Methods for the directed evolution of proteins

A gradient phage-assisted continuous evolution method for screening suppressor tRNAs in Escherichia coli

Suppressor tRNAs, notable for their capability of reading through the stop codon while maintaining normal peptide synthesis, are promising in treating diseases caused by premature termination codons (PTC). However, the lack of effective engineering methods for suppressor tRNAs has curtailed their application potential. Here, we introduce a directed evolution technology that employs phage-assisted continuous evolution (PACE), combined with gradient biosensors featuring various PTCs in the M13 gene III. Utilizing this novel methodology, we have successfully evolved tRNATrp (UGG) reading through the UGA stop codon in Escherichia coli. Massively parallel sequencing revealed that these mutations predominantly occurred in the anticodon loop. Finally, two suppressor tRNATrp (UGA) mutants exhibited over fivefold increases in readthrough efficiency.

The multifaceted role of proteases and modern analytical methods for investigation of their catalytic activity

We discuss the diverse functions of proteases in the context of their biotechnological and medical significance, as well as analytical approaches used to determine the functional activity of these enzymes. An insight into modern approaches to studying the kinetics and specificity of proteases, based on spectral (absorption, fluorescence), mass spectrometric, immunological, calorimetric, and electrochemical methods of analysis is given. We also examine in detail electrochemical systems for determining the activity and specificity of proteases. Particular attention is given to exploring innovative electrochemical systems based on the detection of the electrochemical oxidation signal of amino acid residues, thereby eliminating the need for extra redox labels in the process of peptide synthesis. In the review, we highlight the main prospects for the further development of electrochemical systems for the study of biotechnologically and medically significant proteases, which will enable the miniaturization of the analytical process for determining the catalytic activity of these enzymes.

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.

Lipases for targeted industrial applications, focusing on the development of biotechnologically significant aspects: A comprehensive review of recent trends in protein engineering

Lipases are remarkable biocatalysts, adept at catalyzing the breakdown of diverse compounds into glycerol, fatty acids, and mono- and di-glycerides via hydrolysis. Beyond this, they facilitate esterification, transesterification, alcoholysis, acidolysis, and more, making them versatile in industrial applications. In industrial processes, lipases that exhibit high stability are favored as they can withstand harsh conditions. However, most native lipases are unable to endure adverse conditions, making them unsuitable for industrial use. Protein engineering proves to be a potent technology in the development of lipases that can function effectively under challenging conditions and fulfill criteria for various industrial processes. This review concentrated on new trends in protein engineering to enhance the diversity of lipase genes and employed in silico methods for predicting and comprehensively analyzing target mutations in lipases. Additionally, key molecular factors associated with industrial characteristics of lipases, including thermostability, solvent tolerance, catalytic activity, and substrate preference have been elucidated. The present review delved into how industrial traits can be enhanced through directed evolution (epPCR, gene shuffling), rational design (FRESCO, ASR), combined engineering strategies (i.e. CAST, ISM, and FRISM) as protein engineering methodologies in contexts of biodiesel production, food processing, and applications of detergent, pharmaceutics, and plastic degradation.

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.

In vitro generation of genetic diversity for directed evolution by error-prone artificial DNA synthesis

Generating genetic diversity lies at the heart of directed evolution which has been widely used to engineer genetic parts and gene circuits in synthetic biology. With the ever-expanding application of directed evolution, different approaches of generating genetic diversity are required to enrich the traditional toolbox. Here we show in vitro generation of genetic diversity for directed evolution by error-prone artificial DNA synthesis (epADS). This approach comprises a three-step process which incorporates base errors randomly generated during chemical synthesis of oligonucleotides under specific conditions into the target DNA. Through this method, 200 ~ 4000 folds of diversification in fluorescent strength have been achieved in genes encoding fluorescent proteins. EpADS has also been successfully used to diversify regulatory genetic parts, synthetic gene circuits and even increase microbial tolerance to carbenicillin in a short time period. EpADS would be an alternative tool for directed evolution which may have useful applications in synthetic biology.

A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution

Monitoring changes of signaling molecules and metabolites with high temporal resolution is key to understanding dynamic biological systems. Here, we use directed evolution to develop a genetically encoded ratiometric biosensor for c-di-GMP, a ubiquitous bacterial second messenger regulating important biological processes like motility, surface attachment, virulence and persistence. The resulting biosensor, cdGreen2, faithfully tracks c-di-GMP in single cells and with high temporal resolution over extended imaging times, making it possible to resolve regulatory networks driving bimodal developmental programs in different bacterial model organisms. We further adopt cdGreen2 as a simple tool for in vitro studies, facilitating high-throughput screens for compounds interfering with c-di-GMP signaling and biofilm formation. The sensitivity and versatility of cdGreen2 could help reveal c-di-GMP dynamics in a broad range of microorganisms with high temporal resolution. Its design principles could also serve as a blueprint for the development of similar, orthogonal biosensors for other signaling molecules, metabolites and antibiotics.

Accelerated Engineering of ELP-Based Materials through Hybrid Biomimetic-De Novo Predictive Molecular Design

Efforts to engineer high-performance protein-based materials inspired by nature have mostly focused on altering naturally occurring sequences to confer the desired functionalities, whereas de novo design lags significantly behind and calls for unconventional innovative approaches. Here, using partially disordered elastin-like polypeptides (ELPs) as initial building blocks this work shows that de novo engineering of protein materials can be accelerated through hybrid biomimetic design, which this work achieves by integrating computational modeling, deep neural network, and recombinant DNA technology. This generalizable approach involves incorporating a series of de novo-designed sequences with α-helical conformation and genetically encoding them into biologically inspired intrinsically disordered repeating motifs. The new ELP variants maintain structural conformation and showed tunable supramolecular self-assembly out of thermal equilibrium with phase behavior in vitro. This work illustrates the effective translation of the predicted molecular designs in structural and functional materials. The proposed methodology can be applied to a broad range of partially disordered biomacromolecules and potentially pave the way toward the discovery of novel structural proteins.

Unraveling the Properties of Phage Display Fab Libraries and Their Use in the Selection of Gliadin-Specific Probes by Applying High-Throughput Nanopore Sequencing

Directed evolution is a pivotal strategy for new antibody discovery, which allowed the generation of high-affinity Fabs against gliadin from two antibody libraries in our previous studies. One of the libraries was exclusively derived from celiac patients' mRNA (immune library) while the other was obtained through a protein engineering approach (semi-immune library). Recent advances in high-throughput DNA sequencing techniques are revolutionizing research across genomics, epigenomics, and transcriptomics. In the present work, an Oxford Nanopore in-lab sequencing device was used to comprehensively characterize the composition of the constructed libraries, both at the beginning and throughout the phage-mediated selection processes against gliadin. A customized analysis pipeline was used to select high-quality reads, annotate chain distribution, perform sequence analysis, and conduct statistical comparisons between the different selection rounds. Some immunological attributes of the most representative phage variants after the selection process were also determined. Sequencing results revealed the successful transfer of the celiac immune response features to the immune library and the antibodies derived from it, suggesting the crucial role of these features in guiding the selection of high-affinity recombinant Fabs against gliadin. In summary, high-throughput DNA sequencing has improved our understanding of the selection processes aimed at generating molecular binders against gliadin.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

A Robust Growth-Based Selection Platform to Evolve an Enzyme via Dependency on Noncanonical Tyrosine Analogues

Growth-based selections evaluate the fitness of individual organisms at a population level. In enzyme engineering, such growth selections allow for the rapid and straightforward identification of highly efficient biocatalysts from extensive libraries. However, selection-based improvement of (synthetically useful) biocatalysts is challenging, as they require highly dependable strategies that artificially link their activities to host survival. Here, we showcase a robust and scalable growth-based selection platform centered around the complementation of noncanonical amino acid-dependent bacteria. Specifically, we demonstrate how serial passaging of populations featuring millions of carbamoylase variants autonomously selects biocatalysts with up to 90,000-fold higher initial rates. Notably, selection of replicate populations enriched diverse biocatalysts, which feature distinct amino acid motifs that drastically boost carbamoylase activity. As beneficial substitutions also originated from unintended copying errors during library preparation or cell division, we anticipate that our growth-based selection platform will be applicable to the continuous, autonomous evolution of diverse biocatalysts in the future.

Engineering Microorganisms for Cancer Immunotherapy

Cancer immunotherapy presents a promising approach to fight against cancer by utilizing the immune system. Recently, engineered microorganisms have emerged as a potential strategy in cancer immunotherapy. These microorganisms, including bacteria and viruses, can be designed and modified using synthetic biology and genetic engineering techniques to target cancer cells and modulate the immune system. This review delves into various microorganism-based therapies for cancer immunotherapy, encompassing strategies for enhancing efficacy while ensuring safety and ethical considerations. The development of these therapies holds immense potential in offering innovative personalized treatments for cancer.

High-Temperature Tolerance Protein Engineering through Deep Evolution

Protein engineering aimed at increasing temperature tolerance through iterative mutagenesis and high-throughput screening is often labor-intensive. Here, we developed a deep evolution (DeepEvo) strategy to engineer protein high-temperature tolerance by generating and selecting functional sequences using deep learning models. Drawing inspiration from the concept of evolution, we constructed a high-temperature tolerance selector based on a protein language model, acting as selective pressure in the high-dimensional latent spaces of protein sequences to enrich those with high-temperature tolerance. Simultaneously, we developed a variant generator using a generative adversarial network to produce protein sequence variants containing the desired function. Afterward, the iterative process involving the generator and selector was executed to accumulate high-temperature tolerance traits. We experimentally tested this approach on the model protein glyceraldehyde 3-phosphate dehydrogenase, obtaining 8 variants with high-temperature tolerance from just 30 generated sequences, achieving a success rate of over 26%, demonstrating the high efficiency of DeepEvo in engineering protein high-temperature tolerance.

Computational peptide discovery with a genetic programming approach

The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POETRegex , where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET models and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.

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.

A New Architecture for DNA-Templated Synthesis in Which Abasic Sites Protect Reactants from Degradation

The synthesis of artificial sequence-defined polymers that match and extend the functionality of proteins is an important goal in materials science. One way of achieving this is to program a sequence of chemical reactions between precursor building blocks by means of attached oligonucleotide adapters. However, hydrolysis of the reactive building blocks has so far limited the length and yield of product that can be obtained using DNA-templated reactions. Here, we report an architecture for DNA-templated synthesis in which reactants are tethered at internal abasic sites on opposite strands of a DNA duplex. We show that an abasic site within a DNA duplex can protect a nearby thioester from degradation, significantly increasing the yield of a DNA-templated reaction. This protective effect has the potential to overcome the challenges associated with programmable, sequence-controlled synthesis of long non-natural polymers by extending the lifetime of the reactive building blocks.

Rational evolution for altering the ligand preference of estrogen receptor alpha

Estrogen receptor α is commonly used in synthetic biology to control the activity of genome editing tools. The activating ligands, estrogens, however, interfere with various cellular processes, thereby limiting the applicability of this receptor. Altering its ligand preference to chemicals of choice solves this hurdle but requires adaptation of unspecified ligand-interacting residues. Here, we provide a solution by combining rational protein design with multi-site-directed mutagenesis and directed evolution of stably integrated variants in Saccharomyces cerevisiae. This method yielded an estrogen receptor variant, named TERRA, that lost its estrogen responsiveness and became activated by tamoxifen, an anti-estrogenic drug used for breast cancer treatment. This tamoxifen preference of TERRA was maintained in mammalian cells and mice, even when fused to Cre recombinase, expanding the mammalian synthetic biology toolbox. Not only is our platform transferable to engineer ligand preference of any steroid receptor, it can also profile drug-resistance landscapes for steroid receptor-targeted therapies.

Directed Evolution of Acoustic Reporter Genes Using High-Throughput Acoustic Screening

A major challenge in the fields of biological imaging and synthetic biology is noninvasively visualizing the functions of natural and engineered cells inside opaque samples such as living animals. One promising technology that addresses this limitation is ultrasound (US), with its penetration depth of several cm and spatial resolution on the order of 100 µm. 1 Within the past decade, reporter genes for US have been introduced 2,3 and engineered 4,5 to link cellular functions to US signals via heterologous expression in commensal bacteria and mammalian cells. These acoustic reporter genes (ARGs) represent a novel class of genetically encoded US contrast agent, and are based on air-filled protein nanostructures called gas vesicles (GVs). 6 Just as the discovery of fluorescent proteins was followed by the improvement and diversification of their optical properties through directed evolution, here we describe the evolution of GVs as acoustic reporters. To accomplish this task, we establish high-throughput, semi-automated acoustic screening of ARGs in bacterial cultures and use it to screen mutant libraries for variants with increased nonlinear US scattering. Starting with scanning site saturation libraries for two homologs of the primary GV structural protein, GvpA/B, two rounds of evolution resulted in GV variants with 5- and 14-fold stronger acoustic signals than the parent proteins. We anticipate that this and similar approaches will help high-throughput protein engineering play as large a role in the development of acoustic biomolecules as it has for their fluorescent counterparts.

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.

Controlling Recombination to Evolve Bacteriophages

Recombination among different phages sometimes facilitates their ability to grow on new hosts. Protocols to direct the evolution of phage host range, as might be used in the application of phage therapy, would then benefit from including steps to enable recombination. Applying mathematical and computational models, in addition to experiments using phages T3 and T7, we consider ways that a protocol may influence recombination levels. We first address coinfection, which is the first step to enabling recombination. The multiplicity of infection (MOI, the ratio of phage to cell concentration) is insufficient for predicting (co)infection levels. The force of infection (the rate at which cells are infected) is also critical but is more challenging to measure. Using both a high force of infection and high MOI (>1) for the different phages ensures high levels of coinfection. We also apply a four-genetic-locus model to study protocol effects on recombinant levels. Recombinants accumulate over multiple generations of phage growth, less so if one phage outgrows the other. Supplementing the phage pool with the low-fitness phage recovers some of this 'lost' recombination. Overall, fine tuning of phage recombination rates will not be practical with wild phages, but qualitative enhancement can be attained with some basic procedures.

Empowering Site-Specific Bioconjugations In Vitro and In Vivo: Advances in Sortase Engineering and Sortase-Mediated Ligation

Sortase-mediated ligation (SML) has emerged as a powerful and versatile methodology for site-specific protein conjugation, functionalization/labeling, immobilization, and design of biohybrid molecules and systems. However, the broader application of SML faces several challenges, such as limited activity and stability, dependence on calcium ions, and reversible reactions caused by nucleophilic side-products. Over the past decade, protein engineering campaigns and particularly directed evolution, have been extensively employed to overcome sortase limitations, thereby expanding the potential application of SML in multiple directions, including therapeutics, biorthogonal chemistry, biomaterials, and biosensors. This review provides an overview of achieved advancements in sortase engineering and highlights recent progress in utilizing SML in combination with other state-of-the-art chemical and biological methodologies. The aim is to encourage scientists to employ sortases in their conjugation experiments.

Empowering Protein Engineering through Recombination of Beneficial Substitutions

Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.

Pore Restructuring of Peptide Frameworks by Mutations at Distal Packing Residues

Porous framework materials are highly useful for catalysis, adsorption, and separations. Though they are usually made from inorganic and organic building blocks, recently, folded peptides have been utilized for constructing frameworks, opening up an enormous structure-space for exploration. These peptides assemble in a metal-free fashion using π-stacking, H-bonding, dispersion forces, and the hydrophobic effect. Manipulation of pore-defining H-bonding residues is known to generate new topologies, but the impact of mutations in the hydrophobic packing region facing away from the pores is less obvious. To explore their effects, we synthesized variants of peptide frameworks with mutations in the hydrophobic packing positions and found by single-crystal X-ray crystallography (SC-XRD) that they induce significant changes to the framework pore structure. These structural changes are driven by a need to maximize van der Waals interactions of the nonpolar groups, which are achieved by various mechanisms including helix twisting, chain flipping, chain offsetting, and desymmetrization. Even subtle changes to the van der Waals interface, such as the introduction of a methyl group or isomeric replacement, result in significant pore restructuring. This study shows that the dispersion interactions upholding a peptide material are a rich area for structural engineering.

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.

Single Deletion Unmasks Hidden Anti-Gram-Negative Bacterial Activity of an Insect Defensin-Derived Peptide

Insect defensins are a large family of antimicrobial peptides primarily active against Gram-positive bacteria. Here, we explore their hidden anti-Gram-negative bacterial potential via a nature-guided strategy inspired by natural deletion variants of Drosophila defensins. Referring to these variants, we deleted the equivalent region of an insect defensin with the first cysteine-containing N-terminus, and the last three cysteine-containing C-terminal regions remained. This 15-mer peptide exhibits low solubility and specifically targets Gram-positive bacteria. Further deletion of alanine-9 remarkably improves its solubility, unmasks its hidden anti-Gram-negative bacterial activity, and alters its states in different environments. Intriguingly, compared with the oxidized form, the 14-mer reduced peptide shows increased activity on Gram-positive and Gram-negative bacteria through a membrane-disruptive mechanism. The broad-spectrum activity and tolerance to high-salt environments and human serum, together with no toxicity to mammalian or human cells, make it a promising candidate for the design of new peptide antibiotics against Gram-negative bacterial infections.

In Vitro Enzyme Self-Selection Using Molecular Programs

Directed evolution provides a powerful route for in vitro enzyme engineering. State-of-the-art techniques functionally screen up to millions of enzyme variants using high throughput microfluidic sorters, whose operation remains technically challenging. Alternatively, in vitro self-selection methods, analogous to in vivo complementation strategies, open the way to even higher throughputs, but have been demonstrated only for a few specific activities. Here, we leverage synthetic molecular networks to generalize in vitro compartmentalized self-selection processes. We introduce a programmable circuit architecture that can link an arbitrary target enzymatic activity to the replication of its encoding gene. Microencapsulation of a bacterial expression library with this autonomous selection circuit results in the single-step and screening-free enrichment of genetic sequences coding for programmed enzymatic phenotypes. We demonstrate the potential of this approach for the nicking enzyme Nt.BstNBI (NBI). We applied autonomous selection conditions to enrich for thermostability or catalytic efficiency, manipulating up to 107 microcompartments and 5 × 105 variants at once. Full gene reads of the libraries using nanopore sequencing revealed detailed mutational activity landscapes, suggesting a key role of electrostatic interactions with DNA in the enzyme's turnover. The most beneficial mutations, identified after a single round of self-selection, provided variants with, respectively, 20 times and 3 °C increased activity and thermostability. Based on a modular molecular programming architecture, this approach does not require complex instrumentation and can be repurposed for other enzymes, including those that are not related to DNA chemistry.

Iron Heme Enzyme-Catalyzed Cyclopropanations with Diazirines as Carbene Precursors: Computational Explorations of Diazirine Activation and Cyclopropanation Mechanism

The mechanism of cyclopropanations with diazirines as air-stable and user-friendly alternatives to commonly employed diazo compounds within iron heme enzyme-catalyzed carbene transfer reactions has been studied by means of density functional theory (DFT) calculations of model systems, quantum mechanics/molecular mechanics (QM/MM) calculations, and molecular dynamics (MD) simulations of the iron carbene and the cyclopropanation transition state in the enzyme active site. The reaction is initiated by a direct diazirine-diazo isomerization occurring in the active site of the enzyme. In contrast, an isomerization mechanism proceeding via the formation of a free carbene intermediate in lieu of a direct, one-step isomerization process was observed for model systems. Subsequent reaction with benzyl acrylate takes place through stepwise C-C bond formation via a diradical intermediate, delivering the cyclopropane product. The origin of the observed diastereo- and enantioselectivity in the enzyme was investigated through MD simulations, which indicate a preferred formation of the cis-cyclopropane by steric control.

Using directed evolution to improve hydrogen production in chimeric hydrogenases from algal species

Algae generate hydrogen from sunlight and water utilizing high-energy electrons generated during photosynthesis. The amount of hydrogen produced in heterologous expression of the wild-type hydrogenase is currently insufficient for industrial applications. One approach to improve hydrogen yields is through directed evolution of the DNA of the native hydrogenase. Here, we created 113 chimeric algal hydrogenase gene variants derived from combining segments of three parent hydrogenases, two from Chlamydomonas reinhardtii (CrHydA1 and CrHydA2) and one from Scenedesmus obliquus (HydA1). To generate chimeras, there were seven segments into which each of the parent hydrogenase genes was divided and recombined in a variety of combinations. The chimeric and parental hydrogenase sequences were cloned for heterologous expression in Escherichia coli, and 40 of the resultant enzymes expressed were assayed for H2 production. Chimeric clones that resulted in equal or greater production obtained with the cloned CrHydA1 parent hydrogenase were those comprised of CrHydA1 sequence in segments #1, 2, 3, and/or 4. These best-performing chimeras all contained one common region, segment #2, the part of the sequence known to contain important amino acids involved in proton transfer or hydrogen cluster coordination. The amino acid sequence distances among all chimeric clones to that of the CrHydA1 parent were determined, and the relationship between sequence distances and experimentally-derived H2 production was evaluated. An additional model determined the correlation between electrostatic potential energy surface area ratios and H2 production. The model yielded several algal mutants with predicted hydrogen productions in a range of two to three times that of the wild-type hydrogenase. The mutant data and the model can now be used to predict which specific mutant sequences may result in even higher hydrogen yields. Overall, results provide more precise details in planning future directed evolution to functionally improve algal hydrogenases.

Data Storage Using DNA

The exponential growth of global data has outpaced the storage capacities of current technologies, necessitating innovative storage strategies. DNA, as a natural medium for preserving genetic information, has emerged as a highly promising candidate for next-generation storage medium. Storing data in DNA offers several advantages, including ultrahigh physical density and exceptional durability. Facilitated by significant advancements in various technologies, such as DNA synthesis, DNA sequencing, and DNA nanotechnology, remarkable progress has been made in the field of DNA data storage over the past decade. However, several challenges still need to be addressed to realize practical applications of DNA data storage. In this review, the processes and strategies of in vitro DNA data storage are first introduced, highlighting recent advancements. Next, a brief overview of in vivo DNA data storage is provided, with a focus on the various writing strategies developed to date. At last, the challenges encountered in each step of DNA data storage are summarized and promising techniques are discussed that hold great promise in overcoming these obstacles.

Biocatalytic Stereoselective Oxidation of 2-Arylindoles

3-Hydroxyindolenines can be used to access several structural motifs that are featured in natural products and pharmaceutical compounds, yet the chemical synthesis of 3-hydroxyindolenines is complicated by overoxidation, rearrangements, and complex product mixtures. The selectivity possible in enzymatic reactions can overcome these challenges and deliver enantioenriched products. Herein, we present the development of an asymmetric biocatalytic oxidation of 2-arylindole substrates aided by a curated library of flavin-dependent monooxygenases (FDMOs) sampled from an ancestral sequence space, a sequence similarity network, and a deep-learning-based latent space model. From this library of FDMOs, a previously uncharacterized enzyme, Champase, from the Valley fever fungus, Coccidioides immitis strain RS, was found to stereoselectively catalyze the oxidation of a variety of substituted indole substrates. The promiscuity of this enzyme is showcased by the oxidation of a wide variety of substituted 2-arylindoles to afford the respective 3-hydroxyindolenine products in moderate to excellent yields and up to 95:5 er.

Improving plastic degrading enzymes via directed evolution

Plastic degrading enzymes have immense potential for use in industrial applications. Protein engineering efforts over the last decade have resulted in considerable enhancement of many properties of these enzymes. Directed evolution, a protein engineering approach that mimics the natural process of evolution in a laboratory, has been particularly useful in overcoming some of the challenges of structure-based protein engineering. For example, directed evolution has been used to improve the catalytic activity and thermostability of polyethylene terephthalate (PET)-degrading enzymes, although its use for the improvement of other desirable properties, such as solvent tolerance, has been less studied. In this review, we aim to identify some of the knowledge gaps and current challenges, and highlight recent studies related to the directed evolution of plastic-degrading enzymes.

Interactive computational and experimental approaches improve the sensitivity of periplasmic binding protein-based nicotine biosensors for measurements in biofluids

We developed fluorescent protein sensors for nicotine with improved sensitivity. For iNicSnFR12 at pH 7.4, the proportionality constant for ∆F/F0vs [nicotine] (δ-slope, 2.7 μM-1) is 6.1-fold higher than the previously reported iNicSnFR3a. The activated state of iNicSnFR12 has a fluorescence quantum yield of at least 0.6. We measured similar dose-response relations for the nicotine-induced absorbance increase and fluorescence increase, suggesting that the absorbance increase leads to the fluorescence increase via the previously described nicotine-induced conformational change, the 'candle snuffer' mechanism. Molecular dynamics (MD) simulations identified a binding pose for nicotine, previously indeterminate from experimental data. MD simulations also showed that Helix 4 of the periplasmic binding protein (PBP) domain appears tilted in iNicSnFR12 relative to iNicSnFR3a, likely altering allosteric network(s) that link the ligand binding site to the fluorophore. In thermal melt experiments, nicotine stabilized the PBP of the tested iNicSnFR variants. iNicSnFR12 resolved nicotine in diluted mouse and human serum at 100 nM, the peak [nicotine] that occurs during smoking or vaping, and possibly at the decreasing levels during intervals between sessions. NicSnFR12 was also partially activated by unidentified endogenous ligand(s) in biofluids. Improved iNicSnFR12 variants could become the molecular sensors in continuous nicotine monitors for animal and human biofluids.

The shortest path method (SPM) webserver for computational enzyme design

SPMweb is the online webserver of the Shortest Path Map (SPM) tool for identifying the key conformationally-relevant positions of a given enzyme structure and dynamics. The server is built on top of the DynaComm.py code and enables the calculation and visualization of the SPM pathways. SPMweb is easy-to-use as it only requires three input files: the three-dimensional structure of the protein of interest, and the two matrices (distance and correlation) previously computed from a Molecular Dynamics simulation. We provide in this publication information on how to generate the files for SPM construction even for non-expert users and discuss the most relevant parameters that can be modified. The tool is extremely fast (it takes less than one minute per job), thus allowing the rapid identification of distal positions connected to the active site pocket of the enzyme. SPM applications expand from computational enzyme design, especially if combined with other tools to identify the preferred substitution at the identified position, but also to rationalizing allosteric regulation, and even cryptic pocket identification for drug discovery. The simple user interface and setup make the SPM tool accessible to the whole scientific community. SPMweb is freely available for academia at http://spmosuna.com/.

Establishing a synthetic orthogonal replication system enables accelerated evolution in E. coli

The evolution of new function in living organisms is slow and fundamentally limited by their critical mutation rate. Here, we established a stable orthogonal replication system in Escherichia coli. The orthogonal replicon can carry diverse cargos of at least 16.5 kilobases and is not copied by host polymerases but is selectively copied by an orthogonal DNA polymerase (O-DNAP), which does not copy the genome. We designed mutant O-DNAPs that selectively increase the mutation rate of the orthogonal replicon by two to four orders of magnitude. We demonstrate the utility of our system for accelerated continuous evolution by evolving a 150-fold increase in resistance to tigecycline in 12 days. And, starting from a GFP variant, we evolved a 1000-fold increase in cellular fluorescence in 5 days.

Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based Screening

In this study, we employed a novel approach to improve the serotonin-responsive ssDNA-wrapped single-walled carbon nanotube (ssDNA-SWCNT) nanosensors, combining directed evolution and machine learning-based prediction. Our iterative optimization process is aimed at the sensitivity and selectivity of ssDNA-SWCNT nanosensors. In the three rounds for higher serotonin sensitivity, we substantially improved sensitivity, achieving a remarkable 2.5-fold enhancement in fluorescence response compared to the original sequence. Following this, we directed our efforts towards selectivity for serotonin over dopamine in the two rounds. Despite the structural similarity between these neurotransmitters, we achieved a 1.6-fold increase in selectivity. This innovative methodology, offering high-throughput screening of mutated sequences, marks a significant advancement in biosensor development. The top-performing nanosensors, N2-1 (sensitivity) and L1-14 (selectivity) present promising reference sequences for future studies involving serotonin detection.

Interactive computational and experimental approaches improve the sensitivity of periplasmic binding protein-based nicotine biosensors for measurements in biofluids

We developed fluorescent protein sensors for nicotine with improved sensitivity. For iNicSnFR12 at pH 7.4, the proportionality constant for Δ F / F 0 vs [nicotine] (δ-slope, 2.7 μM-1) is 6.1-fold higher than the previously reported iNicSnFR3a. The activated state of iNicSnFR12 has a fluorescence quantum yield of at least 0.6. We measured similar dose-response relations for the nicotine-induced absorbance increase and fluorescence increase, suggesting that the absorbance increase leads to the fluorescence increase via the previously described nicotine-induced conformational change, the "candle snuffer" mechanism. Molecular dynamics (MD) simulations identified a binding pose for nicotine, previously indeterminate from experimental data. MD simulations also showed that Helix 4 of the periplasmic binding protein (PBP) domain appears tilted in iNicSnFR12 relative to iNicSnFR3a, likely altering allosteric network(s) that link the ligand binding site to the fluorophore. In thermal melt experiments, nicotine stabilized the PBP of the tested iNicSnFR variants. iNicSnFR12 resolved nicotine in diluted mouse and human serum at 100 nM, the peak [nicotine] that occurs during smoking or vaping, and possibly at the decreasing levels during intervals between sessions. NicSnFR12 was also partially activated by unidentified endogenous ligand(s) in biofluids. Improved iNicSnFR12 variants could become the molecular sensors in continuous nicotine monitors for animal and human biofluids.

Biosynthesis, acquisition, regulation, and upcycling of heme: recent advances

Heme, an iron-containing tetrapyrrole in hemoproteins, including: hemoglobin, myoglobin, catalase, cytochrome c, and cytochrome P450, plays critical physiological roles in different organisms. Heme-derived chemicals, such as biliverdin, bilirubin, and phycocyanobilin, are known for their antioxidant and anti-inflammatory properties and have shown great potential in fighting viruses and diseases. Therefore, more and more attention has been paid to the biosynthesis of hemoproteins and heme derivatives, which depends on the adequate heme supply in various microbial cell factories. The enhancement of endogenous biosynthesis and exogenous uptake can improve the intracellular heme supply, but the excess free heme is toxic to the cells. Therefore, based on the heme-responsive regulators, several sensitive biosensors were developed to fine-tune the intracellular levels of heme. In this review, recent advances in the: biosynthesis, acquisition, regulation, and upcycling of heme were summarized to provide a solid foundation for the efficient production and application of high-value-added hemoproteins and heme derivatives.

Advancing Antibody Engineering through Synthetic Evolution and Machine Learning

Abs are versatile molecules with the potential to achieve exceptional binding to target Ags, while also possessing biophysical properties suitable for therapeutic drug development. Protein display and directed evolution systems have transformed synthetic Ab discovery, engineering, and optimization, vastly expanding the number of Ab clones able to be experimentally screened for binding. Moreover, the burgeoning integration of high-throughput screening, deep sequencing, and machine learning has further augmented in vitro Ab optimization, promising to accelerate the design process and massively expand the Ab sequence space interrogated. In this Brief Review, we discuss the experimental and computational tools employed in synthetic Ab engineering and optimization. We also explore the therapeutic challenges posed by developing Abs for infectious diseases, and the prospects for leveraging machine learning-guided protein engineering to prospectively design Abs resistant to viral escape.

Guiding discovery of protein sequence-structure-function modeling

MOTIVATION

Protein engineering techniques are key in designing novel catalysts for a wide range of reactions. Although approaches vary in their exploration of the sequence-structure-function paradigm, they are often hampered by the labor-intensive steps of protein expression and screening. In this work, we describe the development and testing of a high-throughput in silico sequence-structure-function pipeline using AlphaFold2 and fast Fourier transform docking that is benchmarked with enantioselectivity and reactivity predictions for an ancestral sequence library of fungal flavin-dependent monooxygenases.

RESULTS

The predicted enantioselectivities and reactivities correlate well with previously described screens of an experimentally available subset of these proteins and capture known changes in enantioselectivity across the phylogenetic tree representing ancestorial proteins from this family. With this pipeline established as our functional screen, we apply ensemble decision tree models and explainable AI techniques to build sequence-function models and extract critical residues within the binding site and the second-sphere residues around this site. We demonstrate that the top-identified key residues in the control of enantioselectivity and reactivity correspond to experimentally verified residues. The in silico sequence-to-function pipeline serves as an accelerated framework to inform protein engineering efforts from vast informative sequence landscapes contained in protein families, ancestral resurrects, and directed evolution campaigns.

AVAILABILITY

Jupyter notebooks detailing the sequence-structure-function pipeline are available at https://github.com/BrooksResearchGroup-UM/seq_struct_func.

The effects of the chemical modification on immobilized lipase features are affected by the enzyme crowding in the support

In this article, we have analyzed the interactions between enzyme crowding on a given support and its chemical modification (ethylenediamine modification via the carbodiimide route and picryl sulfonic (TNBS) modification of the primary amino groups) on the enzyme activity and stability. Lipase from Thermomyces lanuginosus (TLL) and lipase B from Candida antarctica (CALB) were immobilized on octyl-agarose beads at two very different enzyme loadings, one of them exceeding the capacity of the support, one well under this capacity. Chemical modifications of the highly loaded and lowly loaded biocatalysts gave very different results in terms of activity and stability, which could increase or decrease enzyme activity depending on the enzyme support loading. For example, both lowly loaded biocatalysts increased their activity after modification while the effect was the opposite for the highly loaded biocatalysts. Additionally, the modification with TNBS of highly loaded CALB biocatalyst increased its stability while decrease the activity.

CRISPR base editor-based targeted random mutagenesis (BE-TRM) toolbox for directed evolution

Directed evolution (DE) of desired locus by targeted random mutagenesis (TRM) tools is a powerful approach for generating genetic variations with novel or improved functions, particularly in complex genomes. TRM-based DE involves developing a mutant library of targeted DNA sequences and screening the variants for the desired properties. However, DE methods have for a long time been confined to bacteria and yeasts. Lately, CRISPR/Cas and DNA deaminase-based tools that circumvent enduring barriers such as longer life cycle, small library sizes, and low mutation rates have been developed to facilitate DE in native genetic environments of multicellular organisms. Notably, deaminase-based base editing-TRM (BE-TRM) tools have greatly expanded the scope and efficiency of DE schemes by enabling base substitutions and randomization of targeted DNA sequences. BE-TRM tools provide a robust platform for the continuous molecular evolution of desired proteins, metabolic pathway engineering, creation of a mutant library of desired locus to evolve novel functions, and other applications, such as predicting mutants conferring antibiotic resistance. This review provides timely updates on the recent advances in BE-TRM tools for DE, their applications in biology, and future directions for further improvements. [BMB Reports 2024; 57(1): 30-39].

ACIDES: on-line monitoring of forward genetic screens for protein engineering

Forward genetic screens of mutated variants are a versatile strategy for protein engineering and investigation, which has been successfully applied to various studies like directed evolution (DE) and deep mutational scanning (DMS). While next-generation sequencing can track millions of variants during the screening rounds, the vast and noisy nature of the sequencing data impedes the estimation of the performance of individual variants. Here, we propose ACIDES that combines statistical inference and in-silico simulations to improve performance estimation in the library selection process by attributing accurate statistical scores to individual variants. We tested ACIDES first on a random-peptide-insertion experiment and then on multiple public datasets from DE and DMS studies. ACIDES allows experimentalists to reliably estimate variant performance on the fly and can aid protein engineering and research pipelines in a range of applications, including gene therapy.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2 ) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

A metagenomic library cloning strategy that promotes high-level expression of captured genes to enable efficient functional screening

Functional screening of environmental DNA (eDNA) libraries is a potentially powerful approach to discover enzymatic "unknown unknowns", but is usually heavily biased toward the tiny subset of genes preferentially transcribed and translated by the screening strain. We have overcome this by preparing an eDNA library via partial digest with restriction enzyme FatI (cuts CATG), causing a substantial proportion of ATG start codons to be precisely aligned with strong plasmid-encoded promoter and ribosome-binding sequences. Whereas we were unable to select nitroreductases from standard metagenome libraries, our FatI strategy yielded 21 nitroreductases spanning eight different enzyme families, each conferring resistance to the nitro-antibiotic niclosamide and sensitivity to the nitro-prodrug metronidazole. We showed expression could be improved by co-expressing rare tRNAs and encoded proteins purified directly using an embedded His6-tag. In a transgenic zebrafish model of metronidazole-mediated targeted cell ablation, our lead MhqN-family nitroreductase proved ∼5-fold more effective than the canonical nitroreductase NfsB.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning

The combination of wet-lab experimental data on multi-site combinatorial mutations and machine learning is an innovative method in protein engineering. In this study, we used an innovative sequence-activity relationship (innov'SAR) methodology based on novel descriptors and digital signal processing (DSP) to construct a predictive model. In this paper, 21 experimental (R)-selective amine transaminases from Aspergillus terreus (AT-ATA) were used as an input to predict higher thermostability mutants than those predicted using the existing data. We successfully improved the coefficient of determination (R2) of the model from 0.66 to 0.92. In addition, root-mean-squared deviation (RMSD), root-mean-squared fluctuation (RMSF), solvent accessible surface area (SASA), hydrogen bonds, and the radius of gyration were estimated based on molecular dynamics simulations, and the differences between the predicted mutants and the wild-type (WT) were analyzed. The successful application of the innov'SAR algorithm in improving the thermostability of AT-ATA may help in directed evolutionary screening and open up new avenues for protein engineering.

Engineered bacterial orthogonal DNA replication system for continuous evolution

Continuous evolution can generate biomolecules for synthetic biology and enable evolutionary investigation. The orthogonal DNA replication system (OrthoRep) in yeast can efficiently mutate long DNA fragments in an easy-to-operate manner. However, such a system is lacking in bacteria. Therefore, we developed a bacterial orthogonal DNA replication system (BacORep) for continuous evolution. We achieved this by harnessing the temperate phage GIL16 DNA replication machinery in Bacillus thuringiensis with an engineered error-prone orthogonal DNA polymerase. BacORep introduces all 12 types of nucleotide substitution in 15-kilobase genes on orthogonally replicating linear plasmids with a 6,700-fold higher mutation rate than that of the host genome, the mutation rate of which is unchanged. Here we demonstrate the utility of BacORep-based continuous evolution by generating strong promoters applicable to model bacteria, Bacillus subtilis and Escherichia coli, and achieving a 7.4-fold methanol assimilation increase in B. thuringiensis. BacORep is a powerful tool for continuous evolution in prokaryotic cells.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

Molecular analysis of the type III interferon complex and its applications in protein engineering

Type III interferons (IFNλs) are cytokines with critical roles in the immune system and are attractive therapeutic candidates due to their tissue-specific activity. Despite entering several clinical trials, results have demonstrated limited efficacy and potency, partially attributed to low-affinity protein-protein interactions (PPIs) responsible for receptor complex formation. Subsequently, structural studies of the native IFNλ signaling complexes remain inaccessible. While protein engineering can overcome affinity limitations, tools to investigate low-affinity systems like these remain limited. To provide insights into previous efforts to strengthen the PPIs within this complex, we perform a molecular analysis of the extracellular ternary complexes of IFNλ3 using both computational and experimental approaches. We first use molecular simulations and modeling to quantify differences in PPIs and residue strain fluctuations, generate detailed free energy landscapes, and reveal structural differences between an engineered, high-affinity complex, and a model of the wild-type, low-affinity complex. This analysis illuminates distinct behaviors of these ligands, yielding mechanistic insights into IFNλ complex formation. We then apply these computational techniques in protein engineering and design by utilizing simulation data to identify hotspots of interaction to rationally engineer the native cytokine-receptor complex for increased stability. These simulations are then validated by experimental techniques, showing that a single mutation at a computationally predicted site of interaction between the two receptors increases PPIs and improves complex formation for all IFNλs. This study highlights the power of molecular dynamics simulations for protein engineering and design as applied to the IFNλ family but also presents a potential tool for analysis and engineering of other systems with low-affinity PPIs.