Latent effects of Hsp90 mutants revealed at reduced expression levels

Epitope mapping via in vitro deep mutational scanning methods and its applications

Epitope mapping is a technique employed to define the region of an antigen that elicits an immune response, providing crucial insight into the structural architecture of the antigen as well as epitope-paratope interactions. With this breadth of knowledge, immunotherapies, diagnostics, and vaccines are being developed with a rational and data-supported design. Traditional epitope mapping methods are laborious, time-intensive, and often lack the ability to screen proteins in a high-throughput manner or provide high resolution. Deep mutational scanning (DMS), however, is revolutionizing the field as it can screen all possible single amino acid mutations and provide an efficient and high-throughput way to infer the structures of both linear and three-dimensional epitopes with high resolution. Currently, more than 50 publications take this approach to efficiently identify enhancing or escaping mutations, with many then employing this information to rapidly develop broadly neutralizing antibodies, T-cell immunotherapies, vaccine platforms, or diagnostics. We provide a comprehensive review of the approaches to accomplish epitope mapping while also providing a summation of the development of DMS technology and its impactful applications.

Dominant negative mutations in yeast Hsp90 indicate triage decision mechanism targeting client proteins for degradation

Dominant negative (DN) mutations provide valuable tools for investigating protein mechanisms but can be difficult to isolate because of their toxic effects. We used a mutational scanning approach to identify DN mutations in yeast Hsp90. In a previous mutational scan of the ATPase domain of Hsp90, we noticed that many mutations were at very low frequency after outgrowth in cells coexpressing wildtype Hsp90. Most of these depleted variants were located at the hinge of a lid that closes over ATP. To quantify toxic effects in the hinge regions, we performed mutational scanning using an inducible promoter and identified 113 variants with strong toxic effects. We analyzed individual DN mutations in detail and found that addition of the E33A mutation that prevents ATP hydrolysis by Hsp90 abrogated the DN phenotype. FRET assays performed on individual DN mutants indicate the linkage between ATPase activity and formation of the closed structure is disrupted. DN Hsp90 decreased the expression level of two model Hsp90 clients, glucocorticoid receptor (GR) and v-src kinase. Using MG132, we found that GR was rapidly destabilized in a proteasome-dependent manner. Biochemical analyses indicate that ATP hydrolysis by Hsp90 from open conformations can lead to ubiquitin-dependent client degradation.

Functional Optimization in Distinct Tissues and Conditions Constrains the Rate of Protein Evolution

Understanding the main determinants of protein evolution is a fundamental challenge in biology. Despite many decades of active research, the molecular and cellular mechanisms underlying the substantial variability of evolutionary rates across cellular proteins are not currently well understood. It also remains unclear how protein molecular function is optimized in the context of multicellular species and why many proteins, such as enzymes, are only moderately efficient on average. Our analysis of genomics and functional datasets reveals in multiple organisms a strong inverse relationship between the optimality of protein molecular function and the rate of protein evolution. Furthermore, we find that highly expressed proteins tend to be substantially more functionally optimized. These results suggest that cellular expression costs lead to more pronounced functional optimization of abundant proteins and that the purifying selection to maintain high levels of functional optimality significantly slows protein evolution. We observe that in multicellular species both the rate of protein evolution and the degree of protein functional efficiency are primarily affected by expression in several distinct cell types and tissues, specifically, in developed neurons with upregulated synaptic processes in animals and in young and fast-growing tissues in plants. Overall, our analysis reveals how various constraints from the molecular, cellular, and species' levels of biological organization jointly affect the rate of protein evolution and the level of protein functional adaptation.

Dominant negative mutations in yeast Hsp90 reveal triage decision mechanism targeting client proteins for degradation

Most of the fundamental processes of cells are mediated by proteins. However, the biologically-relevant mechanism of most proteins are poorly understood. Dominant negative mutations have provided a valuable tool for investigating protein mechanisms but can be difficult to isolate because of their toxic effects. We used a mutational scanning approach to identify dominant negative mutations in yeast Hsp90. Hsp90 is a chaperone that forms dynamic complexes with many co-chaperones and client proteins. In vitro analyses have elucidated some key biochemical states and structures of Hsp90, co-chaperones, and clients; however, the biological mechanism of Hsp90 remains unclear. For example, high throughput studies have found that many E3 ubiquitin ligases bind to Hsp90, but it is unclear if these are primarily clients or acting to tag other clients for degradation. We introduced a library of all point mutations in the ATPase domain of Hsp90 into yeast and noticed that 176 were more than 10-fold depleted at the earliest point that we could analyze. There were two hot spot regions of the depleted mutations that were located at the hinges of a loop that closes over ATP. We quantified the dominant negative growth effects of mutations in the hinge regions using a library of mutations driven by an inducible promoter. We analyzed individual dominant negative mutations in detail and found that addition of the E33A mutation that prevents ATP hydrolysis by Hsp90 abrogated the dominant negative phenotype. Pull-down experiments did not reveal any stable binding partners, indicating that the dominant effects were mediated by dynamic complexes. DN Hsp90 decreased the expression level of two model Hsp90 clients, glucocorticoid receptor (GR) and v-src kinase. Using MG132, we found that GR was rapidly destabilized in a proteasome-dependent fashion. These findings provide evidence that the binding of E3 ligases to Hsp90 may serve a quality control function fundamental to eukaryotes.

Contributions of Hyperactive Mutations in Mpro from SARS-CoV-2 to Drug Resistance

The appearance and spread of mutations that cause drug resistance in rapidly evolving diseases, including infections by the SARS-CoV-2 virus, are major concerns for human health. Many drugs target enzymes, and resistance-conferring mutations impact inhibitor binding or enzyme activity. Nirmatrelvir, the most widely used inhibitor currently used to treat SARS-CoV-2 infections, targets the main protease (Mpro) preventing it from processing the viral polyprotein into active subunits. Our previous work systematically analyzed resistance mutations in Mpro that reduce binding to inhibitors; here, we investigate mutations that affect enzyme function. Hyperactive mutations that increase Mpro activity can contribute to drug resistance but have not been thoroughly studied. To explore how hyperactive mutations contribute to resistance, we comprehensively assessed how all possible individual mutations in Mpro affect enzyme function using a mutational scanning approach with a fluorescence resonance energy transfer (FRET)-based yeast readout. We identified hundreds of mutations that significantly increased the Mpro activity. Hyperactive mutations occurred both proximal and distal to the active site, consistent with protein stability and/or dynamics impacting activity. Hyperactive mutations were observed 3 times more than mutations which reduced apparent binding to nirmatrelvir in recent studies of laboratory-grown viruses selected for drug resistance. Hyperactive mutations were also about three times more prevalent than nirmatrelvir binding mutations in sequenced isolates from circulating SARS-CoV-2. Our findings indicate that hyperactive mutations are likely to contribute to the natural evolution of drug resistance in Mpro and provide a comprehensive list for future surveillance efforts.

Insights into Hsp90 mechanism and in vivo functions learned from studies in the yeast, Saccharomyces cerevisiae

The molecular chaperone Hsp90 (Heat shock protein, 90 kDa) is an abundant and essential cytosolic protein required for the stability and/or folding of hundreds of client proteins. Hsp90, along with helper cochaperone proteins, assists client protein folding in an ATP-dependent pathway. The laboratory of Susan Lindquist, in collaboration with other researchers, was the first to establish the yeast Saccharomyces cerevisiae as a model organism to study the functional interaction between Hsp90 and clients. Important insights from studies in her lab were that Hsp90 is essential, and that Hsp90 functions and cochaperone interactions are highly conserved between yeast and mammalian cells. Here, we describe key mechanistic insights into the Hsp90 folding cycle that were obtained using the yeast system. We highlight the early contributions of the laboratory of Susan Lindquist and extend our analysis into the broader use of the yeast system to analyze the understanding of the conformational cycle of Hsp90 and the impact of altered Hsp90 function on the proteome.

Systematic profiling of dominant ubiquitin variants reveals key functional nodes contributing to evolutionary selection

Dominant-negative mutations can help to investigate the biological mechanisms and to understand the selective pressures for multifunctional proteins. However, most studies have focused on recessive mutant effects that occur in the absence of a second functional gene copy, which overlooks the fact that most eukaryotic genomes contain more than one copy of many genes. We have identified dominant effects on yeast growth rate among all possible point mutations in ubiquitin expressed alongside a wild-type allele. Our results reveal more than 400 dominant-negative mutations, indicating that dominant-negative effects make a sizable contribution to selection acting on ubiquitin. Cellular and biochemical analyses of individual ubiquitin variants show that dominant-negative effects are explained by varied accumulation of polyubiquitinated cellular proteins and/or defects in conjugation of ubiquitin variants to ubiquitin ligases. Our approach to identify dominant-negative mutations is general and can be applied to other proteins of interest.

Saccharomyces cerevisiae as a tool for deciphering Hsp90 molecular chaperone function

Yeast is a valuable model organism for their ease of genetic manipulation, rapid growth rate, and relative similarity to higher eukaryotes. Historically, Saccharomyces cerevisiae has played a major role in discovering the function of complex proteins and pathways that are important for human health and disease. Heat shock protein 90 (Hsp90) is a molecular chaperone responsible for the stabilization and activation of hundreds of integral members of the cellular signaling network. Much important structural and functional work, including many seminal discoveries in Hsp90 biology are the direct result of work carried out in S. cerevisiae. Here, we have provided a brief overview of the S. cerevisiae model system and described how this eukaryotic model organism has been successfully applied to the study of Hsp90 chaperone function.

Nucleotide exchange is sufficient for Hsp90 functions in vivo

Hsp90 is an essential eukaryotic chaperone that regulates the activity of many client proteins. Current models of Hsp90 function, which include many conformational rearrangements, specify a requirement of ATP hydrolysis. Here we confirm earlier findings that the Hsp82-E33A mutant, which binds ATP but does not hydrolyze it, supports viability of S. cerevisiae, although it displays conditional phenotypes. We find binding of ATP to Hsp82-E33A induces the conformational dynamics needed for Hsp90 function. Hsp90 orthologs with the analogous EA mutation from several eukaryotic species, including humans and disease organisms, support viability of both S. cerevisiae and Sz. pombe. We identify second-site suppressors of EA that rescue its conditional defects and allow EA versions of all Hsp90 orthologs tested to support nearly normal growth of both organisms, without restoring ATP hydrolysis. Thus, the requirement of ATP for Hsp90 to maintain viability of evolutionarily distant eukaryotic organisms does not appear to depend on energy from ATP hydrolysis. Our findings support earlier suggestions that exchange of ATP for ADP is critical for Hsp90 function. ATP hydrolysis is not necessary for this exchange but provides an important control point in the cycle responsive to regulation by co-chaperones.

Mutational scan inferred binding energetics and structure in intrinsically disordered protein CcdA

Unlike globular proteins, mutational effects on the function of Intrinsically Disordered Proteins (IDPs) are not well-studied. Deep Mutational Scanning of a yeast surface displayed mutant library yields insights into sequence-function relationships in the CcdA IDP. The approach enables facile prediction of interface residues and local structural signatures of the bound conformation. In contrast to previous titration-based approaches which use a number of ligand concentrations, we show that use of a single rationally chosen ligand concentration can provide quantitative estimates of relative binding constants for large numbers of protein variants. This is because the extended interface of IDP ensures that energetic effects of point mutations are spread over a much smaller range than for globular proteins. Our data also provides insights into the much-debated role of helicity and disorder in partner binding of IDPs. Based on this exhaustive mutational sensitivity dataset, a rudimentary model was developed in an attempt to predict mutational effects on binding affinity of IDPs that form alpha-helical structures upon binding.

Epistasis between promoter activity and coding mutations shapes gene evolvability

The evolution of protein-coding genes proceeds as mutations act on two main dimensions: regulation of transcription level and the coding sequence. The extent and impact of the connection between these two dimensions are largely unknown because they have generally been studied independently. By measuring the fitness effects of all possible mutations on a protein complex at various levels of promoter activity, we show that promoter activity at the optimal level for the wild-type protein masks the effects of both deleterious and beneficial coding mutations. Mutations that are deleterious at low activity but masked at optimal activity are slightly destabilizing for individual subunits and binding interfaces. Coding mutations that increase protein abundance are beneficial at low expression but could potentially incur a cost at high promoter activity. We thereby demonstrate that promoter activity in interaction with protein properties can dictate which coding mutations are beneficial, neutral, or deleterious.

Mutational fitness landscape and drug resistance

Robust technology has been developed to systematically quantify fitness landscapes that provide valuable opportunities to improve our understanding of drug resistance and define new avenues to develop drugs with reduced resistance susceptibility. We outline the critical importance of drug resistance studies and the potential for fitness landscape approaches to contribute to this effort. We describe the major technical advancements in mutational scanning, which is the primary approach used to quantify protein fitness landscapes. There are many complex steps to consider in planning and executing mutational scanning projects including developing a selection scheme, generating mutant libraries, tracking the frequency of variants using next-generation sequencing, and processing and interpreting the data. Key experimental parameters impacting each of these steps are discussed to aid in planning fitness landscape studies. There is a strong need for improved understanding of drug resistance, and fitness landscapes provide a promising new approach.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Translational reprogramming in response to accumulating stressors ensures critical threshold levels of Hsp90 for mammalian life

The cytosolic molecular chaperone Hsp90 is essential for eukaryotic life. Although reduced Hsp90 levels correlate with aging, it was unknown whether eukaryotic cells and organisms can tune the basal Hsp90 levels to alleviate physiologically accumulated stress. We have investigated whether and how mice adapt to the deletion of three out of four alleles of the two genes encoding cytosolic Hsp90, with one Hsp90β allele being the only remaining one. While the vast majority of such mouse embryos die during gestation, survivors apparently manage to increase their Hsp90β protein to at least wild-type levels. Our studies reveal an internal ribosome entry site in the 5' untranslated region of the Hsp90β mRNA allowing translational reprogramming to compensate for the genetic loss of Hsp90 alleles and in response to stress. We find that the minimum amount of total Hsp90 required to support viability of mammalian cells and organisms is 50-70% of what is normally there. Those that fail to maintain a threshold level are subject to accelerated senescence, proteostatic collapse, and ultimately death. Therefore, considering that Hsp90 levels can be reduced ≥100-fold in the unicellular budding yeast, critical threshold levels of Hsp90 have markedly increased during eukaryotic evolution.

The High Mutational Sensitivity of ccdA Antitoxin Is Linked to Codon Optimality

Deep mutational scanning studies suggest that synonymous mutations are typically silent and that most exposed, nonactive-site residues are tolerant to mutations. Here, we show that the ccdA antitoxin component of the Escherichia coli ccdAB toxin-antitoxin system is unusually sensitive to mutations when studied in the operonic context. A large fraction (∼80%) of single-codon mutations, including many synonymous mutations in the ccdA gene shows inactive phenotype, but they retain native-like binding affinity towards cognate toxin, CcdB. Therefore, the observed phenotypic effects are largely not due to alterations in protein structure/stability, consistent with a large region of CcdA being intrinsically disordered. E. coli codon preference and strength of ribosome-binding associated with translation of downstream ccdB gene are found to be major contributors of the observed ccdA mutant phenotypes. In select cases, proteomics studies reveal altered ratios of CcdA:CcdB protein levels in vivo, suggesting that the ccdA mutations likely alter relative translation efficiencies of the two genes in the operon. We extend these results by studying single-site synonymous mutations that lead to loss of function phenotypes in the relBE operon upon introduction of rarer codons. Thus, in their operonic context, genes are likely to be more sensitive to both synonymous and nonsynonymous point mutations than inferred previously.

Sequence dependencies and biophysical features both govern cleavage of diverse cut-sites by HIV protease

The infectivity of HIV-1 requires its protease (PR) cleave multiple cut-sites with low sequence similarity. The diversity of cleavage sites has made it challenging to investigate the underlying sequence properties that determine binding and turnover of substrates by PR. We engineered a mutational scanning approach utilizing yeast display, flow cytometry, and deep sequencing to systematically measure the impacts of all individual amino acid changes at 12 positions in three different cut-sites (MA/CA, NC/p1, and p1/p6). The resulting fitness landscapes revealed common physical features that underlie cutting of all three cut-sites at the amino acid positions closest to the scissile bond. In contrast, positions more than two amino acids away from the scissile bond exhibited a strong dependence on the sequence background of the rest of the cut-site. We observed multiple amino acid changes in cut-sites that led to faster cleavage rates, including a preference for negative charge five and six amino acids away from the scissile bond at locations where the surface of protease is positively charged. Analysis of individual cut sites using full-length matrix-capsid proteins indicate that long-distance sequence context can contribute to cutting efficiency such that analyses of peptides or shorter engineered constructs including those in this work should be considered carefully. This work provides a framework for understanding how diverse substrates interact with HIV-1 PR and can be extended to investigate other viral PRs with similar properties.

Comprehensive fitness landscape of SARS-CoV-2 Mpro reveals insights into viral resistance mechanisms

With the continual evolution of new strains of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that are more virulent, transmissible, and able to evade current vaccines, there is an urgent need for effective anti-viral drugs. The SARS-CoV-2 main protease (Mpro) is a leading target for drug design due to its conserved and indispensable role in the viral life cycle. Drugs targeting Mpro appear promising but will elicit selection pressure for resistance. To understand resistance potential in Mpro, we performed a comprehensive mutational scan of the protease that analyzed the function of all possible single amino acid changes. We developed three separate high throughput assays of Mpro function in yeast, based on either the ability of Mpro variants to cleave at a defined cut-site or on the toxicity of their expression to yeast. We used deep sequencing to quantify the functional effects of each variant in each screen. The protein fitness landscapes from all three screens were strongly correlated, indicating that they captured the biophysical properties critical to Mpro function. The fitness landscapes revealed a non-active site location on the surface that is extremely sensitive to mutation, making it a favorable location to target with inhibitors. In addition, we found a network of critical amino acids that physically bridge the two active sites of the Mpro dimer. The clinical variants of Mpro were predominantly functional in our screens, indicating that Mpro is under strong selection pressure in the human population. Our results provide predictions of mutations that will be readily accessible to Mpro evolution and that are likely to contribute to drug resistance. This complete mutational guide of Mpro can be used in the design of inhibitors with reduced potential of evolving viral resistance.

The Adaptive Potential of the Middle Domain of Yeast Hsp90

The distribution of fitness effects (DFEs) of new mutations across different environments quantifies the potential for adaptation in a given environment and its cost in others. So far, results regarding the cost of adaptation across environments have been mixed, and most studies have sampled random mutations across different genes. Here, we quantify systematically how costs of adaptation vary along a large stretch of protein sequence by studying the distribution of fitness effects of the same ≈2,300 amino-acid changing mutations obtained from deep mutational scanning of 119 amino acids in the middle domain of the heat shock protein Hsp90 in five environments. This region is known to be important for client binding, stabilization of the Hsp90 dimer, stabilization of the N-terminal-Middle and Middle-C-terminal interdomains, and regulation of ATPase–chaperone activity. Interestingly, we find that fitness correlates well across diverse stressful environments, with the exception of one environment, diamide. Consistent with this result, we find little cost of adaptation; on average only one in seven beneficial mutations is deleterious in another environment. We identify a hotspot of beneficial mutations in a region of the protein that is located within an allosteric center. The identified protein regions that are enriched in beneficial, deleterious, and costly mutations coincide with residues that are involved in the stabilization of Hsp90 interdomains and stabilization of client-binding interfaces, or residues that are involved in ATPase–chaperone activity of Hsp90. Thus, our study yields information regarding the role and adaptive potential of a protein sequence that complements and extends known structural information.

Structurally distributed surface sites tune allosteric regulation

Our ability to rationally optimize allosteric regulation is limited by incomplete knowledge of the mutations that tune allostery. Are these mutations few or abundant, structurally localized or distributed? To examine this, we conducted saturation mutagenesis of a synthetic allosteric switch in which Dihydrofolate reductase (DHFR) is regulated by a blue-light sensitive LOV2 domain. Using a high-throughput assay wherein DHFR catalytic activity is coupled to E. coli growth, we assessed the impact of 1548 viable DHFR single mutations on allostery. Despite most mutations being deleterious to activity, fewer than 5% of mutations had a statistically significant influence on allostery. Most allostery disrupting mutations were proximal to the LOV2 insertion site. In contrast, allostery enhancing mutations were structurally distributed and enriched on the protein surface. Combining several allostery enhancing mutations yielded near-additive improvements to dynamic range. Our results indicate a path toward optimizing allosteric function through variation at surface sites.

Many proteins exhibit a property called ‘allostery’. In allostery, an input signal at a specific site of a protein – such as a molecule binding, or the protein absorbing a photon of light – leads to a change in output at another site far away. For example, the protein might catalyze a chemical reaction faster or bind to another molecule more tightly in the presence of the input signal. This protein ‘remote control’ allows cells to sense and respond to changes in their environment. An ability to rapidly engineer new allosteric mechanisms into proteins is much sought after because this would provide an approach for building biosensors and other useful tools. One common approach to engineering new allosteric regulation is to combine a ‘sensor’ or input region from one protein with an ‘output’ region or domain from another.

When researchers engineer allostery using this approach of combining input and output domains from different proteins, the difference in the output when the input is ‘on’ versus ‘off’ is often small, a situation called ‘modest allostery’. McCormick et al. wanted to know how to optimize this domain combination approach to increase the difference in output between the ‘on’ and ‘off’ states.

More specifically, McCormick et al. wanted to find out whether swapping out or mutating specific amino acids (each of the individual building blocks that make up a protein) enhances or disrupts allostery. They also wanted to know if there are many possible mutations that change the effectiveness of allostery, or if this property is controlled by just a few amino acids. Finally, McCormick et al. questioned where in a protein most of these allostery-tuning mutations were located.

To answer these questions, McCormick et al. engineered a new allosteric protein by inserting a light-sensing domain (input) into a protein involved in metabolism (a metabolic enzyme that produces a biomolecule called a tetrahydrofolate) to yield a light-controlled enzyme. Next, they introduced mutations into both the ‘input’ and ‘output’ domains to see where they had a greater effect on allostery.

After filtering out mutations that destroyed the function of the output domain, McCormick et al. found that only about 5% of mutations to the ‘output’ domain altered the allosteric response of their engineered enzyme. In fact, most mutations that disrupted allostery were found near the site where the ‘input’ domain was inserted, while mutations that enhanced allostery were sprinkled throughout the enzyme, often on its protein surface. This was surprising in light of the commonly-held assumption that mutations on protein surfaces have little impact on the activity of the ‘output’ domain. Overall, the effect of individual mutations on allostery was small, but McCormick et al. found that these mutations can sometimes be combined to yield larger effects.

McCormick et al.’s results suggest a new approach for optimizing engineered allosteric proteins: by introducing mutations on the protein surface. It also opens up new questions: mechanically, how do surface sites affect allostery? In the future, it will be important to characterize how combinations of mutations can optimize allosteric regulation, and to determine what evolutionary trajectories to high performance allosteric ‘switches’ look like.

Avoidance of protein unfolding constrains protein stability in long-term evolution

Every amino acid residue can influence a protein's overall stability, making stability highly susceptible to change throughout evolution. We consider the distribution of protein stabilities evolutionarily permittable under two previously reported protein fitness functions: flux dynamics and misfolding avoidance. We develop an evolutionary dynamics theory and find that it agrees better with an extensive protein stability data set for dihydrofolate reductase orthologs under the misfolding avoidance fitness function rather than the flux dynamics fitness function. Further investigation with ribonuclease H data demonstrates that not any misfolded state is avoided; rather, it is only the unfolded state. At the end, we discuss how our work pertains to the universal protein abundance-evolutionary rate correlation seen across organisms' proteomes. We derive a closed-form expression relating protein abundance to evolutionary rate that captures Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens experimental trends without fitted parameters.

Understanding the Origins of Loss of Protein Function by Analyzing the Effects of Thousands of Variants on Activity and Abundance

Understanding and predicting how amino acid substitutions affect proteins are keys to our basic understanding of protein function and evolution. Amino acid changes may affect protein function in a number of ways including direct perturbations of activity or indirect effects on protein folding and stability. We have analyzed 6,749 experimentally determined variant effects from multiplexed assays on abundance and activity in two proteins (NUDT15 and PTEN) to quantify these effects and find that a third of the variants cause loss of function, and about half of loss-of-function variants also have low cellular abundance. We analyze the structural and mechanistic origins of loss of function and use the experimental data to find residues important for enzymatic activity. We performed computational analyses of protein stability and evolutionary conservation and show how we may predict positions where variants cause loss of activity or abundance. In this way, our results link thermodynamic stability and evolutionary conservation to experimental studies of different properties of protein fitness landscapes.

Mutations in the Hsp90 N Domain Identify a Site that Controls Dimer Opening and Expand Human Hsp90α Function in Yeast

Hsp90 is a highly conserved molecular chaperone important for the activity of many client proteins. Hsp90 has an N-terminal ATPase domain (N), a middle domain (M) that interacts with clients and a C-terminal dimerization domain (C). "Closing" of dimers around clients is regulated by ATP binding, co-chaperones, and post-translational modifications. ATP hydrolysis coincides with release of mature client and resetting the reaction cycle. Humans have two Hsp90s: hHsp90α and hHsp90β. Although 85% identical, hHsp90β supports Hsp90 function in yeast much better than hHsp90α. Determining the basis of this difference would provide important insight into functional specificity of seemingly redundant Hsp90s, and the evolution of eukaryotic Hsp90 systems and clientele. Here, we found host co-chaperones Sba1, Cpr6 and Cpr7 inhibited hHsp90α function in yeast, and we identified mutations clustering in the N domain that considerably improved hHsp90α function in yeast. The strongest of these rescuer mutations accelerated nucleotide-dependent lid closing, N-M domain docking, and ATPase. It also disrupted binding to Sba1, which prolongs the closed state, and promoted N-M undocking and lid opening. Our data suggest the rescuer mutations improve function of hHsp90α in yeast by accelerating return to the open state. Our findings imply hHsp90α occupies the closed state too long to function effectively in yeast, and define an evolutionarily conserved region of the N domain involved in resetting the Hsp90 reaction cycle.

Altered expression of a quality control protease in E. coli reshapes the in vivo mutational landscape of a model enzyme

Protein mutational landscapes are shaped by the cellular environment, but key factors and their quantitative effects are often unknown. Here we show that Lon, a quality control protease naturally absent in common E. coli expression strains, drastically reshapes the mutational landscape of the metabolic enzyme dihydrofolate reductase (DHFR). Selection under conditions that resolve highly active mutants reveals that 23.3% of all single point mutations in DHFR are advantageous in the absence of Lon, but advantageous mutations are largely suppressed when Lon is reintroduced. Protein stability measurements demonstrate extensive activity-stability tradeoffs for the advantageous mutants and provide a mechanistic explanation for Lon’s widespread impact. Our findings suggest possibilities for tuning mutational landscapes by modulating the cellular environment, with implications for protein design and combatting antibiotic resistance.

Variant effect predictions capture some aspects of deep mutational scanning experiments

BACKGROUND

Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants - missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs.

RESULTS

On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions.

CONCLUSIONS

DMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization.

Comprehensive fitness maps of Hsp90 show widespread environmental dependence

Gene-environment interactions have long been theorized to influence molecular evolution. However, the environmental dependence of most mutations remains unknown. Using deep mutational scanning, we engineered yeast with all 44,604 single codon changes encoding 14,160 amino acid variants in Hsp90 and quantified growth effects under standard conditions and under five stress conditions. To our knowledge, these are the largest determined comprehensive fitness maps of point mutants. The growth of many variants differed between conditions, indicating that environment can have a large impact on Hsp90 evolution. Multiple variants provided growth advantages under individual conditions; however, these variants tended to exhibit growth defects in other environments. The diversity of Hsp90 sequences observed in extant eukaryotes preferentially contains variants that supported robust growth under all tested conditions. Rather than favoring substitutions in individual conditions, the long-term selective pressure on Hsp90 may have been that of fluctuating environments, leading to robustness under a variety of conditions.

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

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

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

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

Changes in gene expression predictably shift and switch genetic interactions

Non-additive interactions between mutations occur extensively and also change across conditions, making genetic prediction a difficult challenge. To better understand the plasticity of genetic interactions (epistasis), we combine mutations in a single protein performing a single function (a transcriptional repressor inhibiting a target gene). Even in this minimal system, genetic interactions switch from positive (suppressive) to negative (enhancing) as the expression of the gene changes. These seemingly complicated changes can be predicted using a mathematical model that propagates the effects of mutations on protein folding to the cellular phenotype. More generally, changes in gene expression should be expected to alter the effects of mutations and how they interact whenever the relationship between expression and a phenotype is nonlinear, which is the case for most genes. These results have important implications for understanding genotype-phenotype maps and illustrate how changes in genetic interactions can often—but not always—be predicted by hierarchical mechanistic models.

Non-additive genetic interactions are plastic and can complicate genetic prediction. Here, using deep mutagenesis of the lambda repressor, Li et al. reveal that changes in gene expression can alter the strength and direction of genetic interactions between mutations in many genes and develop mathematical models for predicting them.

Constrained Mutational Sampling of Amino Acids in HIV-1 Protease Evolution

The evolution of HIV-1 protein sequences should be governed by a combination of factors including nucleotide mutational probabilities, the genetic code, and fitness. The impact of these factors on protein sequence evolution is interdependent, making it challenging to infer the individual contribution of each factor from phylogenetic analyses alone. We investigated the protein sequence evolution of HIV-1 by determining an experimental fitness landscape of all individual amino acid changes in protease. We compared our experimental results to the frequency of protease variants in a publicly available data set of 32,163 sequenced isolates from drug-naïve individuals. The most common amino acids in sequenced isolates supported robust experimental fitness, indicating that the experimental fitness landscape captured key features of selection acting on protease during viral infections of hosts. Amino acid changes requiring multiple mutations from the likely ancestor were slightly less likely to support robust experimental fitness than single mutations, consistent with the genetic code favoring chemically conservative amino acid changes. Amino acids that were common in sequenced isolates were predominantly accessible by single mutations from the likely protease ancestor. Multiple mutations commonly observed in isolates were accessible by mutational walks with highly fit single mutation intermediates. Our results indicate that the prevalence of multiple-base mutations in HIV-1 protease is strongly influenced by mutational sampling.

Power law fitness landscapes and their ability to predict fitness

Whether or not evolution by natural selection is predictable depends on the existence of general patterns shaping the way mutations interact with the genetic background. This interaction, also known as epistasis, has been observed during adaptation (macroscopic epistasis) and in individual mutations (microscopic epistasis). Interestingly, a consistent negative correlation between the fitness effect of beneficial mutations and background fitness (known as diminishing returns epistasis) has been observed across different species and conditions. We tested whether the adaptation pattern of an additional species, Schizosaccharomyces pombe, followed the same trend. We used strains that differed by the presence of large karyotype differences and observed the same pattern of fitness convergence. Using these data along with published datasets, we measured the ability of different models to describe adaptation rates. We found that a phenotype-fitness landscape shaped like a power law is able to correctly predict adaptation dynamics in a variety of species and conditions. Furthermore we show that this model can provide a link between the observed macroscopic and microscopic epistasis. It may be very useful in the development of algorithms able to predict the adaptation of microorganisms from measures of the current phenotypes. Overall, our results suggest that even though adaptation quickly slows down, populations adapting to lab conditions may be quite far from a fitness peak.

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

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

Pervasive contingency and entrenchment in a billion years of Hsp90 evolution

Interactions among mutations within a protein have the potential to make molecular evolution contingent and irreversible, but the extent to which epistasis actually shaped historical evolutionary trajectories is unclear. To address this question, we experimentally measured how the fitness effects of historical sequence substitutions changed during the billion-year evolutionary history of the heat shock protein 90 (Hsp90) ATPase domain beginning from a deep eukaryotic ancestor to modern Saccharomyces cerevisiae We found a pervasive influence of epistasis. Of 98 derived amino acid states that evolved along this lineage, about half compromise fitness when introduced into the reconstructed ancestral Hsp90. And the vast majority of ancestral states reduce fitness when introduced into the extant S. cerevisiae Hsp90. Overall, more than 75% of historical substitutions were contingent on permissive substitutions that rendered the derived state nondeleterious, became entrenched by subsequent restrictive substitutions that made the ancestral state deleterious, or both. This epistasis was primarily caused by specific interactions among sites rather than a general effect on the protein's tolerance to mutation. Our results show that epistasis continually opened and closed windows of mutational opportunity over evolutionary timescales, producing histories and biological states that reflect the transient internal constraints imposed by the protein's fleeting sequence states.

Experimental Determination and Prediction of the Fitness Effects of Random Point Mutations in the Biosynthetic Enzyme HisA

The distribution of fitness effects of mutations is a factor of fundamental importance in evolutionary biology. We determined the distribution of fitness effects of 510 mutants that each carried between 1 and 10 mutations (synonymous and nonsynonymous) in the hisA gene, encoding an essential enzyme in the l-histidine biosynthesis pathway of Salmonella enterica. For the full set of mutants, the distribution was bimodal with many apparently neutral mutations and many lethal mutations. For a subset of 81 single, nonsynonymous mutants most mutations appeared neutral at high expression levels, whereas at low expression levels only a few mutations were neutral. Furthermore, we examined how the magnitude of the observed fitness effects was correlated to several measures of biophysical properties and phylogenetic conservation.We conclude that for HisA: (i) The effect of mutations can be masked by high expression levels, such that mutations that are deleterious to the function of the protein can still be neutral with regard to organism fitness if the protein is expressed at a sufficiently high level; (ii) the shape of the fitness distribution is dependent on the extent to which the protein is rate-limiting for growth; (iii) negative epistatic interactions, on an average, amplified the combined effect of nonsynonymous mutations; and (iv) no single sequence-based predictor could confidently predict the fitness effects of mutations in HisA, but a combination of multiple predictors could predict the effect with a SD of 0.04 resulting in 80% of the mutations predicted within 12% of their observed selection coefficients.

Synonymous Mutations at the Beginning of the Influenza A Virus Hemagglutinin Gene Impact Experimental Fitness

The fitness effects of synonymous mutations can provide insights into biological and evolutionary mechanisms. We analyzed the experimental fitness effects of all single-nucleotide mutations, including synonymous substitutions, at the beginning of the influenza A virus hemagglutinin (HA) gene. Many synonymous substitutions were deleterious both in bulk competition and for individually isolated clones. Investigating protein and RNA levels of a subset of individually expressed HA variants revealed that multiple biochemical properties contribute to the observed experimental fitness effects. Our results indicate that a structural element in the HA segment viral RNA may influence fitness. Examination of naturally evolved sequences in human hosts indicates a preference for the unfolded state of this structural element compared to that found in swine hosts. Our overall results reveal that synonymous mutations may have greater fitness consequences than indicated by simple models of sequence conservation, and we discuss the implications of this finding for commonly used evolutionary tests and analyses.

A statistical framework for analyzing deep mutational scanning data

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

No Evidence That Protein Noise-Induced Epigenetic Epistasis Constrains Gene Expression Evolution

Changes in gene expression can affect phenotypes and therefore both its level and stochastic variability are frequently under selection. It has recently been proposed that epistatic interactions influence gene expression evolution: gene pairs where simultaneous knockout is more deleterious than expected should evolve reduced expression noise to avoid concurrent low expression of both proteins. In apparent support, yeast genes with many epistatic partners have low expression variation both among isogenic individuals and between species. However, the specific predictions and basic assumptions of this verbal model remain untested. Using bioinformatics analysis, we first demonstrate that the model's predictions are unsupported by available large-scale data. Based on quantitative biochemical modeling, we then show that epistasis between expression reductions (epigenetic epistasis) is not expected to aggravate the fitness cost of stochastic expression, which is in sharp contrast to the verbal argument. This nonintuitive result can be readily explained by the typical diminishing return of fitness on gene activity and by the fact that expression noise not only decreases but also increases the abundance of proteins. Overall, we conclude that stochastic variation in epistatic partners is unlikely to drive noise minimization or constrain gene expression divergence on a genomic scale.

An Hsp90 co-chaperone protein in yeast is functionally replaced by site-specific posttranslational modification in humans

Heat shock protein 90 (Hsp90) is an essential eukaryotic molecular chaperone. To properly chaperone its clientele, Hsp90 proceeds through an ATP-dependent conformational cycle influenced by posttranslational modifications (PTMs) and assisted by a number of co-chaperone proteins. Although Hsp90 conformational changes in solution have been well-studied, regulation of these complex dynamics in cells remains unclear. Phosphorylation of human Hsp90α at the highly conserved tyrosine 627 has previously been reported to reduce client interaction and Aha1 binding. Here we report that these effects are due to a long-range conformational impact inhibiting Hsp90α N-domain dimerization and involving a region of the middle domain/carboxy-terminal domain interface previously suggested to be a substrate binding site. Although Y627 is not phosphorylated in yeast, we demonstrate that the non-conserved yeast co-chaperone, Hch1, similarly affects yeast Hsp90 (Hsp82) conformation and function, raising the possibility that appearance of this PTM in higher eukaryotes represents an evolutionary substitution for HCH1.

Optimization of lag phase shapes the evolution of a bacterial enzyme

Mutations provide the variation that drives evolution, yet their effects on fitness remain poorly understood. Here we explore how mutations in the essential enzyme adenylate kinase (Adk) of Escherichia coli affect multiple phases of population growth. We introduce a biophysical fitness landscape for these phases, showing how they depend on molecular and cellular properties of Adk. We find that Adk catalytic capacity in the cell (the product of activity and abundance) is the major determinant of mutational fitness effects. We show that bacterial lag times are at a well-defined optimum with respect to Adk's catalytic capacity, while exponential growth rates are only weakly affected by variation in Adk. Direct pairwise competitions between strains show how environmental conditions modulate the outcome of a competition where growth rates and lag times have a tradeoff, shedding light on the multidimensional nature of fitness and its importance in the evolutionary optimization of enzymes.

Estimating the prevalence of functional exonic splice regulatory information

In addition to coding information, human exons contain sequences necessary for correct splicing. These elements are known to be under purifying selection and their disruption can cause disease. However, the density of functional exonic splicing information remains profoundly uncertain. Several groups have experimentally investigated how mutations at different exonic positions affect splicing. They have found splice information to be distributed widely in exons, with one estimate putting the proportion of splicing-relevant nucleotides at >90%. These results suggest that splicing could place a major pressure on exon evolution. However, analyses of sequence conservation have concluded that the need to preserve splice regulatory signals only slightly constrains exon evolution, with a resulting decrease in the average human rate of synonymous evolution of only 1–4%. Why do these two lines of research come to such different conclusions? Among other reasons, we suggest that the methods are measuring different things: one assays the density of sites that affect splicing, the other the density of sites whose effects on splicing are visible to selection. In addition, the experimental methods typically consider short exons, thereby enriching for nucleotides close to the splice junction, such sites being enriched for splice-control elements. By contrast, in part owing to correction for nucleotide composition biases and to the assumption that constraint only operates on exon ends, the conservation-based methods can be overly conservative.

Correlation of fitness landscapes from three orthologous TIM barrels originates from sequence and structure constraints

Sequence divergence of orthologous proteins enables adaptation to environmental stresses and promotes evolution of novel functions. Limits on evolution imposed by constraints on sequence and structure were explored using a model TIM barrel protein, indole-3-glycerol phosphate synthase (IGPS). Fitness effects of point mutations in three phylogenetically divergent IGPS proteins during adaptation to temperature stress were probed by auxotrophic complementation of yeast with prokaryotic, thermophilic IGPS. Analysis of beneficial mutations pointed to an unexpected, long-range allosteric pathway towards the active site of the protein. Significant correlations between the fitness landscapes of distant orthologues implicate both sequence and structure as primary forces in defining the TIM barrel fitness landscape and suggest that fitness landscapes can be translocated in sequence space. Exploration of fitness landscapes in the context of a protein fold provides a strategy for elucidating the sequence-structure-fitness relationships in other common motifs.

The TIM barrel fold is an evolutionarily conserved motif found in proteins with a variety of enzymatic functions. Here the authors explore the fitness landscape of the TIM barrel protein IGPS and uncover evolutionary constraints on both sequence and structure, accompanied by long range allosteric interactions.

Saturation Mutagenesis of the HIV-1 Envelope CD4 Binding Loop Reveals Residues Controlling Distinct Trimer Conformations

The conformation of HIV-1 envelope (Env) glycoprotein trimers is key in ensuring protection against waves of neutralizing antibodies generated during infection, while maintaining sufficient exposure of the CD4 binding site (CD4bs) for viral entry. The CD4 binding loop on Env is an early contact site for CD4 while penetration of a proximal cavity by CD4 triggers Env conformational changes for entry. The role of residues in the CD4 binding loop in regulating the conformation of the trimer and trimer association domain (TAD) was investigated using a novel saturation mutagenesis approach. Single mutations identified, resulted in distinct trimer conformations affecting CD4bs exposure, the glycan shield and the TAD across diverse HIV-1 clades. Importantly, mutations that improve access to the CD4bs without exposing the immunodominant V3 loop were identified. The different trimer conformations identified will affect the specificity and breadth of nabs elicited in vivo and are important to consider in design of Env immunogens for vaccines.

Spike proteins on the surface of HIV virus particles bind to CD4 receptors on the surface of immune cells and trigger infection. The immune system in an infected person attacks the virus spikes by producing antibodies that bind and neutralize them. To combat this immune attack, HIV continually alters the structure of the spike and thus escapes host antibodies. However, this process must still preserve sites on the spike that bind CD4 receptors for infection. Here, we investigated how the spike regulates its structure. We used a systematic approach to investigate every possible mutation covering a region of the spike critical for binding the CD4 receptor and controlling overall structure. We identified different sites and mechanisms that control the spike structure for diverse HIV-1 strains and impact the exposure of the binding site for CD4 along with targets for neutralizing antibodies. Our observations will help guide the design of spike structures for vaccines that induce neutralizing antibodies effective against different HIV-1 strains across the globe.

Modulating the function of a β-1,3-glucanosyltransferase to that of an endo-β-1,3-glucanase by structure-based protein engineering

A glycoside hydrolase (GH) family 17 β-1,3-glucanosyltransferase (RmBgt17A) from Rhizomucor miehei CAU432 (CGMCC No. 4967) shared very low sequence homology (∼20 % identity) with that of other β-1,3-glucanases,despite their similar structural folds. Structural comparison and sequence alignment between RmBgt17A and GH family 17 β-1,3-glucanases suggested important roles for three residues (Tyr102, Trp157, and Glu158) located in the substrate-binding cleft of RmBgt17A in transglycosylation activity. A series of site-directed mutagenesis studies indicated that a single Glu-to-Ala mutation (E158A) modulates the function of RmBgt17A to that of a β-1,3-glucanase. Mutant E158A exhibited high hydrolytic activity (39.95 U/mg) toward reduced laminarin, 348.5-fold higher than the wild type. Optimal pH and temperature of the purified RmBgt17A-E158A were 4.5 and 55 °C, respectively. TLC analysis suggested that RmBgt17A-E158A is an endo-β-1,3-glucanase. Our study provides novel insight into protein engineering of the substrate-binding cleft of glycoside hydrolases to modulate the function of transglycosylation and hydrolysis.

Deep Sequencing of Random Mutant Libraries Reveals the Active Site of the Narrow Specificity CphA Metallo-β-Lactamase is Fragile to Mutations

CphA is a Zn²⁺-dependent metallo-β-lactamase that efficiently hydrolyzes only carbapenem antibiotics. To understand the sequence requirements for CphA function, single codon random mutant libraries were constructed for residues in and near the active site and mutants were selected for E. coli growth on increasing concentrations of imipenem, a carbapenem antibiotic. At high concentrations of imipenem that select for phenotypically wild-type mutants, the active-site residues exhibit stringent sequence requirements in that nearly all residues in positions that contact zinc, the substrate, or the catalytic water do not tolerate amino acid substitutions. In addition, at high imipenem concentrations a number of residues that do not directly contact zinc or substrate are also essential and do not tolerate substitutions. Biochemical analysis confirmed that amino acid substitutions at essential positions decreased the stability or catalytic activity of the CphA enzyme. Therefore, the CphA active - site is fragile to substitutions, suggesting active-site residues are optimized for imipenem hydrolysis. These results also suggest that resistance to inhibitors targeted to the CphA active site would be slow to develop because of the strong sequence constraints on function.

Molecular Determinants of Mutant Phenotypes, Inferred from Saturation Mutagenesis Data

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

A Statistical Guide to the Design of Deep Mutational Scanning Experiments

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

Selection on metabolic pathway function in the presence of mutation-selection-drift balance leads to rate-limiting steps that are not evolutionarily stable

Background

While commonly assumed in the biochemistry community that the control of metabolic pathways is thought to be critical to cellular function, it is unclear if metabolic pathways generally have evolutionarily stable rate limiting (flux controlling) steps.

Results

A set of evolutionary simulations using a kinetic model of a metabolic pathway was performed under different conditions to evaluate the evolutionary stability of rate limiting steps. Simulations used combinations of selection for steady state flux, selection against the cost of molecular biosynthesis, and selection against the accumulation of high concentrations of a deleterious intermediate. Two mutational regimes were used, one with mutations that on average were neutral to molecular phenotype and a second with a preponderance of activity-destroying mutations. The evolutionary stability of rate limiting steps was low in all simulations with non-neutral mutational processes. Clustering of parameter co-evolution showed divergent inter-molecular evolutionary patterns under different evolutionary regimes.

Conclusions

This study provides a null model for pathway evolution when compensatory processes dominate with potential applications to predicting pathway functional change. This result also suggests a possible mechanism in which studies in statistical genetics that aim to associate a genotype to a phenotype assuming independent action of variants may be mis-specified through a mis-characterization of the link between individual gene function and pathway function. A better understanding of the genotype-phenotype map has potential applications in differentiating between compensatory changes and directional selection on pathways as well as detecting SNPs and fixed differences that might have phenotypic effects.

Reviewers

This article was reviewed by Arne Elofsson, David Ardell, and Shamil Sunyaev.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-016-0133-6) contains supplementary material, which is available to authorized users.

Determination of ubiquitin fitness landscapes under different chemical stresses in a classroom setting

Ubiquitin is essential for eukaryotic life and varies in only 3 amino acid positions between yeast and humans. However, recent deep sequencing studies indicate that ubiquitin is highly tolerant to single mutations. We hypothesized that this tolerance would be reduced by chemically induced physiologic perturbations. To test this hypothesis, a class of first year UCSF graduate students employed deep mutational scanning to determine the fitness landscape of all possible single residue mutations in the presence of five different small molecule perturbations. These perturbations uncover 'shared sensitized positions' localized to areas around the hydrophobic patch and the C-terminus. In addition, we identified perturbation specific effects such as a sensitization of His68 in HU and a tolerance to mutation at Lys63 in DTT. Our data show how chemical stresses can reduce buffering effects in the ubiquitin proteasome system. Finally, this study demonstrates the potential of lab-based interdisciplinary graduate curriculum.

DOI:http://dx.doi.org/10.7554/eLife.15802.001

The ability of an organism to grow and reproduce, that is, it’s “fitness”, is determined by how its genes interact with the environment. Yeast is a model organism in which researchers can control the exact mutations present in the yeast’s genes (its genotype) and the conditions in which the yeast cells live (their environment). This allows researchers to measure how a yeast cell’s genotype and environment affect its fitness.

Ubiquitin is a protein that many organisms depend on to manage cell stress by acting as a tag that targets other proteins for degradation. Essential proteins such as ubiquitin often remain unchanged by mutation over long periods of time. As a result, these proteins evolve very slowly. Like all proteins, ubiquitin is built from a chain of amino acid molecules linked together, and the ubiquitin proteins of yeast and humans are made of almost identical sequences of amino acids.

Although ubiquitin has barely changed its sequence over evolution, previous studies have shown that – under normal growth conditions in the laboratory – most amino acids in ubiquitin can be mutated without any loss of cell fitness. This led Mavor et al. to hypothesize that treating the yeast cells with chemicals that cause cell stress might lead to amino acids in ubiquitin becoming more sensitive to mutation.

To test this idea, a class of graduate students at the University of California, San Francisco grew yeast cells with different ubiquitin mutations together, and with different chemicals that induce cell stress, and measured their growth rates. Sequencing the ubiquitin gene in the thousands of tested yeast cells revealed that three of the chemicals cause a shared set of amino acids in ubiquitin to become more sensitive to mutation.

This result suggests that these amino acids are important for the stress response, possibly by altering the ability of yeast cells to target certain proteins for degradation. Conversely, another chemical causes yeast to become more tolerant to changes in the ubiquitin sequence. The experiments also link changes in particular amino acids in ubiquitin to specific stress responses.

Mavor et al. show that many of ubquitin’s amino acids are sensitive to mutation under different stress conditions, while others can be mutated to form different amino acids without effecting fitness. By testing the effects of other chemicals, future experiments could further characterize how the yeast’s genotype and environment interact.

DOI:http://dx.doi.org/10.7554/eLife.15802.002

Systematic Mutant Analyses Elucidate General and Client-Specific Aspects of Hsp90 Function

To probe the mechanism of the Hsp90 chaperone that is required for the maturation of many signaling proteins in eukaryotes, we analyzed the effects of all individual amino acid changes in the ATPase domain on yeast growth rate. The sensitivity of a position to mutation was strongly influenced by proximity to the phosphates of ATP, indicating that ATPase-driven conformational changes impose stringent physical constraints on Hsp90. To investigate how these constraints may vary for different clients, we performed biochemical analyses on a panel of Hsp90 mutants spanning the full range of observed fitness effects. We observed distinct effects of nine Hsp90 mutations on activation of v-src and glucocorticoid receptor (GR), indicating that different chaperone mechanisms can be utilized for these clients. These results provide a detailed guide for understanding Hsp90 mechanism and highlight the potential for inhibitors of Hsp90 that target a subset of clients.

Quantifying and understanding the fitness effects of protein mutations: Laboratory versus nature

The last decade has seen a growing number of experiments aimed at systematically mapping the effects of mutations in different proteins, and of attempting to correlate their biophysical and biochemical effects with organismal fitness. While insightful, systematic laboratory measurements of fitness effects present challenges and difficulties. Here, we discuss the limitations associated with such measurements, and in particular the challenge of correlating the effects of mutations at the single protein level ("protein fitness") with their effects on organismal fitness. A variety of experimental setups are used, with some measuring the direct effects on protein function and others monitoring the growth rate of a model organism carrying the protein mutants. The manners by which fitness effects are calculated and presented also vary, and the conclusions, including the derived distributions of fitness effects of mutations, vary accordingly. The comparison of the effects of mutations in the laboratory to the natural protein diversity, namely to amino acid changes that have fixed in the course of millions of years of evolution, is also debatable. The results of laboratory experiments may, therefore, be less relevant to understanding long-term inter-species variations yet insightful with regard to short-term polymorphism, for example, in the study of the effects of human SNPs.