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Overall, our approach enables variant-to-function insights for disease genes in a cost-effective manner that can be broadly implemented by standard research laboratories.

Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning

Throughout evolution, protein families undergo substantial sequence divergence while preserving structure and function. Although most mutations are deleterious, evolution can explore sequence space via epistatic networks of intramolecular interactions that alleviate the harmful mutations. However, comprehensive analysis of such epistatic networks across protein families remains limited. Thus, we conduct a family wide analysis of the B1 metallo-β-lactamases, combining experiments (deep mutational scanning, DMS) on two distant homologs (NDM-1 and VIM-2) and computational analyses (in silico DMS based on Direct Coupling Analysis, DCA) of 100 homologs. The methods jointly reveal and quantify prevalent epistasis, as ~1/3rd of equivalent mutations are epistatic in DMS. From DCA, half of the positions have a >6.5 fold difference in effective number of tolerated mutations across the entire family. Notably, both methods locate residues with the strongest epistasis in regions of intermediate residue burial, suggesting a balance of residue packing and mutational freedom in forming epistatic networks. We identify entrenched WT residues between NDM-1 and VIM-2 in DMS, which display statistically distinct behaviors in DCA from non-entrenched residues. Entrenched residues are not easily compensated by changes in single nearby interactions, reinforcing existing findings where a complex epistatic network compounds smaller effects from many interacting residues.

ParSE-seq: a calibrated multiplexed assay to facilitate the clinical classification of putative splice-altering variants

Interpreting the clinical significance of putative splice-altering variants outside canonical splice sites remains difficult without time-intensive experimental studies. To address this, we introduce Parallel Splice Effect Sequencing (ParSE-seq), a multiplexed assay to quantify variant effects on RNA splicing. We first apply this technique to study hundreds of variants in the arrhythmia-associated gene SCN5A. Variants are studied in 'minigene' plasmids with molecular barcodes to allow pooled variant effect quantification. We perform experiments in two cell types, including disease-relevant induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). The assay strongly separates known control variants from ClinVar, enabling quantitative calibration of the ParSE-seq assay. Using these evidence strengths and experimental data, we reclassify 29 of 34 variants with conflicting interpretations and 11 of 42 variants of uncertain significance. In addition to intronic variants, we show that many synonymous and missense variants disrupted RNA splicing. Two splice-altering variants in the assay also disrupt splicing and sodium current when introduced into iPSC-CMs by CRISPR-Cas9 editing. ParSE-seq provides high-throughput experimental data for RNA-splicing to support precision medicine efforts and can be readily adopted to study other loss-of-function genotype-phenotype relationships.

Deep neural networks for predicting the affinity landscape of protein-protein interactions

Studies determining protein-protein interactions (PPIs) by deep mutational scanning have focused mainly on a narrow range of affinities within complexes and thus include only partial coverage of the mutation space of given proteins. By inserting an affinity-reducing N-terminal alanine in the N-terminal domain of the tissue inhibitor of metalloproteinases-2 (N-TIMP2), we overcame the limitation of its narrow affinity range for matrix metalloproteinase 9 (MMP9CAT). We trained deep neural networks (DNNs) to quantitatively predict the binding affinity of unobserved wild-type variants and variants carrying an N-terminal alanine. Good correlation was obtained between predicted and observed log2 enrichment ratio (ER) values, which also correlated with the affinity of N-TIMP2 variants to MMP9CAT. Our ability to predict affinities of unobserved N-TIMP2 variants was confirmed on an independent dataset of experimentally validated N-TIMP2 proteins. This ability is of significant importance in the field of PPI prediction and for developing therapies targeting these interactions.

High throughput mutational scanning of a protein via alchemistry on a high-performance computing resource

Antibiotic resistance presents a significant challenge to public health, as bacteria can develop resistance to antibiotics through random mutations during their life cycles, making the drugs ineffective. Understanding how these mutations contribute to drug resistance at the molecular level is crucial for designing new treatment approaches. Recent advancements in molecular biology tools have made it possible to conduct comprehensive analyses of protein mutations. Computational methods for assessing molecular fitness, such as binding energies, are not as precise as experimental techniques like deep mutational scanning. Although full atomistic alchemical free energy calculations offer the necessary precision, they are seldom used to assess high throughput data as they require significantly more computational resources. We generated a computational library using deep mutational scanning for dihydrofolate reductase (DHFR), a protein commonly studied in antibiotic resistance research. Due to resource limitations, we analyzed 33 out of 159 positions, identifying 16 single amino acid replacements. Calculations were conducted for DHFR in its drug-free state and in the presence of two different inhibitors. We demonstrate the feasibility of such calculations, made possible due to the enhancements in computational resources and their optimized use.

Culture Wars: Empirically Determining the Best Approach for Plasmid Library Amplification

DNA libraries are critical components of many biological assays. These libraries are often kept in plasmids that are amplified in E. coli to generate sufficient material for an experiment. Library uniformity is critical for ensuring that every element in the library is tested similarly and is thought to be influenced by the culture approach used during library amplification. We tested five commonly used culturing methods for their ability to uniformly amplify plasmid libraries: liquid, semisolid agar, cell spreader-spread plates with high or low colony density, and bead-spread plates. Each approach was evaluated with two library types: a random 80-mer library, representing high complexity and low coverage of similar sequence lengths, and a human TF ORF library, representing low complexity and high coverage of diverse sequence lengths. We found that no method was better than liquid culture, which produced relatively uniform libraries regardless of library type. However, when libraries were transformed with high coverage, the culturing method had minimal impact on uniformity or amplification bias. Plating libraries was the worst approach by almost every measure for both library types and, counterintuitively, produced the strongest biases against long sequence representation. Semisolid agar amplified most elements of the library uniformly but also included outliers with orders of magnitude higher abundance. For amplifying DNA libraries, liquid culture, the simplest method, appears to be best.

Deep mutational scanning quantifies DNA binding and predicts clinical outcomes of PAX6 variants

Nonsense and missense mutations in the transcription factor PAX6 cause a wide range of eye development defects, including aniridia, microphthalmia and coloboma. To understand how changes of PAX6:DNA binding cause these phenotypes, we combined saturation mutagenesis of the paired domain of PAX6 with a yeast one-hybrid (Y1H) assay in which expression of a PAX6-GAL4 fusion gene drives antibiotic resistance. We quantified binding of more than 2700 single amino-acid variants to two DNA sequence elements. Mutations in DNA-facing residues of the N-terminal subdomain and linker region were most detrimental, as were mutations to prolines and to negatively charged residues. Many variants caused sequence-specific molecular gain-of-function effects, including variants in position 71 that increased binding to the LE9 enhancer but decreased binding to a SELEX-derived binding site. In the absence of antibiotic selection, variants that retained DNA binding slowed yeast growth, likely because such variants perturbed the yeast transcriptome. Benchmarking against known patient variants and applying ACMG/AMP guidelines to variant classification, we obtained supporting-to-moderate evidence that 977 variants are likely pathogenic and 1306 are likely benign. Our analysis shows that most pathogenic mutations in the paired domain of PAX6 can be explained simply by the effects of these mutations on PAX6:DNA association, and establishes Y1H as a generalisable assay for the interpretation of variant effects in transcription factors.

Mapping MAVE data for use in human genomics applications

The large-scale experimental measures of variant functional assays submitted to MaveDB have the potential to provide key information for resolving variants of uncertain significance, but the reporting of results relative to assayed sequence hinders their downstream utility. The Atlas of Variant Effects Alliance mapped multiplexed assays of variant effect data to human reference sequences, creating a robust set of machine-readable homology mappings. This method processed approximately 2.5 million protein and genomic variants in MaveDB, successfully mapping 98.61% of examined variants and disseminating data to resources such as the UCSC Genome Browser and Ensembl Variant Effect Predictor.

Deep Mutational Scanning in Disease-related Genes with Saturation Mutagenesis-Reinforced Functional Assays (SMuRF)

Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods hamper crowd-sourcing approaches toward genome-wide resolution of variants in disease-related genes. Our framework, Saturation Mutagenesis-Reinforced Functional assays (SMuRF), addresses these issues by offering simple and cost-effective saturation mutagenesis, as well as streamlining functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes FKRP and LARGE1, we generated functional scores for all possible coding single nucleotide variants, which aid in resolving clinically reported variants of uncertain significance. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Our approach opens new directions for enabling variant-to-function insights for disease genes in a manner that is broadly useful for crowd-sourcing implementation across standard research laboratories.

Optimization of a deep mutational scanning workflow to improve quantification of mutation effects on protein-protein interactions

Deep Mutational Scanning (DMS) assays are powerful tools to study sequence-function relationships by measuring the effects of thousands of sequence variants on protein function. During a DMS experiment, several technical artefacts might distort non-linearly the functional score obtained, potentially biasing the interpretation of the results. We therefore tested several technical parameters in the deepPCA workflow, a DMS assay for protein-protein interactions, in order to identify technical sources of non-linearities. We found that parameters common to many DMS assays such as amount of transformed DNA, timepoint of harvest and library composition can cause non-linearities in the data. Designing experiments in a way to minimize these non-linear effects will improve the quantification and interpretation of mutation effects.

Sampling Strategies for Experimentally Mapping Molecular Fitness Landscapes Using High-Throughput Methods

Empirical studies of genotype-phenotype-fitness maps of proteins are fundamental to understanding the evolutionary process, in elucidating the space of possible genotypes accessible through mutations in a landscape of phenotypes and fitness effects. Yet, comprehensively mapping molecular fitness landscapes remains challenging since all possible combinations of amino acid substitutions for even a few protein sites are encoded by an enormous genotype space. High-throughput mapping of genotype space can be achieved using large-scale screening experiments known as multiplexed assays of variant effect (MAVEs). However, to accommodate such multi-mutational studies, the size of MAVEs has grown to the point where a priori determination of sampling requirements is needed. To address this problem, we propose calculations and simulation methods to approximate minimum sampling requirements for multi-mutational MAVEs, which we combine with a new library construction protocol to experimentally validate our approximation approaches. Analysis of our simulated data reveals how sampling trajectories differ between simulations of nucleotide versus amino acid variants and among mutagenesis schemes. For this, we show quantitatively that marginal gains in sampling efficiency demand increasingly greater sampling effort when sampling for nucleotide sequences over their encoded amino acid equivalents. We present a new library construction protocol that efficiently maximizes sequence variation, and demonstrate using ultradeep sequencing that the library encodes virtually all possible combinations of mutations within the experimental design. Insights learned from our analyses together with the methodological advances reported herein are immediately applicable toward pooled experimental screens of arbitrary design, enabling further assay upscaling and expanded testing of genotype space.

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage

Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.

Scaling up Functional Analyses of the G Protein-Coupled Receptor Rhodopsin

Eukaryotic cells use G protein-coupled receptors (GPCRs) to convert external stimuli into internal signals to elicit cellular responses. However, how mutations in GPCR-coding genes affect GPCR activation and downstream signaling pathways remain poorly understood. Approaches such as deep mutational scanning show promise in investigations of GPCRs, but a high-throughput method to measure rhodopsin activation has yet to be achieved. Here, we scale up a fluorescent reporter assay in budding yeast that we engineered to study rhodopsin's light-activated signal transduction. Using this approach, we measured the mutational effects of over 1200 individual human rhodopsin mutants, generated by low-frequency random mutagenesis of the GPCR rhodopsin (RHO) gene. Analysis of the data in the context of rhodopsin's three-dimensional structure reveals that transmembrane helices are generally less tolerant to mutations compared to flanking helices that face the lipid bilayer, which suggest that mutational tolerance is contingent on both the local environment surrounding specific residues and the specific position of these residues in the protein structure. Comparison of functional scores from our screen to clinically identified rhodopsin disease variants found many pathogenic mutants to be loss of function. Lastly, functional scores from our assay were consistent with a complex counterion mechanism involved in ligand-binding and rhodopsin activation. Our results demonstrate that deep mutational scanning is possible for rhodopsin activation and can be an effective method for revealing properties of mutational tolerance that may be generalizable to other transmembrane proteins.

Advancing Antibody Engineering through Synthetic Evolution and Machine Learning

Abs are versatile molecules with the potential to achieve exceptional binding to target Ags, while also possessing biophysical properties suitable for therapeutic drug development. Protein display and directed evolution systems have transformed synthetic Ab discovery, engineering, and optimization, vastly expanding the number of Ab clones able to be experimentally screened for binding. Moreover, the burgeoning integration of high-throughput screening, deep sequencing, and machine learning has further augmented in vitro Ab optimization, promising to accelerate the design process and massively expand the Ab sequence space interrogated. In this Brief Review, we discuss the experimental and computational tools employed in synthetic Ab engineering and optimization. We also explore the therapeutic challenges posed by developing Abs for infectious diseases, and the prospects for leveraging machine learning-guided protein engineering to prospectively design Abs resistant to viral escape.

Deep mutational scanning of proteins in mammalian cells

Protein mutagenesis is essential for unveiling the molecular mechanisms underlying protein function in health, disease, and evolution. In the past decade, deep mutational scanning methods have evolved to support the functional analysis of nearly all possible single-amino acid changes in a protein of interest. While historically these methods were developed in lower organisms such as E. coli and yeast, recent technological advancements have resulted in the increased use of mammalian cells, particularly for studying proteins involved in human disease. These advancements will aid significantly in the classification and interpretation of variants of unknown significance, which are being discovered at large scale due to the current surge in the use of whole-genome sequencing in clinical contexts. Here, we explore the experimental aspects of deep mutational scanning studies in mammalian cells and report the different methods used in each step of the workflow, ultimately providing a useful guide toward the design of such studies.

Utility of AlphaMissense predictions in Asparagine Synthetase deficiency variant classification

AlphaMissense is a recently developed method that is designed to classify missense variants into pathogenic, benign, or ambiguous categories across the entire human proteome. Asparagine Synthetase Deficiency (ASNSD) is a developmental disorder associated with severe symptoms, including congenital microcephaly, seizures, and premature death. Diagnosing ASNSD relies on identifying mutations in the asparagine synthetase (ASNS) gene through DNA sequencing and determining whether these variants are pathogenic or benign. Pathogenic ASNS variants are predicted to disrupt the protein's structure and/or function, leading to asparagine depletion within cells and inhibition of cell growth. AlphaMissense offers a promising solution for the rapid classification of ASNS variants established by DNA sequencing and provides a community resource of pathogenicity scores and classifications for newly diagnosed ASNSD patients. Here, we assessed AlphaMissense's utility in ASNSD by benchmarking it against known critical residues in ASNS and evaluating its performance against a list of previously reported ASNSD-associated variants. We also present a pipeline to calculate AlphaMissense scores for any protein in the UniProt database. AlphaMissense accurately attributed a high average pathogenicity score to known critical residues within the two ASNS active sites and the connecting intramolecular tunnel. The program successfully categorized 78.9% of known ASNSD-associated missense variants as pathogenic. The remaining variants were primarily labeled as ambiguous, with a smaller proportion classified as benign. This study underscores the potential role of AlphaMissense in classifying ASNS variants in suspected cases of ASNSD, potentially providing clarity to patients and their families grappling with ongoing diagnostic uncertainty.

ParSE-seq: A Calibrated Multiplexed Assay to Facilitate the Clinical Classification of Putative Splice-altering Variants

Background

Interpreting the clinical significance of putative splice-altering variants outside 2-base pair canonical splice sites remains difficult without functional studies.

Methods

We developed Parallel Splice Effect Sequencing (ParSE-seq), a multiplexed minigene-based assay, to test variant effects on RNA splicing quantified by high-throughput sequencing. We studied variants in SCN5A, an arrhythmia-associated gene which encodes the major cardiac voltage-gated sodium channel. We used the computational tool SpliceAI to prioritize exonic and intronic candidate splice variants, and ClinVar to select benign and pathogenic control variants. We generated a pool of 284 barcoded minigene plasmids, transfected them into Human Embryonic Kidney (HEK293) cells and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), sequenced the resulting pools of splicing products, and calibrated the assay to the American College of Medical Genetics and Genomics scheme. Variants were interpreted using the calibrated functional data, and experimental data were compared to SpliceAI predictions. We further studied some splice-altering missense variants by cDNA-based automated patch clamping (APC) in HEK cells and assessed splicing and sodium channel function in CRISPR-edited iPSC-CMs.

Results

ParSE-seq revealed the splicing effect of 224 SCN5A variants in iPSC-CMs and 244 variants in HEK293 cells. The scores between the cell types were highly correlated (R2=0.84). In iPSCs, the assay had concordant scores for 21/22 benign/likely benign and 24/25 pathogenic/likely pathogenic control variants from ClinVar. 43/112 exonic variants and 35/70 intronic variants with determinate scores disrupted splicing. 11 of 42 variants of uncertain significance were reclassified, and 29 of 34 variants with conflicting interpretations were reclassified using the functional data. SpliceAI computational predictions correlated well with experimental data (AUC = 0.96). We identified 20 unique SCN5A missense variants that disrupted splicing, and 2 clinically observed splice-altering missense variants of uncertain significance had normal function when tested with the cDNA-based APC assay. A splice-altering intronic variant detected by ParSE-seq, c.1891-5C>G, also disrupted splicing and sodium current when introduced into iPSC-CMs at the endogenous locus by CRISPR editing.

Conclusions

ParSE-seq is a calibrated, multiplexed, high-throughput assay to facilitate the classification of candidate splice-altering variants.

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 .

Genotype-phenotype landscapes for immune-pathogen coevolution

Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.

Molecular Mechanisms and Clinical Phenotypes of GJB2 Missense Variants

The GJB2 gene is the most common gene responsible for hearing loss (HL) worldwide, and missense variants are the most abundant type. GJB2 pathogenic missense variants cause nonsyndromic HL (autosomal recessive and dominant) and syndromic HL combined with skin diseases. However, the mechanism by which these different missense variants cause the different phenotypes is unknown. Over 2/3 of the GJB2 missense variants have yet to be functionally studied and are currently classified as variants of uncertain significance (VUS). Based on these functionally determined missense variants, we reviewed the clinical phenotypes and investigated the molecular mechanisms that affected hemichannel and gap junction functions, including connexin biosynthesis, trafficking, oligomerization into connexons, permeability, and interactions between other coexpressed connexins. We predict that all possible GJB2 missense variants will be described in the future by deep mutational scanning technology and optimizing computational models. Therefore, the mechanisms by which different missense variants cause different phenotypes will be fully elucidated.

Phosphosite Scanning reveals a complex phosphorylation code underlying CDK-dependent activation of Hcm1

Ordered cell cycle progression is coordinated by cyclin dependent kinases (CDKs). CDKs often phosphorylate substrates at multiple sites clustered within disordered regions. However, for most substrates, it is not known which phosphosites are functionally important. We developed a high-throughput approach, Phosphosite Scanning, that tests the importance of each phosphosite within a multisite phosphorylated domain. We show that Phosphosite Scanning identifies multiple combinations of phosphosites that can regulate protein function and reveals specific phosphorylations that are required for phosphorylation at additional sites within a domain. We applied this approach to the yeast transcription factor Hcm1, a conserved regulator of mitotic genes that is critical for accurate chromosome segregation. Phosphosite Scanning revealed a complex CDK-regulatory circuit that mediates Cks1-dependent phosphorylation of key activating sites in vivo. These results illuminate the mechanism of Hcm1 activation by CDK and establish Phosphosite Scanning as a powerful tool for decoding multisite phosphorylated domains.

Deep mutational scanning of essential bacterial proteins can guide antibiotic development

Deep mutational scanning is a powerful approach to investigate a wide variety of research questions including protein function and stability. Here, we perform deep mutational scanning on three essential E. coli proteins (FabZ, LpxC and MurA) involved in cell envelope synthesis using high-throughput CRISPR genome editing, and study the effect of the mutations in their original genomic context. We use more than 17,000 variants of the proteins to interrogate protein function and the importance of individual amino acids in supporting viability. Additionally, we exploit these libraries to study resistance development against antimicrobial compounds that target the selected proteins. Among the three proteins studied, MurA seems to be the superior antimicrobial target due to its low mutational flexibility, which decreases the chance of acquiring resistance-conferring mutations that simultaneously preserve MurA function. Additionally, we rank anti-LpxC lead compounds for further development, guided by the number of resistance-conferring mutations against each compound. Our results show that deep mutational scanning studies can be used to guide drug development, which we hope will contribute towards the development of novel antimicrobial therapies.

Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy

Over the last decade, next-generation sequencing (NGS) methods have become increasingly used in various areas of human genomics. In routine clinical care, their use is already implemented in oncology to profile the mutational landscape of a tumor, as well as in rare disease diagnostics. However, its utilization in pharmacogenomics is largely lacking behind. Recent population-scale genome data has revealed that human pharmacogenes carry a plethora of rare genetic variations that are not interrogated by conventional array-based profiling methods and it is estimated that these variants could explain around 30% of the genetically encoded functional pharmacogenetic variability.To interpret the impact of such variants on drug response a multitude of computational tools have been developed, but, while there have been major advancements, it remains to be shown whether their accuracy is sufficient to improve personalized pharmacogenetic recommendations in robust trials. In addition, conventional short-read sequencing methods face difficulties in the interrogation of complex pharmacogenes and high NGS test costs require stringent evaluations of cost-effectiveness to decide about reimbursement by national healthcare programs. Here, we illustrate current challenges and discuss future directions toward the clinical implementation of NGS to inform genotype-guided decision-making.

Deep mutational scanning and massively parallel kinetics of plasminogen activator inhibitor-1 functional stability to probe its latency transition

Plasminogen activator inhibitor-1 (PAI-1), a member of the serine protease inhibitor superfamily of proteins, is unique among serine protease inhibitors for exhibiting a spontaneous conformational change to a latent or inactive state. The functional half-life for this transition at physiologic temperature and pH is ∼1 to 2 h. To better understand the molecular mechanisms underlying this transition, we now report on the analysis of a comprehensive PAI-1 variant library expressed on filamentous phage and selected for functional stability after 48 h at 37 °C. Of the 7201 possible single amino acid substitutions in PAI-1, we identified 439 that increased the functional stability of PAI-1 beyond that of the WT protein. We also found 1549 single amino acid substitutions that retained inhibitory activity toward the canonical target protease of PAI-1 (urokinase-like plasminogen activator), whereas exhibiting functional stability less than or equal to that of WT PAI-1. Missense mutations that increase PAI-1 functional stability are concentrated in highly flexible regions within the PAI-1 structure. Finally, we developed a method for simultaneously measuring the functional half-lives of hundreds of PAI-1 variants in a multiplexed, massively parallel manner, quantifying the functional half-lives for 697 single missense variants of PAI-1 by this approach. Overall, these findings provide novel insight into the mechanisms underlying the latency transition of PAI-1 and provide a database for interpreting human PAI-1 genetic variants.

Accurate protein stability predictions from homology models

Calculating changes in protein stability (ΔΔG) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate ΔΔGs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of ΔΔG values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested ΔΔGs and templates for homology modeling across a broad range of sequence identities, and selected three methods for ΔΔG calculations to test. We find that ΔΔG-values predicted from homology models compare equally well to experimental ΔΔGs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using ΔΔGs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.

Defining the sensitivity landscape of EGFR variants to tyrosine kinase inhibitors

Tyrosine kinase inhibitor (TKI) is a standard treatment for patients with NSCLC harboring constitutively active epidermal growth factor receptor (EGFR) mutations. However, most rare EGFR mutations lack treatment regimens except for the well-studied ones. We constructed two EGFR variant libraries containing substitutions, deletions, or insertions using the saturation mutagenesis method. All the variants were located in the EGFR mutation hotspot (exons 18-21). The sensitivity of these variants to afatinib, erlotinib, gefitinib, icotinib, and osimertinib was systematically studied by determining their enrichment in massively parallel cytotoxicity assays using an endogenous EGFR-depleted cell line. A total of 3914 and 70,475 variants were detected in the constructed EGFR Substitution-Deletion (Sub-Del) and exon 20 Insertion (Ins) libraries. Of the 3914 Sub-Del variants, 221 proliferated fast in the control assay and were sensitive to EGFR-TKIs. For the 70,475 Ins variants, insertions at amino acid positions 770-774 were highly enriched in all 5 TKI cytotoxicity assays. Moreover, the top 5% of the enriched insertion variants included a glycine or serine insertion at high frequency. We present a comprehensive reference for the sensitivity of EGFR variants to five commonly used TKIs. The approach used here should be applicable to other genes and targeted drugs. BACKGROUND: Tyrosine kinase inhibitors (TKIs) therapy is a standard treatment for patients with advanced non-small-cell lung carcinoma (NSCLC) when activating epidermal growth factor receptor (EGFR) mutations are detected. However, except for the well-studied EGFR mutations, most EGFR mutations lack treatment regimens. TRANSLATIONAL SIGNIFICANCE: The results demonstrated that patients with rare EGFR mutations were most likely to benefit from osimertinib therapy compared to afatinib, erlotinib, gefitinib, or icotinib therapy. This study provides a case of deep mutational scanning that simultaneously assayed substitution, deletion, and insertion variants. This approach is applicable for other oncogenes and targeted drugs.

Mechanisms of protein evolution

How do proteins evolve? How do changes in sequence mediate changes in protein structure, and in turn in function? This question has multiple angles, ranging from biochemistry and biophysics to evolutionary biology. This review provides a brief integrated view of some key mechanistic aspects of protein evolution. First, we explain how protein evolution is primarily driven by randomly acquired genetic mutations and selection for function, and how these mutations can even give rise to completely new folds. Then, we also comment on how phenotypic protein variability, including promiscuity, transcriptional and translational errors, may also accelerate this process, possibly via "plasticity-first" mechanisms. Finally, we highlight open questions in the field of protein evolution, with respect to the emergence of more sophisticated protein systems such as protein complexes, pathways, and the emergence of pre-LUCA enzymes.

Humanized yeast to model human biology, disease and evolution

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

Epistatic drift causes gradual decay of predictability in protein evolution

Epistatic interactions can make the outcomes of evolution unpredictable, but no comprehensive data are available on the extent and temporal dynamics of changes in the effects of mutations as protein sequences evolve. Here, we use phylogenetic deep mutational scanning to measure the functional effect of every possible amino acid mutation in a series of ancestral and extant steroid receptor DNA binding domains. Across 700 million years of evolution, epistatic interactions caused the effects of most mutations to become decorrelated from their initial effects and their windows of evolutionary accessibility to open and close transiently. Most effects changed gradually and without bias at rates that were largely constant across time, indicating a neutral process caused by many weak epistatic interactions. Our findings show that protein sequences drift inexorably into contingency and unpredictability, but that the process is statistically predictable, given sufficient phylogenetic and experimental data.

Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering

Proteins are Nature's molecular machinery and comprise diverse roles while consisting of chemically similar building blocks. In recent years, protein engineering and design have become important research areas, with many applications in the pharmaceutical, energy, and biocatalysis fields, among others-where the aim is to ultimately create a protein given desired structural and functional properties. It is often critical to model the relationship between a protein's sequence, folded structure, and biological function to assist in such protein engineering pursuits. However, significant challenges remain in concretely mapping an amino acid sequence to specific protein properties and biological activities. Mutations may enhance or diminish molecular protein function, and the epistatic interactions between mutations result in an inherently complex mapping between genetic modifications and protein function. Therefore, estimating the quantitative effects of mutations on protein function(s) remains a grand challenge of biology, bioinformatics, and many related fields and would rapidly accelerate protein engineering tasks when successful. Such estimation is often known as variant effect prediction (VEP). However, progress has been demonstrated in recent years with the development of machine learning (ML) methods in modeling the relationship between mutations and protein function. In this Review, recent advances in variant effect prediction (VEP) are discussed as tools for protein engineering, focusing on techniques incorporating gains from the broader ML community and challenges in estimating biomolecular functional differences. Primary developments highlighted include convolutional neural networks, graph neural networks, and natural language embeddings for protein sequences.

Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A general calculus of fitness landscapes finds genes under selection in cancers

Genetic variants drive the evolution of traits and diseases. We previously modeled these variants as small displacements in fitness landscapes and estimated their functional impact by differentiating the evolutionary relationship between genotype and phenotype. Conversely, here we integrate these derivatives to identify genes steering specific traits. Over cancer cohorts, integration identified 460 likely tumor-driving genes. Many have literature and experimental support but had eluded prior genomic searches for positive selection in tumors. Beyond providing cancer insights, these results introduce a general calculus of evolution to quantify the genotype-phenotype relationship and discover genes associated with complex traits and diseases.

Environmental selection and epistasis in an empirical phenotype-environment-fitness landscape

Fitness landscapes, mappings of genotype/phenotype to their effects on fitness, are invaluable concepts in evolutionary biochemistry. Although widely discussed, measurements of phenotype-fitness landscapes in proteins remain scarce. Here, we quantify all single mutational effects on fitness and phenotype (EC₅₀) of VIM-2 β-lactamase across a 64-fold range of ampicillin concentrations. We then construct a phenotype-fitness landscape that takes variations in environmental selection pressure into account. We found that a simple, empirical landscape accurately models the ~39,000 mutational data points, suggesting that the evolution of VIM-2 can be predicted on the basis of the selection environment. Our landscape provides new quantitative knowledge on the evolution of the β-lactamases and proteins in general, particularly their evolutionary dynamics under subinhibitory antibiotic concentrations, as well as the mechanisms and environmental dependence of non-specific epistasis.

Linking protein structural and functional change to mutation using amino acid networks

The function of a protein is strongly dependent on its structure. During evolution, proteins acquire new functions through mutations in the amino-acid sequence. Given the advance in deep mutational scanning, recent findings have found functional change to be position dependent, notwithstanding the chemical properties of mutant and mutated amino acids. This could indicate that structural properties of a given position are potentially responsible for the functional relevance of a mutation. Here, we looked at the relation between structure and function of positions using five proteins with experimental data of functional change available. In order to measure structural change, we modeled mutated proteins via amino-acid networks and quantified the perturbation of each mutation. We found that structural change is position dependent, and strongly related to functional change. Strong changes in protein structure correlate with functional loss, and positions with functional gain due to mutations tend to be structurally robust. Finally, we constructed a computational method to predict functionally sensitive positions to mutations using structural change that performs well on all five proteins with a mean precision of 74.7% and recall of 69.3% of all functional positions.

Microfluidic deep mutational scanning of the human executioner caspases reveals differences in structure and regulation

The human caspase family comprises 12 cysteine proteases that are centrally involved in cell death and inflammation responses. The members of this family have conserved sequences and structures, highly similar enzymatic activities and substrate preferences, and overlapping physiological roles. In this paper, we present a deep mutational scan of the executioner caspases CASP3 and CASP7 to dissect differences in their structure, function, and regulation. Our approach leverages high-throughput microfluidic screening to analyze hundreds of thousands of caspase variants in tightly controlled in vitro reactions. The resulting data provides a large-scale and unbiased view of the impact of amino acid substitutions on the proteolytic activity of CASP3 and CASP7. We use this data to pinpoint key functional differences between CASP3 and CASP7, including a secondary internal cleavage site, CASP7 Q196 that is not present in CASP3. Our results will open avenues for inquiry in caspase function and regulation that could potentially inform the development of future caspase-specific therapeutics.

Deep Mutational Scanning of Protein-Protein Interactions Between Partners Expressed from Their Endogenous Loci In Vivo

Deep mutational scanning (DMS) generates mutants of a protein of interest in a comprehensive manner. CRISPR-Cas9 technology enables large-scale genome editing with high efficiency. Using both DMS and CRISPR-Cas9 therefore allows us to investigate the effects of thousands of mutations inserted directly in the genome. Combined with protein-fragment complementation assay (PCA), which enables the quantitative measurement of protein-protein interactions (PPIs) in vivo, these methods allow for the systematic assessment of the effects of mutations on PPIs in living cells. Here, we describe a method leveraging DMS, CRISPR-Cas9, and PCA to study the effect of point mutations on PPIs mediated by protein domains in yeast.

Engineering Proteins by Combining Deep Mutational Scanning and Yeast Display

Protein engineering using display platforms such as yeast display and phage display has allowed discovery of proteins with therapeutic and industrial applications. Antibodies and T cell receptors developed for therapeutic applications are often engineered by constructing libraries of mutations in loops of five to ten residues called complementarity determining regions that are in proximity to the antigen. In the past decade, deep mutational scanning has become a powerful tool in a protein engineer's toolbox, as it allows one to compare the impact of all 20 amino acids at each position, across the length of the protein. Thus, a single experiment can provide a sequence-activity landscape with information about hotspots or suboptimal binding sites in the original proteins. These residues or regions may be overlooked by engineering methods that are driven solely by structures or directed evolution of error-prone PCR libraries. Here, we describe experimental methods to engineer proteins by combining yeast display and deep mutational scanning mutagenesis, using T cell receptors as an example.

Deep mutational scanning for therapeutic antibody engineering

The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.

Scanning mutagenesis of RNA-binding protein ProQ reveals a quality control role for the Lon protease

The FinO-domain protein ProQ belongs to a widespread family of RNA-binding proteins (RBPs) involved in gene regulation in bacterial chromosomes and mobile elements. While the cellular RNA targets of ProQ have been established in diverse bacteria, the functionally crucial ProQ residues remain to be identified under physiological conditions. Following our discovery that ProQ deficiency alleviates growth suppression of Salmonella with succinate as the sole carbon source, an experimental evolution approach was devised to exploit this phenotype. By coupling mutational scanning with loss-of-function selection, we identified multiple ProQ residues in both the amino-terminal FinO domain and the variable carboxy-terminal region that are required for ProQ activity. Two carboxy-terminal mutations abrogated ProQ function and mildly impaired binding of a model RNA target. In contrast, several mutations in the FinO domain rendered ProQ both functionally inactive and unable to interact with target RNA in vivo. Alteration of the FinO domain stimulated the rapid turnover of ProQ by Lon-mediated proteolysis, suggesting a quality control mechanism that prevents the accumulation of nonfunctional ProQ molecules. We extend this observation to Hfq, the other major sRNA chaperone of enteric bacteria. The Hfq Y55A mutant protein, defective in RNA-binding and oligomerization, proved to be labile and susceptible to degradation by Lon. Taken together, our findings connect the major AAA+ family protease Lon with RNA-dependent quality control of Hfq and ProQ, the two major sRNA chaperones of Gram-negative bacteria.

Amyloid β structural polymorphism, associated toxicity and therapeutic strategies

A review of the multidisciplinary scientific literature reveals a large variety of amyloid-β (Aβ) oligomeric species, differing in molecular weight, conformation and morphology. These species, which may assemble via either on- or off-aggregation pathways, exhibit differences in stability, function and neurotoxicity, according to different experimental settings. The conformations of the different Aβ species are stabilized by intra- and inter-molecular hydrogen bonds and by electrostatic and hydrophobic interactions, all depending on the chemical and physical environment (e.g., solvent, ions, pH) and interactions with other molecules, such as lipids and proteins. This complexity and the lack of a complete understanding of the relationship between the different Aβ species and their toxicity is currently dictating the nature of the inhibitor (or inducer)-based approaches that are under development for interfering with (or inducing) the formation of specific species and Aβ oligomerization, and for interfering with the associated downstream neurotoxic effects. Here, we review the principles that underlie the involvement of different Aβ oligomeric species in neurodegeneration, both in vitro and in preclinical studies. In addition, we provide an overview of the existing inhibitors (or inducers) of Aβ oligomerization that serve as potential therapeutics for neurodegenerative diseases. The review, which covers the exciting studies that have been published in the past few years, comprises three main parts: 1) on- and off-fibrillar assembly mechanisms and Aβ structural polymorphism; 2) interactions of Aβ with other molecules and cell components that dictate the Aβ aggregation pathway; and 3) targeting the on-fibrillar Aβ assembly pathway as a therapeutic approach.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Climbing Up and Down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein-Protein Complexes

Protein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease-inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.

scAAVengr, a transcriptome-based pipeline for quantitative ranking of engineered AAVs with single-cell resolution

Background

Adeno-associated virus (AAV)-mediated gene therapies are rapidly advancing to the clinic, and AAV engineering has resulted in vectors with increased ability to deliver therapeutic genes. Although the choice of vector is critical, quantitative comparison of AAVs, especially in large animals, remains challenging.

Methods

Here, we developed an efficient single-cell AAV engineering pipeline (scAAVengr) to simultaneously quantify and rank efficiency of competing AAV vectors across all cell types in the same animal.

Results

To demonstrate proof-of-concept for the scAAVengr workflow, we quantified - with cell-type resolution - the abilities of naturally occurring and newly engineered AAVs to mediate gene expression in primate retina following intravitreal injection. A top performing variant identified using this pipeline, K912, was used to deliver SaCas9 and edit the rhodopsin gene in macaque retina, resulting in editing efficiency similar to infection rates detected by the scAAVengr workflow. scAAVengr was then used to identify top-performing AAV variants in mouse brain, heart, and liver following systemic injection.

Conclusions

These results validate scAAVengr as a powerful method for development of AAV vectors.

Funding

This work was supported by funding from the Ford Foundation, NEI/NIH, Research to Prevent Blindness, Foundation Fighting Blindness, UPMC Immune Transplant and Therapy Center, and the Van Sloun fund for canine genetic research.

Deep mutational scanning of the plasminogen activator inhibitor-1 functional landscape

The serine protease inhibitor (SERPIN) plasminogen activator inhibitor-1 (PAI-1) is a key regulator of the fibrinolytic system, inhibiting the serine proteases tissue- and urokinase-type plasminogen activator (tPA and uPA, respectively). Missense variants render PAI-1 non-functional through misfolding, leading to its turnover as a protease substrate, or to a more rapid transition to the latent/inactive state. Deep mutational scanning was performed to evaluate the impact of amino acid sequence variation on PAI-1 inhibition of uPA using an M13 filamentous phage display system. Error prone PCR was used to construct a mutagenized PAI-1 library encompassing ~ 70% of potential single amino acid substitutions. The relative effects of 27% of all possible missense variants on PAI-1 inhibition of uPA were determined using high-throughput DNA sequencing. 826 missense variants demonstrated conserved inhibitory activity while 1137 resulted in loss of PAI-1 inhibitory function. The least evolutionarily conserved regions of PAI-1 were also identified as being the most tolerant of missense mutations. The results of this screen confirm previous low-throughput mutational studies, including those of the reactive center loop. These data provide a powerful resource for explaining structure-function relationships for PAI-1 and for the interpretation of human genomic sequence variants.

PacBio sequencing output increased through uniform and directional fivefold concatenation

Advances in sequencing technology have allowed researchers to sequence DNA with greater ease and at decreasing costs. Main developments have focused on either sequencing many short sequences or fewer large sequences. Methods for sequencing mid-sized sequences of 600-5,000 bp are currently less efficient. For example, the PacBio Sequel I system yields ~ 100,000-300,000 reads with an accuracy per base pair of 90-99%. We sought to sequence several DNA populations of ~ 870 bp in length with a sequencing accuracy of 99% and to the greatest depth possible. We optimised a simple, robust method to concatenate genes of ~ 870 bp five times and then sequenced the resulting DNA of ~ 5,000 bp by PacBioSMRT long-read sequencing. Our method improved upon previously published concatenation attempts, leading to a greater sequencing depth, high-quality reads and limited sample preparation at little expense. We applied this efficient concatenation protocol to sequence nine DNA populations from a protein engineering study. The improved method is accompanied by a simple and user-friendly analysis pipeline, DeCatCounter, to sequence medium-length sequences efficiently at one-fifth of the cost.

Applications of Deep Mutational Scanning in Virology

Several recently developed high-throughput techniques have changed the field of molecular virology. For example, proteomics studies reveal complete interactomes of a viral protein, genome-wide CRISPR knockout and activation screens probe the importance of every single human gene in aiding or fighting a virus, and ChIP-seq experiments reveal genome-wide epigenetic changes in response to infection. Deep mutational scanning is a relatively novel form of protein science which allows the in-depth functional analysis of every nucleotide within a viral gene or genome, revealing regions of importance, flexibility, and mutational potential. In this review, we discuss the application of this technique to RNA viruses including members of the Flaviviridae family, Influenza A Virus and Severe Acute Respiratory Syndrome Coronavirus 2. We also briefly discuss the reverse genetics systems which allow for analysis of viral replication cycles, next-generation sequencing technologies and the bioinformatics tools that facilitate this research.