Deep Mutational Scanning Research Articles

High-throughput Variant Resolution Utilizing Deep Mutational Scanning in Sarcoglycanopathies (P13-13.004)

High-throughput Variant Resolution Utilizing Deep Mutational Scanning in Sarcoglycanopathies (P13-13.004)

Systematic Conformation‐to‐Phenotype Mapping via Limited Protein Sequencing

Non‐native conformations drive protein misfolding diseases, frustrate bioengineering efforts, and fuel molecular evolution. No current experimental technique is well‐suited for elucidating them, and computational techniques lack training data. Deep mutational scanning can comprehensively map complex protein fitness landscapes but not their conformational landscapes. We describe an approach to systematically discover, stabilize, and resolve native and non‐native conformations of a protein, in vitroor ex vivo, and directly link them to their molecular, organismal, or evolutionary phenotypes, using high‐throughput disulfide scanning (HTDS) of the entire protein. To reveal characteristic disulfide bonding patterns of chromatographically resolved conformers, we devised a limited protein deep‐sequencing method that identifies the exact sequence positions of two Cys residues simultaneously within the same polypeptide chain. HTDS of the E. coli periplasmic chaperone HdeA revealed phenotypically distinct classes of disordered hydrophobic conformers. HTDS can determine phenotypic effects of non‐native conformations for many proteins that function in disulfide‐permissive environments.

Meditopia: Meditope‐Enabled Bonds and their Affinity with Cetuximab

A meditope is a peptide found in the fragment antigen‐binding (Fab) region of an antibody, and can be used to bind substances, such as monoclonal antibodies (mAbs) and pharmaceuticals. We centered on four mutations that can increase the affinity between the meditope and the mAb, allowing for a stronger bond. Our focus was on the anti‐epidermal growth factor receptor mAb cetuximab, which tracks the overexpression of the epidermal growth factor receptor (EGFR) by cancer cells. Utilizing a meditope, cetuximab can be linked to chemotherapy, creating a more effective treatment that only attacks cancer cells, significantly reducing possible long‐term side effects. In this study, yeast display and deep mutational scanning tracked, enhanced, and joined four mutations within the meditope, increasing affinity, or bond strength, for cetuximab 10‐fold, from Kd 130 nm to 15 nm. Those mutations led to heightened cetuximab recruitment to EGFR‐overexpressing cancer cells, and therefore more efficient targeting of cancer. The methods used in this study can develop other effective meditope‐linked monoclonal antibodies and chemotherapy treatments. The Mahtomedi MSOE Center for BioMolecular Modeling MAPS Team will use 3‐D modeling and printing technology to examine structure‐function relationships of meditope‐mAb binding within cetuximab. The visual model will be a valuable tool in developing our story.

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

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

Detailed analysis of antibody responses to SARS-CoV-2 vaccination and infection in macaques.

Macaques are a commonly used model for studying immunity to human viruses, including for studies of SARS-CoV-2 infection and vaccination. However, it is unknown whether macaque antibody responses resemble the response in humans. To answer this question, we employed a phage-based deep mutational scanning approach (Phage-DMS) to compare which linear epitopes are targeted on the SARS-CoV-2 Spike protein in convalescent humans, convalescent (re-infected) rhesus macaques, mRNA-vaccinated humans, and repRNA-vaccinated pigtail macaques. We also used Phage-DMS to determine antibody escape pathways within each epitope, enabling a granular comparison of antibody binding specificities at the locus level. Overall, we identified some common epitope targets in both macaques and humans, including in the fusion peptide (FP) and stem helix-heptad repeat 2 (SH-H) regions. Differences between groups included a response to epitopes in the N-terminal domain (NTD) and C-terminal domain (CTD) in vaccinated humans but not vaccinated macaques, as well as recognition of a CTD epitope and epitopes flanking the FP in convalescent macaques but not convalescent humans. There was also considerable variability in the escape pathways among individuals within each group. Sera from convalescent macaques showed the least variability in escape overall and converged on a common response with vaccinated humans in the SH-H epitope region, suggesting highly similar antibodies were elicited. Collectively, these findings suggest that the antibody response to SARS-CoV-2 in macaques shares many features with humans, but with substantial differences in the recognition of certain epitopes and considerable individual variability in antibody escape profiles, suggesting a diverse repertoire of antibodies that can respond to major epitopes in both humans and macaques. Differences in macaque species and exposure type may also contribute to these findings.

Mapping the energetic and allosteric landscapes of protein binding domains.

Allosteric communication between distant sites in proteins is central to biological regulation but still poorly characterized, limiting understanding, engineering and drug development1-6. An important reason for this is the lack of methods to comprehensively quantify allostery in diverse proteins. Here we address this shortcoming and present a method that uses deep mutational scanning to globally map allostery. The approach uses an efficient experimental design to infer en masse the causal biophysical effects of mutations by quantifying multiple molecular phenotypes-here we examine binding and protein abundance-in multiple genetic backgrounds and fitting thermodynamic models using neural networks. We apply the approach to two of the most common protein interaction domains found in humans, an SH3 domain and a PDZ domain, to produce comprehensive atlases of allosteric communication. Allosteric mutations are abundant, with a large mutational target space of network-altering 'edgetic' variants. Mutations are more likely to be allosteric closer to binding interfaces, at glycine residues and at specific residues connecting to an opposite surface within the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should enable the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped.

MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles

Abstract Mutations, which result in amino acid substitutions, influence the stability of proteins and their binding to biomolecules. A molecular understanding of the effects of protein mutations is both of biotechnological and medical relevance. Empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. In silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of known mutations. Often software such as FoldX, while fast and reliable, lack the necessary automation features to apply them in a high-throughput manner. We introduce MutateX, a software to automate the prediction of ΔΔGs associated with the systematic mutation of each residue within a protein, or protein complex to all other possible residue types, using the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles, upon post-translational modifications and in multimeric assemblies. At the heart of MutateX lies an automated pipeline engine that handles input preparation, parallelization and outputs publication-ready figures. We illustrate the MutateX protocol applied to different case studies. The results of the high-throughput scan provided by our tools can help in different applications, such as the analysis of disease-associated mutations, to complement experimental deep mutational scans, or assist the design of variants for industrial applications. MutateX is a collection of Python tools that relies on open-source libraries. It is available free of charge under the GNU General Public License from https://github.com/ELELAB/mutatex.

Current and Future Approaches to Classify VUSs in LGMD-Related Genes.

Next-generation sequencing (NGS) has revealed large numbers of genetic variants in LGMD-related genes, with most of them classified as variants of uncertain significance (VUSs). VUSs are genetic changes with unknown pathological impact and present a major challenge in genetic test interpretation and disease diagnosis. Understanding the phenotypic consequences of VUSs can provide clinical guidance regarding LGMD risk and therapy. In this review, we provide a brief overview of the subtypes of LGMD, disease diagnosis, current classification systems for investigating VUSs, and a potential deep mutational scanning approach to classify VUSs in LGMD-related genes.

Phylogenetic inference of changes in amino acid propensities with single-position resolution.

Fitness conferred by the same allele may differ between genotypes and environments, and these differences shape variation and evolution. Changes in amino acid propensities at protein sites over the course of evolution have been inferred from sequence alignments statistically, but the existing methods are data-intensive and aggregate multiple sites. Here, we develop an approach to detect individual amino acids that confer different fitness in different groups of species from combined sequence and phylogenetic data. Using the fact that the probability of a substitution to an amino acid depends on its fitness, our method looks for amino acids such that substitutions to them occur more frequently in one group of lineages than in another. We validate our method using simulated evolution of a protein site under different scenarios and show that it has high specificity for a wide range of assumptions regarding the underlying changes in selection, while its sensitivity differs between scenarios. We apply our method to the env gene of two HIV-1 subtypes, A and B, and to the HA gene of two influenza A subtypes, H1 and H3, and show that the inferred fitness changes are consistent with the fitness differences observed in deep mutational scanning experiments. We find that changes in relative fitness of different amino acid variants within a site do not always trigger episodes of positive selection and therefore may not result in an overall increase in the frequency of substitutions, but can still be detected from changes in relative frequencies of different substitutions.

Stabilization of the SARS-CoV-2 receptor binding domain by protein core redesign and deep mutational scanning.

Stabilizing antigenic proteins as vaccine immunogens or diagnostic reagents is a stringent case of protein engineering and design as the exterior surface must maintain recognition by receptor(s) and antigen-specific antibodies at multiple distinct epitopes. This is a challenge, as stability enhancing mutations must be focused on the protein core, whereas successful computational stabilization algorithms typically select mutations at solvent-facing positions. In this study, we report the stabilization of SARS-CoV-2 Wuhan Hu-1 Spike receptor binding domain using a combination of deep mutational scanning and computational design, including the FuncLib algorithm. Our most successful design encodes I358F, Y365W, T430I, and I513L receptor binding domain mutations, maintains recognition by the receptor ACE2 and a panel of different anti-receptor binding domain monoclonal antibodies, is between 1 and 2°C more thermally stable than the original receptor binding domain using a thermal shift assay, and is less proteolytically sensitive to chymotrypsin and thermolysin than the original receptor binding domain. Our approach could be applied to the computational stabilization of a wide range of proteins without requiring detailed knowledge of active sites or binding epitopes. We envision that this strategy may be particularly powerful for cases when there are multiple or unknown binding sites.

Directed mutational scanning reveals a balance between acidic and hydrophobic residues in strong human activation domains.

SUMMARYAcidic activation domains are intrinsically disordered regions of the transcription factors that bind coactivators. The intrinsic disorder and low evolutionary conservation of activation domains have made it difficult to identify the sequence features that control activity. To address this problem, we designed thousands of variants in seven acidic activation domains and measured their activities with a high-throughput assay in human cell culture. We found that strong activation domain activity requires a balance between the number of acidic residues and aromatic and leucine residues. These findings motivated a predictor of acidic activation domains that scans the human proteome for clusters of aromatic and leucine residues embedded in regions of high acidity. This predictor identifies known activation domains and accurately predicts previously unidentified ones. Our results support a flexible acidic exposure model of activation domains in which the acidic residues solubilize hydrophobic motifs so that they can interact with coactivators. A record of this paper’s transparent peer review process is included in the supplemental information.

In-depth Sequence-Function Characterization Reveals Multiple Pathways to Enhance Enzymatic Activity.

Deep mutational scanning (DMS) has recently emerged as a powerful method to study protein sequence-function relationships but it has not been well-explored as a guide to enzyme engineering and identifying pathways by which their catalytic cycle may be improved. We report such a demonstration in this work using a Phenylalanine ammonia-lyase (PAL), which deaminates L-phenylalanine to trans-cinnamic acid and has widespread application in chemo-enzymatic synthesis, agriculture, and medicine. In particular, the PAL from Anabaena variabilis (AvPAL*) has garnered significant attention as the active ingredient in Pegvaliase®, the only FDA-approved drug treating classical Phenylketonuria (PKU). Although an extensive body of literature exists on the structure, substrate-specificity, and catalytic cycle, protein-wide sequence determinants of function remain unknown, as do intermediate reaction steps that limit turnover frequency, all of which has hindered rational engineering of these enzymes. Here, we created a detailed sequence-function landscape of AvPAL* by performing DMS and revealed 112 mutations at 79 functionally relevant sites that affect a positive change in enzyme fitness. Using fitness values and structure-function analysis, we picked a subset of positions for comprehensive single- and multi-site saturation mutagenesis and identified combinations of mutations that led to improved reaction kinetics in cell-free and cellular contexts. We then performed QM/MM and MD to understand the mechanistic role of the most beneficial mutations and observed that different mutants confer improvements via different mechanisms, including stabilizing transition and intermediate states, improving substrate diffusion into the active site, and decreasing product inhibition. This work demonstrates how DMS can be combined with computational analysis to effectively identify significant mutations that enhance enzyme activity along with the underlying mechanisms by which these mutations confer their benefit.

Using deep mutational scanning to identify the determinants of antibiotic resistance

Current drug discovery platforms have proven incapable of supplying enough new antibiotic types to meet the challenge of rapidly developing clinical resistances. New techniques that specifically incorporate potential resistance adaptations during design are necessary. In cases of known specific resistance mechanisms, a biophysical approach may allow rational development of existing drugs that escape resistance. In this work, we use deep mutational scanning (DMS), a next-generation sequencing-based technique that reports on the site-specific fitness effects of mutations across an entire protein, to investigate the contribution of Virginiamycin acetyltransferase (VatD) to several streptogramin analogues.

Evolutionary Landscapes of Host-Virus Arms Races.

Vertebrate immune systems suppress viral infection using both innate restriction factors and adaptive immunity. Viruses mutate to escape these defenses, driving hosts to counterevolve to regain fitness. This cycle recurs repeatedly, resulting in an evolutionary arms race whose outcome depends on the pace and likelihood of adaptation by host and viral genes. Although viruses evolve faster than their vertebrate hosts, their proteins are subject to numerous functional constraints that impact the probability of adaptation. These constraints are globally defined by evolutionary landscapes, which describe the fitness and adaptive potential of all possible mutations. We review deep mutational scanning experiments mapping the evolutionary landscapes of both host and viral proteins engaged in arms races. For restriction factors and some broadly neutralizing antibodies, landscapes favor the host, which may help to level the evolutionary playing field against rapidly evolving viruses. We discuss the biophysical underpinnings of these landscapes and their therapeutic implications.

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.

Comprehensive interrogation of the ADAR2 deaminase domain for engineering enhanced RNA editing activity and specificity.

Adenosine deaminases acting on RNA (ADARs) can be repurposed to enable programmable RNA editing, however their exogenous delivery leads to transcriptome-wide off-targeting, and additionally, enzymatic activity on certain RNA motifs, especially those flanked by a 5' guanosine is very low thus limiting their utility as a transcriptome engineering toolset. Towards addressing these issues, we first performed a novel deep mutational scan of the ADAR2 deaminase domain, directly measuring the impact of every amino acid substitution across 261 residues, on RNA editing. This enabled us to create a domain-wide mutagenesis map while also revealing a novel hyperactive variant with improved enzymatic activity at 5'-GAN-3' motifs. As overexpression of ADAR enzymes, especially hyperactive variants, can lead to significant transcriptome-wide off-targeting, we next engineered a split-ADAR2 deaminase which resulted in >100-fold more specific RNA editing as compared to full-length deaminase overexpression. Taken together, we anticipate this systematic engineering of the ADAR2 deaminase domain will enable broader utility of the ADAR toolset for RNA biotechnology applications.

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

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

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.

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 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 assessmentof 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.