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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

From bench to biofactory: high-throughput technologies and automated workflows to accelerate biomanufacturing

Microbial production of target molecules has advanced significantly in recent years driven by innovations in enzyme engineering, DNA synthesis, and genomic editing. However, to access the massive potential of microbial production, a vast parametric space remains to be investigated to optimize these biobased processes for a robust bioeconomy. Here, we review the current state of the art, some key challenges and possible solutions. We see a critical role of automation, high-throughput technologies, self-driving and cloud labs, and data management to enable Artificial Intelligence/Machine Learning and mechanistic models to overcome the design space challenges and accelerate the development of novel bio-based solutions. Accurate models will expedite the development and scale-up of engineered microbes for a range of final products from many starting materials.

Petzold, Christopher J↗
DOE OSTI2024

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline

Abstract As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.

36 MATERIALS SCIENCE↗
DOE OSTI2024

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.

biocatalysis↗
DOE OSTI2025

Controlled Enzyme Cargo Loading in Engineered Bacterial Microcompartment Shells

Bacterial microcompartments (BMCs) are nanometer-scale organelles with a protein-based shell that serve to colocalize and encapsulate metabolic enzymes. They may provide a range of benefits to improve pathway catalysis, including substrate channeling and selective permeability. Several groups are working toward using BMC shells as a platform for enhancing engineered metabolic pathways. The microcompartment shell of Haliangium ochraceum (HO) has emerged as a versatile and modular shell system that can be expressed and assembled outside its native host and with non-native cargo. Further, the HO shell has been modified to use the engineered protein conjugation system SpyCatcher–SpyTag for non-native cargo loading. Here, we used a model enzyme, triose phosphate isomerase (Tpi), to study non-native cargo loading into four HO shell variants and begin to understand maximal shell loading levels. We also measured activity of Tpi encapsulated in the HO shell variants and found that activity was determined by the amount of cargo loaded and was not strongly impacted by the predicted permeability of the shell variant to large molecules. All shell variants tested could be used to generate active, Tpi-loaded versions, but the simplest variants assembled most robustly. We propose that the simple variant is the most promising for continued development as a metabolic engineering platform.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Engineering PHL7 for Improved Poly(Ethylene Terephthalate) Depolymerization via Rational Design and Directed Evolution

Enzymatic depolymerization of poly(ethylene terephthalate) (PET) has emerged as a promising approach for polyester recycling, and, to date, many natural and engineered PET hydrolase enzymes have been reported. For industrial use, PET hydrolases must achieve high depolymerization extent and exhibit excellent thermostability. Here, we engineered a natural PET hydrolase, Polyester Hydrolase Leipzig #7 (PHL7), through rational design and directed evolution using a high-throughput screening platform. Four new enzymes were engineered with enhanced properties compared with the parent enzyme, wild-type PHL7 (PHL7-WT), and other benchmark PET hydrolases, under the tested conditions. In bioreactors, the exemplary engineered enzyme, PHL7-Jemez, exhibited improved ability to depolymerize amorphous PET film compared with PHL7-WT at 2.9% and 20% substrate loadings, with 37% and 270% higher hydrolysis, respectively, after 48 h. This study develops several state-of-the-art PET hydrolases and demonstrates a directed evolution platform to engineer high-performance enzymes, which can accelerate enzyme discovery toward improved biocatalytic recycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Enzyme property prediction using artificial intelligence

Artificial intelligence (AI)-driven enzyme property prediction enables rapid discovery and engineering of enzymes for a wide range of biotechnological and therapeutic applications. Here, we first introduce the key components in AI model development, including enzyme datasets, protein representation methods, and model architectures. We then highlight a variety of AI tools developed for the prediction of enzyme properties and functional annotations, including enzyme structure, kinetic parameters, substrate specificity, thermostability, solubility, Enzyme Commission number, and Gene Ontology term. Moreover, we describe representative downstream applications enabled by these AI tools. Finally, we discuss some challenges and opportunities as well as future prospects.

Yuan, Le [University of Illinois at Urbana-Champai↗
DOE OSTI2025

Evaluating the limitations of Bayesian metabolic control analysis

AbstractBayesian Metabolic Control Analysis (BMCA) has emerged as a promising framework for inferring metabolic control coefficients in data-limited scenarios by integrating Bayesian inference with linlog rate laws. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCA’s ability to infer elasticity values, flux control coefficients (FCCs), and concentration control coefficients (CCCs) under varying data availability conditions using three synthetic metabolic network models. Our findings highlight the strengths and weaknesses of BMCA, guiding its application in metabolic engineering and emphasizing the need for methodological refinements.Author summaryUnderstanding how enzymes control metabolic pathways is crucial for optimizing biomanufacturing and synthetic biology applications. Bayesian Metabolic Control Analysis (BMCA) is a promising computational method that integrates Bayesian inference with metabolic control analysis to estimate key control parameters, even in cases with limited experimental data. However, the accuracy and limitations of BMCA remain unclear. In this study, we systematically evaluate BMCA using three synthetic metabolic networks to determine how different types of physiological data impact its predictive performance. We find that BMCA requires flux and enzyme concentration data for accurate predictions, while external metabolite concentrations contribute little. Additionally, BMCA fails to predict elasticity values beyond a magnitude of 1.5 and reliably infer allosteric regulation, even when strong regulatory interactions exist. In addition, BMCA does not accurately rank metabolic control points, which may limit its utility in identifying key enzymes in engineered pathways. Our work provides practical insights into when and how BMCA can be applied, guiding future research in metabolic modeling and control analysis.

Shin, Janis (ORCID:0000000216572455)↗
DOE OSTI2024

Catalyzing the future: recent advances in chemical synthesis using enzymes

Biocatalysis has the potential to address the need for more sustainable organic synthesis routes. Pro-tein engineering can tune enzymes to perform in cascade reactions and for efficient synthesis of en-antiomerically enriched compounds, using both natural and new-to-nature reaction pathways. This review highlights recent achievements in biocatal-ysis, especially the development of novel enzymatic syntheses to access versatile small molecule inter-mediates and complex biomolecules. Biocatalytic strategies for the degradation of persistent pollu-tants and approaches for biomass valorization are also discussed. Here, the transition of chemical synthesis to a greener future will be accelerated by imple-menting enzymes and engineering them for high performance and new activities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Circularity in Sequence-Controlled Copolyamides Enabled by Regioselective Enzymatic Hydrolysis

Sequence-controlled polymers enable precise control over macromolecular structures and function, but both their synthesis and end-of-life management remain fundamental challenges. Achieving high sequence fidelity is synthetically demanding, and conventional depolymerization methods lack regioselectivity, leading to irreversible loss of encoded molecular information and limiting polymer circularity. Enzymatic catalysis offers a potential solution by combining substrate specificity with selective bond cleavage. Here, we report the synthesis, characterization, and regioselective enzymatic depolymerization of poly- (X,AMA), a sequence-controlled copolyamide composed of alternating hexamethylenediamine−adipic acid (MA) and pxylylenediamine− adipic acid (XA) repeat units. Poly(X,AMA) was synthesized via solid-state polycondensation (SSP) of sequence-defined oligomers, enabling precise control over repeat-unit order. Polymer microstructure and sequence fidelity were confirmed by 13 C NMR spectroscopy and MALDI−TOF mass spectrometry. Comparison with a statistical copolymer analogue and Nylon-66 demonstrated pronounced differences in crystallinity, morphology, and thermal behavior arising from sequence control. Screening of 96 Nylon hydrolase homologues against poly(X,AMA) revealed strongly enzyme-dependent depolymerization profiles. While tetrad formation was generally favored, enzymes displayed pronounced sequence selectivity, preferentially releasing distinct sequence-defined tetrads XAMA or MAXA. SSP of sequence-defined tetrad MAXA produced a copolyamide with near identical monomer ordering as poly(X,AMA). Computational modeling of enzyme−substrate complexes identified structural features consistent with the observed regioselectivity. Together, these results establish selective enzymatic depolymerization as a viable strategy for the circular recycling of sequence-controlled polymers and provide a foundation for the rational engineering of enzymes for programmable polymer deconstruction.

Amides↗
DOE OSTI2025

Oxidative Funneling of PVDC (CRADA Final Report)

Poly(vinylidene chloride) (PVDC) is a major polymer product from the Participant. PVDC-containing plastics are not commonly recycled. To overcome the challenges associated with recycling PVDC and multi-layer materials, we propose a tandem catalytic-biological process to convert PVDC containing waste to upcycled, tunable products such as polyhydroxyalkanoates (PHAs). PHAs are commercially relevant, biodegradable polymers that are useful in packaging, biomedical, and personal care applications. This technology, which the Contractor has named oxidative funneling (OxFun), uses a metal-promoted autoxidation step to depolymerize co-mingled polymers to a mixture of oxygenated compounds, which an engineered bacterium can funnel to a single bioproduct: here, medium chain length polyhydroxyalkanoates (mcl-PHAs). The proposed oxidation chemistry is amenable to inclusion of additional polymers, and the biocatalyst can be engineered to convert the deconstruction products to a variety of valuable chemicals, thus presenting a tunable system for both feedstock variability and target product. Pseudomonas putida KT2440 – a metabolically robust microbe that has been engineered and proven viable for upcycling deconstructed polystyrene (PS), high density polyethlene (HDPE), and polyethylene terephthalate (PET) – can be engineered with enzymes capable of de-chlorinating PVDC deconstruction products (e.g., 2,2-dichloroacetate) in addition to PE-derived substrates into mcl-PHAs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A map of the rubisco biochemical landscape

Rubisco is the primary CO 2 -fixing enzyme of the biosphere, yet it has slow kinetics. The roles of evolution and chemical mechanism in constraining its biochemical function remain debated. Engineering efforts aimed at adjusting the biochemical parameters of rubisco have largely failed, although recent results indicate that the functional potential of rubisco has a wider scope than previously known. Here we developed a massively parallel assay, using an engineered Escherichia coli 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 CO 2 concentration enabled inference of enzyme velocity and apparent CO 2 affinity parameters for thousands of substitutions. This approach identified many highly conserved positions that tolerate mutation and rare mutations that improve CO 2 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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Engineering an aldoxime dehydratase with high activity and isomer tolerance for biosynthesis of an O -protected primary cyanohydrin

O-protected primary cyanohydrins (glycolonitriles) are important building blocks for many difunctionalized compounds and precursors to known bioactive molecules. Their synthesis, however, utilizes toxic cyanide, which raises significant safety concerns for industrial synthesis. Here, in this study, we present a cyanide-free enzymatic synthesis of an o-benzyl protected primary cyanohydrin from an (E)- or (Z)-α-oxygen protected aldoxime using an engineered aldoxime dehydratase enzyme from Bacillus sp. OxB-1 (OxdB). In contrast to many evolved enzymes that tend to “specialize” as their activity increases, we used directed evolution to engineer OxdB for efficient dehydration of both isomers in a mixture of (E)- or (Z)-α-oxygen aldoximes with high activity and substrate loading to achieve near quantitative yield. Using this enzyme, we further demonstrate a cyanide-free chemoenzymatic pathway to an o-protected primary cyanohydrin starting from a readily available aldehyde, where the aldehyde is first condensed with hydroxylamine, followed by dehydration using our evolved enzyme. This pathway was readily scaled up to 1 g scale with high substrate loading, demonstrating its utility in industrial synthesis of these important building block functional groups.

Aldoxime dehydratase↗
DOE OSTI2025

Machine Learning-Guided Identification of PET Hydrolases from Natural Diversity

The enzymatic depolymerization of poly(ethylene terephthalate) (PET) is emerging as a leading chemical recycling technology for waste polyester. As part of this endeavor, new candidate enzymes identified from natural diversity can serve as useful starting points for enzyme evolution and engineering. In this study, we improved upon HMM searches by applying an iterative machine learning strategy to identify 400 putative PET-degrading enzymes (PET hydrolases) from naturally occurring homologs. Using high-throughput (HTP) experimental techniques, we successfully expressed and purified >200 enzyme candidates and assayed them for PET hydrolysis activity as a function of pH, temperature, and substrate crystallinity. From this library, we discovered 91 previously unknown PET hydrolases, 35 of which retain activity at pH 4.5 on crystalline material, which are conditions relevant to developing more efficient commercial processes. Notably, four enzymes showed equal to or higher activity than LCC-ICCG, a benchmark PET hydrolase, at this challenging condition in our screening assay, and 11 of which have pH optima <7. Using these data, we identified regions of PETases statistically correlated to activity at lower pH. We additionally investigated the effect of condition-specific activity data on trained machine learning predictors and found a precision (putative hit rate) improvement of up to 30% compared to a Hidden Markov Model alone. Our findings show that by pointing enzyme discovery toward conditions of interest with multiple rounds of experimental and machine learning, we can discover large sets of active enzymes and explore factors associated with activity at those conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Imine Reductase-Catalyzed, Radical-Mediated Asymmetric Cyano Group Migration

Functional group migration (FGM) reactions represent a fundamental class of transformations in organic chemistry, enabling the repositioning of functional moieties in nonobvious ways. However, catalytic asymmetric radical-mediated FGMs remain rare due to the inherent challenges of achieving catalyst-controlled enantioselectivity over free radical intermediates. Herein, we repurpose imine reductases (IREDs), a class of biotechnologically important enzymes known for their substrate promiscuity, to enable the first examples of catalytic asymmetric cyano group migration via a radical mechanism. An orthogonal set of radical enzymes, including PbaIREDCym and SmiIREDCym, was engineered, allowing both 1,4- and 1,5-cyano group migration reactions to occur in an enantiodivergent fashion. The use of the nonionic surfactant TPGS-1000 was found to improve both the yield and enantioselectivity of these cyano migration reactions. Furthermore, this biocatalytic process exhibited a broad substrate scope and is readily scalable, affording a rare example of chiral nonamine product assembly with imine reductases. More broadly, stereoselective radical biocatalysis with engineered IREDs and other versatile enzymes provides a potentially general solution to challenging asymmetric FGM reactions.

Biocatalysis↗
DOE OSTI2024

Streamlining heterologous expression of top carbonic anhydrases in Escherichia coli : bioinformatic and experimental approaches

Carbonic anhydrase (CA) enzymes facilitate the reversible hydration of CO 2 to bicarbonate ions and protons. Identifying efficient and robust CAs and expressing them in model host cells, such as Escherichia coli, enables more efficient engineering of these enzymes for industrial CO 2 capture. However, expression of CAs in E. coli is challenging due to the possible formation of insoluble protein aggregates, or inclusion bodies. This makes the production of soluble and active CA protein a prerequisite for downstream applications. In this study, we streamlined the process of CA expression by selecting seven top CA candidates and used two bioinformatic tools to predict their solubility for expression in E. coli. The prediction results place these enzymes in two categories: low and high solubility. Our expression of high solubility score CAs (namely CA5-SspCA, CA6-SazCAtrunc, CA7-PabCA and CA8-PhoCA) led to significantly higher protein yields (5 to 75 mg purified protein per liter) in flask cultures, indicating a strong correlation between the solubility prediction score and protein expression yields. Furthermore, phylogenetic tree analysis demonstrated CA class-specific clustering patterns for protein solubility and production yields. Unexpectedly, we also found that the unique N-terminal, 11-amino acid segment found after the signal sequence (not present in its homologs), was essential for CA6-SazCA activity. Overall, this work demonstrated that protein solubility prediction, phylogenetic tree analysis, and experimental validation are potent tools for identifying top CA candidates and then producing soluble, active forms of these enzymes in E. coli. The comprehensive approaches we report here should be extendable to the expression of other heterogeneous proteins in E. coli.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Metabolic engineering strategies for producing decanoic acid and related oleochemicals: 1-decanol, 2-nonanone, and poly(3-hydroxydecanoate) in Escherichia coli

Medium-chain (mc-) oleochemicals are an important class of renewable chemicals with broad industrial applications; however, their sustainable microbial production remains challenging. In this study, we developed a versatile metabolic engineering and fed-batch strategy to produce C 10 -oleochemicals in Escherichia coli. Central to this approach is an engineered mc-acyl-ACP thioesterase Cl FatB3-tr-D10S with C 10 species accounting for around 70% of the total fatty acids produced. To expand product diversity, we established a decanoyl-CoA pool through co-expression of fadD, enabling downstream conversion into multiple product classes. Through pathway tuning, enzyme bioprospecting, strain engineering and fermentation optimization strategies, we demonstrated selective production of 1-decanol, 2-nonanone and poly(3-hydroxydecanoate) (C 10 -PHA). Production of decanoic acid and 1-decanol were achieved by optimizing expression of Cl fatB3-tr-D10S and, Mt fadD6 and Ma acr, respectively. Leveraging β-oxidation enabled the production of β-ketoacyl-CoA intermediates, which were converted to 2-nonanone via heterologous Mlu fadE, Vf fadB and Ps fadM expression. Additionally, expression of phaJ2 and phaC2 facilitated the conversion of decanoyl-CoA pool into C 10 -PHA homopolymer. Altogether, this work demonstrates a versatile and tunable platform for medium-chain oleochemical production.

1-Decanol↗
DOE OSTI2026

Engineering Enantiocomplementary Protoglobins for Stereoconvergent Construction of N -Alkylated α-Aminoketones

The synthesis of enantiopure compounds from a mixture of E/Z alkenes represents a notable challenge in synthetic chemistry. While enzymes excel in achieving unparalleled selectivity, their inherent specificity often confines activity to a single stereoisomeric substrate, consequently restricting the overall efficiency of such transformations. Here, we demonstrate that protoglobin-derived hemoproteins can catalyze stereoconvergent intermolecular amination using simple N-alkyl hydroxylamines as nitrene precursors, a transformation which remains elusive in synthetic chemistry. These engineered enzymes process E/Z mixtures of silyl enol ethers, enabling the precise incorporation of N-alkyl amino moieties (−NHAlkyl) into diverse molecular structures (up to 79% yield and 95% ee). Two complementary protoglobin variants were engineered using directed evolution to enable enantiodivergent synthesis of both enantiomers of α-aminoketones. This enzymatic platform achieves stereoconvergent and enantiodivergent transformations, facilitating the conversion of simple chemicals into an array of valuable pharmaceutical compounds featuring aminoketone functionalities.

Alcohols↗
DOE OSTI2026

Data for A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-foldimprovement in substrate preference and 16-fold improvement in ethyl-transferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

AI/ML↗