ACS Catalysis

Enzymes offer unparalleled selectivity and sustainability for chemical synthesis, yet their widespread industrial application is often hindered by the slow and uncertain process of discovering and optimizing suitable biocatalysts. While directed evolution remains the gold standard for enzyme optimization, its success hinges on the availability of a starting enzyme with measurable activity, a persistent bottleneck for many desired functions. Designing libraries likely to contain such functional starting points remains a major challenge. In this work, we use the GenSLM protein language model (PLM) along with a series of filters to generate novel sequences of the {beta}-subunit of tryptophan synthase (TrpB) that express in Escherichia coli, are stable, and are catalytically active in the absence of a TrpA partner. Many generated TrpBs also demonstrated significant substrate promiscuity, accepting non-canonical substrates typically inaccessible to natural TrpBs. Remarkably, several outperformed both natural and laboratory-optimized TrpBs on native and non-canonical substrates. Comparative analysis of the most active and promiscuous generated TrpB and its closest natural homolog confirmed that this enhanced functional versatility does not stem from the natural enzyme, highlighting the creative potential of generative models. Our results demonstrate that the model can generate enzymes which not only preserve natural structure and function but also acquire non-natural properties, establishing PLMs as powerful tools for biocatalyst discovery and engineering, with the potential in some cases to bypass further optimization.

White-rot fungi secrete a repertoire of high-redox potential oxidoreductases to efficiently decompose lignin. Of these enzymes, versatile peroxidases (VPs) are the most promiscuous biocatalysts. VPs are attractive enzymes for research and industrial use, but their recombinant production is extremely challenging. To date, only a single VP has been structurally characterized and optimized for recombinant functional expression, stability and activity. Computational enzyme optimization methods can be applied to many enzymes in parallel, but they require accurate structures. Here, we demonstrate that model structures computed by deep-learning based ab initio structure prediction methods are reliable starting points for one-shot PROSS stability-design calculations. Four designed VPs encoding as many as 43 mutations relative to the wild type enzymes are functionally expressed in yeast whereas their wild type parents are not. Three of these designs exhibit substantial and useful diversity in reactivity profile and tolerance to environmental conditions. The reliability of the new generation of structure predictors and design methods increases the scale and scope of computational enzyme optimization, enabling efficient discovery and exploitation of the functional diversity in natural enzyme families.

Bacterial aryl malonate decarboxylase is a cofactor-free enzyme that generates a wide spectrum of -chiral carboxylic acids in outstanding optical purity, including several non-steroidal anti-inflammatory drugs and chiral building blocks. The well-characterized AMDase from Bordetella bronchiseptica (BbAMDase) and related enzymes of the same family have three main limitations: (i) low stability, both operational and thermal, and (ii) limited substrate spectrum regarding the size of the smaller substituent on the -C-atom and (iii) low stereoselectivity towards -alkenyl--alkyl malonic acids. To address these limitations, we expanded the structural diversity of the AMDase family by ancestral sequence reconstruction (ASR). The phylogenetic analysis of the decarboxylase revealed conserved structural motifs and key amino acids in the hydrophobic active-site cavity, a catalytic motif crucial for activity and selectivity of the enzyme. The analysis highlighted the natural distribution of amino acid exchanges that had been previously identified in enzyme engineering campaigns. AMDase ancestors showed higher stability, activity, and, in one case, also stereoselectivity than BbAMDase. While the up to 10 {degrees}C higher unfolding temperature of AMDase ancestors is a frequent result in ASR, the improvement of the half-life time of 294-fold of ancestor N131 was surprising. Ancestor N31 formed 2-methyl-but-3-enoic acid from its corresponding malonic acid in an optical purity of 99.7% eeR. The extant BbAMDase produces this compound in much lower optical purity (96.8% eeR), which corresponds to a 1.4 kcal{middle dot}mol-1 difference of the transition state free energy of the two reaction paths leading to the different enantiomers. Furthermore, the stereoselectivity of the ancestors was completely inverted by switch of the catalytic cysteine residues G74C/C188G.

Enzyme reactions, both in Nature and technical applications, commonly occur at the interface of immiscible phases. Nevertheless, stringent descriptions of interfacial enzyme catalysis remain sparse, and this is partly due to a shortage of coherent experimental data to guide and assess such work. We have produced and kinetically characterized 83 cellulases, which revealed a conspicuous linear free energy relationship (LFER) between the strength of substrate binding and the activation barrier. This common scaling occurred despite the investigated enzymes were structurally and mechanistically diverse. We suggest that the scaling reflects basic physical restrictions of the hydrolytic process and that evolutionary selection have condensed cellulase phenotypes near the line. One consequence of the LFER is that the activity of a cellulase can be estimated from substrate binding strength, irrespectively of structural and mechanistic details, and this appears promising for in silico selection and design within this industrially important group of enzymes. On a more general note, the LFER may identify a link to inorganic heterogeneous catalysis, and hence open for the implementation of approaches from this field within interfacial enzymology.

O_SCPLOWLC_SCPLOW-Threonine aldolases (LTAs) are attractive biocatalysts for synthesizing {beta}-hydroxy--amino acids (HAAs) via C-C bond formation in pharmaceuticals, although their industrial applications suffer from low activity and diastereoselectivity. Herein, we describe the discovery of a new LTA from Neptunomonas marine (NmLTA) that displays both ideal enzymatic activity (64.8 U/mg) and diastereoselectivity (89.5% diastereomeric excess; de) for the desired product O_SCPLOWLC_SCPLOW-threo-4-methylsulfonylphenylserine (O_SCPLOWLC_SCPLOW-threo-MPTS). Using X-ray crystallography, site-directed mutagenesis, and computational modeling, we propose a "dual-conformation" mechanism for the diastereoselectivity control of NmLTA, whereby the incoming 4-methylsulfonylbenzaldehyde (4-MTB) could potentially bind at the NmLTA active site in two distinct orientations, potentially forming two diastereoisomers (threo- or erythro-form products). Importantly, two key NmLTA residues H140 and Y319 play critical roles in fine-tuning the binding mode of 4-MTB, supported by our site-mutagenesis assays. Uncovering of the catalytic mechanism in NmLTA guides us to further improve the diastereoselectivity of this enzyme. A triple variant of NmLTA (N18S/Q39R/Y319L; SRL) exhibited both improved diastereoselectivity (de value > 99%) and enzymatic activity (95.7 U/mg) for the synthesis of O_SCPLOWLC_SCPLOW-threo-MPTS compared with that of wild type. The preparative gram-scale synthesis for O_SCPLOWLC_SCPLOW-threo-MPTS with the SRL variant produced a space-time yield of up to 9.0 g L-1h-1, suggesting a potential role as a robust C-C bond synthetic tool for industrial synthesis of HAAs at a preparative scale. Finally, the SRL variant accepted a wider range of aromatic aldehyde derivatives as substrates and exhibited improved diastereoselectivity toward para-site substituents. This work provides deep structural insights into the molecular mechanism underlying the catalysis in NmLTA and pinpoints the key structural motifs responsible for regulating the diastereoselectivity control, thereby guiding future attempts for protein engineering of various LTAs from different sources.

Ketosynthase domains govern chain transfer and substrate selectivity in modular polyketide synthases (PKS), yet their functional tunability in native contexts remains poorly understood. We performed phylogenetically guided mutagenesis of the KS5 domain from the Streptomyces cinnamonensis monensin PKS and evaluated 72 variants in vivo across wild-type and reductive-loop-null backgrounds. This revealed discrete active-site motifs that control productivity, redox-state specificity, and extender-unit selection, functions traditionally ascribed to other PKS domains. AlphaFold3 structural mapping linked these motifs to substrate-tunnel and catalytic-core features, providing a mechanistic basis for the observed phenotypes. Our findings demonstrate that KS domains can be rationally re-tuned to overcome productivity bottlenecks and alter specificity in intact PKSs, offering a route to improved yields and expanded chemical diversity in engineered polyketides.

Fluorinases have high potential for industrial biofluorination but any applications have been precluded by low catalytic efficiency and resistance to active site engineering. In this work, we employed PRIZM, a computational workflow utilizing an existing low-N dataset and zero-shot models for in silico prediction of activity-enhancing mutations at distal sites. The combination of these predictions with expert opinion led to the identification of 21 fluorinase mutants with enhanced relative activities, while 3 variants showed increased melting temperatures. A mutation in the hexameric interface, K237R, resulted in the largest stability gain, a more than 3.2-fold improvement in catalytic efficiency at 57{degrees}C, and an 8-fold increase in relative activity at 62{degrees}C. These results highlight the potential of distal fluorinase engineering for improving properties required to realize its industrial applications.

We present a fully computational workflow for de novo design of efficient enzymes using backbone fragments from natural proteins and without recourse to iterative experimental optimization. The best designed Kemp eliminase exhibits >140 mutations from any natural protein, high stability (>85 {degrees}C) and unprecedented catalytic efficiency (12,700 M-1s-1), surpassing previous computational designs by two orders of magnitude. We find that mutations both inside and outside the active site contribute synergistically to the high observed activity and stability. Mutation of an aromatic residue used in all prior Kemp eliminase designs increases efficiency to >105 M-1s-1. Our approach addresses critical limitations in design methodology, generating stable, high-efficiency, new-to-nature enzymes in complex folds and enables testing hypotheses on the fundamentals of biocatalysis through a limited experimental effort.

The de novo design of enzymes critically tests our understanding of natural enzymes and enables design of novel catalysts. Here, we identify the features responsible for the catalytic efficiency of a highly proficient de novo enzyme generated through computational design and optimized by directed evolution. Computational, spectroscopic, and biochemical studies reveal successfully designed features, including precise alignment of catalytic residues, transition state stabilization, and environmental tuning. In the most evolved enzyme, the binding of a transition state analog also led to widespread increases in backbone rigidity and conformational stability throughout the protein, except within a helix near the active site entrance, where the introduction of Gly and Pro increased dynamics and catalytic activity. Thus, the entire protein contributes to catalysis in the most optimized enzyme. These studies provide principles for designing efficient enzymes.

Active-site redesign frequently yields modest improvements because residues controlling physical steps like substrate binding and product release lie outside the active site. Efficient catalysis requires a cooperative catalytic network of residues that support both the chemical and physical steps of catalysis. Using ancestral hydroxynitrile lyase HNL1, an /{beta}-hydrolase with poor esterase activity, we tested this framework directly. Matching all active-site residues to a proficient esterase improved KM five-fold but left kcat unchanged, confirming that chemical machinery alone is insufficient. Activity-weighted sequence comparison (SigniSite) across ten homologous HNLs and esterases identified 38 positions disfavoring esterase activity. Experimental refinement yielded a minimal set of fifteen substitutions (HNL1-15) with [~]60-fold higher kcat and [~]400-fold higher kcat/KM. Single-substitution reversion analysis confirmed that all fifteen substitutions are essential and provided evidence for strong cooperativity between them. X-ray crystal structures of HNL1 and HNL1-15 reveal three coordinated structural changes: reshaping the substrate-binding pocket to favor productive ester binding, restoring access to the oxyanion hole, and opening an additional tunnel for product egress and water entry. These changes arise through backbone rearrangements and altered flexibility rather than direct active-site contacts, explaining why the responsible positions escape conservation-based detection. Because cooperativity masks individual contributions, engineering such networks may require step-specific assays -- measuring binding, acylation, or product release directly -- rather than screening composite kcat.

Cyclohexanone monooxygenases (CHMO) consume molecular oxygen and NADPH to catalyze the valuable oxidation of cyclic ketones. However, CHMO usage is restricted by poor thermostability and stringent specificity for NADPH. Efforts to engineer CHMO have been limited by the sensitivity of the enzyme to perturbations in conformational dynamics and long-range interactions that cannot be predicted. We demonstrate a pair of aerobic, high-throughput growth selection platforms in Escherichia coli for oxygenase evolution, based on NADPH or NADH redox balance. We utilize the NADPH-dependent selection in the directed evolution of thermostable CHMO and discover the variant CHMO GV (A245G-A288V) with a 2.7-fold improvement in residual activity compared to the wild type after 40 {degrees}C incubation. Addition of a previously reported mutation resulted in A245G-A288V-T415C which has further improved thermostability at 45 {degrees}C. We apply the NADH-dependent selection to alter the cofactor specificity of CHMO to accept NADH, a less expensive cofactor than NADPH. We identified the variant CHMO DTNP (S208D-K326T-K349N-L143P) with a 21-fold cofactor specificity switch from NADPH to NADH compared to the wild type. Molecular modeling indicates that CHMO GV experiences more favorable residue packing and backbone torsions, and CHMO DTNP activity is driven by cooperative fine-tuning of cofactor contacts. Our introduced tools for oxygenase evolution enable the rapid engineering of properties critical to industrial scalability.

The fluorinase enzyme, the only known biocatalyst forming stable carbon-fluorine bonds, operates with extremely low efficiency, catalyzing one reaction every 2-12 minutes. This severely limits its utility for sustainable biofluorination, and its sluggish activity remains poorly understood. We suppressed its aggregation through directed mutagenesis and elucidated the kinetic mechanism using a novel mathematical framework that fits complex kinetic and oligomerization data. This analysis revealed that >80% of enzyme molecules are inactive under standard conditions due to two dead-end pathways. The designed W50F+A279R mutant preferentially formed hexamers and displayed enhanced catalytic efficiency in this oligomeric state. When coupled with mechanism-based optimization of the reaction medium, including enzymatic removal of the inhibitory product, the catalytic turnover rate reached 12.5 {+/-} 2.1 min-{superscript 1}, representing [~]60-fold increase compared with previously reported turnover rates of the wild-type enzyme. Our work provides a mechanistic blueprint for fluorinase enhancement and a generalizable mathematical framework for analyzing kinetics of multimeric enzymes.

Directed evolution (DE) inspired by natural evolution (NE) has been achieving tremendous successes in protein/enzyme engineering. However, the conventional one-protein-for-one-task DE cannot match the multi-proteins-for-multi-tasks NE in terms of screening throughput and efficiency, thus often failing to meet the fast-growing demands for biocatalysts with desired properties. In this study, we design a novel multi-enzyme-for-multi-substrate (MEMS) DE model and establish the proof-of-concept by running a NE-mimicking and higher-throughput screening on the basis of two-P450s-against-seven-substrates (2Px7S) in one pot. With the significantly improved throughput and hit-rate, we witness a series of convergent evolution events of the two archetypal cytochrome P450 enzymes (P450 BM3 and P450cam) in laboratory. Further structural analysis of the two functionally convergent P450 variants provide important insights into how distinct active-sites can reach a common catalytic goal.

Polyurethanes (PURs) represent a significant challenge in plastic waste management due to their chemical resilience and limited recycling options. In this study, we report the identification and characterization of six novel bacterial urethanases, expanding the enzymatic repertoire for targeted PUR depolymerization. These enzymes demonstrated carbamate-cleaving activity optimally under alkaline conditions, maintaining stability across a pH range of 7 to 10 and varying thermal and solvent tolerances. Among the candidate enzymes, u17, u10, and u15 collectively exhibited high activity, catalytic efficiency, and thermostability, establishing a strong foundation for further optimization. Building on these results, u15 emerged as particularly notable for its catalytic efficiency on the carbamate model substrate di-urethane ethylene methylenedianiline, DUE-MDA, with a kcat/KM of 51.8 {+/-} 0.1 (s-1mM-1). and this motivated its selection for detailed structural analysis. High-resolution crystallography of u15 revealed key active-site architecture, including the conserved amidase signature catalytic triad and flexible loop regions that influence substrate binding and specificity. Molecular docking and molecular dynamics simulations further elucidated substrate binding determinants of u15 during urethane bond hydrolysis. Docking of DUE-MDA revealed two distinct substrate orientations (Pose A and Pose B) differing in the positioning of the carbamate group relative to Ser177. Pose A was more stable and catalytically competent, maintaining the substrate within the oxyanion hole and sustaining optimal geometry for nucleophilic attack by Ser177. Comparable behavior was observed for the partially hydrolyzed intermediate mono-urethane ethylene methylenedianiline, MUE-MDA, indicating a conserved binding mode across substrates. Collectively, these findings highlight amidase signature urethanases as valuable scaffolds for advancing sustainable and scalable biocatalytic recycling of polyurethanes. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/705263v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@127bf23org.highwire.dtl.DTLVardef@75c29corg.highwire.dtl.DTLVardef@13bbf30org.highwire.dtl.DTLVardef@18504a4_HPS_FORMAT_FIGEXP M_FIG C_FIG

Evolutionary constraints significantly limit the diversity of naturally occurring enzymes, thereby reducing the sequence repertoire available for enzyme discovery and engineering. Recent breakthroughs in protein structure prediction and de novo design, powered by artificial intelligence, now enable us to create enzymes with desired functions without relying on traditional genome mining. Here, we demonstrate a computational strategy for creating new-to-nature PET hydrolases by leveraging the known catalytic mechanisms and implementing multiple deep learning algorithms and molecular computations. This strategy includes the extraction of functional motifs from a template enzyme (here we use leaf-branch compost cutinase, LCC), regeneration of new protein scaffolds, computational screening, experimental validation, and sequence refinement. We successfully replicate PET hydrolytic activity with designer enzymes that are at least 30% shorter in sequence length than LCC. Among them, RsPETase 1 stands out due to its robust expressibility. It exhibits comparable activity to IsPETase and considerable thermostability with a melting temperature of 56 {degrees}C, despite sharing only 34% sequence similarity with LCC. This work suggests that enzyme diversity can be expanded by recapitulating functional motifs with computationally built protein scaffolds, thus generating opportunities to acquire highly active and robust enzymes that do not exist in nature.

Phenylalanine ammonia-lyases (PALs) deaminate L-phenylalanine to trans-cinnamic acid and ammonium and have idespread application in chemo-enzymatic synthesis, agriculture, and medicine. In particular, the PAL from Anabaena variabilis (Trichormus variabilis) has garnered significant attention as the active ingredient in Pegvaliase(R), the only FDA-approved drug treating classical phenylketonuria (PKU). Although an extensive body of literature exists on structure, substrate-specificity, and catalytic mechanism, protein-wide sequence determinants of function remain unknown, which limits the ability to rationally engineer these enzymes. Previously, we developed a high-throughput screen (HTS) for PAL, and here, we leverage it to create a detailed sequence-function landscape of PAL by performing deep mutational scanning (DMS). Our method revealed 79 hotspots that affected a positive change in enzyme fitness, many of which have not been reported previously. Using fitness values and structure-function analysis, we picked a subset of residues for comprehensive single- and multi-site saturation mutagenesis to improve the catalytic activity of PAL and identified combinations of mutations that led to improvement in reaction kinetics in cell-free and cellular contexts. To understand the mechanistic role of the most beneficial mutations, we performed QM/MM and MD and observed that different mutants confer improved catalytic activity via different mechanisms, including stabilizing first transition and intermediate states and improving substrate diffusion into the active site, and decreased product inhibition. Thus, this work provides a comprehensive sequence-function relationship for PAL, identifies positions that improve PAL activity when mutated and assesses their mechanisms of action.

Sucrose synthase (SuSy) has been suggested as a key enabling enzyme for uridine diphosphate glucose (UDP-Glc) regeneration in glycosyltransferase-catalyzed biotransformations. However, its stability and efficiency in industrially relevant conditions have not been characterized or engineered, limiting its industrial readiness. Here, we combined enzyme discovery and characterization with comprehensive semi-rational enzyme engineering strategies, to optimize SuSys catalytic activity, thermostability, solvent tolerance, and soluble expression. The engineered variants were significantly more stable than wild-type, with up to 13.6 {degrees}C increase in melting temperature, over two orders of magnitude improvement in half-lives at elevated temperatures, and approximately three orders of magnitude increase in total turnover number. Additionally, the optimized variants retained up to 75% relative activity at 60 {degrees}C in the presence of 25% (v/v) DMSO, which the wild-type shows near complete loss of activity. Structural and molecular dynamics analyses reveal how mutations modulate conformational dynamics and hydrophobic packing, favoring catalytically competent conformations. Using methyl anthranilate glycosylation as a representative biotransformation, we demonstrate that the engineered SuSy variants consistently outperform both wild-type SuSy and stoichiometric UDP-Glc systems, enabling efficient UDP-Glc regeneration at reduced enzyme and sugar donor loadings. Finally, techno-economic and environmental assessments further indicate that implementation of engineered SuSy reduces reaction cost by approximately 6- and 2-fold relative to UDP-Glc and wild-type systems, respectively, while achieving average reductions of 3- and 2-fold in environmental end-point impacts. These results established SuSy engineering as a critical enabler for sustainable glycosylation reactions.

Efficient regeneration of cofactors is vital for the establishment of continuous biocatalytic processes. Formate is an ideal electron donor for cofactor regeneration due to its general availability, low reduction potential, and benign byproduct (CO2). However, formate dehydrogenases (FDHs) are usual specific to NAD+, such that NADPH regeneration with formate is challenging. Previous studies reported naturally occurring FDHs or engineered FDHs that accept NADP+, but these enzymes show low kinetic efficiencies and specificities. Here, we harness the power of natural selection to engineer FDH variants to simultaneously optimize three properties: kinetic efficiency with NADP+, specificity towards NADP+, and affinity towards formate. By simultaneously mutating multiple residues of FDH from Pseudomonas sp. 101, which exhibits no initial activity towards NADP+, we generate a library of >106 variants. We introduce this library into an E. coli strain that cannot produce NADPH. By selecting for growth with formate as sole NADPH source, we isolate several enzyme variants that support efficient NADPH regeneration. We find that the kinetically superior enzyme variant, harboring five mutations, has 5-fold higher efficiency and 13-fold higher specificity than the best enzyme previously engineered, while retaining high affinity towards formate. By using molecular dynamics simulations, we reveal the contribution of each mutation to the superior kinetics of this variant. We further determine how non-additive epistatic effects improve multiple parameters simultaneously. Our work demonstrates the capacity of in vivo selection to identify superior enzyme variants carrying multiple mutations which would be almost impossible to find using conventional screening methods.

Enzymes used on industrial scale are routinely engineered for best performance. However, exhaustive mutagenesis campaigns using the twenty canonical proteinogenic amino acids rapidly reach an evolutionary ceiling, where gains in activity compromise other critical properties such as thermal endurance. Although non-canonical amino acids (ncAA) expand the chemical space, most are costly for use on an industrial scale and significantly perturb structure. Here, we demonstrate that the evolutionary ceiling of highly optimized polyethylene terephthalate (PET) hydrolases (PETases) can be broken with azatryptophans that (i) differ minimally from their canonical tryptophan, (ii) are genetically encoded, and (iii) are produced in high yield by enzymatic biosynthesis from inexpensive precursors. The first genetic encoding systems are described for 4-azatryptophan, 5-azatryptophan, and 6-azatryptophan, achieving single, site-selective isosteric CH [->] N substitutions that enhancing the catalytic activity while preserving thermal stability. The fluorescence of 6AW provides a uniquely sensitive reporter of side-chain solvent exposure, which is critical for PETase activity and shown to vary between five different PETases. Furthermore, Azatryptophan-bearing enzymes are inexpensive to produce. To benchmark PETase activity, a rapid fluorescence-based kinetic assay, PETra, is introduced, which delivers consistency and reproducibility by using a soluble substrate yet correlates strongly with the hydrolysis of solid PET.

Biological reducing power is carried by nicotinamide adenine dinucleotide (phosphate) (NAD(P)/H), which supports cellular functions and cannot be specifically directed to engineered metabolic pathways. Nicotinamide mononucleotide (NMN(H)) has emerged as an orthogonal redox cofactor to address this challenge, yet strategies remain elusive for creating NMN(H)-specific enzymes that no longer interact with the cellular NAD(P)/H pools. Previous designs avoided perturbing the most ancient and conserved GxGxxG motif in Rossmann fold enzymes, the root cause of persistent NAD(P)/H recognition. Herein, we demonstrated that this motif, though long considered essential, is in fact mutable to yield active NMN+-specific enzymes. This is implemented on two unrelated model enzymes chosen to garner orthogonal reducing power from either the cheap electron source phosphite or glycolysis. On phosphite dehydrogenase (PTDH), variants NRC-01 and NRC-02 eliminated electron leaking to numerous NAD(P)H-dependent side reactions in whole cells and crude cell lysates while driving NMNH-dependent biotransformation with [~]240-fold higher productivity than existing catalysts. On glyceraldehyde-3-phosphate dehydrogenase (GapA), variant RSQ featured a [~]2.9x104-fold cofactor specificity switch to NMN+ from NAD+. Combined Rosetta modeling, systematic structural alignment, and experimental results revealed that Rossmann fold reprogramming, paired with engineered structural reinforcement, may offer a general route to orthogonal redox biocatalysts.