Using Evolution to Guide Protein Engineering: The Devil IS in the Details

Engineering inhibitory repeat domains of Pin-II type proteinase inhibitors indicate their high structural-functional tolerance to mutagenesis

Plant proteinase inhibitors (PIs) are critical in defending against biotic stress. Most PIs contain an inhibitory repeat domain (IRD), which serves as the functional component, displaying a high degree of sequence and structural conservation. In this study, we examined the structural and functional resilience of IRDs using a combination of computational modeling and experimental validation. We have taken an evolution-based approach to enhance the PIs effectiveness of two previously identified Capsicum annuum IRDs, IRD4 and IRD10. Through in silico site-saturation mutagenesis of IRD4 and IRD10, we identified key sites associated with enhanced PI activity for targeted mutagenesis. Binding energy predictions for a mutant IRD library, tested against target proteases, suggested that positions R11 and N32 in IRD4 and N32 and H33 in IRD10 were promising candidates for further modification to improve inhibitory potential. Subsequent experimental validation revealed that the mutant proteins IRD4_R11K and IRD4_N32S exhibited stronger chymotrypsin inhibition than the wild-type (WT) IRD4. Similarly, the mutants IRD10_N32S and IRD10_H33 N demonstrated improved trypsin inhibition relative to the WT IRD10. These findings indicate that engineered IRD variants can tolerate structural changes while maintaining or enhancing their inhibitory activity against target proteases. Overall, this study demonstrates the potential of engineering PIs to increase their structural and functional resilience, offering new opportunities for biotechnological applications.

Identification of positions in human aldolase a that are neutral for apparent KM

According to evolutionary theory, many naturally-occurring amino acid substitutions are expected to be neutral or near-neutral, with little effect on protein structure or function. Accordingly, most changes observed in human exomes are also expected to be neutral. As such, accurate algorithms for identifying medically-relevant changes must discriminate rare, non-neutral substitutions against a background of neutral substitutions. However, due to historical biases in biochemical experiments, the data available to train and validate prediction algorithms mostly contains non-neutral substitutions, with few examples of neutral substitutions. Thus, available training sets have the opposite composition of the desired test sets. Towards improving a dataset of these critical negative controls, we have concentrated on identifying neutral positions - those positions for which most of the possible 19 amino acid substitutions have little effect on protein structure or function. Here, we used a strategy based on multiple sequence alignments to identify putative neutral positions in human aldolase A, followed by biochemical assays for 147 aldolase substitutions. Results showed that most variants had little effect on either the apparent Michaelis constant for substrate fructose-1,6-bisphosphate or its apparent cooperativity. Thus, these data are useful for training and validating prediction algorithms. In addition, we created a database of these and other biochemically characterized aldolase variants along with aldolase sequences and characteristics derived from sequence and structure analyses. This database is publicly available at https://github.com/liskinsk/Aldolase-variant-and-sequence-database.

Dynamics-based protein network features accurately discriminate neutral and rheostat positions

In some proteins, a unique class of nonconserved positions is characterized by their ability to generate diverse functional outcomes through single amino acid substitutions. Due to their ability to tune protein function, accurately identifying such "rheostat" positions is crucial for protein design, for understanding the impact of mutations observed in humans, and for predicting the evolution of pathogen drug resistance. However, identifying rheostat positions has been challenging, due-in part-to the absence of a clear structural relationship with binding sites. In this study, experimental data from our previous study of the Escherichia coli lactose repressor protein (LacI) was used to identify rheostat positions for which mutations tune in vivo EC50 for the allosteric ligand "IPTG." We next used the rheostat assignments to test the hypothesis that rheostat positions have unique dynamic features that will enable their identification. To that end, we integrated all-atom molecular dynamics simulations with perturbation residue response analysis. Results first revealed distinct dynamic behavior in IPTG-bound LacI compared with apo LacI, which was consistent with IPTG's role as an allosteric inducer. Next, we used a variety of dynamic features to build a classification model that discriminates experimentally characterized rheostat positions in LacI from positions with other types of substitution outcomes. In parallel, we built a second classifier model based on the 3D structural "static" network features of LacI. In comparative studies, the dynamic model better identified rheostat positions that were >8 Å from the binding site. In summary, our study provides insights into the dynamic characteristics of rheostat positions and suggests that models built on dynamic features may be useful for predicting the locations of rheostat positions in a wide range of proteins.

Rheostatic contributions to protein stability can obscure a position's functional role

Rheostat positions, which can be substituted with various amino acids to tune protein function across a range of outcomes, are a developing area for advancing personalized medicine and bioengineering. Current methods cannot accurately predict which proteins contain rheostat positions or their substitution outcomes. To compare the prevalence of rheostat positions in homologs, we previously investigated their occurrence in two pyruvate kinase (PYK) isozymes. Human liver PYK contained numerous rheostat positions that tuned the apparent affinity for the substrate phosphoenolpyruvate (Kapp-PEP) across a wide range. In contrast, no functional rheostat positions were identified in Zymomonas mobilis PYK (ZmPYK). Further, the set of ZmPYK substitutions included an unusually large number that lacked measurable activity. We hypothesized that the inactive substitution variants had reduced protein stability, precluding detection of Kapp-PEP tuning. Using modified buffers, robust enzymatic activity was obtained for 19 previously-inactive ZmPYK substitution variants at three positions. Surprisingly, both previously-inactive and previously-active substitution variants all had Kapp-PEP values close to wild-type. Thus, none of the three positions were functional rheostat positions, and, unlike human liver PYK, ZmPYK's Kapp-PEP remained poorly tunable by single substitutions. To directly assess effects on stability, we performed thermal denaturation experiments for all ZmPYK substitution variants. Many diminished stability, two enhanced stability, and the three positions showed different thermal sensitivity to substitution, with one position acting as a "stability rheostat." The differences between the two PYK homologs raises interesting questions about the underlying mechanism(s) that permit functional tuning by single substitutions in some proteins but not in others.

De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein-DNA binding

Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences, in vivo genomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained on in vitro TF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific, in vivo binding profiles. Conversely, deep learning models, trained on in vivo TF binding assays, effectively predict and explain genomic occupancy profiles as a function of complex regulatory sequence syntax, albeit without a clear biophysical interpretation. To reconcile these complementary models of in vitro and in vivo TF binding, we developed Affinity Distillation (AD), a method that extracts thermodynamic affinities de-novo from deep learning models of TF chromatin immunoprecipitation (ChIP) experiments by marginalizing away the influence of genomic sequence context. Applied to neural networks modeling diverse classes of yeast and mammalian TFs, AD predicts energetic impacts of sequence variation within and surrounding motifs on TF binding as measured by diverse in vitro assays with superior dynamic range and accuracy compared to motif-based methods. Furthermore, AD can accurately discern affinities of TF paralogs. Our results highlight thermodynamic affinity as a key determinant of in vivo binding, suggest that deep learning models of in vivo binding implicitly learn high-resolution affinity landscapes, and show that these affinities can be successfully distilled using AD. This new biophysical interpretation of deep learning models enables high-throughput in silico experiments to explore the influence of sequence context and variation on both intrinsic affinity and in vivo occupancy.

Engineering the kinetic stability of a β-trefoil protein by tuning its topological complexity

Kinetic stability, defined as the rate of protein unfolding, is central to determining the functional lifetime of proteins, both in nature and in wide-ranging medical and biotechnological applications. Further, high kinetic stability is generally correlated with high resistance against chemical and thermal denaturation, as well as proteolytic degradation. Despite its significance, specific mechanisms governing kinetic stability remain largely unknown, and few studies address the rational design of kinetic stability. Here, we describe a method for designing protein kinetic stability that uses protein long-range order, absolute contact order, and simulated free energy barriers of unfolding to quantitatively analyze and predict unfolding kinetics. We analyze two β-trefoil proteins: hisactophilin, a quasi-three-fold symmetric natural protein with moderate stability, and ThreeFoil, a designed three-fold symmetric protein with extremely high kinetic stability. The quantitative analysis identifies marked differences in long-range interactions across the protein hydrophobic cores that partially account for the differences in kinetic stability. Swapping the core interactions of ThreeFoil into hisactophilin increases kinetic stability with close agreement between predicted and experimentally measured unfolding rates. These results demonstrate the predictive power of readily applied measures of protein topology for altering kinetic stability and recommend core engineering as a tractable target for rationally designing kinetic stability that may be widely applicable.

Evolution of tunnels in α/β-hydrolase fold proteins—What can we learn from studying epoxide hydrolases?

The evolutionary variability of a protein’s residues is highly dependent on protein region and function. Solvent-exposed residues, excluding those at interaction interfaces, are more variable than buried residues whereas active site residues are considered to be conserved. The abovementioned rules apply also to α/β-hydrolase fold proteins—one of the oldest and the biggest superfamily of enzymes with buried active sites equipped with tunnels linking the reaction site with the exterior. We selected soluble epoxide hydrolases as representative of this family to conduct the first systematic study on the evolution of tunnels. We hypothesised that tunnels are lined by mostly conserved residues, and are equipped with a number of specific variable residues that are able to respond to evolutionary pressure. The hypothesis was confirmed, and we suggested a general and detailed way of the tunnels’ evolution analysis based on entropy values calculated for tunnels’ residues. We also found three different cases of entropy distribution among tunnel-lining residues. These observations can be applied for protein reengineering mimicking the natural evolution process. We propose a ‘perforation’ mechanism for new tunnels design via the merging of internal cavities or protein surface perforation. Based on the literature data, such a strategy of new tunnel design could significantly improve the enzyme’s performance and can be applied widely for enzymes with buried active sites.

So far very little is known about proteins tunnels evolution. The goal of this study is to evaluate the evolution of tunnels in the family of soluble epoxide hydrolases—representatives of numerous α/β-hydrolase fold enzymes. As a result two types of tunnels evolution analysis were proposed (a general and a detailed approach), as well as a ‘perforation’ mechanism which can mimic native evolution in proteins and can be used as an additional strategy for enzymes redesign.

Dynamic coupling of residues within proteins as a mechanistic foundation of many enigmatic pathogenic missense variants

Many pathogenic missense mutations are found in protein positions that are neither well-conserved nor fall in any known functional domains. Consequently, we lack any mechanistic underpinning of dysfunction caused by such mutations. We explored the disruption of allosteric dynamic coupling between these positions and the known functional sites as a possible mechanism for pathogenesis. In this study, we present an analysis of 591 pathogenic missense variants in 144 human enzymes that suggests that allosteric dynamic coupling of mutated positions with known active sites is a plausible biophysical mechanism and evidence of their functional importance. We illustrate this mechanism in a case study of β-Glucocerebrosidase (GCase) in which a vast majority of 94 sites harboring Gaucher disease-associated missense variants are located some distance away from the active site. An analysis of the conformational dynamics of GCase suggests that mutations on these distal sites cause changes in the flexibility of active site residues despite their distance, indicating a dynamic communication network throughout the protein. The disruption of the long-distance dynamic coupling caused by missense mutations may provide a plausible general mechanistic explanation for biological dysfunction and disease.

Evolution-Based Protein Engineering for Antifungal Peptide Improvement

Antimicrobial peptides (AMPs) have been considered as the alternatives to antibiotics because of their less susceptibility to microbial resistance. However, compared with conventional antibiotics they show relatively low activity and the consequent high cost and nonspecific cytotoxicity, hindering their clinical application. What’s more, engineering of AMPs is a great challenge due to the inherent complexity in their sequence, structure, and function relationships. Here, we report an evolution-based strategy for improving the antifungal activity of a nematode-sourced defensin (Cremycin-5). This strategy utilizes a sequence-activity comparison between Cremycin-5 and its functionally diverged paralogs to identify sites associated with antifungal activity for screening of enhanceable activity-modulating sites for subsequent saturation mutagenesis. Using this strategy, we identified a site (Glu-15) whose mutations with nearly all other types of amino acids resulted in a universally enhanced activity against multiple fungal species, which is thereby defined as a Universally Enhanceable Activity-Modulating Site (UEAMS). Especially, Glu15Lys even exhibited >9-fold increased fungicidal potency against several clinical isolates of Candida albicans through inhibiting cytokinesis. This mutant showed high thermal and serum stability and quicker killing kinetics than clotrimazole without detectable hemolysis. Molecular dynamic simulations suggest that the mutations at the UEAMS likely limit the conformational flexibility of a distant functional residue via allostery, enabling a better peptide–fungus interaction. Further sequence, structural, and mutational analyses of the Cremycin-5 ortholog uncover an epistatic interaction between the UEAMS and another site that may constrain its evolution. Our work lights one new road to success of engineering AMP drug leads.

Substitutions at Nonconserved Rheostat Positions Modulate Function by Rewiring Long-Range, Dynamic Interactions

Amino acid substitutions at nonconserved protein positions can have noncanonical and "long-distance" outcomes on protein function. Such outcomes might arise from changes in the internal protein communication network, which is often accompanied by changes in structural flexibility. To test this, we calculated flexibilities and dynamic coupling for positions in the linker region of the lactose repressor protein. This region contains nonconserved positions for which substitutions alter DNA-binding affinity. We first chose to study 11 substitutions at position 52. In computations, substitutions showed long-range effects on flexibilities of DNA-binding positions, and the degree of flexibility change correlated with experimentally measured changes in DNA binding. Substitutions also altered dynamic coupling to DNA-binding positions in a manner that captured other experimentally determined functional changes. Next, we broadened calculations to consider the dynamic coupling between 17 linker positions and the DNA-binding domain. Experimentally, these linker positions exhibited a wide range of substitution outcomes: Four conserved positions tolerated hardly any substitutions ("toggle"), ten nonconserved positions showed progressive changes from a range of substitutions ("rheostat"), and three nonconserved positions tolerated almost all substitutions ("neutral"). In computations with wild-type lactose repressor protein, the dynamic couplings between the DNA-binding domain and these linker positions showed varied degrees of asymmetry that correlated with the observed toggle/rheostat/neutral substitution outcomes. Thus, we propose that long-range and noncanonical substitutions outcomes at nonconserved positions arise from rewiring long-range communication among functionally important positions. Such calculations might enable predictions for substitution outcomes at a range of nonconserved positions.

MHCEpitopeEnergy, a Flexible Rosetta-Based Biotherapeutic Deimmunization Platform

As non-"self" macromolecules, biotherapeutics can trigger an immune response that can reduce drug efficacy, require patients to be taken off therapy, or even cause life-threatening reactions. To enable the flexible and facile design of protein biotherapeutics while reducing the prevalence of T-cell epitopes that drive immune recognition, we have integrated into the Rosetta protein design suite a new scoring term that allows design protocols to account for predicted or experimentally identified epitopes in the optimized objective function. This flexible scoring term can be used in any Rosetta design trajectory, can be targeted to specific regions of a protein, and can be readily extended to work with a variety of epitope predictors. By performing extensive design runs with varied design parameter choices for three case study proteins as well as a larger diverse benchmark, we show that the incorporation of this scoring term enables the effective exploration of an alternative, deimmunized sequence space to discover diverse proteins that are potentially highly deimmunized while retaining physical and chemical qualities similar to those yielded by equivalent nondeimmunizing sequence design protocols.

Recent advances in user-friendly computational tools to engineer protein function

Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein-protein and protein-nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.

Insulin therapy; a valuable legacy and its future perspective

Proteins and peptides are widely used in various areas including pharmaceutical, health, food, textile and biofuel industries. At present, pharmaceutical proteins and peptides have attracted the attention of many researchers. These types of drugs are superior to chemical drugs in many ways so that every year the number of drugs with a protein or peptide moiety is increasing. Due to high performance and low side effects, the demand for these drugs has increased year by year. The beginning of the protein and peptide drug industry dates back to 1982 with the introduction of the protein hormone insulin into the field of treatment. From this year onwards, a new number of protein and peptide drugs have entered the field of treatment every year. In this article, we focus on human therapeutic insulin. First, the history of the hormone will be introduced, then-current methods for insulin therapy will be discussed and finally, the treatments by this hormone in the future will be pointed. Reading this article would be very helpful for nano researchers, biochemists, organic chemists, material scientists and other people who are interested in soft and hard matters interfaces.

Rheostat positions: A new classification of protein positions relevant to pharmacogenomics

To achieve the full potential of pharmacogenomics, one must accurately predict the functional out comes that arise from amino acid substitutions in proteins. Classically, researchers have focused on understanding the consequences of individual substitutions. However, literature surveys have shown that most substitutions were created at evolutionarily conserved positions. Awareness of this bias leads to a shift in perspective, from considering the outcomes of individual substitutions to understanding the roles of individual protein positions. Conserved positions tend to act as "toggle" switches, with most substitutions abolishing function. However, nonconserved positions have been found equally capable of affecting protein function. Indeed, many nonconserved positions act like functional dimmer switches ("rheostat" positions): This is revealed when multiple substitutions are made at a single position. Each substitution has a different functional outcome; the set of substitutions spans arange of outcomes. Finally, some nonconserved positions appear neutral, capable of accommodating all amino acid types without modifying function. This manuscript reviews the currently-known properties of rheost at positions, with examples shown for pyruvate kinase, organic anion transporting polypeptide 1B1, the beta-lactamase inhibitory protein, and angiotensin-converting enzyme 2. Outcomes observed for rheostat positions have implications for the rational design of drug analogs and allosteric drugs. Furthermore, this new framework - comprising three types of protein positions - provides a new approach to interpreting disease and population-based databases of amino acid changes. In conclusion, although a full understanding of substitution out comes at rheostat positions poses a challenge, utilization of this new frame of reference will further advance the application of pharmacogenomics.

Identification of biochemically neutral positions in liver pyruvate kinase

Understanding how each residue position contributes to protein function has been a long-standing goal in protein science. Substitution studies have historically focused on conserved protein positions. However, substitutions of nonconserved positions can also modify function. Indeed, we recently identified nonconserved positions that have large substitution effects in human liver pyruvate kinase (hLPYK), including altered allosteric coupling. To facilitate a comparison of which characteristics determine when a nonconserved position does vs does not contribute to function, the goal of the current work was to identify neutral positions in hLPYK. However, existing hLPYK data showed that three features commonly associated with neutral positions-high sequence entropy, high surface exposure, and alanine scanning-lacked the sensitivity needed to guide experimental studies. We used multiple evolutionary patterns identified in a sequence alignment of the PYK family to identify which positions were least patterned, reasoning that these were most likely to be neutral. Nine positions were tested with a total of 117 amino acid substitutions. Although exploring all potential functions is not feasible for any protein, five parameters associated with substrate/effector affinities and allosteric coupling were measured for hLPYK variants. For each position, the aggregate functional outcomes of all variants were used to quantify a "neutrality" score. Three positions showed perfect neutral scores for all five parameters. Furthermore, the nine positions showed larger neutral scores than 17 positions located near allosteric binding sites. Thus, our strategy successfully enriched the dataset for positions with neutral and modest substitutions.

Allostery and Epistasis: Emergent Properties of Anisotropic Networks

Understanding the underlying mechanisms behind protein allostery and non-additivity of substitution outcomes (i.e., epistasis) is critical when attempting to predict the functional impact of mutations, particularly at non-conserved sites. In an effort to model these two biological properties, we extend the framework of our metric to calculate dynamic coupling between residues, the Dynamic Coupling Index (DCI) to two new metrics: (i) EpiScore, which quantifies the difference between the residue fluctuation response of a functional site when two other positions are perturbed with random Brownian kicks simultaneously versus individually to capture the degree of cooperativity of these two other positions in modulating the dynamics of the functional site and (ii) DCI_asym, which measures the degree of asymmetry between the residue fluctuation response of two sites when one or the other is perturbed with a random force. Applied to four independent systems, we successfully show that EpiScore and DCI_asym can capture important biophysical properties in dual mutant substitution outcomes. We propose that allosteric regulation and the mechanisms underlying non-additive amino acid substitution outcomes (i.e., epistasis) can be understood as emergent properties of an anisotropic network of interactions where the inclusion of the full network of interactions is critical for accurate modeling. Consequently, mutations which drive towards a new function may require a fine balance between functional site asymmetry and strength of dynamic coupling with the functional sites. These two tools will provide mechanistic insight into both understanding and predicting the outcome of dual mutations.

funtrp: identifying protein positions for variation driven functional tuning

Evaluating the impact of non-synonymous genetic variants is essential for uncovering disease associations and mechanisms of evolution. An in-depth understanding of sequence changes is also fundamental for synthetic protein design and stability assessments. However, the variant effect predictor performance gain observed in recent years has not kept up with the increased complexity of new methods. One likely reason for this might be that most approaches use similar sets of gene and protein features for modeling variant effects, often emphasizing sequence conservation. While high levels of conservation highlight residues essential for protein activity, much of the variation observable in vivo is arguably weaker in its impact, thus requiring evaluation at a higher level of resolution. Here, we describe functionNeutral/Toggle/Rheostatpredictor (funtrp), a novel computational method that categorizes protein positions based on the position-specific expected range of mutational impacts: Neutral (weak/no effects), Rheostat (function-tuning positions), or Toggle (on/off switches). We show that position types do not correlate strongly with familiar protein features such as conservation or protein disorder. We also find that position type distribution varies across different protein functions. Finally, we demonstrate that position types can improve performance of existing variant effect predictors and suggest a way forward for the development of new ones.

The Role of Conformational Dynamics and Allostery in Modulating Protein Evolution

Advances in sequencing techniques and statistical methods have made it possible not only to predict sequences of ancestral proteins but also to identify thousands of mutations in the human exome, some of which are disease associated. These developments have motivated numerous theories and raised many questions regarding the fundamental principles behind protein evolution, which have been traditionally investigated horizontally using the tip of the phylogenetic tree through comparative studies of extant proteins within a family. In this article, we review a vertical comparison of the modern and resurrected ancestral proteins. We focus mainly on the dynamical properties responsible for a protein's ability to adapt new functions in response to environmental changes. Using the Dynamic Flexibility Index and the Dynamic Coupling Index to quantify the relative flexibility and dynamic coupling at a site-specific, single-amino-acid level, we provide evidence that the migration of hinges, which are often functionally critical rigid sites, is a mechanism through which proteins can rapidly evolve. Additionally, we show that disease-associated mutations in proteins often result in flexibility changes even at positions distal from mutational sites, particularly in the modulation of active site dynamics.

Homolog comparisons further reconcile in vitro and in vivo correlations of protein activities by revealing over-looked physiological factors

To bridge biological and biochemical disciplines, the relationship between in vitro protein biochemical function and in vivo activity must be established. Such studies can (a) help determine whether properties measured in simple, dilute solutions extrapolate to the complex in vivo conditions and (b) illuminate cryptic biological factors that are new avenues for study. We have explored the in vivo-in vitro relationship for chimeras built from LacI/GalR transcription regulators. In prior studies of individual chimeras, amino acid changes that altered in vitro DNA binding affinity exhibited correlated changes in in vivo transcription repression. However, discrepancies arose when the two datasets were compared to each other: Although their DNA binding domains were identical and their in vitro binding affinities spanned the same range, their in vivo repression ranges differed by >50-fold. Here, we determined that the presence of endogenous ligand for one chimera further exacerbated the offset, but that different abilities to simultaneously bind and "loop" two DNA operators resolves the discrepancy. Indeed, results suggest that the lac operon can be looped by even weakly interacting repressor dimers. For looping-competent repressors, we measured in vitro binding to the secondary operator. Surprisingly, this was largely insensitive to amino acid changes in the repressor protein, which reflects altered specificity; this supports the emerging view that the locations of specificity determining positions can be unique to each protein homolog. In aggregate, this work illustrates how a comparative approach can enrich understanding of the in vivo-in vitro relationship and suggest unexpected avenues for future study.

RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions

Human mutations often cause amino acid changes (variants) that can alter protein function or stability. Some variants fall at protein positions that experimentally exhibit "rheostatic" mutation outcomes (different amino acid substitutions lead to a range of functional outcomes). In ongoing studies of rheostat positions, we encountered the need to aggregate experimental results from multiple variants, to describe the overall roles of individual positions. Here, we present "RheoScale" which generates quantitative scores to discriminate rheostat positions from those with "toggle" (most substitutions abolish function) or "neutral" (most substitutions have wild-type function) outcomes. RheoScale scores facilitate correlations of experimental data (such as binding affinity or stability) with structural and bioinformatic analyses. The RheoScale calculator is encoded into a Microsoft Excel workbook and an R script. Example analyses are shown for three model protein systems, including one assessed via deep mutational scanning. The RheoScale calculator quickly and efficiently provided quantitative descriptions that were in good agreement with prior qualitative observations. As an example application, scores were compared to the example proteins' structures; strong rheostat positions tended to occur in dynamic locations. In the future, RheoScale scores can be easily integrated into computational studies to facilitate improved algorithms for predicting outcomes of human variants.

Engineering substrate promiscuity in halophilic alcohol dehydrogenase (HvADH2) by in silico design

An alcohol dehydrogenase from the halophilic archaeon Haloferax volcanii (HvADH2) has been engineered by rational design to broaden its substrate scope towards the conversion of a range of aromatic substrates, including flurbiprofenol, that is an intermediate of the non-steroidal anti-inflammatory drug, flurbiprofen. Wild-type HvADH2 showed minimal activity with flurbiprofenol (11.1 mU/mg). A homology model of HvADH2 was built and docking experiments with this substrate revealed that the biphenyl rings of flurbiprofenol formed strong interactions with residues F85 and F108, preventing its optimal binding in the active site. Mutations at position 85 however did not increase activity. Site directed mutagenesis at position F108 allowed the identification of three variants showing a significant (up to 2.3-fold) enhancement of activity towards flurbiprofenol, when compared to wild-type HvADH2. Interestingly, F108G variant did not show the classic inhibition in the presence of (R)-enantiomer when tested with rac-1-phenylethanol, underling its potential in racemic resolution of secondary alcohols.

Computational predictors fail to identify amino acid substitution effects at rheostat positions

Many computational approaches exist for predicting the effects of amino acid substitutions. Here, we considered whether the protein sequence position class - rheostat or toggle - affects these predictions. The classes are defined as follows: experimentally evaluated effects of amino acid substitutions at toggle positions are binary, while rheostat positions show progressive changes. For substitutions in the LacI protein, all evaluated methods failed two key expectations: toggle neutrals were incorrectly predicted as more non-neutral than rheostat non-neutrals, while toggle and rheostat neutrals were incorrectly predicted to be different. However, toggle non-neutrals were distinct from rheostat neutrals. Since many toggle positions are conserved, and most rheostats are not, predictors appear to annotate position conservation better than mutational effect. This finding can explain the well-known observation that predictors assign disproportionate weight to conservation, as well as the field's inability to improve predictor performance. Thus, building reliable predictors requires distinguishing between rheostat and toggle positions.