Revealing enzyme functional architecture via high-throughput microfluidic enzyme kinetics

A constant pH molecular dynamics and experimental study on the effect of different pH on the structure of urease from Sporosarcina pasteurii

CONTEXT

Urease is pivotal in microbial-induced calcium carbonate precipitation (MICP), where its catalytic efficiency directly governs calcium carbonate formation. However, practical MICP applications in extreme environments (e.g., acidic mine drainage, industrial waste sites) are hindered by limited understanding of urease behavior under extreme pH conditions. This study combines laboratory experiments and constant pH molecular dynamics (CpHMD) simulations to investigate how pH variations (3-11) affect the structural stability of Sporosarcina pasteurii urease, focusing on its α-subunit (PDB: 4CEU). Experimental validation identified pH 7-8 as optimal for urease activity, aligning with molecular dynamics results showing minimal structural deviations (RMSD) and stable protonation states under neutral to mildly alkaline conditions. Extreme pH (3, 4, 11) disrupted active-site geometry and induced charge fluctuations, impairing catalytic function. CpHMD simulations revealed that the α-subunit retains structural integrity at pH 7-8, suggesting potential reassembly post-environmental stress. This work bridges gaps in enzymatic stability under harsh conditions, offering insights for optimizing MICP in geotechnical and environmental remediation applications.

METHODS

The study combined experimental and computational approaches. Sporosarcina pasteurii urease activity was experimentally assessed across pH 3-11 by monitoring urea hydrolysis-induced conductivity changes. Computational analyses employed GROMACS constant pH with the CHARMM36 force field to perform pH-dependent molecular dynamics simulations. The urease structure was solvated, neutralized, energy-minimized, and subjected to constant pH simulations. Structural stability, active site dynamics, and protonation states of titratable residues were analyzed via RMSD, hydrogen bonds, solvent-accessible surface area (SASA), and Epock 1.0.5. Free energy landscapes and residue interactions were evaluated using principal component analysis (PCA) and λ-dynamics. Experimental data were processed with OriginPro 2024b and Python, linking pH-induced conformational shifts to enzymatic function.

Isolation and characterization of antioxidant and anti-tyrosinase activities of Cosmos caudatus leaf polysaccharides using microwave-assisted extraction

This study investigates the extraction of Cosmos caudatus leaf polysaccharides (CCLP) using citric acid monohydrate (CAM) as the extraction medium. Moreover, the extraction was assisted with microwave-assisted extraction due to its advantages as high extraction efficiency with low energy consumption and short extraction period. Optimal conditions yielded a maximum of 36.06 % which achieved by Box-Behnken design analysis. Characterization studies revealed that CCLP has β-configuration with branching properties, indicated by the presence of methyl, acetyl, and sugar ring structures. CCLP exhibit low methoxyl and considered as glucose-rich polysaccharides due to glucose as its major monosaccharide compositions. Additionally, CCLP demonstrated good gelling properties, moderate viscosity and high-water solubility which further supported by high water and oil-holding capacities, enhancing its formulation potential. Bioactivity evaluation revealed significant antioxidant properties with IC50 values of 2.15 mg/mL and 8.00 mg/mL for DPPH and ABTS radicals, respectively. CCLP also exhibited potent tyrosinase inhibition, with IC50 values of 1.69 mg/mL for monophenolase and 1.31 mg/mL for diphenolase. Furthermore, its photoprotective potential, reflected by a sun protection factor (SPF) of 25.33 %, highlights its potential utility in skincare applications. These findings suggest that CCLP, with its unique structural features and strong bioactivities is a promising bioactive ingredient for managing hyperpigmentation.

Hydrophilic/Omniphobic Droplet Arrays for High-throughput and Quantitative Enzymology

Engineered enzymes with enhanced or novel functions are specific catalysts with wide-ranging applications in industry and medicine. Here, we introduce droplet array microfluidic enzyme kinetics (DA-MEK), a high-throughput enzyme screening platform that combines water-in-air droplet microarrays formed on patterned superhydrophilic/omniphobic surfaces with cell-free protein synthesis to enable cost-effective expression and quantitative kinetic characterization of enzyme variants. By printing DNA templates encoding enzyme variants onto hydrophilic spots, stamping slides to add cell-free expression mix, and imaging the resulting arrays, we demonstrate reproducible expression of enzyme variants across hundreds of microwells per slide, with line of sight toward replicating this across larger libraries. By specifically patterning slides with antibodies, we further demonstrate parallel immobilization, purification, and iterative characterization of the expressed variants. Subsequent stamping of fluorogenic substrates and time-lapse imaging allows determination of Michaelis-Menten parameters for each variant, with measured catalytic efficiencies spanning 5 orders of magnitude and agreeing well with values obtained via traditional microtiter plate assays. DA-MEK consumes orders of magnitude less reagents than plate-based assays, while providing accurate and detailed kinetic information for both beneficial and deleterious mutations. In future work, we anticipate that DA-MEK will provide a powerful and versatile platform to accelerate enzyme engineering and enable screening of large variant libraries under diverse conditions.

Promiscuity Guided Evolution of Decarboxylative Aldolases for Synthesis of Tertiary γ-Hydroxy Amino Acids

Many applications of enzymes benefit from activity on structurally diverse substrates. Here, we sought to engineer the decarboxylative aldolase UstD to perform a challenging C-C bond forming reaction with ketone electrophiles. The parent enzyme had only low levels of activity, portending multiple rounds of directed evolution and a possibility that mutations may inadvertently increase the specificity of the enzyme for a single model screening substrate. We show how to intentionally guide UstD towards generality through multi-generational directed evolution using substrate-multiplexed screening (SUMS). Mutations outside of the active site that impact catalytic function were immediately revealed by shifts in promiscuity, even when the overall activity was lower. By re-targeting these distal residues that couple to the active site with saturation mutagenesis, broadly activating mutations were readily identified. When analyzing active site mutants, SUMS identified both specialist enzymes that would have more limited utility as well as generalist enzymes with complementary activity on diverse substrates. These new UstD enzymes catalyze convergent synthesis of non-canonical amino acids bearing tertiary alcohol side chains. This methodology is easy to implement and enables the rapid and effective evolution of enzymes to catalyze desirable new functions.

A structural perspective on enzymes and their catalytic mechanisms

In this perspective, we analyse the progress made in our knowledge of enzyme sequences, structures and functions in the last 2 years. We review how much new enzyme data have been garnered and annotated, derived from the study of proteins using structural and computational approaches. Recent advances towards capturing 'Catalysis in silico' are described, including knowledge and predictions of enzyme structures, their interactions and mechanisms. We highlight the flood of enzyme data, driven by metagenomic sequencing, the improved enzyme data resources, the high coverage in Protein Data Bank of E.C. classes and the AI-driven structure prediction techniques that facilitate the accurate prediction of protein structures. We note the focus on disordered regions in the context of enzyme regulation and specificity and comment on emerging bioinformatic approaches that capture reaction mechanisms computationally for comparing and predicting enzyme mechanisms. We also consider the drivers of progress in this field in the next five years.

Engineering highly active nuclease enzymes with machine learning and high-throughput screening

Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged fitness landscape and costly experiments. In this work, we present TeleProt, a machine learning (ML) framework that blends evolutionary and experimental data to design diverse protein libraries, and employ it to improve the catalytic activity of a nuclease enzyme that degrades biofilms that accumulate on chronic wounds. After multiple rounds of high-throughput experiments, TeleProt found a significantly better top-performing enzyme than directed evolution (DE), had a better hit rate at finding diverse, high-activity variants, and was even able to design a high-performance initial library using no prior experimental data. We have released a dataset of 55,000 nuclease variants, one of the most extensive genotype-phenotype enzyme activity landscapes to date, to drive further progress in ML-guided design. A record of this paper's transparent peer review process is included in the supplemental information.

'Need for speed: high throughput' - mass spectrometry approaches for high-throughput directed evolution screening of natural product enzymes

Covering: 2015 to 2024Mass spectrometry (MS)-based methods have been implemented extensively for enzyme engineering due to their label-free nature, making them suitable for screening a wide range of biochemical systems. Over the past decade, advancements in mass spectrometry, separation science, and the implementation of hyphenated methods have allowed for more streamlined analysis of large volumes of samples while maximizing the richness and dimensionality of the data collected. In this review we highlight recent advancements in mass spectrometry that have allowed for more efficient, robust, and rigorous enzyme engineering for various applications relating to natural products chemistry.

Robust Prediction of Enzyme Variant Kinetics with RealKcat

Accurate prediction of kinetic parameters is crucial for understanding known and tailoring novel enzymes for biocatalysis. Current models fail to capture mutation effects on catalytically essential residues, limiting their utility in enzyme design. We grid-searched through ten model architectures (25,671 hyperparameter combinations) to identify a gradient-based additive framework called RealKcat, trained on 27,176 experimental entries curated manually (KinHub-27k) by screening 2,158 articles. Clustering catalytic turnover (k cat ) and substrate affinity (K M ) by rational orders of magnitude, RealKcat achieves >85% test accuracy, demonstrating highest sensitivity to mutation-induced variability thus far, and is the first-of-its-kind-model to demonstrate complete loss of activity upon deletion of the catalytic apparatus. Finally, state-of-the-art k cat validation accuracy (96%) on alkaline phosphatase (PafA) mutant industrial dataset confirms RealKcat's generalizability in learning per-residue catalytic relevance.

A map of the rubisco biochemical landscape

Rubisco is the primary CO2-fixing enzyme of the biosphere1, yet it has slow kinetics2. The roles of evolution and chemical mechanism in constraining its biochemical function remain debated3,4. Engineering efforts aimed at adjusting the biochemical parameters of rubisco have largely failed5, although recent results indicate that the functional potential of rubisco has a wider scope than previously known6. Here we developed a massively parallel assay, using an engineered Escherichia coli7 in which enzyme activity is coupled to growth, to systematically map the sequence-function landscape of rubisco. Composite assay of more than 99% of single-amino acid mutants versus CO2 concentration enabled inference of enzyme velocity and apparent CO2 affinity parameters for thousands of substitutions. This approach identified many highly conserved positions that tolerate mutation and rare mutations that improve CO2 affinity. These data indicate that non-trivial biochemical changes are readily accessible and that the functional distance between rubiscos from diverse organisms can be traversed, laying the groundwork for further enzyme engineering efforts.

A pivotal switch in β-domain determines the substrate selectivity of terpene synthases involved in Gardenia jasminoides floral scent synthesis

Plants have evolved a diverse array of secondary metabolites to enhance their adaptability to environmental stresses, with volatile terpenoids being a notable example. Gardenia (Gardenia jasminoides), celebrated for its unique fragrance, is a key natural source of volatile terpenoids. Using our chromosome-level genome and transcriptome data of G. jasminoides, we previously identified six terpene synthases (TPSs) involved in the production of its floral scent. Here, we functionally characterized these six key TPS enzymes, aligning their product profiles with volatile organic compound (VOC) analysis. Notably, we identified two highly similar TPSs, GjTPS1 and GjTPS2, which share high sequence homology but differ in substrate selectivity. Through AI-based predictions and site-directed mutagenesis, we pinpointed a single amino acid in the β-domain that acts as a "switch," modulating substrate selectivity-an unusual finding, as this residue is outside the active site region. Comparative genomic analyses with Coffea canephora and other eudicots revealed that G. jasminoides TPS genes primarily expanded through dispersed duplications (DSD) and tandem duplications (TD). Our study is the first to reveal a "switch" in a previously deemed "non-functional" region, regulating substrate selectivity in TPS proteins. These insights could facilitate breeding elite Gardenia varieties and support the rational engineering of terpene synthases.

Nanozymes with Modulable Inhibition Transfer Pathways for Thiol and Cell Identification

The elementary mechanism and site studies of nanozyme-based inhibition reactions are ambiguous and urgently require advanced nanozymes as mediators to elucidate the inhibition effect. To this end, we develop a class of nanozymes featuring single Cu-N catalytic configurations and B-O sites as binding configurations on a porous nitrogen-doped carbon substrate (B6/CuSA) for inducing modulable inhibition transfer at the atomic level. The full redistribution of electrons across the Cu-N sites, induced by B-O sites incorporation, yields B6/CuSA with enhanced peroxidase-like activity versus CuSA. More importantly, CuSA with single Cu-N sites features in cysteine binding and expresses a competitive inhibition through coordination bonds, with an inhibition constant of 0.048 mM. Benefiting from the modulable binding way in nanozymes, B6/CuSA possesses mixed binding approaches for cysteine through noncovalent bonds and delivers a record-mixed inhibition interaction with a competitive inhibition constant of 0.054 mM and a noncompetitive inhibition constant of 0.71 mM. Based on the modulable inhibition of B6/CuSA and CuSA, a multichannel sensor array accomplishes the detection of various cancer cells, normal cells, and thiols. The design principle of this work is endowed with guidelines for the preliminary inhibition mechanism evaluation of massive potential thiols, cell discrimination, and disease prediction.

Rapid in silico directed evolution by a protein language model with EVOLVEpro

Directed protein evolution is central to biomedical applications but faces challenges such as experimental complexity, inefficient multiproperty optimization, and local maxima traps. Although in silico methods that use protein language models (PLMs) can provide modeled fitness landscape guidance, they struggle to generalize across diverse protein families and map to protein activity. We present EVOLVEpro, a few-shot active learning framework that combines PLMs and regression models to rapidly improve protein activity. EVOLVEpro surpasses current methods, yielding up to 100-fold improvements in desired properties. We demonstrate its effectiveness across six proteins in RNA production, genome editing, and antibody binding applications. These results highlight the advantages of few-shot active learning with minimal experimental data over zero-shot predictions. EVOLVEpro opens new possibilities for artificial intelligence-guided protein engineering in biology and medicine.

CPI-Pred: A deep learning framework for predicting functional parameters of compound-protein interactions

Recent advancements in deep learning have enabled functional annotation of genome sequences, facilitating the discovery of new enzymes and metabolites. However, accurately predicting compound-protein interactions (CPI) from sequences remains challenging due to the complexity of these interactions and the sparsity and heterogeneity of available data, which constrain the generalization of patterns across their solution space. In this work, we introduce CPI-Pred, a versatile deep learning model designed to predict compound-protein interaction function. CPI-Pred integrates compound representations derived from a novel message-passing neural network and enzyme representations generated by state-of-the-art protein language models, leveraging innovative sequence pooling and cross-attention mechanisms. To train and evaluate CPI-Pred, we compiled the largest dataset of enzyme kinetic parameters to date, encompassing four key metrics: the Michaelis-Menten constant (K M), enzyme turnover number (k cat), catalytic efficiency (k cat /K M), and inhibition constant (K I). These kinetic parameters are critical for elucidating enzyme function in metabolic contexts and understanding their regulation by compounds within biological networks. We demonstrate that CPI-Pred can predict diverse types of CPI using only the amino acid sequence of enzymes and structural representations of compounds, outperforming state-of-the-art models on unseen compounds and structurally dissimilar enzymes. Over workflow provides a valuable tool for tackling a range of metabolic engineering challenges, including the designing of novel enzyme sequences and compounds, such as enzyme inhibitors. Additionally, the datasets curated in this study offer a valuable resource for the scientific community, serving as a benchmark for machine learning models focused on enzyme activity and promiscuity prediction.

Quantifying protein unfolding kinetics with a high-throughput microfluidic platform

Even after folding, proteins transiently sample unfolded or partially unfolded intermediates, and these species are often at risk of irreversible alteration (e.g. via proteolysis, aggregation, or post-translational modification). Kinetic stability, in addition to thermodynamic stability, can directly impact protein lifetime, abundance, and the formation of alternative, sometimes disruptive states. However, we have very few measurements of protein unfolding rates or how mutations alter these rates, largely due to technical challenges associated with their measurement. To address this need, we developed SPARKfold (Simultaneous Proteolysis Assay Revealing Kinetics of Folding), a microfluidic platform to express, purify, and measure unfolding rate constants for >1000 protein variants in parallel via on-chip native proteolysis. To demonstrate the power and potential of SPARKfold, we determined unfolding rate constants for 1,104 protein samples in parallel. We built a library of 31 dihydrofolate reductase (DHFR) orthologs with up to 78 chamber replicates per variant to provide the statistical power required to evaluate the system's ability to resolve subtle effects. SPARKfold rate constants for 5 constructs agreed with those obtained using traditional techniques across a 150-fold range, validating the accuracy of the technique. Comparisons of mutant kinetic effects via SPARKfold with previously published measurements impacts on folding thermodynamics provided information about the folding transition state and pathways via φ analysis. Overall, SPARKfold enables rapid characterization of protein variants to dissect the nature of the unfolding transition state. In future work, SPARKfold can reveal mutations that drive misfolding and aggregation and enable rational design of kinetically hyperstable variants for industrial use in harsh environments.

SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations

The accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of catalytic residues in enzymes. Here, we introduce SCREEN, a graph neural network for the high-throughput prediction of catalytic residues via the integration of enzyme functional and structural information. SCREEN constructs residue representations based on spatial arrangements and incorporates enzyme function priors into such representations through contrastive learning. We demonstrate that SCREEN (1) consistently outperforms currently-available predictors; (2) provides accurate results when applied to inferred enzyme structures; and (3) generalizes well to enzymes dissimilar from those in the training set. We also show that the putative catalytic residues predicted by SCREEN mimic key structural and biophysical characteristics of native catalytic residues. Moreover, using experimental datasets, we show that SCREEN's predictions can be used to distinguish residues with a high mutation tolerance from those likely to cause functional loss when mutated, indicating that this tool might be used to infer disease-associated mutations. SCREEN is publicly available at https://github.com/BioColLab/SCREEN and https://ngdc.cncb.ac.cn/biocode/tool/7580.

Mutations to transcription factor MAX allosterically increase DNA selectivity by altering folding and binding pathways

Understanding how proteins discriminate between preferred and non-preferred ligands ('selectivity') is essential for predicting biological function and a central goal of protein engineering efforts, yet the biophysical mechanisms underpinning selectivity remain poorly understood. Towards this end, we study how variants of the promiscuous transcription factor (TF) MAX (H. sapiens) alter DNA specificity and selectivity, yielding >1700 Kds and >500 rate constants in complex with multiple DNA sequences. Twenty-two of the 240 assayed MAX point mutations enhance selectivity, yet none of these mutations occur at residues that contact nucleotides in published structures. By applying thermodynamic and kinetic models to these results and previous observations for the highly similar yet far more selective TF Pho4 (S. cerevisiae), we find that these mutations enhance selectivity by altering partitioning between or affinity within conformations with different intrinsic selectivity, providing a mechanistic basis for allosteric modulation of ligand selectivity. These results highlight the importance of conformational heterogeneity in determining sequence selectivity and can guide future efforts to engineer selective proteins.

Cell-Free Gene Expression: Methods and Applications

Cell-free gene expression (CFE) systems empower synthetic biologists to build biological molecules and processes outside of living intact cells. The foundational principle is that precise, complex biomolecular transformations can be conducted in purified enzyme or crude cell lysate systems. This concept circumvents mechanisms that have evolved to facilitate species survival, bypasses limitations on molecular transport across the cell wall, and provides a significant departure from traditional, cell-based processes that rely on microscopic cellular "reactors." In addition, cell-free systems are inherently distributable through freeze-drying, which allows simple distribution before rehydration at the point-of-use. Furthermore, as cell-free systems are nonliving, they provide built-in safeguards for biocontainment without the constraints attendant on genetically modified organisms. These features have led to a significant increase in the development and use of CFE systems over the past two decades. Here, we discuss recent advances in CFE systems and highlight how they are transforming efforts to build cells, control genetic networks, and manufacture biobased products.

Combining Oligo Pools and Golden Gate Cloning to Create Protein Variant Libraries or Guide RNA Libraries for CRISPR Applications

Oligo pools are array-synthesized, user-defined mixtures of single-stranded oligonucleotides that can be used as a source of synthetic DNA for library cloning. While currently offering the most affordable source of synthetic DNA, oligo pools also come with limitations such as a maximum synthesis length (approximately 350 bases), a higher error rate compared to alternative synthesis methods, and the presence of truncated molecules in the pool due to incomplete synthesis. Here, we provide users with a comprehensive protocol that details how oligo pools can be used in combination with Golden Gate cloning to create user-defined protein mutant libraries, as well as single-guide RNA libraries for CRISPR applications. Our methods are optimized to work within the Yeast Toolkit Golden Gate scheme, but are in principle compatible with any other Golden Gate-based modular cloning toolkit and extendable to other restriction enzyme-based cloning methods beyond Golden Gate. Our methods yield high-quality, affordable, in-house variant libraries.

Massively parallel genetic perturbation reveals the energetic architecture of an amyloid beta nucleation reaction

Amyloid protein aggregates are pathological hallmarks of more than fifty human diseases but how soluble proteins nucleate to form amyloids is poorly understood. Here we use combinatorial mutagenesis, a kinetic selection assay, and machine learning to massively perturb the energetics of the nucleation reaction of amyloid beta (Aβ42), the protein that aggregates in Alzheimer's disease. In total, we quantify the nucleation rates of >140,000 variants of Aβ42. This allows us to accurately quantify the changes in reaction activation energy for all possible amino acid substitutions in a protein for the first time and, in addition, to quantify >600 energetic interactions between mutations. The data reveal the simple and interpretable genetic architecture of an amyloid nucleation reaction. Strikingly, strong energetic couplings are rare and identify a subset of structural contacts in mature fibrils. Together with the activation energy changes, this strongly suggests that the Aβ42 nucleation reaction transition state is structured in a short C-terminal region, providing a structural model for the reaction that may initiate Alzheimer's disease. We believe this approach can be widely applied to probe the energetics and transition state structures of protein reactions.

Biochemistry and genetics are coming together to improve our understanding of genotype to phenotype relationships

Since genome sequencing became accessible, determining how specific differences in genotypes lead to complex phenotypes such as disease has become one of the key goals in biomedicine. Predicting effects of sequence variants on cellular or organismal phenotype faces several challenges. First, variants simultaneously affect multiple protein properties and predicting their combined effect is complex. Second, effects of changes in a single protein propagate through the cellular network, which we only partially understand. In this review, we emphasize the importance of both biochemistry and genetics in addressing these challenges. Moreover, we highlight work that blurs the distinction between biochemistry and genetics fields to provide new insights into the genotype-to-phenotype relationships.

Redefining Roles: A Paradigm Shift in Tryptophan-Kynurenine Metabolism for Innovative Clinical Applications

The tryptophan-kynurenine (KYN) pathway has long been recognized for its essential role in generating metabolites that influence various physiological processes. Traditionally, these metabolites have been categorized into distinct, often opposing groups, such as pro-oxidant versus antioxidant, excitotoxic/neurotoxic versus neuroprotective. This dichotomous framework has shaped much of the research on conditions like neurodegenerative and neuropsychiatric disorders, as well as cancer, where metabolic imbalances are a key feature. The effects are significantly influenced by various factors, including the concentration of metabolites and the particular cellular milieu in which they are generated. A molecule that acts as neuroprotective at low concentrations may exhibit neurotoxic effects at elevated levels. The oxidative equilibrium of the surrounding environment can alter the function of KYN from an antioxidant to a pro-oxidant. This narrative review offers a comprehensive examination and analysis of the contemporary understanding of KYN metabolites, emphasizing their multifaceted biological functions and their relevance in numerous physiological and pathological processes. This underscores the pressing necessity for a paradigm shift in the comprehension of KYN metabolism. Understanding the context-dependent roles of KYN metabolites is vital for novel therapies in conditions like Alzheimer's disease, multiple sclerosis, and cancer. Comprehensive pathway modulation, including balancing inflammatory signals and enzyme regulation, offers promising avenues for targeted, effective treatments.

High-throughput antibody screening with high-quality factor nanophotonics and bioprinting

Empirical investigation of the quintillion-scale, functionally diverse antibody repertoires that can be generated synthetically or naturally is critical for identifying potential biotherapeutic leads, yet remains burdensome. We present high-throughput nanophotonics- and bioprinter-enabled screening (HT-NaBS), a multiplexed assay for large-scale, sample-efficient, and rapid characterization of antibody libraries. Our platform is built upon independently addressable pixelated nanoantennas exhibiting wavelength-scale mode volumes, high-quality factors (high-Q) exceeding 5000, and pattern densities exceeding one million sensors per square centimeter. Our custom-built acoustic bioprinter enables individual sensor functionalization via the deposition of picoliter droplets from a library of capture antigens at rates up to 25,000 droplets per second. We detect subtle differentiation in the target binding signature through spatially-resolved spectral imaging of hundreds of resonators simultaneously, elucidating antigen-antibody binding kinetic rates, affinity constant, and specificity. We demonstrate HT-NaBS on a panel of antibodies targeting SARS-CoV-2, Influenza A, and Influenza B antigens, with a sub-picomolar limit of detection within 30 minutes. Furthermore, through epitope binning analysis, we demonstrate the competence and diversity of a library of native antibodies targeting functional epitopes on a priority pathogen (H5N1 bird flu) and on glycosylated therapeutic Cetuximab antibodies against epidermal growth factor receptor. With a roadmap to image tens of thousands of sensors simultaneously, this high-throughput, resource-efficient, and label-free platform can rapidly screen for high-affinity and broad epitope coverage, accelerating biotherapeutic discovery and de novo protein design.

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

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

Evolutionary-Scale Enzymology Enables Biochemical Constant Prediction Across a Multi-Peaked Catalytic Landscape

Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function, evolution, and design. Here, we expand an emerging microfluidic platform to measure catalytic constants-k cat and K M-for hundreds of diverse naturally occurring sequences and mutants of the model enzyme Adenylate Kinase (ADK). This enables us to dissect the sequence-catalysis landscape's topology, navigability, and mechanistic underpinnings, revealing distinct catalytic peaks organized by structural motifs. These results challenge long-standing hypotheses in enzyme adaptation, demonstrating that thermophilic enzymes are not slower than their mesophilic counterparts. Combining the rich representations of protein sequences provided by deep-learning models with our custom high-throughput kinetic data yields semi-supervised models that significantly outperform existing models at predicting catalytic parameters of naturally occurring ADK sequences. Our work demonstrates a promising strategy for dissecting sequence-catalysis landscapes across enzymatic evolution and building family-specific models capable of accurately predicting catalytic constants, opening new avenues for enzyme engineering and functional prediction.

Activity-Based DNA-Encoded Library Screening for Selective Inhibitors of Eukaryotic Translation

Small molecule probes exist for only ∼2% of human proteins because most lack functional binding pockets or cannot be assayed for high-throughput screening. Selective translation modulation circumvents canonical druggability and assay development constraints by using in vitro transcription-translation (IVTT) as a universal biochemical screening assay. We developed an IVTT activity assay by fusing a GFP reporter to various target gene sequences and screened the target sequences for inhibitors in microfluidic picoliter-scale droplets using a 5,348-member translation inhibitor DNA-encoded library (DEL). Screening a proof-of-concept PCSK9-GFP reporter yielded many hits; 6/7 hits inhibited PCSK9-GFP IVTT (IC50 1-20 μM), and the lead hit reduced PCSK9 levels in HepG2 cells. Preliminary selectivity was informed by counterscreening the DEL against a frameshift mutant PCSK9-GFP reporter. A plug-and-play approach to assay development and screening was demonstrated by scouting the DEL for activity using reporter genes of targets with difficult-to-assay or even unknown function (RPL27, KRASG12D, MST1, USO1). This microfluidic IVTT DEL screening platform could scale probe discovery to the human proteome and perhaps more broadly across the tree of life.

Enzoology: understanding enzyme interactions and epistasis in the cell

Recent work from Nguyen et al. unveils massively parallel measurements of epistatic interactions between two enzymes, dihydrofolate reductase and thymidylate synthase, in their natural cellular context. Almost 3000 mutations of DHFR in three TYMS backgrounds reveal a complex interaction network. The authors capture much of this complexity using a simple model.

On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering

Protein design and directed evolution have separately contributed enormously to protein engineering. Without being mutually exclusive, the former relies on computation from first principles, while the latter is a combinatorial approach based on chance. Advances in ultrahigh throughput (uHT) screening, next generation sequencing and machine learning may create alternative routes to engineered proteins, where functional information linked to specific sequences is interpreted and extrapolated in silico. In particular, the miniaturisation of functional tests in water-in-oil emulsion droplets with picoliter volumes and their rapid generation and analysis (>1 kHz) allows screening of >107-membered libraries in a day. Subsequently, decoding the selected clones by short or long-read sequencing methods leads to large sequence-function datasets that may allow extrapolation from experimental directed evolution to further improved mutants beyond the observed hits. In this work, we explore experimental strategies for how to draw up 'fitness landscapes' in sequence space with uHT droplet microfluidics, review the current state of AI/ML in enzyme engineering and discuss how uHT datasets may be combined with AI/ML to make meaningful predictions and accelerate biocatalyst engineering.

Killer yeasts: expanding frontiers in the age of synthetic biology

Killer yeasts secrete protein toxins that are selectively lethal to other yeast and filamentous fungi. These exhibit exceptional genetic and functional diversity, and have several biotechnological applications. However, despite decades of research, several limitations hinder their widespread adoption. In this perspective we contend that technical advances in synthetic biology present an unprecedented opportunity to unlock the full potential of yeast killer systems across a spectrum of applications. By leveraging these new technologies, engineered killer toxins may emerge as a pivotal new tool to address antifungal resistance and food security. Finally, we speculate on the biotechnological potential of re-engineering host double-stranded (ds) RNA mycoviruses, from which many toxins derive, as a safe and noninfectious system to produce designer RNA.

High-throughput affinity measurements of direct interactions between activation domains and co-activators

Sequence-specific activation by transcription factors is essential for gene regulation1,2. Key to this are activation domains, which often fall within disordered regions of transcription factors3,4 and recruit co-activators to initiate transcription5. These interactions are difficult to characterize via most experimental techniques because they are typically weak and transient6,7. Consequently, we know very little about whether these interactions are promiscuous or specific, the mechanisms of binding, and how these interactions tune the strength of gene activation. To address these questions, we developed a microfluidic platform for expression and purification of hundreds of activation domains in parallel followed by direct measurement of co-activator binding affinities (STAMMPPING, for Simultaneous Trapping of Affinity Measurements via a Microfluidic Protein-Protein INteraction Generator). By applying STAMMPPING to quantify direct interactions between eight co-activators and 204 human activation domains (>1,500 K ds), we provide the first quantitative map of these interactions and reveal 334 novel binding pairs. We find that the metazoan-specific co-activator P300 directly binds >100 activation domains, potentially explaining its widespread recruitment across the genome to influence transcriptional activation. Despite sharing similar molecular properties (e.g. enrichment of negative and hydrophobic residues), activation domains utilize distinct biophysical properties to recruit certain co-activator domains. Co-activator domain affinity and occupancy are well-predicted by analytical models that account for multivalency, and in vitro affinities quantitatively predict activation in cells with an ultrasensitive response. Not only do our results demonstrate the ability to measure affinities between even weak protein-protein interactions in high throughput, but they also provide a necessary resource of over 1,500 activation domain/co-activator affinities which lays the foundation for understanding the molecular basis of transcriptional activation.

Thermo-responsive aqueous two-phase system for two-level compartmentalization

Hierarchical compartmentalization responding to changes in intracellular and extracellular environments is ubiquitous in living eukaryotic cells but remains a formidable task in synthetic systems. Here we report a two-level compartmentalization approach based on a thermo-responsive aqueous two-phase system (TR-ATPS) comprising poly(N-isopropylacrylamide) (PNIPAM) and dextran (DEX). Liquid membraneless compartments enriched in PNIPAM are phase-separated from the continuous DEX solution via liquid-liquid phase separation at 25 °C and shrink dramatically with small second-level compartments generated at the interface, resembling the structure of colloidosome, by increasing the temperature to 35 °C. The TR-ATPS can store biomolecules, program the spatial distribution of enzymes, and accelerate the overall biochemical reaction efficiency by nearly 7-fold. The TR-ATPS inspires on-demand, stimulus-triggered spatiotemporal enrichment of biomolecules via two-level compartmentalization, creating opportunities in synthetic biology and biochemical engineering.

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.

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures

Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes' kcat is always time-consuming. Recently, the prediction of kcat using deep learning models has mitigated this problem. However, the accuracy and robustness in kcat prediction still needs to be improved significantly, particularly when dealing with enzymes with low sequence similarity compared to those within the training dataset. Herein, we present DeepEnzyme, a cutting-edge deep learning model that combines the most recent Transformer and Graph Convolutional Network (GCN) to capture the information of both the sequence and 3D-structure of a protein. To improve the prediction accuracy, DeepEnzyme was trained by leveraging the integrated features from both sequences and 3D-structures. Consequently, DeepEnzyme exhibits remarkable robustness when processing enzymes with low sequence similarity compared to those in the training dataset by utilizing additional features from high-quality protein 3D-structures. DeepEnzyme also makes it possible to evaluate how point mutations affect the catalytic activity of the enzyme, which helps identify residue sites that are crucial for the catalytic function. In summary, DeepEnzyme represents a pioneering effort in predicting enzymes' kcat values with improved accuracy and robustness compared to previous algorithms. This advancement will significantly contribute to our comprehension of enzyme function and its evolutionary patterns across species.

Rapid protein evolution by few-shot learning with a protein language model

Directed evolution of proteins is critical for applications in basic biological research, therapeutics, diagnostics, and sustainability. However, directed evolution methods are labor intensive, cannot efficiently optimize over multiple protein properties, and are often trapped by local maxima. In silico-directed evolution methods incorporating protein language models (PLMs) have the potential to accelerate this engineering process, but current approaches fail to generalize across diverse protein families. We introduce EVOLVEpro, a few-shot active learning framework to rapidly improve protein activity using a combination of PLMs and protein activity predictors, achieving improved activity with as few as four rounds of evolution. EVOLVEpro substantially enhances the efficiency and effectiveness of in silico protein evolution, surpassing current state-of-the-art methods and yielding proteins with up to 100-fold improvement of desired properties. We showcase EVOLVEpro for five proteins across three applications: T7 RNA polymerase for RNA production, a miniature CRISPR nuclease, a prime editor, and an integrase for genome editing, and a monoclonal antibody for epitope binding. These results demonstrate the advantages of few-shot active learning with small amounts of experimental data over zero-shot predictions. EVOLVEpro paves the way for broader applications of AI-guided protein engineering in biology and medicine.

Carbohydrate-active enzyme (CAZyme) discovery and engineering via (Ultra)high-throughput screening

Carbohydrate-active enzymes (CAZymes) constitute a diverse set of enzymes that catalyze the assembly, degradation, and modification of carbohydrates. These enzymes have been fashioned into potent, selective catalysts by millennia of evolution, and yet are also highly adaptable and readily evolved in the laboratory. To identify and engineer CAZymes for different purposes, (ultra)high-throughput screening campaigns have been frequently utilized with great success. This review provides an overview of the different approaches taken in screening for CAZymes and how mechanistic understandings of CAZymes can enable new approaches to screening. Within, we also cover how cutting-edge techniques such as microfluidics, advances in computational approaches and synthetic biology, as well as novel assay designs are leading the field towards more informative and effective screening approaches.

Expanding chemistry through in vitro and in vivo biocatalysis

Living systems contain a vast network of metabolic reactions, providing a wealth of enzymes and cells as potential biocatalysts for chemical processes. The properties of protein and cell biocatalysts-high selectivity, the ability to control reaction sequence and operation in environmentally benign conditions-offer approaches to produce molecules at high efficiency while lowering the cost and environmental impact of industrial chemistry. Furthermore, biocatalysis offers the opportunity to generate chemical structures and functions that may be inaccessible to chemical synthesis. Here we consider developments in enzymes, biosynthetic pathways and cellular engineering that enable their use in catalysis for new chemistry and beyond.

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

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

The effectiveness of selection in a species affects the direction of amino acid frequency evolution

Nearly neutral theory predicts that species with higher effective population size (N e ) are better able to purge slightly deleterious mutations. We compare evolution in high-N e vs. low-N e vertebrates to reveal which amino acid frequencies are subject to subtle selective preferences. We take three complementary approaches, two measuring flux and one measuring outcomes. First, we fit non-stationary substitution models of amino acid flux using maximum likelihood, comparing the high-N e clade of rodents and lagomorphs to its low-N e sister clade of primates and colugos. Second, we compare evolutionary outcomes across a wider range of vertebrates, via correlations between amino acid frequencies and N e . Third, we dissect the details of flux in human, chimpanzee, mouse, and rat, as scored by parsimony - this also enables comparison to a historical paper. All three methods agree on which amino acids are preferred under more effective selection. Preferred amino acids tend to be smaller, less costly to synthesize, and to promote intrinsic structural disorder. Parsimony-induced bias in the historical study produces an apparent reduction in structural disorder, perhaps driven by slightly deleterious substitutions. Within highly exchangeable pairs of amino acids, arginine is strongly preferred over lysine, and valine over isoleucine, consistent with more effective selection preferring a marginally larger free energy of folding. These two preferences match differences between thermophiles and mesophilic relatives. These results reveal the biophysical consequences of mutation-selection-drift balance, and demonstrate the utility of nearly neutral theory for understanding protein evolution.

The full spectrum of SLC22 OCT1 mutations illuminates the bridge between drug transporter biophysics and pharmacogenomics

Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.

Ancestral terpene cyclases: From fundamental science to applications in biosynthesis

Terpenes constitute one of the largest family of natural products with potent applications as renewable platform chemicals and medicines. The low activity, selectivity and stability displayed by terpene biosynthetic machineries can constitute an obstacle towards achieving expedient biosynthesis of terpenoids in processes that adhere to the 12 principles of green chemistry. Accordingly, engineering of terpene synthase enzymes is a prerequisite for industrial biotechnology applications, but obstructed by their complex catalysis that depend on reactive carbocationic intermediates that are prone to undergo bifurcation mechanisms. Rational redesign of terpene synthases can be tedious and requires high-resolution structural information, which is not always available. Furthermore, it has proven difficult to link sequence space of terpene synthase enzymes to specific product profiles. Herein, the author shows how ancestral sequence reconstruction (ASR) can favorably be used as a protein engineering tool in the redesign of terpene synthases without the need of a structure, and without excessive screening. A detailed workflow of ASR is presented along with associated limitations, with a focus on applying this methodology on terpene synthases. From selected examples of both class I and II enzymes, the author advocates that ancestral terpene cyclases constitute valuable assets to shed light on terpene-synthase catalysis and in enabling accelerated biosynthesis.

The genetic landscape of a metabolic interaction

While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focus on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We use deep mutational scanning to quantify the growth rate effect of 2696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems.

Rugged fitness landscapes minimize promiscuity in the evolution of transcriptional repressors

How a protein's function influences the shape of its fitness landscape, smooth or rugged, is a fundamental question in evolutionary biochemistry. Smooth landscapes arise when incremental mutational steps lead to a progressive change in function, as commonly seen in enzymes and binding proteins. On the other hand, rugged landscapes are poorly understood because of the inherent unpredictability of how sequence changes affect function. Here, we experimentally characterize the entire sequence phylogeny, comprising 1,158 extant and ancestral sequences, of the DNA-binding domain (DBD) of the LacI/GalR transcriptional repressor family. Our analysis revealed an extremely rugged landscape with rapid switching of specificity, even between adjacent nodes. Further, the ruggedness arises due to the necessity of the repressor to simultaneously evolve specificity for asymmetric operators and disfavors potentially adverse regulatory crosstalk. Our study provides fundamental insight into evolutionary, molecular, and biophysical rules of genetic regulation through the lens of fitness landscapes.

A map of the rubisco biochemical landscape

Rubisco is the primary CO2 fixing enzyme of the biosphere yet has slow kinetics. The roles of evolution and chemical mechanism in constraining the sequence landscape of rubisco remain debated. In order to map sequence to function, we developed a massively parallel assay for rubisco using an engineered E. coli where enzyme function is coupled to growth. By assaying >99% of single amino acid mutants across CO2 concentrations, we inferred enzyme velocity and CO2 affinity for thousands of substitutions. We identified many highly conserved positions that tolerate mutation and rare mutations that improve CO2 affinity. These data suggest that non-trivial kinetic improvements are readily accessible and provide a comprehensive sequence-to-function mapping for enzyme engineering efforts.

Frustration can Limit the Adaptation of Promiscuous Enzymes Through Gene Duplication and Specialisation

Virtually all enzymes catalyse more than one reaction, a phenomenon known as enzyme promiscuity. It is unclear whether promiscuous enzymes are more often generalists that catalyse multiple reactions at similar rates or specialists that catalyse one reaction much more efficiently than other reactions. In addition, the factors that shape whether an enzyme evolves to be a generalist or a specialist are poorly understood. To address these questions, we follow a three-pronged approach. First, we examine the distribution of promiscuity in empirical enzymes reported in the BRENDA database. We find that the promiscuity distribution of empirical enzymes is bimodal. In other words, a large fraction of promiscuous enzymes are either generalists or specialists, with few intermediates. Second, we demonstrate that enzyme biophysics is not sufficient to explain this bimodal distribution. Third, we devise a constraint-based model of promiscuous enzymes undergoing duplication and facing selection pressures favouring subfunctionalization. The model posits the existence of constraints between the catalytic efficiencies of an enzyme for different reactions and is inspired by empirical case studies. The promiscuity distribution predicted by our constraint-based model is consistent with the empirical bimodal distribution. Our results suggest that subfunctionalization is possible and beneficial only in certain enzymes. Furthermore, the model predicts that conflicting constraints and selection pressures can cause promiscuous enzymes to enter a 'frustrated' state, in which competing interactions limit the specialisation of enzymes. We find that frustration can be both a driver and an inhibitor of enzyme evolution by duplication and subfunctionalization. In addition, our model predicts that frustration becomes more likely as enzymes catalyse more reactions, implying that natural selection may prefer catalytically simple enzymes. In sum, our results suggest that frustration may play an important role in enzyme evolution.

Advanced strategies in high-throughput droplet screening for enzyme engineering

Enzymes, as biocatalysts, play a cumulatively important role in environmental purification and industrial production of chemicals and pharmaceuticals. However, natural enzymes are limited by their physiological properties in practice, which need to be modified driven by requirements. Screening and isolating certain enzyme variants or ideal industrial strains with high yielding of target product enzymes is one of the main directions of enzyme engineering research. Droplet-based high-throughput screening (DHTS) technology employs massive monodisperse emulsion droplets as microreactors to achieve single strain encapsulation, as well as continuous monitoring for the inside mutant library. It can effectively sort out strains or enzymes with desired characteristics, offering a throughput of 108 events per hour. Much of the early literature focused on screening various engineered strains or designing signalling sorting strategies based on DHTS technology. However, the field of enzyme engineering lacks a comprehensive overview of advanced methods for microfluidic droplets and their cutting-edge developments in generation and manipulation. This review emphasizes the advanced strategies and frontiers of microfluidic droplet generation and manipulation facilitating enzyme engineering development. We also introduce design for various screening signals that cooperate with DHTS and devote to enzyme engineering.

New insights in bacterial organophosphorus cycling: From human pathogens to environmental bacteria

In terrestrial and aquatic ecosystems, phosphorus (P) availability controls primary production, with consequences for climate regulation and global food security. Understanding the microbial controls on the global P cycle is a prerequisite for minimising our reliance on non-renewable phosphate rock reserves and reducing pollution associated with excessive P fertiliser use. This recognised importance has reinvigorated research into microbial P cycling, which was pioneered over 75 years ago through the study of human pathogenic bacteria-host interactions. Immobilised organic P represents a significant fraction of the total P pool. Hence, microbes have evolved a plethora of mechanisms to transform this fraction into labile inorganic phosphate, the building block for numerous biological molecules. The 'genomics era' has revealed an extraordinary diversity of organic P cycling genes exist in the environment and studies going 'back to the lab' are determining how this diversity relates to function. Through this integrated approach, many hitherto unknown genes and proteins that are involved in microbial P cycling have been discovered. Not only do these fundamental discoveries push the frontier of our knowledge, but several examples also provide exciting opportunities for biotechnology and present possible solutions for improving the sustainability of how we grow our food, both locally and globally. In this review, we provide a comprehensive overview of bacterial organic P cycling, covering studies on human pathogens and how this knowledge is informing new discoveries in environmental microbiology.

Resource allocation in mammalian systems

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function

Advances in personalized medicine and protein engineering require accurately predicting outcomes of amino acid substitutions. Many algorithms correctly predict that evolutionarily-conserved positions show "toggle" substitution phenotypes, which is defined when a few substitutions at that position retain function. In contrast, predictions often fail for substitutions at the less-studied "rheostat" positions, which are defined when different amino acid substitutions at a position sample at least half of the possible functional range. This review describes efforts to understand the impact and significance of rheostat positions: (1) They have been observed in globular soluble, integral membrane, and intrinsically disordered proteins; within single proteins, their prevalence can be up to 40%. (2) Substitutions at rheostat positions can have biological consequences and ∼10% of substitutions gain function. (3) Although both rheostat and "neutral" (defined when all substitutions exhibit wild-type function) positions are nonconserved, the two classes have different evolutionary signatures. (4) Some rheostat positions have pleiotropic effects on function, simultaneously modulating multiple parameters (e.g., altering both affinity and allosteric coupling). (5) In structural studies, substitutions at rheostat positions appear to cause only local perturbations; the overall conformations appear unchanged. (6) Measured functional changes show promising correlations with predicted changes in protein dynamics; the emergent properties of predicted, dynamically coupled amino acid networks might explain some of the complex functional outcomes observed when substituting rheostat positions. Overall, rheostat positions provide unique opportunities for using single substitutions to tune protein function. Future studies of these positions will yield important insights into the protein sequence/function relationship.

The molecular basis for cellular function of intrinsically disordered protein regions

Intrinsically disordered protein regions exist in a collection of dynamic interconverting conformations that lack a stable 3D structure. These regions are structurally heterogeneous, ubiquitous and found across all kingdoms of life. Despite the absence of a defined 3D structure, disordered regions are essential for cellular processes ranging from transcriptional control and cell signalling to subcellular organization. Through their conformational malleability and adaptability, disordered regions extend the repertoire of macromolecular interactions and are readily tunable by their structural and chemical context, making them ideal responders to regulatory cues. Recent work has led to major advances in understanding the link between protein sequence and conformational behaviour in disordered regions, yet the link between sequence and molecular function is less well defined. Here we consider the biochemical and biophysical foundations that underlie how and why disordered regions can engage in productive cellular functions, provide examples of emerging concepts and discuss how protein disorder contributes to intracellular information processing and regulation of cellular function.

Understanding activity-stability tradeoffs in biocatalysts by enzyme proximity sequencing

Understanding the complex relationships between enzyme sequence, folding stability and catalytic activity is crucial for applications in industry and biomedicine. However, current enzyme assay technologies are limited by an inability to simultaneously resolve both stability and activity phenotypes and to couple these to gene sequences at large scale. Here we present the development of enzyme proximity sequencing, a deep mutational scanning method that leverages peroxidase-mediated radical labeling with single cell fidelity to dissect the effects of thousands of mutations on stability and catalytic activity of oxidoreductase enzymes in a single experiment. We use enzyme proximity sequencing to analyze how 6399 missense mutations influence folding stability and catalytic activity in a D-amino acid oxidase from Rhodotorula gracilis. The resulting datasets demonstrate activity-based constraints that limit folding stability during natural evolution, and identify hotspots distant from the active site as candidates for mutations that improve catalytic activity without sacrificing stability. Enzyme proximity sequencing can be extended to other enzyme classes and provides valuable insights into biophysical principles governing enzyme structure and function.

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.