How protein stability and new functions trade off

The Arabidopsis AtSWEET13 transporter discriminates sugars by selective facial and positional substrate recognition

Transporters are targeted by endogenous metabolites and exogenous molecules to reach cellular destinations, but it is generally not understood how different substrate classes exploit the same transporter's mechanism. Any disclosure of plasticity in transporter mechanism when treated with different substrates becomes critical for developing general selectivity principles in membrane transport catalysis. Using extensive molecular dynamics simulations with an enhanced sampling approach, we select the Arabidopsis sugar transporter AtSWEET13 as a model system to identify the basis for glucose versus sucrose molecular recognition and transport. Here we find that AtSWEET13 chemical selectivity originates from a conserved substrate facial selectivity demonstrated when committing alternate access, despite mono-/di-saccharides experiencing differing degrees of conformational and positional freedom throughout other stages of transport. However, substrate interactions with structural hallmarks associated with known functional annotations can help reinforce selective preferences in molecular transport.

Simultaneous enhancement of multiple functional properties using evolution-informed protein design

A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β-lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications.

Structural and Dynamic Features of Acinetobacter baumannii OXA-66 β-Lactamase Explain Its Stability and Evolution of Novel Variants

OXA-66 is a member of the OXA-51 subfamily of class D β-lactamases native to the Acinetobacter genus that includes Acinetobacter baumannii, one of the ESKAPE pathogens and a major cause of drug-resistant nosocomial infections. Although both wild type OXA-66 and OXA-51 have low catalytic activity, they are ubiquitous in the Acinetobacter genomes. OXA-51 is also remarkably thermostable. In addition, newly emerging, single and double amino acid variants show increased activity against carbapenems, indicating that the OXA-51 subfamily is growing and gaining clinical significance. In this study, we used molecular dynamics simulations, X-ray crystallography, and thermal denaturation data to examine and compare the dynamics of OXA-66 wt and its gain-of-function variants: I129L (OXA-83), L167V (OXA-82), P130Q (OXA-109), P130A, and W222L (OXA-234). Our data indicate that OXA-66 wt also has a high melting temperature, and its remarkable stability is due to an extensive and rigid hydrophobic bridge formed by a number of residues around the active site and harbored by the three loops, P, Ω, and β5-β6. Compared to the WT enzyme, the mutants exhibit higher flexibility only in the loop regions, and are more stable than other robust carbapenemases, such as OXA-23 and OXA-24/40. All the mutants show increased rotational flexibility of residues I129 and W222, which allows carbapenems to bind. Overall, our data support the hypothesis that structural features in OXA-51 and OXA-66 promote evolution of multiple highly stable variants with increased clinical relevance in A. baumannii.

Effects of selection stringency on the outcomes of directed evolution

Directed evolution makes mutant lineages compete in climbing complicated sequence-function landscapes. Given this underlying complexity it is unclear how selection stringency, a ubiquitous parameter of directed evolution, impacts the outcome. Here we approach this question in terms of the fitnesses of the candidate variants at each round and the heterogeneity of their distributions of fitness effects. We show that even if the fittest mutant is most likely to yield the fittest mutants in the next round of selection, diversification can improve outcomes by sampling a larger variety of fitness effects. We find that heterogeneity in fitness effects between variants, larger population sizes, and evolution over a greater number of rounds all encourage diversification.

Review of predicting protein stability changes upon variations

Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.

Mutational robustness and the role of buffer genes in evolvability

Organisms rely on mutations to fuel adaptive evolution. However, many mutations impose a negative effect on fitness. Cells may have therefore evolved mechanisms that affect the phenotypic effects of mutations, thus conferring mutational robustness. Specifically, so-called buffer genes are hypothesized to interact directly or indirectly with genetic variation and reduce its effect on fitness. Environmental or genetic perturbations can change the interaction between buffer genes and genetic variation, thereby unmasking the genetic variation's phenotypic effects and thus providing a source of variation for natural selection to act on. This review provides an overview of our understanding of mutational robustness and buffer genes, with the chaperone gene HSP90 as a key example. It discusses whether buffer genes merely affect standing variation or also interact with de novo mutations, how mutational robustness could influence evolution, and whether mutational robustness might be an evolved trait or rather a mere side-effect of complex genetic interactions.

Utilization of low-stability variants in protein evolutionary engineering

Evolutionary engineering involves repeated mutations and screening and is widely used to modify protein functions. However, it is important to diversify evolutionary pathways to eliminate the bias and limitations of the variants by using traditionally unselected variants. In this study, we focused on low-stability variants that are commonly excluded from evolutionary processes and tested a method that included an additional restabilization step. The esterase from the thermophilic bacterium Alicyclobacillus acidocaldarius was used as a model protein, and its activity at its optimum temperature of 65 °C was improved by evolutionary experiments using random mutations by error-prone PCR. After restabilization using low-stability variants with low-temperature (37 °C) activity, several re-stabilizing variants were obtained from a large number of variant libraries. Some of the restabilized variants achieved by removing the destabilizing mutations showed higher activity than that of the wild-type protein. This implies that low-stability variants with low-temperature activity can be re-evolved for future use. This method will enable further diversification of evolutionary pathways.

Changes in Active Site Loop Conformation Relate to the Transition toward a Novel Enzymatic Activity

Enzymatic promiscuity, the ability of enzymes to catalyze multiple, distinct chemical reactions, has been well documented and is hypothesized to be a major driver of the emergence of new enzymatic functions. Yet, the molecular mechanisms involved in the transition from one activity to another remain debated and elusive. Here, we evaluated the redesign of the active site binding cleft of lactonase SsoPox using structure-based design and combinatorial libraries. We created variants with largely improved catalytic abilities against phosphotriesters, the best ones being >1000-fold better compared to the wild-type enzyme. The observed shifts in activity specificity are large, and some variants completely lost their initial activity. The selected combinations of mutations have considerably reshaped the active site cavity via side chain changes but mostly through large rearrangements of the active site loops and changes to their conformations, as revealed by a suite of crystal structures. This suggests that a specific active site loop configuration is critical to the lactonase activity. Interestingly, analysis of high-resolution structures hints at the potential role of conformational sampling and its directionality in defining the enzyme activity profile.

Opportunities and challenges in design and optimization of protein function

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

Viral Receptor-Binding Protein Evolves New Function through Mutations That Cause Trimer Instability and Functional Heterogeneity

When proteins evolve new activity, a concomitant decrease in stability is often observed because the mutations that confer new activity can destabilize the native fold. In the conventional model of protein evolution, reduced stability is considered a purely deleterious cost of molecular innovation because unstable proteins are prone to aggregation and are sensitive to environmental stressors. However, recent work has revealed that nonnative, often unstable protein conformations play an important role in mediating evolutionary transitions, raising the question of whether instability can itself potentiate the evolution of new activity. We explored this question in a bacteriophage receptor-binding protein during host-range evolution. We studied the properties of the receptor-binding protein of bacteriophage λ before and after host-range evolution and demonstrated that the evolved protein is relatively unstable and may exist in multiple conformations with unique receptor preferences. Through a combination of structural modeling and in vitro oligomeric state analysis, we found that the instability arises from mutations that interfere with trimer formation. This study raises the intriguing possibility that protein instability might play a previously unrecognized role in mediating host-range expansions in viruses.

High temperature delays and low temperature accelerates evolution of a new protein phenotype

Since the origin of life, temperatures on earth have fluctuated both on short and long time scales. How such changes affect the rate at which Darwinian evolution can bring forth new phenotypes remains unclear. On the one hand, high temperature may accelerate phenotypic evolution because it accelerates most biological processes. On the other hand, it may slow phenotypic evolution, because proteins are usually less stable at high temperatures and therefore less evolvable. Here, to test these hypotheses experimentally, we evolved a green fluorescent protein in E. coli towards the new phenotype of yellow fluorescence at different temperatures. Yellow fluorescence evolved most slowly at high temperature and most rapidly at low temperature, in contradiction to the first hypothesis. Using high-throughput population sequencing, protein engineering, and biochemical assays, we determined that this is due to the protein-destabilizing effect of neofunctionalizing mutations. Destabilization is highly detrimental at high temperature, where neofunctionalizing mutations cannot be tolerated. Their detrimental effects can be mitigated through excess stability at low temperature, leading to accelerated adaptive evolution. By modifying protein folding stability, temperature alters the accessibility of mutational paths towards high-fitness genotypes. Our observations have broad implications for our understanding of how temperature changes affect evolutionary adaptations and innovations.

Network of epistatic interactions in an enzyme active site revealed by large-scale deep mutational scanning

Cooperative interactions between amino acids are critical for protein function. A genetic reflection of cooperativity is epistasis, which is when a change in the amino acid at one position changes the sequence requirements at another position. To assess epistasis within an enzyme active site, we utilized CTX-M β-lactamase as a model system. CTX-M hydrolyzes β-lactam antibiotics to provide antibiotic resistance, allowing a simple functional selection for rapid sorting of modified enzymes. We created all pairwise mutations across 17 active site positions in the β-lactamase enzyme and quantitated the function of variants against two β-lactam antibiotics using next-generation sequencing. Context-dependent sequence requirements were determined by comparing the antibiotic resistance function of double mutations across the CTX-M active site to their predicted function based on the constituent single mutations, revealing both positive epistasis (synergistic interactions) and negative epistasis (antagonistic interactions) between amino acid substitutions. The resulting trends demonstrate that positive epistasis is present throughout the active site, that epistasis between residues is mediated through substrate interactions, and that residues more tolerant to substitutions serve as generic compensators which are responsible for many cases of positive epistasis. Additionally, we show that a key catalytic residue (Glu166) is amenable to compensatory mutations, and we characterize one such double mutant (E166Y/N170G) that acts by an altered catalytic mechanism. These findings shed light on the unique biochemical factors that drive epistasis within an enzyme active site and will inform enzyme engineering efforts by bridging the gap between amino acid sequence and catalytic function.

Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes

The potential of engineered enzymes in industrial applications is often limited by their expression levels, thermal stability, and catalytic diversity. De novo enzyme design faces challenges due to the complexity of enzymatic catalysis. An alternative approach involves expanding natural enzyme capabilities for new substrates and parameters. Here, we introduce CoSaNN (Conformation Sampling using Neural Network), an enzyme design strategy using deep learning for structure prediction and sequence optimization. CoSaNN controls enzyme conformations to expand chemical space beyond simple mutagenesis. It employs a context-dependent approach for generating enzyme designs, considering non-linear relationships in sequence and structure space. We also developed SolvIT, a graph NN predicting protein solubility in Escherichia coli, optimizing enzyme expression selection from larger design sets. Using this method, we engineered enzymes with superior expression levels, with 54% expressed in E. coli, and increased thermal stability, with over 30% having higher Tm than the template, with no high-throughput screening. Our research underscores AI's transformative role in protein design, capturing high-order interactions and preserving allosteric mechanisms in extensively modified enzymes, and notably enhancing expression success rates. This method's ease of use and efficiency streamlines enzyme design, opening broad avenues for biotechnological applications and broadening field accessibility.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

Machine learning for functional protein design

Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.

Sequence-developability mapping of affibody and fibronectin paratopes via library-scale variant characterization

Protein developability is requisite for use in therapeutic, diagnostic, or industrial applications. Many developability assays are low throughput, which limits their utility to the later stages of protein discovery and evolution. Recent approaches enable experimental or computational assessment of many more variants, yet the breadth of applicability across protein families and developability metrics is uncertain. Here, three library-scale assays-on-yeast protease, split green fluorescent protein (GFP), and non-specific binding-were evaluated for their ability to predict two key developability outcomes (thermal stability and recombinant expression) for the small protein scaffolds affibody and fibronectin. The assays' predictive capabilities were assessed via both linear correlation and machine learning models trained on the library-scale assay data. The on-yeast protease assay is highly predictive of thermal stability for both scaffolds, and the split-GFP assay is informative of affibody thermal stability and expression. The library-scale data was used to map sequence-developability landscapes for affibody and fibronectin binding paratopes, which guides future design of variants and libraries.

Identification of the SIRT1 gene's most harmful non-synonymous SNPs and their effects on functional and structural features-an in silico analysis

Introduction

The sirtuin (Silent mating type information regulation 2 homolog)1(SIRT1) protein plays a vital role in many disorders such as diabetes, cancer, obesity, inflammation, and neurodegenerative and cardiovascular diseases. The objective of this in silico analysis of SIRT1's functional single nucleotide polymorphisms (SNPs) was to gain valuable insight into the harmful effects of non-synonymous SNPs (nsSNPs) on the protein. The objective of the study was to use bioinformatics methods to investigate the genetic variations and modifications that may have an impact on the SIRT1 gene's expression and function.

Methods

nsSNPs of SIRT1 protein were collected from the dbSNP site, from its three (3) different protein accession IDs. These were then fed to various bioinformatic tools such as SIFT, Provean, and I- Mutant to find the most deleterious ones. Functional and structural effects were examined using the HOPE server and I-Tasser. Gene interactions were predicted by STRING software. The SIFT, Provean, and I-Mutant tools detected the most deleterious three nsSNPs (rs769519031, rs778184510, and rs199983221).

Results

Out of 252 nsSNPs, SIFT analysis showed that 94 were deleterious, Provean listed 67 dangerous, and I-Mutant found 58 nsSNPs resulting in lowered stability of proteins. HOPE modelling of rs199983221 and rs769519031 suggested reduced hydrophobicity due to Ile 4Thr and Ile223Ser resulting in decreased hydrophobic interactions. In contrast, on modelling rs778184510, the mutant protein had a higher hydrophobicity than the wild type.

Conclusions

Our study reports that three nsSNPs (D357A, I223S, I4T) are the most damaging mutations of the SIRT1 gene. Mutations may result in altered protein structure and functions. Such altered protein may be the basis for various disorders. Our findings may be a crucial guide in establishing the pathogenesis of various disorders.

Hyperstable Synthetic Mini-Proteins as Effective Ligand Scaffolds

Small, single-domain protein scaffolds are compelling sources of molecular binding ligands with the potential for efficient physiological transport, modularity, and manufacturing. Yet, mini-proteins require a balance between biophysical robustness and diversity to enable new functions. We tested the developability and evolvability of millions of variants of 43 designed libraries of synthetic 40-amino acid βαββ proteins with diversified sheet, loop, or helix paratopes. We discovered a scaffold library that yielded hundreds of binders to seven targets while exhibiting high stability and soluble expression. Binder discovery yielded 6-122 nM affinities without affinity maturation and Tms averaging ≥78 °C. Broader βαββ libraries exhibited varied developability and evolvability. Sheet paratopes were the most consistently developable, and framework 1 was the most evolvable. Paratope evolvability was dependent on target, though several libraries were evolvable across many targets while exhibiting high stability and soluble expression. Select βαββ proteins are strong starting points for engineering performant binders.

Determinants of Developability and Evolvability of Synthetic Miniproteins as Ligand Scaffolds

Binding ligands empower molecular therapeutics and diagnostics. Despite an array of protein scaffolds engineered for binding, the biophysical elements that drive developability and evolvability are not fully understood. In particular, engineering novel function while maintaining biophysical integrity within the context of small, single-domain proteins is challenged by integration of the structural framework and the evolved binding site. Miniproteins present a challenge to our limits of protein engineering capability and provide advantages in physiological targeting, modularity for multi-functional constructs, and unique binding modes. Herein, we evaluate the ability of hyperstable synthetic miniproteins, originally designed for foldedness, to function as binding scaffolds. We synthesized 45 combinatorial libraries, with 109 variants, systematically varied across two topologies, each with five starting frameworks and four or five diverse, structurally distinct paratopes, to elucidate their impact on evolvability and developability. We evaluated evolvability with yeast display binding selections against four targets. High-throughput assays -stability via yeast display and soluble expression via split-GFP in E. coli - measured developability. The comprehensive, robust dataset demonstrates how protein topology, parental framework, and paratope structure and location all impact scaffold performance. A hyperstable framework and localized diversity are not sufficient for an effective scaffold, but several designs of these elements within synthetic miniproteins designed solely for stability result in scaffold libraries with effective evolvability and developability. Engineered variants were well-folded, thermally stable, and bound target with single-digit nanomolar affinity. Thus, hyperstable synthetic miniproteins can serve as precursors to developable, evolvable mini-scaffolds with unique potential for physiological transport, modularity, and binding modes.

MEnTaT: A machine-learning approach for the identification of mutations to increase protein stability

Enhancing protein thermal stability is important for biomedical and industrial applications as well as in the research laboratory. Here, we describe a simple machine-learning method which identifies amino acid substitutions that contribute to thermal stability based on comparison of the amino acid sequences of homologous proteins derived from bacteria that grow at different temperatures. A key feature of the method is that it compares the sequences based not simply on the amino acid identity, but rather on the structural and physicochemical properties of the side chain. The method accurately identified stabilizing substitutions in three well-studied systems and was validated prospectively by experimentally testing predicted stabilizing substitutions in a polyamine oxidase. In each case, the method outperformed the widely used bioinformatic consensus approach. The method can also provide insight into fundamental aspects of protein structure, for example, by identifying how many sequence positions in a given protein are relevant to temperature adaptation.

Computational design of highly efficient thermostable MHET hydrolases and dual enzyme system for PET recycling

Recently developed enzymes for the depolymerization of polyethylene terephthalate (PET) such as FAST-PETase and LCC-ICCG are inhibited by the intermediate PET product mono(2-hydroxyethyl) terephthalate (MHET). Consequently, the conversion of PET enzymatically into its constituent monomers terephthalic acid (TPA) and ethylene glycol (EG) is inefficient. In this study, a protein scaffold (1TQH) corresponding to a thermophilic carboxylesterase (Est30) was selected from the structural database and redesigned in silico. Among designs, a double variant KL-MHETase (I171K/G130L) with a similar protein melting temperature (67.58 °C) to that of the PET hydrolase FAST-PETase (67.80 °C) exhibited a 67-fold higher activity for MHET hydrolysis than FAST-PETase. A fused dual enzyme system comprising KL-MHETase and FAST-PETase exhibited a 2.6-fold faster PET depolymerization rate than FAST-PETase alone. Synergy increased the yield of TPA by 1.64 fold, and its purity in the released aromatic products reached 99.5%. In large reaction systems with 100 g/L substrate concentrations, the dual enzyme system KL36F achieved over 90% PET depolymerization into monomers, demonstrating its potential applicability in the industrial recycling of PET plastics. Therefore, a dual enzyme system can greatly reduce the reaction and separation cost for sustainable enzymatic PET recycling.

Bioindustrial applications of thermostable Endoglucanase purified from Trichoderma viride towards the conversion of agrowastes to value-added products

Importance of biocatalytic reactions and biotransformations mediated by fungal enzymes has increased tremendously in various industries. Endoglucanase obtained from Trichoderma viride has been utilized for bioconversion of agrowastes; wheat straw (WS) and corn stover (CS) as biomass into citric acid and single cell protein (SCP) as value-added products. The enzyme was purified to apparent homogeneity with Mr:44.67 kDa; purification-fold, yield, specific activity to be 19.5-, 29.2%, and 150.4 Units.mg-1, respectively, with thermostability up to 70 °C. The enzyme showed a novel N-terminal peptide and its computational analysis revealed a conserved 'SG' amino acid sequence alike microbial cellulases. The experimental results have shown the potential of endoglucanase for the conversion of agrowastes; wheat straw (WS) and corn stover (CS) into citric acid, maximum yield (KgM-3) found in submerged (WS:50;CS:45) fermentation process. Single-cell protein (SCP) production in WS (68 KgM-3) hydrolysate was superior to both CS hydrolysate (60 KgM-3) and YEPD (standard medium) (58 KgM-3).

Viral Evolution Shaped by Host Proteostasis Networks

Understanding the factors that shape viral evolution is critical for developing effective antiviral strategies, accurately predicting viral evolution, and preventing pandemics. One fundamental determinant of viral evolution is the interplay between viral protein biophysics and the host machineries that regulate protein folding and quality control. Most adaptive mutations in viruses are biophysically deleterious, resulting in a viral protein product with folding defects. In cells, protein folding is assisted by a dynamic system of chaperones and quality control processes known as the proteostasis network. Host proteostasis networks can determine the fates of viral proteins with biophysical defects, either by assisting with folding or by targeting them for degradation. In this review, we discuss and analyze new discoveries revealing that host proteostasis factors can profoundly shape the sequence space accessible to evolving viral proteins. We also discuss the many opportunities for research progress proffered by the proteostasis perspective on viral evolution and adaptation.

Molecular determinants of protein evolvability

The plethora of biological functions that sustain life is rooted in the remarkable evolvability of proteins. An emerging view highlights the importance of a protein's initial state in dictating evolutionary success. A deeper comprehension of the mechanisms that govern the evolvability of these initial states can provide invaluable insights into protein evolution. In this review, we describe several molecular determinants of protein evolvability, unveiled by experimental evolution and ancestral sequence reconstruction studies. We further discuss how genetic variation and epistasis can promote or constrain functional innovation and suggest putative underlying mechanisms. By establishing a clear framework for these determinants, we provide potential indicators enabling the forecast of suitable evolutionary starting points and delineate molecular mechanisms in need of deeper exploration.

Bioinformatics and Structural Analysis of Antigenic Variation in the Hemagglutinin Gene of the Influenza A(H1N1)pdm09 Virus Circulating in Shiraz (2013 to 2015)

Circulating influenza A virus provided an excellent opportunity to study the adaptation of the influenza A(H1N1)pdm09 virus to the human host. Particularly, due to the availability of sequences taken from isolates, we could monitor amino acid changes and the stability of mutations that occurred in hemagglutinin (HA). HA is crucial to viral infection because it binds to ciliated cell receptors and mediates the fusion of cells and viral membranes; because antibodies that bind to HA may block virus entry to the cell, this protein is subjected to high selective pressure. In this study, the locations of mutations in the structures of mutant HA were analyzed and the three-dimensional (3D) structures of these mutations were modeled in I-TASSER. Also, the location of these mutations was visualized and studied using Swiss PDB Viewer software and the PyMOL Molecular Graphics System. The crystal structure of the HA from A/California/07/2009 (3LZG) was used for further analysis. The new noncovalent bond formations in mutant luciferases were analyzed via WHAT IF and PIC, and protein stability was evaluated in the iStable server. We identified 33 and 23 mutations in A/Shiraz/106/2015 and A/California/07/2009 isolates, respectively; some mutations are located on the antigenic sites of Sa, Sb, Ca1, Ca2, and Cb HA1 and the fusion peptide of HA2. The results show that with the mutation some interactions are lost and new interactions are formed with other amino acids. The results of the free-energy analysis suggested that these new interactions have a destabilizing effect, which needs confirmation experimentally. IMPORTANCE Due to the fact that the mutations that occurred in the influenza virus HA cause the instability of the protein produced by the virus and antigenic changes and the escape of the virus from the immune system, the mutations that occurred in A/Shiraz/1/2013 were investigated in terms of energy level and stability. The mutations located in a globular portion of the HA are S188T, Q191H, S270P, K285Q, and P299L. On the other hand, the E374K, E46K-B, S124N-B, and I321V mutations are located in the stem portion of the HA (HA2). The change V252L mutation eliminates interactions with Ala181, Phe147, Leu151, and Trp153 and forms new interactions with Gly195, Asn264, Phe161, Met244, Tyr246, Leu165, and Trp167 which can change the stability of the HA structure. The K166Q mutation, which is located within the antigenic site Sa, causes the virus to escape from the immune response.

Protein folding rate evolution upon mutations

Despite the spectacular success of cutting-edge protein fold prediction methods, many critical questions remain unanswered, including why proteins can reach their native state in a biologically reasonable time. A satisfactory answer to this simple question could shed light on the slowest folding rate of proteins as well as how mutations-amino-acid substitutions and/or post-translational modifications-might affect it. Preliminary results indicate that (i) Anfinsen's dogma validity ensures that proteins reach their native state on a reasonable timescale regardless of their sequence or length, and (ii) it is feasible to determine the evolution of protein folding rates without accounting for epistasis effects or the mutational trajectories between the starting and target sequences. These results have direct implications for evolutionary biology because they lay the groundwork for a better understanding of why, and to what extent, mutations-a crucial element of evolution and a factor influencing it-affect protein evolvability. Furthermore, they may spur significant progress in our efforts to solve crucial structural biology problems, such as how a sequence encodes its folding.

Mutations observed in somatic evolution reveal underlying gene mechanisms

Highly sensitive DNA sequencing techniques have allowed the discovery of large numbers of somatic mutations in normal tissues. Some mutations confer a competitive advantage over wild-type cells, generating expanding clones that spread through the tissue. Competition between mutant clones leads to selection. This process can be considered a large scale, in vivo screen for mutations increasing cell fitness. It follows that somatic missense mutations may offer new insights into the relationship between protein structure, function and cell fitness. We present a flexible statistical method for exploring the selection of structural features in data sets of somatic mutants. We show how this approach can evidence selection of specific structural features in key drivers in aged tissues. Finally, we show how drivers may be classified as fitness-enhancing and fitness-suppressing through different patterns of mutation enrichment. This method offers a route to understanding the mechanism of protein function through in vivo mutant selection.

Enzyme Stability-Activity Trade-Off: New Insights from Protein Stability Weaknesses and Evolutionary Conservation

A general limitation of the use of enzymes in biotechnological processes under sometimes nonphysiological conditions is the complex interplay between two key quantities, enzyme activity and stability, where the increase of one is often associated with the decrease of the other. A precise stability-activity trade-off is necessary for the enzymes to be fully functional, but its weight in different protein regions and its dependence on environmental conditions is not yet elucidated. To advance this issue, we used the formalism that we have recently developed to effectively identify stability strength and weakness regions in protein structures and applied it to a large set of globular enzymes with known experimental structure and catalytic sites. Our analysis showed a striking oscillatory pattern of free energy compensation centered on the catalytic region. Indeed, catalytic residues are usually nonoptimal with respect to stability, but residues in the first shell around the catalytic site are, on the average, stability strengths and thus compensate for this lack of stability; residues in the second shell are weaker again, and so on. This trend is consistent across all enzyme families. It is accompanied by a similar, but less pronounced, pattern of residue conservation across evolution. In addition, we analyzed cold- and heat-adapted enzymes separately and highlighted different patterns of stability strengths and weaknesses, which provide insight into the longstanding problem of catalytic rate enhancement in cold environments. The successful comparison of our stability and conservation results with experimental fitness data, obtained by deep mutagenesis scanning, led us to propose criteria for improving catalytic activity while maintaining enzyme stability, a key goal in enzyme design.

Genomic characterization of SARS-CoV-2 in Egypt: insights into spike protein thermodynamic stability

The overall pattern of the SARS-CoV-2 pandemic so far has been a series of waves; surges in new cases followed by declines. The appearance of novel mutations and variants underlie the rises in infections, making surveillance of SARS-CoV-2 mutations and prediction of variant evolution of utmost importance. In this study, we sequenced 320 SARS-CoV-2 viral genomes isolated from patients from the outpatient COVID-19 clinic in the Children's Cancer Hospital Egypt 57357 (CCHE 57357) and the Egypt Center for Research and Regenerative Medicine (ECRRM). The samples were collected between March and December 2021, covering the third and fourth waves of the pandemic. The third wave was found to be dominated by Nextclade 20D in our samples, with a small number of alpha variants. The delta variant was found to dominate the fourth wave samples, with the appearance of omicron variants late in 2021. Phylogenetic analysis reveals that the omicron variants are closest genetically to early pandemic variants. Mutation analysis shows SNPs, stop codon mutation gain, and deletion/insertion mutations, with distinct patterns of mutations governed by Nextclade or WHO variant. Finally, we observed a large number of highly correlated mutations, and some negatively correlated mutations, and identified a general inclination toward mutations that lead to enhanced thermodynamic stability of the spike protein. Overall, this study contributes genetic and phylogenetic data, as well as provides insights into SARS-CoV-2 viral evolution that may eventually help in the prediction of evolving mutations for better vaccine development and drug targets.

Changes in Active Site Loop Conformation Relate to the Transition toward a Novel Enzymatic Activity

Enzymatic promiscuity, the ability of enzymes to catalyze multiple, distinct chemical reactions, has been well documented and is hypothesized to be a major driver for the emergence of new enzymatic functions. Yet, the molecular mechanisms involved in the transition from one activity to another remain debated and elusive. Here, we evaluated the redesign of the active site binding cleft of the lactonase SsoPox using structure-based design and combinatorial libraries. We created variants with largely improved catalytic abilities against phosphotriesters, the best ones being > 1,000-fold better compared to the wild-type enzyme. The observed shifts in activity specificity are large, ~1,000,000-fold and beyond, since some variants completely lost their initial activity. The selected combinations of mutations have considerably reshaped the active site cavity via side chain changes but mostly through large rearrangements of the active site loops, as revealed by a suite of crystal structures. This suggests that specific active site loop configuration is critical to the lactonase activity. Interestingly, analysis of high-resolution structures hints at the potential role of conformational sampling and its directionality in defining an enzyme activity profile.

Designed active-site library reveals thousands of functional GFP variants

Mutations in a protein active site can lead to dramatic and useful changes in protein activity. The active site, however, is sensitive to mutations due to a high density of molecular interactions, substantially reducing the likelihood of obtaining functional multipoint mutants. We introduce an atomistic and machine-learning-based approach, called high-throughput Functional Libraries (htFuncLib), that designs a sequence space in which mutations form low-energy combinations that mitigate the risk of incompatible interactions. We apply htFuncLib to the GFP chromophore-binding pocket, and, using fluorescence readout, recover >16,000 unique designs encoding as many as eight active-site mutations. Many designs exhibit substantial and useful diversity in functional thermostability (up to 96 °C), fluorescence lifetime, and quantum yield. By eliminating incompatible active-site mutations, htFuncLib generates a large diversity of functional sequences. We envision that htFuncLib will be used in one-shot optimization of activity in enzymes, binders, and other proteins.

Simultaneous enhancement of multiple functional properties using evolution-informed protein design

Designing optimized proteins is important for a range of practical applications. Protein design is a rapidly developing field that would benefit from approaches that enable many changes in the amino acid primary sequence, rather than a small number of mutations, while maintaining structure and enhancing function. Homologous protein sequences contain extensive information about various protein properties and activities that have emerged over billions of years of evolution. Evolutionary models of sequence co-variation, derived from a set of homologous sequences, have proven effective in a range of applications including structure determination and mutation effect prediction. In this work we apply one of these models (EVcouplings) to computationally design highly divergent variants of the model protein TEM-1 β-lactamase, and characterize these designs experimentally using multiple biochemical and biophysical assays. Nearly all designed variants were functional, including one with 84 mutations from the nearest natural homolog. Surprisingly, all functional designs had large increases in thermostability and most had a broadening of available substrates. These property enhancements occurred while maintaining a nearly identical structure to the wild type enzyme. Collectively, this work demonstrates that evolutionary models of sequence co-variation (1) are able to capture complex epistatic interactions that successfully guide large sequence departures from natural contexts, and (2) can be applied to generate functional diversity useful for many applications in protein design.

Global Analysis of Multi-Mutants to Improve Protein Function

The identification of amino acid substitutions that both enhance the stability and function of a protein is a key challenge in protein engineering. Technological advances have enabled assaying thousands of protein variants in a single high-throughput experiment, and more recent studies use such data in protein engineering. We present a Global Multi-Mutant Analysis (GMMA) that exploits the presence of multiply-substituted variants to identify individual amino acid substitutions that are beneficial for the stability and function across a large library of protein variants. We have applied GMMA to a previously published experiment reporting on >54,000 variants of green fluorescent protein (GFP), each with known fluorescence output, and each carrying 1-15 amino acid substitutions (Sarkisyan et al., 2016). The GMMA method achieves a good fit to this dataset while being analytically transparent. We show experimentally that the six top-ranking substitutions progressively enhance GFP. More broadly, using only a single experiment as input our analysis recovers nearly all the substitutions previously reported to be beneficial for GFP folding and function. In conclusion, we suggest that large libraries of multiply-substituted variants may provide a unique source of information for protein engineering.

Using High-Throughput Molecular Dynamics Simulation to Enhance the Computational Design of Kemp Elimination Enzymes

Computational enzyme design has been successfully applied to identify new alternatives to natural enzymes for the biosynthesis of important compounds. However, the moderate catalytic activities of de novo designed enzymes indicate that the modeling accuracy of current computational enzyme design methods should be improved. Here, high-throughput molecular dynamics simulations were used to enhance computational enzyme design, thus allowing the identification of variants with higher activities in silico. Different time schemes of high-throughput molecular dynamics simulations were tested to identify the catalytic features of evolved Kemp eliminases. The 20 × 1 ns molecular dynamics simulation scheme was sufficiently accurate and computationally viable to screen the computationally designed massive variants of Kemp elimination enzymes. The developed hybrid computational strategy was used to redesign the most active Kemp eliminase, HG3.17, and five variants were generated and experimentally confirmed to afford higher catalytic efficiencies than that of HG3.17, with one double variant (D52Q/A53S) exhibiting a 55% increase. The hybrid computational enzyme design strategy is general and computationally economical, with which we anticipate the efficient creation of practical enzymes for industrial biocatalysis.

In vivo client proteins of the chaperonin GroEL-GroES provide insight into the role of chaperones in protein evolution

Protein folding is often hampered by intermolecular protein aggregation, which can be prevented by a variety of chaperones in the cell. Bacterial chaperonin GroEL is a ring-shaped chaperone that forms complexes with its cochaperonin GroES, creating central cavities to accommodate client proteins (also referred as substrate proteins) for folding. GroEL and GroES (GroE) are the only indispensable chaperones for bacterial viability, except for some species of Mollicutes such as Ureaplasma. To understand the role of chaperonins in the cell, one important goal of GroEL research is to identify a group of obligate GroEL/GroES clients. Recent advances revealed hundreds of in vivo GroE interactors and obligate chaperonin-dependent clients. This review summarizes the progress on the in vivo GroE client repertoire and its features, mainly for Escherichia coli GroE. Finally, we discuss the implications of the GroE clients for the chaperone-mediated buffering of protein folding and their influences on protein evolution.

Ancestral Sequence Reconstruction Identifies Structural Changes Underlying the Evolution of Ideonella sakaiensis PETase and Variants with Improved Stability and Activity

The improved production, recycling, and removal of plastic waste, such as polyethylene terephthalate (PET), are pressing environmental and economic issues for society. Biocatalytic (enzymatic) PET depolymerization is potentially a sustainable, low-energy solution to PET recycling, especially when compared with current disposal methods such as landfills, incineration, or gasification. IsPETase has been extensively studied for its use in PET depolymerization; however, its evolution from cutinases is not fully understood, and most engineering studies have neglected the majority of the available sequence space remote from the active site. In this study, ancestral protein reconstruction (ASR) has been used to trace the evolutionary trajectory from ancient serine hydrolases to IsPETase, while ASR and the related design approach, protein repair one-stop shop, were used to identify enzyme variants with improved activity and stability. Kinetic and structural characterization of these variants reveals new insights into the evolution of PETase activity and the role of second-shell mutations around the active site. Among the designed and reconstructed variants, we identified several with melting points 20 °C higher than that of IsPETase and two variants with significantly higher catalytic activity.

Engineering DszC Mutants from Transition State Macrodipole Considerations and Evolutionary Sequence Analysis

We describe an approach to identify enzyme mutants with increased turnover using the enzyme DszC as a case study. Our approach is based on recalculating the barriers of alanine mutants through single-point energy calculations at the hybrid QM/MM level in the wild-type reactant and transition state geometries. We analyze the difference in the electron density between the reactant and transition state to identify sites/residues where electrostatic interactions stabilize the transition state over the reactants. We also assess the insertion of a unit probe charge to identify positions in which the introduction of charged residues lowers the barrier.

Evaluation of AlphaFold structure-based protein stability prediction on missense variations in cancer

Identifying pathogenic missense variants in hereditary cancer is critical to the efforts of patient surveillance and risk-reduction strategies. For this purpose, many different gene panels consisting of different number and/or set of genes are available and we are particularly interested in a panel of 26 genes with a varying degree of hereditary cancer risk consisting of ABRAXAS1, ATM, BARD1, BLM, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, EPCAM, MEN1, MLH1, MRE11, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, STK11, TP53, and XRCC2. In this study, we have compiled a collection of the missense variations reported in any of these 26 genes. More than a thousand missense variants were collected from ClinVar and the targeted screen of a breast cancer cohort of 355 patients which contributed to this set with 160 novel missense variations. We analyzed the impact of the missense variations on protein stability by five different predictors including both sequence- (SAAF2EC and MUpro) and structure-based (Maestro, mCSM, CUPSAT) predictors. For the structure-based tools, we have utilized the AlphaFold (AF2) protein structures which comprise the first structural analysis of this hereditary cancer proteins. Our results agreed with the recent benchmarks that computed the power of stability predictors in discriminating the pathogenic variants. Overall, we reported a low-to-medium-level performance for the stability predictors in discriminating pathogenic variants, except MUpro which had an AUROC of 0.534 (95% CI [0.499-0.570]). The AUROC values ranged between 0.614-0.719 for the total set and 0.596-0.682 for the set with high AF2 confidence regions. Furthermore, our findings revealed that the confidence score for a given variant in the AF2 structure could alone predict pathogenicity more robustly than any of the tested stability predictors with an AUROC of 0.852. Altogether, this study represents the first structural analysis of the 26 hereditary cancer genes underscoring 1) the thermodynamic stability predicted from AF2 structures as a moderate and 2) the confidence score of AF2 as a strong descriptor for variant pathogenicity.

Mutation-driven parallel evolution in emergence of ACE2-utilizing sarbecoviruses

Mutation and recombination are two major genetic mechanisms that drive the evolution of viruses. They both exert an interplay during virus evolution, in which mutations provide a first ancestral source of genetic diversity for subsequent recombination. Sarbecoviruses are a group of evolutionarily related β-coronaviruses including human severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2 and a trove of related animal viruses called SARS-like CoVs (SL-CoVs). This group of members either use or not use angiotensin-converting enzyme 2 (ACE2) as their entry receptor, which has been linked to the properties of their spike protein receptor binding domains (RBDs). This raises an outstanding question regarding how ACE2 binding originated within sarbecoviruses. Using a combination of analyses of phylogenies, ancestral sequences, structures, functions and molecular dynamics, we provide evidence in favor of an evolutionary scenario, in which three distinct ancestral RBDs independently developed the ACE2 binding trait via parallel amino acid mutations. In this process, evolutionary intermediate RBDs might be firstly formed through loop extensions to offer key functional residues accompanying point mutations to remove energetically unfavorable interactions and to change the dynamics of the functional loops, all required for ACE2 binding. Subsequent optimization in the context of evolutionary intermediates led to the independent emergence of ACE2-binding RBDs in the SARS-CoV and SARS-CoV-2 clades of Asian origin and the clade comprising SL-CoVs of European and African descent. These findings will help enhance our understanding of mutation-driven evolution of sarbecoviruses in their early history.

Analyses of Mutation Displacements from Homology Models

Evaluation of the structural perturbations introduced by a single amino acid mutation is the main issue for protein structural biology. We propose here to present some recent advances in methods, allowing the splitting of distortion between the actual substitution effect and the contribution of the local flexibility of the position where the mutation occurs. Its main drawback is the need of many structures with a single mutation in each of them. To bypass this difficulty, we propose to use molecular modeling tools, with several software enabling us to build a model from a template, given the sequence. As a proof of concept, we rely on a gold standard, the human lysozyme. Both wild-type and three mutant structures are available in the PDB. Two of these mutations result in amyloid fibril formation, and the last one is neutral. As a conclusion, irrespective of the algorithm used for modeling, side chain conformations at the site of mutation are reliable, although long-range effects are out of reach of these tools.

In Silico Structural Analysis Predicting the Pathogenicity of PLP1 Mutations in Multiple Sclerosis

The X chromosome gene PLP1 encodes myelin proteolipid protein (PLP), the most prevalent protein in the myelin sheath surrounding the central nervous system. X-linked dysmyelinating disorders such as Pelizaeus-Merzbacher disease (PMD) or spastic paraplegia type 2 (SPG2) are typically caused by point mutations in PLP1. Nevertheless, numerous case reports have shown individuals with PLP1 missense point mutations which also presented clinical symptoms and indications that were consistent with the diagnostic criteria of multiple sclerosis (MS), a disabling disease of the brain and spinal cord with no current cure. Computational structural biology methods were used to assess the impact of these mutations on the stability and flexibility of PLP structure in order to determine the role of PLP1 mutations in MS pathogenicity. The analysis showed that most of the variants can alter the functionality of the protein structure such as R137W variants which results in loss of helix and H140Y which alters the ordered protein interface. In silico genomic methods were also performed to predict the significance of these mutations associated with impairments in protein functionality and could suggest a better definition for therapeutic strategies and clinical application in MS patients.

Experimental and In Silico Analysis of TEM β-Lactamase Adaptive Evolution

Multiple mutations often have non-additive (epistatic) phenotypic effects. Epistasis is of fundamental biological relevance but is not well understood mechanistically. Adaptive evolution, i.e., the evolution of new biochemical activities, is rich in epistatic interactions. To better understand the principles underlying epistasis during genetic adaptation, we studied the evolution of TEM-1 β-lactamase variants exhibiting cefotaxime resistance. We report the collection of a library of 487 observed evolutionary trajectories for TEM-1 and determine the epistasis status based on cefotaxime resistance phenotype for 206 combinations of 2-3 TEM-1 mutations involving 17 positions under adaptive selective pressure. Gain-of-function (GOF) mutations are gatekeepers for adaptation. To see if GOF phenotypes can be inferred based solely on sequence data, we calculated the enrichment of GOF mutations in the different categories of epistatic pairs. Our results suggest that this is possible because GOF mutations are particularly enriched in sign and reciprocal sign epistasis, which leave a major imprint on the sequence space accessible to evolution. We also used FoldX to explore the relationship between thermodynamic stability and epistasis. We found that mutations in observed evolutionary trajectories tend to destabilize the folded structure of the protein, albeit their cumulative effects are consistently below the protein's free energy of folding. The destabilizing effect is stronger for epistatic pairs, suggesting that modest or local alterations in folding stability can modulate catalysis. Finally, we report a significant relationship between epistasis and the degree to which two protein positions are structurally and dynamically coupled, even in the absence of ligand.

Mutation-driven evolution of antibacterial function in an ancestral antifungal scaffold: Significance for peptide engineering

Mutation-driven evolution of novel function on an old gene has been documented in many development- and adaptive immunity-related genes but is poorly understood in immune effector molecules. Drosomycin-type antifungal peptides (DTAFPs) are a family of defensin-type effectors found in plants and ecdysozoans. Their primitive function was to control fungal infection and then co-opted for fighting against bacterial infection in plants, insects, and nematodes. This provides a model to study the structural and evolutionary mechanisms behind such functional diversification. In the present study, we determined the solution structure of mehamycin, a DTAFP from the Northern root-knot nematode Meloidogyne hapla with antibacterial activity and an 18-mer insert, and studied the mutational effect through using a mutant with the insert deleted. Mehamycin adopts an expected cysteine-stabilized α-helix and β-sheet fold in its core scaffold and the inserted region, called single Disulfide Bridge-linked Domain (abbreviated as sDBD), forms an extended loop protruding from the scaffold. The latter folds into an amphipathic architecture stabilized by one disulfide bridge, which likely confers mehamycin a bacterial membrane permeability. Deletion of the sDBD remarkably decreased the ability but accompanying an increase in thermostability, indicative of a structure-function trade-off in the mehamycin evolution. Allosteric analysis revealed an interior interaction between the two domains, which might promote point mutations at some key sites of the core domain and ultimately give rise to the emergence of antibacterial function. Our work may be valuable in guiding protein engineering of mehamycin to improve its activity and stability.

Bacterial Hsp90 predominantly buffers but does not potentiate the phenotypic effects of deleterious mutations during fluorescent protein evolution

Chaperones facilitate the folding of other ("client") proteins and can thus affect the adaptive evolution of these clients. Specifically, chaperones affect the phenotype of proteins via two opposing mechanisms. On the one hand, they can buffer the effects of mutations in proteins and thus help preserve an ancestral, premutation phenotype. On the other hand, they can potentiate the effects of mutations and thus enhance the phenotypic changes caused by a mutation. We study that how the bacterial Hsp90 chaperone (HtpG) affects the evolution of green fluorescent protein. To this end, we performed directed evolution of green fluorescent protein under low and high cellular concentrations of Hsp90. Specifically, we evolved green fluorescent protein under both stabilizing selection for its ancestral (green) phenotype and directional selection toward a new (cyan) phenotype. While Hsp90 did only affect the rate of adaptive evolution transiently, it did affect the phenotypic effects of mutations that occurred during adaptive evolution. Specifically, Hsp90 allowed strongly deleterious mutations to accumulate in evolving populations by buffering their effects. Our observations show that the role of a chaperone for adaptive evolution depends on the organism and the trait being studied.

Optimizing Antibody Affinity and Developability Using a Framework-CDR Shuffling Approach-Application to an Anti-SARS-CoV-2 Antibody

The computational methods used for engineering antibodies for clinical development have undergone a transformation from three-dimensional structure-guided approaches to artificial-intelligence- and machine-learning-based approaches that leverage the large sequence data space of hundreds of millions of antibodies generated by next-generation sequencing (NGS) studies. Building on the wealth of available sequence data, we implemented a computational shuffling approach to antibody components, using the complementarity-determining region (CDR) and the framework region (FWR) to optimize an antibody for improved affinity and developability. This approach uses a set of rules to suitably combine the CDRs and FWRs derived from naturally occurring antibody sequences to engineer an antibody with high affinity and specificity. To illustrate this approach, we selected a representative SARS-CoV-2-neutralizing antibody, H4, which was identified and isolated previously based on the predominant germlines that were employed in a human host to target the SARS-CoV-2-human ACE2 receptor interaction. Compared to screening vast CDR libraries for affinity enhancements, our approach identified fewer than 100 antibody framework-CDR combinations, from which we screened and selected an antibody (CB79) that showed a reduced dissociation rate and improved affinity against the SARS-CoV-2 spike protein (7-fold) when compared to H4. The improved affinity also translated into improved neutralization (>75-fold improvement) of SARS-CoV-2. Our rapid and robust approach for optimizing antibodies from parts without the need for tedious structure-guided CDR optimization will have broad utility for biotechnological applications.

Directed Evolution of Soluble α-1,2-Fucosyltransferase Using Kanamycin Resistance Protein as a Phenotypic Reporter for Efficient Production of 2'-Fucosyllactose

2'-Fucosyllactose (2'-FL), the most abundant fucosylated oligosaccharide in human milk, has multiple beneficial effects on human health. However, its biosynthesis by metabolically engineered Escherichia coli is often hampered owing to the insolubility and instability of α-1,2-fucosyltransferase (the rate-limiting enzyme). In this study, we aimed to enhance 2'-FL production by increasing the expression of soluble α-1,2-fucosyltransferase from Helicobacter pylori (FucT2). Because structural information regarding FucT2 has not been unveiled, we decided to improve the expression of soluble FucT2 in E. coli via directed evolution using a protein solubility biosensor that links protein solubility to antimicrobial resistance. For such a system to be viable, the activity of kanamycin resistance protein (Kan^R) should be dependent on FucT2 solubility. Kan^R was fused to the C-terminus of mutant libraries of FucT2, which were generated using a combination of error-prone PCR and DNA shuffling. Notably, one round of the directed evolution process, which consisted of mutant library generation and selection based on kanamycin resistance, resulted in a significant increase in the expression level of soluble FucT2. As a result, a batch fermentation with the ΔL M15 pBCGW strain, expressing the FucT2 mutant (F#1-5) isolated from the first round of the directed evolution process, resulted in the production of 0.31 g/l 2'-FL with a yield of 0.22 g 2'-FL/g lactose, showing 1.72- and 1.51-fold increase in the titer and yield, respectively, compared to those of the control strain. The simple and powerful method developed in this study could be applied to enhance the solubility of other unstable enzymes.

Orthogonal glycolytic pathway enables directed evolution of noncanonical cofactor oxidase

Noncanonical cofactor biomimetics (NCBs) such as nicotinamide mononucleotide (NMN+) provide enhanced scalability for biomanufacturing. However, engineering enzymes to accept NCBs is difficult. Here, we establish a growth selection platform to evolve enzymes to utilize NMN+-based reducing power. This is based on an orthogonal, NMN+-dependent glycolytic pathway in Escherichia coli which can be coupled to any reciprocal enzyme to recycle the ensuing reduced NMN+. With a throughput of >106 variants per iteration, the growth selection discovers a Lactobacillus pentosus NADH oxidase variant with ~10-fold increase in NMNH catalytic efficiency and enhanced activity for other NCBs. Molecular modeling and experimental validation suggest that instead of directly contacting NCBs, the mutations optimize the enzyme's global conformational dynamics to resemble the WT with the native cofactor bound. Restoring the enzyme's access to catalytically competent conformation states via deep navigation of protein sequence space with high-throughput evolution provides a universal route to engineer NCB-dependent enzymes.

Design and optimization of enzymatic activity in a de novo β-barrel scaffold

While native scaffolds offer a large diversity of shapes and topologies for enzyme engineering, their often unpredictable behavior in response to sequence modification makes de novo generated scaffolds an exciting alternative. Here we explore the customization of the backbone and sequence of a de novo designed eight stranded β‐barrel protein to create catalysts for a retro‐aldolase model reaction. We show that active and specific catalysts can be designed in this fold and use directed evolution to further optimize activity and stereoselectivity. Our results support previous suggestions that different folds have different inherent amenability to evolution and this property could account, in part, for the distribution of natural enzymes among different folds.

Stability and expression of SARS-CoV-2 spike-protein mutations

Protein fold stability likely plays a role in SARS-CoV-2 S-protein evolution, together with ACE2 binding and antibody evasion. While few thermodynamic stability data are available for S-protein mutants, many systematic experimental data exist for their expression. In this paper, we explore whether such expression levels relate to the thermodynamic stability of the mutants. We studied mutation-induced SARS-CoV-2 S-protein fold stability, as computed by three very distinct methods and eight different protein structures to account for method- and structure-dependencies. For all methods and structures used (24 comparisons), computed stability changes correlate significantly (99% confidence level) with experimental yeast expression from the literature, such that higher expression is associated with relatively higher fold stability. Also significant, albeit weaker, correlations were seen between stability and ACE2 binding effects. The effect of thermodynamic fold stability may be direct or a correlate of amino acid or site properties, notably the solvent exposure of the site. Correlation between computed stability and experimental expression and ACE2 binding suggests that functional properties of the SARS-CoV-2 S-protein mutant space are largely determined by a few simple features, due to underlying correlations. Our study lends promise to the development of computational tools that may ideally aid in understanding and predicting SARS-CoV-2 S-protein evolution.