As demand for the rare earth elements (REEs) that power new and emerging technologies increases rapidly, there is a growing need for next-generation separations systems that can provide more selective isolation of these critical, but difficult-to-purify materials. Due to their evolvability and compatibility with existing separations platforms, polypeptides represent an ideal and environmentally-friendly chassis with which to meet this challenge; however, there exist no high-throughput approaches to selecting polypeptides with improved affinity and selectivity towards the various REEs, chiefly due to the difficulty of detecting a selectable phenotype. Here, we present work towards overcoming this obstacle by developing a bacterial display platform for the direct sensing of REE binding in living bacterial cells. Our approach exploits the antenna effect, which allows a sufficiently proximal fluorophore to sensitize a bound europium ion and stimulate emission through resonant energy transfer, and makes use of genetic code expansion to insert a fluorescent amino acid directly into the polypeptide backbone in cellulo. We find that targeting a model lanthanide-binding peptide to the periplasm enables the accumulation of sufficient concentrations of this binder in an easily accessible subcellular localization such that robust signal can be detected. Through optimization of encoding, sensitization, and handling, we further improve our signal up to >20-fold over control cells lacking binders and demonstrate facile differentiation and characterization of several variant peptides. Our work paves the way for high-throughput synthetic evolution of REE binding polypeptides with expanded functionality.

The fetal form of the insulin receptor (IRA) is predominantly expressed in many cancers, where IR signaling promotes progression of the disease. Alternatively, the adult IR isoform (IRB) is predominantly expressed in normal tissues. Previous attempts to target IR indiscriminately via dual IR/IGF1R tyrosine kinase inhibitors frequently failed due to on-target off-tumor effects. However, an IRA-specific therapeutic has the potential to specifically target cancer while sparing normal tissues. Furthermore, an IRA-specific binder would advance research regarding this disease-associated isoform. Yet, isoform-specific IR binders have not been developed. Herein, we engineer six high-affinity, IRA-specific protein binders from a synthetic miniprotein scaffold. These miniprotein variants have IRA KDs ranging from 0.98-7.6 nM with no appreciable IRB binding at 100 nM ligand. Five of the six variants are synergistic agonists. One variant (V1) is a passive, IRA-specific binder with a KD = 6.3 nM and no IRB binding at 1 μM ligand as well as no IGF1R binding at 100 nM, which provides compelling potential as a targeted therapeutic. Additionally, we present the discovery process for these molecules, a yeast display directed evolution campaign utilizing a combination of sorting techniques. Retrospective analysis of the discovery process revealed the importance of alternating selections for target affinity and isoform specificity to generate binders with a favorable combination of these properties.

Mapping transient protein-protein interactions remain a major challenge in studying viral host-pathogen interfaces. While some virus-host interactions are stable and readily captured, the majority are highly dynamic, reflecting the need for viral proteins to engage distinct host factors at different stages of the life cycle. Here, we employ a protein engineering strategy based on the site-specific incorporation of the unnatural acid p-azido-L-phenylalanine (AzF) to enable photo-crosslinking proteomic analysis of the SARS-CoV-2 accessory protein Orf3a in live cells. Genetic installation of AzF at residue K198 of Orf3a permitted UV-induced covalent capture of proximal host interacting proteins, overcoming challenges associated with membrane localization and limited protein abundance. A total of 248 high-confidence Orf3a-interacting proteins were reproducibly identified and subjected to gene ontology analysis, revealing enrichment in innate immune signaling, antiviral defense, RNA processing, and viral replication-associated pathways. Orf3a is an accessory protein that functions as a viroporin and traffics across multiple cellular compartments, and was found to interact with host RNA helicases, RNA-binding proteins, immune regulators, and metabolic enzymes implicated in SARS-CoV-2 infection. Together, these results demonstrate that genetically encoded, site-specific photo-crosslinking enables selective capture of transient interactions that are often missed by nonspecific 254 nm UV crosslinking approaches and highlights Orf3a as a multifunctional protein that engages diverse host pathways. More broadly, this study establishes a generalizable framework for leveraging unnatural amino acid-based protein engineering approaches to interrogate dynamic host-pathogen interactions.

Bacteriophage M13 substrate libraries are a useful tool to discover novel protease substrates. In this study, we have mapped 7-mer protease substrates cleaved by native Escherichia coli proteases during phage outgrowth. Cleavage was monitored by the loss of a biotinylated AviTag at the N-terminus of the recombinant M13 minor coat protein, protein III. Using this method, we isolated 120 phage-displayed substrates that contain dibasic amino acid motifs (Lys-Lys, Arg-Arg, Lys-Arg, and Arg-Lys), of which the Lys-Lys motif has not been previously described using substrate phage. Since dibasic motifs are known to be substrates of the outer membrane protein proteases OmpP and OmpT, the phage library was transferred into a mutant strain deficient in both protease activities, and the selection process was repeated. Sequencing of recovered clones revealed no enrichment of the four dibasic motifs, demonstrating that all four motifs are substrates of Omptin proteases. These findings indicate that phage propagation in Omptin-expressing bacteria leads to the loss of dibasic amino acid motifs from phage-displayed combinatorial peptide, protein, and antibody libraries. This suggests that the presence of dibasic residues in proteins and peptides displayed on bacteriophage M13 should necessitate the use of OmpT- and OmpP-deficient bacterial hosts.

Protein bioconjugates have a wide range of applications, including for targeted drug delivery and in vivo diagnostics. Expanding the chemistry available to link protein to cargo remains a critical consideration for the development of novel bioconjugates for unmet needs. Currently, most FDA-approved protein bioconjugates are based on the antibody structure, which constrains the available conjugation chemistries and applications. Alternative scaffold proteins have potential as targeting molecules that advance the synthetic possibilities. Here, we report two methods of high-yield incorporation of azide-containing groups into an Fn3 domain protein scaffold to enable click chemistry conjugation. One approach incorporates noncanonical amino acid azidohomoalanine during recombinant protein expression, and the other method develops a linker system using a unique thiol residue in the Fn3 structure. These synthetic approaches enabled orthogonal, dual labeling. The resulting conjugates retain functionality in receptor binding assays, validating these methods for diverse applications in engineering scaffold proteins for bioconjugate applications.

Generative models have transformed structural bioinformatics, enabling antibody and nanobody design against protein epitopes; however, nanobody engineering for small molecule sensing remains largely experimental. In this work, we evaluate seven state-of-the-art structure predictors, AlphaFold3, Chai-1, Boltz-2x, RoseTTAFold3, Protenix, FlowDock and OmegaFold, on nanobody-small molecule complexes. Most predictors accurately reproduced nanobody and CDR geometries but struggled with ligand placement and orientation, although co-folding improved overall accuracy. Contact analysis revealed that CDR1, rather than CDR3, was predominantly involved in ligand binding. Intrinsic confidence scores correlated poorly with experimental accuracy and showed limited power in distinguishing binders from non-binders. Increasing the number of samples and seeds yielded modest gains in accuracy, whereas additional recycles did not. These findings highlight both the strengths and limitations of structure prediction methods for nanobody-small molecule complexes.

With this status report, we aim to provide a timely snapshot of the protein engineering field as a broad and rapidly advancing discipline that integrates computational, molecular biology, structure-guided, evolutionary, and synthetic approaches to create new and improved proteins with tailored structures and useful functions. The report is organized into eight thematic areas spanning core methodologies and major application domains, including enzymes, therapeutics, detection, synthetic biology, and materials. Contributions from experts across these areas highlight both the historical foundations and recent advances in their respective fields, with particular emphasis on the growing influence of machine learning and artificial intelligence-based methods. Emerging from this broad overview is a central message: protein engineering appears to be entering a golden age, defined by a rapidly accelerating pace of progress, even as significant challenges in design, screening, and real-world application remain. Looking ahead, the continued integration of computational and experimental strategies is poised to further accelerate the impact of protein engineering across an expanding range of economically and societally important sectors, from therapeutics and molecular imaging to diagnostics, plastic recycling, and industrial chemistry.

Engineering proteins for therapeutic applications is a field that has seen substantial growth in the past two decades, but challenges remain. A major deficiency in current strategies is the lack of a means to efficiently screen for binding to key epitopes to yield a desired function. To meet this challenge, we designed a tethered yeast surface display construct that leverages high local concentrations by tethering a candidate protein binder to a target protein of interest (POI) via a flexible peptide linker. These high local concentrations enable screening of epitope-specific binders based on decreased signal due to inhibitory function when the construct is assayed alongside a competitive POI binder. We demonstrate that epitope-specific screening and enrichment is possible based on fluorescent output and study key optimization parameters. We anticipate this technique will accelerate binder development by reducing the need for downstream low throughput epitope mapping of isolated proteins.

Beta-glucosidase (EC 3.2.1.21) is crucial to biorefinery industries because it is the bottleneck enzyme in the enzymatic conversion of cellobiose to glucose, a key component of the enzyme complex that converts lignocellulosic biomass into glucose. The enzyme is inhibited by its own product, glucose, which accumulates and blocks the catalytic tunnel or the product exit region. This is the main drawback of this enzyme. In this work, we engineered AfBgl1 to enhance its thermal stability. FireProt was used to generate a mutant library, and the mutants with the highest free energies (Q302P, S187W, and S301D) were selected for further studies. The comparative analyses revealed that the mutant protein Q302P showed increased substrate affinity (Km of Q302P = 0.3 mM and AfBgl1 = 1.42 mM), activity-based thermostability (up to 70°C), and increased glucose tolerance (Ki of Q302P = 188.65 mM and AfBgl1 = 83.96 mM). Interestingly, a mutation based on free energy gains these properties of glucose tolerance and catalytic efficiency, which is generally unconventional. The mutational effect has been justified and explained through structural analysis and molecular dynamics simulations, indicating that the mutation's position is crucial for triggering this robust effect. The study delineates the scope of free-energy-based protein engineering beyond thermostability and highlights the influence of local fluctuations on the dynamics of the catalytic tunnel.

Interleukin-7 (IL-7) is indispensable for T cell development and homeostasis, and clinical studies have demonstrated its ability to restore T cell numbers in lymphopenic conditions. However, the therapeutic application of IL-7 has been limited by its short half-life and potential immunogenicity of recombinant variants. To overcome these challenges, we engineered a fully human tandem bispecific antibody, TB4, that functions as a potent IL-7 receptor agonist by cis-targeting IL-7 receptor alpha (IL-7Rα) and the common γ chain (γc). The tetravalent tandem scFv-Fc architecture, coupled with optimized linker length, conferred markedly enhanced binding affinity and avidity compared to the knobs-into-holes bispecific format, with domain orientation designed to limit unintended γc binding. At the cellular level, TB4 engaged both receptor subunits and exhibited reduced internalization dynamics, features that are likely to contribute to the sustained STAT5 phosphorylation observed in primary human T cells. Functionally, TB4 supported long-term T cell survival and expansion, but selectively promoted the expansion of CD4+ memory subsets, especially effector memory cells re-expressing CD45RA populations, in contrast to the broader effects of native IL-7. Transcriptomic profiling further revealed that while both TB4 and IL-7 activated proliferative gene programs, TB4 uniquely drove an antiviral and innate immune signature. Collectively, these findings establish TB4 as a next-generation IL-7R agonist antibody with a differentiated mechanism of action and potential as a precision immunomodulator that can selectively expand and reprogram T cells.

Directed evolution for protein engineering, as currently practiced in the biotechnology and pharmaceutical industries, is both tedious and expensive. Computationally driven protein design has the potential to expedite the engineering process and generate high-quality variants at a lower cost than traditional approaches. We investigated the effectiveness of two different computational methods as triaging tools for prioritizing target positions and identifying specific mutations that are likely to improve protein thermodynamic stability. Our benchmarking study used a comprehensive dataset consisting of 174,945 mutations across 180 distinct proteins and evaluated the ESM (Evolutionary Scale Modeling) protein language model alongside a physics-based method, MM/GBSA (Molecular Mechanics Generalized Born Surface Area). We found prediction biases in each method but also determined that these biases can be mitigated by applying the two methods in a complementary manner. We propose a hybrid mutation prioritization and selection strategy that achieves better accuracy than either method alone. Through re-ranking, the combined prioritization strategy attained a higher overall average ROC (receiver operating characteristic) AUC (area under curve) of 0.743 across the dataset compared to either MM/GBSA alone (0.685) or ESM Log Odds alone (0.597). The integrated framework can be adapted and applied to newer AI and physics-based models as the field advances.

Structural dynamics play a crucial role in protein function, and tuning these dynamics through mutagenesis has emerged as a promising strategy for enhancing activity. However, identifying dynamics hotspots for protein engineering remains a labor-intensive challenge. Here, we demonstrate that NMR peak intensity analysis-a rapid, qualitative method with residue-level resolution-can identify functionally relevant dynamic regions with high precision. Using a family of red fluorescent proteins (RFPs) as a case study, we reveal that flexibility in specific regions of their structures correlates with function. Specifically, as quantum yield increases, the side of the β-barrel closest to the chromophore phenolate moiety becomes more rigid, while the opposite side, closest to the acylimine group, gains flexibility. Notably, the phenolate face corresponds to a mutational hotspot frequently targeted in directed evolution campaigns aimed at enhancing brightness, underscoring its functional significance. B-factor analysis of non-cryogenic X-ray crystal structures further supports our findings. Our results establish NMR peak intensity analysis as a promising tool for mapping functional dynamics hotspots to guide protein engineering campaigns.

Recent years have seen remarkable clinical success with therapies that harness the patient's immune system, with checkpoint inhibitors in oncology as a prominent example. Non-invasive monitoring of immune activation in vivo has the potential to accelerate both basic immunology research and clinical drug development, offering a valuable means to track responses to emerging immunotherapies. CD69 is a rapidly induced activation marker on lymphocytes and other leukocytes, making it an attractive imaging target. For such applications, affibody molecules offer distinct advantages as radio imaging tracers due to their small size, high affinity, and rapid pharmacokinetics, resulting in excellent imaging contrast. Here, we combined directed evolution with computational design to optimise a CD69-binding affibody molecule. An alanine scan of the parental binder informed the construction of a diversification library, which was displayed on Escherichia coli and subjected to iterative MACS and FACS selections with stringent off-rate competition. The top selection hit was subsequently refined through site-directed mutagenesis, including variants suggested by a general protein language model. The resulting lead, Z1525, bound human CD69 with single-digit nanomolar affinity and showed improved thermal stability while retaining solubility and refolding capacity, consistent with suitability for radiolabelling and in vivo targeting. Most importantly, it displayed selective binding to CD69 on stimulated Jurkat cells, with negligible binding to resting cells. These results establish E. coli display with off-rate-driven selection as an efficient strategy for affibody affinity maturation and demonstrate that protein language models can effectively guide improvements in folding stability. Z1525 represents a promising radio imaging tracer candidate for monitoring immune activation in vivo and warrants further preclinical development.

Proteins and peptides underpin essential biological functions and technological applications, from targeting disease-relevant interactions to providing broad enzymatic activities. However, engineering molecules with desired properties remains difficult, owing to complex sequence-structure-function relationships and the lack of data on specific systems. Experimental selection strategies, including directed evolution, phage display, and mRNA display, address this challenge by leveraging high diversity libraries and iterative enrichment under defined selection pressures. This allows for the identification of candidates without requiring extensive prior knowledge, and can generate extensive datasets for use in machine learning. While many selection systems exist, comparisons across different selection approaches are hindered by the lack of a unifying analytical framework. Here, we developed a toolset of broadly applicable analyses for assessing selection dynamics in multi-round or multi-condition experiments, ranging from position level analysis of sequence properties to full sequence space mappings through protein language model embeddings. Performing analyses across different systems, we identify desirable traits in selection experiments including enrichment of distinct sequence patterns and correlation between enrichment and final desired functions. Notably, even under weak selection regimes with all sequences <1% frequency, functional sequences (e.g., 70nM IC50 binder to SARS-CoV-2 main protease) are still consistently enriched. We also find repeated selections of the same starting library can help differentiate selection effects of varying conditions (e.g., different delivery of metal ligand) from system noise. These findings, along with the toolset, can be used to guide experimental design, interpretation, and troubleshooting across protein and peptide discovery platforms.