How protein stability and new functions trade off

Dimerization of the deaminase domain and locking interactions with Cas9 boost base editing efficiency in ABE8e

CRISPR-based DNA adenine base editors (ABEs) hold remarkable promises to address human genetic diseases caused by point mutations. ABEs were developed by combining CRISPR-Cas9 with a transfer RNA (tRNA) adenosine deaminase enzyme and through directed evolution, conferring the ability to deaminate DNA. However, the molecular mechanisms driving the efficient DNA deamination in the evolved ABEs remain unresolved. Here, extensive molecular simulations and biochemical experiments reveal the biophysical basis behind the astonishing base editing efficiency of ABE8e, the most efficient ABE to date. We demonstrate that the ABE8e's DNA deaminase domain, TadA8e, forms remarkably stable dimers compared to its tRNA-deaminating progenitor and that the strength of TadA dimerization is crucial for DNA deamination. The TadA8e dimer forms robust interactions involving its R98 and R129 residues, the RuvC domain of Cas9 and the DNA. These locking interactions are exclusive to ABE8e, distinguishing it from its predecessor, ABE7.10, and are indispensable to boost DNA deamination. Additionally, we identify three critical residues that drive the evolution of ABE8e toward improved base editing by balancing the enzyme's activity and stability, reinforcing the TadA8e dimer and improving the ABE8e's functionality. These insights offer new directions to engineer superior ABEs, advancing the design of safer precision genome editing tools.

Heterogeneous folding landscapes and predetermined breaking points within a protein family

The accurate prediction of protein structures with artificial intelligence has been a spectacular success. Yet, how proteins fold into their native structures inside the cell remains incompletely understood. Of particular interest is to rationalize how proteins interact with the protein homeostasis network, an organism specific set of protein folding and quality control enzymes. Failure of protein homeostasis leads to widespread misfolding and aggregation, and thus neurodegeneration. Here, I present a comparative analysis of the folding of 16 single-domain proteins from the same organism across a protein family, the Saccharomyces cerevisiae small GTPases. Using computational modeling to directly probe protein folding dynamics, this work shows how near identical structures from the same folding environment can exhibit heterogeneous folding landscapes. Remarkably, yeast small GTPases are found to unfold along different pathways either via the N- or C-terminus initiated by structure-encoded predetermined breaking points. Degrons as recognition signals for ubiquitin-dependent degradation were systematically absent from the initial unfolding sites, as if to protect from too rapid degradation upon spontaneous unfolding or before completion of the folding. The presented results highlight a direct coordination of folding pathway and protein homeostasis interaction signals across a protein family. A deeper understanding of the interdependence of proteins with their folding environment will help to rationalize and combat diseases linked to protein misfolding and dysregulation. More generally, this work underlines the importance of understanding protein folding in the cellular context, and highlights valuable constraints towards a systems-level understanding of protein homeostasis.

The Hsp90 Molecular Chaperone as a Global Modifier of the Genotype-Phenotype-Fitness Map: An Evolutionary Perspective

Global modifier genes influence the mapping of genotypes onto phenotypes and fitness through their epistatic interactions with genetic variants on a massive scale. The first such factor to be identified, Hsp90, is a highly conserved molecular chaperone that plays a central role in protein homeostasis. Hsp90 is a "hub of hubs" that chaperones proteins engaged in many key cellular and developmental regulatory networks. These clients, which are enriched in kinases, transcription factors, and E3 ubiquitin ligases, drive diverse cellular functions and are themselves highly connected. By contrast to many other hub proteins, the abundance and activity of Hsp90 changes substantially in response to shifting environmental conditions. As a result, Hsp90 modifies the functional impact of many genetic variants simultaneously in a manner that depends on environmental stress. Studies in diverse organisms suggest that this coupling between Hsp90 function and challenging environments exerts a substantial impact on what parts of the genome are visible to natural selection, expanding adaptive opportunities when most needed. In this Perspective, we explore the multifaceted role of Hsp90 as global modifier of the genotype-phenotype-fitness map as well as its implications for evolution in nature and the clinic.

Intrinsic Disorder and Other Malleable Arsenals of Evolved Protein Multifunctionality

Microscopic evolution at the functional biomolecular level is an ongoing process. Leveraging functional and high-throughput assays, along with computational data mining, has led to a remarkable expansion of our understanding of multifunctional protein (and gene) families over the past few decades. Various molecular and intermolecular mechanisms are now known that collectively meet the cumulative multifunctional demands in higher organisms along an evolutionary path. This multitasking ability is attributed to a certain degree of intrinsic or adapted flexibility at the structure-function level. Evolutionary diversification of structure-function relationships in proteins highlights the functional importance of intrinsically disordered proteins/regions (IDPs/IDRs) which are highly dynamic biological soft matter. Multifunctionality is favorably supported by the fluid-like shapes of IDPs/IDRs, enabling them to undergo disorder-to-order transitions upon binding to different molecular partners. Other new malleable members of the protein superfamily, such as those involved in fold-switching, also undergo structural transitions. This new insight diverges from all traditional notions of functional singularity in enzyme classes and emphasizes a far more complex, multi-layered diversification of protein functionality. However, a thorough review in this line, focusing on flexibility and function-driven structural transitions related to evolved multifunctionality in proteins, is currently missing. This review attempts to address this gap while broadening the scope of multifunctionality beyond single protein sequences. It argues that protein intrinsic disorder is likely the most striking mechanism for expressing multifunctionality in proteins. A phenomenological analogy has also been drawn to illustrate the increasingly complex nature of modern digital life, driven by the need for multitasking, particularly involving media.

New-to-nature CO2-dependent acetyl-CoA assimilation enabled by an engineered B12-dependent acyl-CoA mutase

Acetyl-CoA is a key metabolic intermediate and the product of various natural and synthetic one-carbon (C1) assimilation pathways. While an efficient conversion of acetyl-CoA into other central metabolites, such as pyruvate, is imperative for high biomass yields, available aerobic pathways typically release previously fixed carbon in the form of CO2. To overcome this loss of carbon, we develop a new-to-nature pathway, the Lcm module, in this study. The Lcm module provides a direct link between acetyl-CoA and pyruvate, is shorter than any other oxygen-tolerant route and notably fixes CO2, instead of releasing it. The Lcm module relies on the new-to-nature activity of a coenzyme B12-dependent mutase for the conversion of 3-hydroxypropionyl-CoA into lactyl-CoA. We demonstrate Lcm activity of the scaffold enzyme 2-hydroxyisobutyryl-CoA mutase from Bacillus massiliosenegalensis, and further improve catalytic efficiency 10-fold by combining in vivo targeted hypermutation and adaptive evolution in an engineered Escherichia coli selection strain. Finally, in a proof-of-principle, we demonstrate the complete Lcm module in vitro. Overall, our work demonstrates a synthetic CO2-incorporating acetyl-CoA assimilation route that expands the metabolic solution space of central carbon metabolism, providing options for synthetic biology and metabolic engineering.

A Dual-Focus Workflow for Simultaneously Engineering High Activity and Thermal Stability in Methyl Parathion Hydrolase

Industrial fermentation applications typically require enzymes that exhibit high stability and activity at high temperatures. However, efforts to simultaneously improve these properties are usually limited by a trade-off between stability and activity. This report describes a computational strategy to enhance both activity and thermal stability of the mesophilic organophosphate-degrading enzyme, methyl parathion hydrolase (MPH). To predict hotspot mutation sites, we assembled a library of features associated with the target properties for each residue and then prioritized candidate sites by hierarchical clustering. Subsequent in silico screening with multiple algorithms to simulate selective pressures yielded a subset of 23 candidate mutations. Iterative parallel screening of mutations that improved thermal stability and activity yielded, MPHase-m5b, which exhibited 13.3 °C higher Tm and 4.2 times higher catalytic activity than wild-type (WT) MPH over a wide temperature range. Systematic analysis of crystal structures, molecular dynamics (MD) simulations, and quantum mechanics/molecular mechanics (QM/MM) calculations revealed a wider entrance to the active site that increased substrate access with an extensive network of interactions outside the active site that reinforced αβ/βα sandwich architecture to improve thermal stability. This study thus provides an advanced, rational design framework to improve efficiency in engineering highly active, thermostable biocatalysts for industrial applications.

Ancestral Sequence Reconstruction to Enable Biocatalytic Synthesis of Azaphilones

Biocatalysis can be powerful in organic synthesis but is often limited by enzymes' substrate scope and selectivity. Developing a biocatalytic step involves identifying an initial enzyme for the target reaction followed by optimization through rational design, directed evolution, or both. These steps are time consuming, resource-intensive, and require expertise beyond typical organic chemistry. Thus, an effective strategy for streamlining the process from enzyme identification to implementation is essential to expanding biocatalysis. Here, we present a strategy combining bioinformatics-guided enzyme mining and ancestral sequence reconstruction (ASR) to resurrect enzymes for biocatalytic synthesis. Specifically, we achieve an enantioselective synthesis of azaphilone natural products using two ancestral enzymes: a flavin-dependent monooxygenase (FDMO) for stereodivergent oxidative dearomatization and a substrate-selective acyltransferase (AT) for the acylation of the enzymatically installed hydroxyl group. This cascade, stereocomplementary to established chemoenzymatic routes, expands access to enantiomeric linear tricyclic azaphilones. By leveraging the co-occurrence and coevolution of FDMO and AT in azaphilone biosynthetic pathways, we identified an AT candidate, CazE, and addressed its low solubility and stability through ASR, obtaining a more soluble, stable, promiscuous, and reactive ancestral AT (AncAT). Sequence analysis revealed AncAT as a chimeric composition of its descendants with enhanced reactivity likely due to ancestral promiscuity. Flexible receptor docking and molecular dynamics simulations showed that the most reactive AncAT promotes a reactive geometry between substrates. We anticipate that our bioinformatics-guided, ASR-based approach can be broadly applied in target-oriented synthesis, reducing the time required to develop biocatalytic steps and efficiently access superior biocatalysts.

The origin of mutational epistasis

The interconnected processes of protein folding, mutations, epistasis, and evolution have all been the subject of extensive analysis throughout the years due to their significance for structural and evolutionary biology. The origin (molecular basis) of epistasis-the non-additive interactions between mutations-is still, nonetheless, unknown. The existence of a new perspective on protein folding, a problem that needs to be conceived as an 'analytic whole', will enable us to shed light on the origin of mutational epistasis at the simplest level-within proteins-while also uncovering the reasons why the genetic background in which they occur, a key component of molecular evolution, could foster changes in epistasis effects. Additionally, because mutations are the source of epistasis, more research is needed to determine the impact of post-translational modifications, which can potentially increase the proteome's diversity by several orders of magnitude, on mutational epistasis and protein evolvability. Finally, a protein evolution thermodynamic-based analysis that does not consider specific mutational steps or epistasis effects will be briefly discussed. Our study explores the complex processes behind the evolution of proteins upon mutations, clearing up some previously unresolved issues, and providing direction for further research.

Rational design of disulfide bonds to increase thermostability of Rhodococcus opacus catechol 1,2 dioxygenase

Catechol 1,2 dioxygenase is a versatile enzyme with several potential applications. However, due to its low thermostability, its industrial potential is not being met. In this study, the thermostability of a mesophilic catechol 1,2 dioxygenase from the species Rhodococcus opacus was enhanced via the introduction of disulphide bonds into its structure. Engineered designs (56) were obtained using computational prediction applications, with a set of hypothesized selection criteria narrowing the list to 9. Following recombinant production and purification, several of the designs demonstrated substantially improved protein thermostability. Notably, variant K96C-D278C yielded improvements including a 4.6°C increase in T50, a 725% increase in half-life, a 5.5°C increase in Tm, and a >10-fold increase in total turnover number compared to wild type. Stacking of best designs was not productive. Overall, current state-of-the-art prediction algorithms were effective for design of disulfide-thermostabilized catechol 1,2 dioxygenase.

Investigating role of positively selected genes and mutation sites of ERG11 in drug resistance of Candida albicans

The steep increase in acquired drug resistance in Candida isolates has posed a great challenge in the clinical management of candidiasis globally. Information of genes and codon sites that are positively selected during evolution can provide insights into the mechanisms driving antifungal resistance in Candida. This study aimed to create a manually curated list of genes of Candida spp. reported to be associated with antifungal resistance in literature, and further investigate the structure-function implications of positively selected genes and mutation sites. Sequence analysis of antifungal drug resistance associated gene sequences from various species and strains of Candida revealed that ERG11 and MRR1 of C. albicans were positively selected during evolution. Four sites in ERG11 and two sites in MRR1 of C. albicans were positively selected and associated with drug resistance. These four sites (132, 405, 450, and 464) of ERG11 are predictive markers for azole resistance and have evolved over time. A well-characterized crystal structure of sterol-14-α-demethylase (CYP51) encoded by ERG11 is available in PDB. Therefore, the stability of CYP51 in complex with fluconazole was evaluated using MD simulations and molecular docking studies for two mutations (Y132F and Y132H) reported to be associated with azole resistance in literature. These mutations induced high flexibility in functional motifs of CYP51. It was also observed that residues such as I304, G308, and I379 of CYP51 play a critical role in fluconazole binding affinity. The insights gained from this study can further guide drug design strategies addressing antimicrobial resistance.

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.

Computational Stabilization of a Non-Heme Iron Enzyme Enables Efficient Evolution of New Function

Deep learning tools for enzyme design are rapidly emerging, and there is a critical need to evaluate their effectiveness in engineering workflows. Here we show that the deep learning-based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially relevant non-native functions. This superfamily has diverse catalytic functions and could provide a rich new source of biocatalysts for synthesis and industrial processes. Through systematic comparisons of directed evolution trajectories for a non-native, remote C(sp3)-H hydroxylation reaction, we demonstrate that the stabilized redesign can be evolved more efficiently than the wild-type enzyme. After three rounds of directed evolution, we obtained a 6-fold activity increase from the wild-type parent and an 80-fold increase from the stabilized variant. To generate the initial stabilized variant, we identified multiple structural and sequence constraints to preserve catalytic function. We applied these criteria to produce stabilized, catalytically active variants of a second Fe(II)/αKG enzyme, suggesting that the approach is generalizable to additional members of the Fe(II)/αKG superfamily. ProteinMPNN is user-friendly and widely accessible, and our results provide a framework for the routine implementation of deep learning-based protein stabilization tools in directed evolution workflows for novel biocatalysts.

The ribosome lowers the entropic penalty of protein folding

Most proteins fold during biosynthesis on the ribosome1, and co-translational folding energetics, pathways and outcomes of many proteins have been found to differ considerably from those in refolding studies2-10. The origin of this folding modulation by the ribosome has remained unknown. Here we have determined atomistic structures of the unfolded state of a model protein on and off the ribosome, which reveal that the ribosome structurally expands the unfolded nascent chain and increases its solvation, resulting in its entropic destabilization relative to the peptide chain in isolation. Quantitative 19F NMR experiments confirm that this destabilization reduces the entropic penalty of folding by up to 30 kcal mol-1 and promotes formation of partially folded intermediates on the ribosome, an observation that extends to other protein domains and is obligate for some proteins to acquire their active conformation. The thermodynamic effects also contribute to the ribosome protecting the nascent chain from mutation-induced unfolding, which suggests a crucial role of the ribosome in supporting protein evolution. By correlating nascent chain structure and dynamics to their folding energetics and post-translational outcomes, our findings establish the physical basis of the distinct thermodynamics of co-translational protein folding.

Sequence-Directed Covalent Protein-RNA Linkages in a Single Step Using Engineered HUH-Tags

Replication-initiating HUH-endonucleases (Reps) are enzymes that form covalent bonds with single-stranded DNA (ssDNA) in a sequence specific manner to initiate rolling circle replication. These nucleases have been co-opted for use in biotechnology as sequence specific protein-ssDNA bioconjugation fusion partners dubbed 'HUH-tags'. Here, we describe the engineering and in vitro characterization of a series of laboratory evolved HUH-tags capable of forming robust sequence-directed covalent bonds with unmodified RNA substrates. We show that promiscuous Rep-RNA interaction can be enhanced through directed evolution from nearly undetectable levels in wildtype enzymes to robust reactivity in final engineered iterations. Taken together, these engineered HUH-tags represent a promising platform for enabling site-specific protein-RNA covalent bioconjugation in vitro, potentially mediating a host of new applications and offering a valuable addition to the HUH-tag repertoire.

A combinatorially complete epistatic fitness landscape in an enzyme active site

Protein engineering often targets binding pockets or active sites which are enriched in epistasis-nonadditive interactions between amino acid substitutions-and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences-a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.

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.

Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function

Directed evolution has emerged as a powerful tool for engineering new biocatalysts. However, introducing new catalytic residues can be destabilizing, and it is generally beneficial to start with a stable enzyme parent. Here we show that the deep learning-based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially-relevant non-native functions. For the Fe(II)/αKG enzyme tP4H, we performed site-saturation mutagenesis with both the wild-type and stabilized design variant and screened for activity increases in a non-native C-H hydroxylation reaction. We observed substantially larger increases in non-native activity for variants obtained from the stabilized scaffold compared to those from the wild-type enzyme. ProteinMPNN is user-friendly and widely-accessible, and straightforward structural criteria were sufficient to obtain stabilized, catalytically-functional variants of the Fe(II)/αKG enzymes tP4H and GriE. Our work suggests that stabilization by computational sequence redesign could be routinely implemented as a first step in directed evolution campaigns for novel biocatalysts.

Enhancing predictions of protein stability changes induced by single mutations using MSA-based Language Models

MOTIVATION

Protein Language Models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a Protein Language Model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data pre-processing to mitigate overfitting.

RESULTS

We ensure comprehensive comparisons by fine-tuning various pre-trained models, taking advantage of analyses such as ablation studies and baselines evaluation. Our methodology introduces a stringent policy to reduce the widespread issue of data leakage, rigorously removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations.

AVAILABILITY AND IMPLEMENTATION

Code and data at https://github.com/RitAreaSciencePark/PLM4Muts.

SUPPLEMENTARY INFORMATION

Supplementary Information is available at Bioinformatics online.

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.

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.