Using atomistic solution scattering modelling to elucidate the role of the Fc glycans in human IgG4.

Human immunoglobulin G (IgG) exists as four subclasses IgG1-4, each of which has two Fab subunits joined by two hinges to a Fc subunit. IgG4 has the shortest hinge with 12 residues. The Fc subunit has two glycan chains, but the importance of glycosylation is not fully understood in IgG4. Here, to evaluate the stability and structure of non-glycosylated IgG4, we performed a multidisciplinary structural study of glycosylated and deglycosylated human IgG4 A33 for comparison with our similar study of human IgG1 A33. After deglycosylation, IgG4 was found to be monomeric by analytical ultracentrifugation; its sedimentation coefficient of 6.52 S was reduced by 0.27 S in reflection of its lower mass. X-ray and neutron solution scattering showed that the overall Guinier radius of gyration RG and its cross-sectional values after deglycosylation were almost unchanged. In the P(r) distance distribution curves, the two M1 and M2 peaks that monitor the two most common distances within IgG4 were unchanged following deglycosylation. Further insight from Monte Carlo simulations for glycosylated and deglycosylated IgG4 came from 111,382 and 117,135 possible structures respectively. Their comparison to the X-ray and neutron scattering curves identified several hundred best-fit models for both forms of IgG4. Principal component analyses showed that glycosylated and deglycosylated IgG4 exhibited different conformations from each other. Within the constraint of unchanged RG and M1-M2 values, the glycosylated IgG4 models showed more restricted Fc conformations compared to deglycosylated IgG4, but no other changes. Kratky plots supported this interpretation of greater disorder upon deglycosylation, also observed in IgG1. Overall, these more variable Fc conformations may demonstrate a generalisable impact of deglycosylation on Fc structures, but with no large conformational changes in IgG4 unlike those seen in IgG1.

Investigation of the Solid-State Interactions in Lyophilized Human G-CSF Using Hydrogen-Deuterium Exchange Mass Spectrometry.

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) previously elucidated the interactions between excipients and proteins for liquid granulocyte colony stimulating factor (G-CSF) formulations, confirming predictions made using computational structure docking. More recently, solid-state HDX mass spectrometry (ssHDX-MS) was developed for proteins in the lyophilized state. Deuterium uptake in ssHDX-MS has been shown for various proteins, including monoclonal antibodies, to be highly correlated with storage stability, as measured by protein aggregation and chemical degradation. As G-CSF is known to lose activity through aggregation upon lyophilization, we applied the ssHDX-MS method with peptide mapping to four different lyophilized formulations of G-CSF to compare the impact of three excipients on local structure and exchange dynamics. HDX at 22 °C was confirmed to correlate well with the monomer content remaining after lyophilization and storage at -20 °C, with sucrose providing the greatest protection, and then phenylalanine, mannitol, and no excipient leading to progressively less protection. Storage at 45 °C led to little difference in final monomer content among the formulations, and so there was no discernible relationship with total deuterium uptake on ssHDX. Incubation at 45 °C may have led to a structural conformation and/or aggregation mechanism no longer probed by HDX at 22 °C. Such a conformational change was observed previously at 37 °C for liquid-formulated G-CSF using NMR. Peptide mapping revealed that tolerance to lyophilization and -20 °C storage was linked to increased stability in the small helix, loop AB, helix C, and loop CD. LC-MS HDX and NMR had previously linked loop AB and loop CD to the formation of a native-like state (N*) prior to aggregation in liquid formulations, suggesting a similar structural basis for G-CSF aggregation in the liquid and solid states.

Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants.

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

Two-substrate enzyme engineering using small libraries that combine the substrate preferences from two different variant lineages.

Improving the range of substrates accepted by enzymes with high catalytic activity remains an important goal for the industrialisation of biocatalysis. Many enzymes catalyse two-substrate reactions which increases the complexity in engineering them for the synthesis of alternative products. Often mutations are found independently that can improve the acceptance of alternatives to each of the two substrates. Ideally, we would be able to combine mutations identified for each of the two alternative substrates, and so reprogramme new enzyme variants that synthesise specific products from their respective two-substrate combinations. However, as we have previously observed for E. coli transketolase, the mutations that improved activity towards aromatic acceptor aldehydes, did not successfully recombine with mutations that switched the donor substrate to pyruvate. This likely results from several active site residues having multiple roles that can affect both of the substrates, as well as structural interactions between the mutations themselves. Here, we have designed small libraries, including both natural and non-natural amino acids, based on the previous mutational sites that impact on acceptance of the two substrates, to achieve up to 630× increases in kcat for the reaction with 3-formylbenzoic acid (3-FBA) and pyruvate. Computational docking was able to determine how the mutations shaped the active site to improve the proximity of the 3-FBA substrate relative to the enamine-TPP intermediate, formed after the initial reaction with pyruvate. This work opens the way for small libraries to rapidly reprogramme enzyme active sites in a plug and play approach to catalyse new combinations of two-substrate reactions.

Residue Effect-Guided Design: Engineering of S. Solfataricus ß-Glycosidase to Enhance Its Thermostability and Bioproduction of Ginsenoside Compound K.

ß-Glycosidase from Sulfolobus solfataricus (SS-BGL) is a highly effective biocatalyst for the synthesis of compound K (CK) from glycosylated protopanaxadiol ginsenosides. In order to improve the thermal stability of SS-BGL, molecular dynamics simulations were used to determine the residue-level binding energetics of ginsenoside Rd in the SS-BGL-Rd docked complex and to identify the top ten critical contributors. Target sites for mutations were determined using dynamic cross-correlation mapping of residues via the Ohm server to identify networks of distal residues that interact with the key binding residues. Target mutations were determined rationally based on site characteristics. Single mutants and then recombination of top hits led to the two most promising variants SS-BGL-Q96E/N97D/N302D and SS-BGL-Q96E/N97D/N128D/N302D with 2.5-fold and 3.3-fold increased half-lives at 95 °C, respectively. The enzyme activities relative to those of wild-type for ginsenoside conversion were 161 and 116%, respectively..

Column-free optical deconvolution of intrinsic fluorescence for a monoclonal antibody and its product-related impurities.

The quantification of monoclonal antibody (mAb) aggregates and fragments using high pressure liquid chromatography-size exclusion chromatography (HPLC-SEC) typically requires off-line measurements that are time-consuming and therefore not compatible with real-time monitoring. However, it has been crucial to manufacturing and process development, and remains the industrial standard in the assessment of product-related impurities. Here we demonstrate that our previously established intrinsic time-resolved fluorescence (TRF) approach can be used to quantify the bioprocess critical quality attribute (CQA) of antibody product purity at various stages of a typical downstream process, with the potential to be developed for in-line bioprocess monitoring. This was directly benchmarked against industry-standard HPLC-SEC. Strong linear correlations were observed between outputs from TRF spectroscopy and HPLC-SEC, for the monomer and aggregate-fragment content, with R2 coefficients of 0.99 and 0.69, respectively. At total protein concentrations above 1.41 mg/mL, HPLC-SEC UV-Vis chromatograms displayed signs of detector saturation which reduced the accuracy of protein quantification, thus requiring additional sample dilution steps. By contrast, TRF spectroscopy increased in accuracy at these concentrations due to higher signal-to-noise ratios. Our approach opens the potential for reducing the time and labour required for validating aggregate content in mAb bioprocess stages from the several hours required for HPLC-SEC to a few minutes per sample.

Crystal structures and molecular dynamics simulations of a humanised antibody fragment at acidic to basic pH.

Antibody-fragment (Fab) therapy development has the potential to be accelerated by computational modelling and simulations that predict their target binding, stability, formulation, manufacturability, and the impact of further protein engineering. Such approaches are currently predicated on starting with good crystal structures that closely represent those found under the solution conditions to be simulated. A33 Fab, is an undeveloped immunotherapeutic antibody candidate that was targeted to the human A33 antigen homogeneously expressed in 95% cases of primary and metastatic colorectal cancers. It is now used as a very well characterised testing ground for developing analytics, formulation and protein engineering strategies, and to gain a deeper understanding of mechanisms of destabilisation, representative of the wider therapeutic Fab platform. In this article, we report the structure of A33 Fab in two different crystal forms obtained at acidic and basic pH. The structures overlapped with RMSD of 1.33 Å overall, yet only 0.5 Å and 0.76 Å for the variable- and constant regions alone. While most of the differences were within experimental error, the switch linker between the variable and the constant regions showed some small differences between the two pHs. The two structures then enabled a direct evaluation of the impact of initial crystal structure selection on the outcomes of molecular dynamics simulations under different conditions, and their subsequent use for determining best fit solution structures using previously obtained small-angle x-ray scattering (SAXS) data. The differences in the two structures did not have a major impact on MD simulations regardless of the pH, other than a slight persistence of structure affecting the solvent accessibility of one of the predicted APR regions of A33 Fab. Interestingly, despite being obtained at pH 4 and pH 9, the two crystal structures were more similar to the SAXS solution structures obtained at pH 7, than to those at pH 4 or pH 9. Furthermore, the P65 crystal structure from pH 4 was also a better representation of the solution structures at any other pH, than was the P1 structure obtained at pH 9. Thus, while obtained at different pH, the two crystal structures may represent highly (P65) and lesser (P1) populated species that both exist at pH 7 in solution. These results now lay the foundation for confident MD simulations of A33 Fab that rationalise or predict behaviours in a range of conditions.

Lyophilization to enable distribution of ChAdOx1 and ChAdOx2 adenovirus-vectored vaccines without refrigeration.

Distribution of vaccines which require refrigerated or frozen storage can be challenging and expensive. The adenovirus vector platform has been widely used for COVID-19 vaccines while several further candidate vaccines using the platform are in clinical development. In current liquid formulations, adenoviruses require distribution at 2-8 °C. The development of formulations suitable for ambient temperature distribution would be advantageous. Previous peer-reviewed reports of adenovirus lyophilization are relatively limited. Here, we report the development of a formulation and process for lyophilization of simian adenovirus-vectored vaccines based on the ChAdOx1 platform. We describe the iterative selection of excipients using a design of experiments approach, and iterative cycle improvement to achieve both preservation of potency and satisfactory cake appearance. The resulting method achieved in-process infectivity titre loss of around 50%. After drying, there was negligible further loss over a month at 30 °C. Around 30% of the predrying infectivity remained after a month at 45 °C. This performance is likely to be suitable for 'last leg' distribution at ambient temperature. This work may also facilitate the development of other product presentations using dried simian adenovirus-vectored vaccines.

Challenges in scaling up AAV-based gene therapy manufacturing.

Accelerating the scale up of adeno-associated virus (AAV) manufacture is highly desirable to meet the increased demand for gene therapies. However, the development of bioprocesses for AAV gene therapies remains time-consuming and challenging. The quality by design (QbD) approach ensures bioprocess designs that meet the desired product quality and safety profile. Rapid stress tests, developability screens, and scale-down technologies have the potential to streamline AAV product and manufacturing bioprocess development within the QbD framework. Here we review how their successful use for antibody manufacture development is translating to AAV, but also how this will depend critically on improved analytical methods and adaptation of the tools as more understanding is gained on the critical attributes of AAV required for successful therapy.

Enhanced Thermal Stability and Reduced Aggregation in an Antibody Fab Fragment at Elevated Concentrations.

The aggregation of protein therapeutics such as antibodies remains a major challenge in the biopharmaceutical industry. The present study aimed to characterize the impact of the protein concentration on the mechanisms and potential pathways for aggregation, using the antibody Fab fragment A33 as the model protein. Aggregation kinetics were determined for 0.05 to 100 mg/mL Fab A33, at 65 °C. A surprising trend was observed whereby increasing the concentration decreased the relative aggregation rate, ln(v) (% day-1), from 8.5 at 0.05 mg/mL to 4.4 at 100 mg/mL. The absolute aggregation rate (mol L-1 h-1) increased with the concentration following a rate order of approximately 1 up to a concentration of 25 mg/mL. Above this concentration, there was a transition to an apparently negative rate order of -1.1 up to 100 mg/mL. Several potential mechanisms were examined as possible explanations. A greater apparent conformational stability at 100 mg/mL was observed from an increase in the thermal transition midpoint (Tm) by 7-9 °C, relative to those at 1-4 mg/mL. The associated change in unfolding entropy (△Svh) also increased by 14-18% at 25-100 mg/mL, relative to those at 1-4 mg/mL, indicating reduced conformational flexibility in the native ensemble. Addition of Tween or the crowding agents Ficoll and dextran, showed that neither surface adsorption, diffusion limitations nor simple volume crowding affected the aggregation rate. Fitting of kinetic data to a wide range of mechanistic models implied a reversible two-state conformational switch mechanism from aggregation-prone monomers (N*) into non-aggregating native forms (N) at higher concentrations. kD measurements from DLS data also suggested a weak self-attraction while remaining colloidally stable, consistent with macromolecular self-crowding within weakly associated reversible oligomers. Such a model is also consistent with compaction of the native ensemble observed through changes in Tm and △Svh.

Stability Convergence in Antibody Coformulations.

Combined administration of antibody therapeutics has proven to be beneficial for patients with cancer or infectious diseases. As a result, there is a growing trend toward multiple antibodies premixed into a single product form and delivered to patients as a fixed-dose coformulation. However, combining antibodies into a single coformulation could be challenging as proteins have the potential to interact and alter their stability and degradation profiles in the mixture, compared to that in isolation. We show that in two specific antibody-antibody coformulations, the more stable antibody component increased the stability of the less stable component, which in return destabilized the more stable component, hence exhibiting an overall convergence of stability in the coformulation.

The global landscape of approved antibody therapies.

Antibody therapies have become an important class of therapeutics in recent years as they have exhibited outstanding efficacy and safety in the treatment of several major diseases including cancers, immune-related diseases, infectious disease and hematological disease. There has been significant progress in the global research and development landscape of antibody therapies in the past decade. In this review, we have collected available data from the Umabs Antibody Therapies Database (Umabs-DB, https://umabs.com) as of 30 June 2022. The Umabs-DB shows that 162 antibody therapies have been approved by at least one regulatory agency in the world, including 122 approvals in the US, followed by 114 in Europe, 82 in Japan and 73 in China, whereas biosimilar, diagnostic and veterinary antibodies are not included in our statistics. Although the US and Europe have been at the leading position for decades, rapid advancement has been witnessed in Japan and China in the past decade. The approved antibody therapies include 115 canonical antibodies, 14 antibody-drug conjugates, 7 bispecific antibodies, 8 antibody fragments, 3 radiolabeled antibodies, 1 antibody-conjugate immunotoxin, 2 immunoconjugates and 12 Fc-Fusion proteins. They have been developed against 91 drug targets, of which PD-1 is the most popular, with 14 approved antibody-based blockades for cancer treatment in the world. This review outlined the global landscape of the approved antibody therapies with respect to the regulation agencies, therapeutic targets and indications, aiming to provide an insight into the trends of the global development of antibody therapies.

Directed evolution for soluble and active periplasmic expression of bovine enterokinase in Escherichia coli.

Bovine enterokinase light chain (EKL) is an industrially useful protease for accurate removal of affinity-purification tags from high-value biopharmaceuticals. However, recombinant expression in Escherichia coli produces insoluble inclusion bodies, requiring solubilisation, refolding, and autocatalytic activation to recover functional enzyme. Error-prone PCR and DNA shuffling of the EKL gene, T7 promoter, lac operon, ribosome binding site, and pelB leader sequence, yielded 321 unique variants after screening ~ 6500 colonies. The best variants had > 11,000-fold increased total activity in lysates, producing soluble enzyme that no longer needed refolding. Further characterisation identified the factors that improved total activity from an inactive and insoluble starting point. Stability was a major factor, whereby melting temperatures > 48.4 °C enabled good expression at 37 °C. Variants generally did not alter catalytic efficiency as measured by kcat/Km, which improved for only one variant. Codon optimisation improved the total activity in lysates produced at 37 °C. However, non-optimised codons and expression at 30 °C gave the highest activity through improved protein quality, with increased kcat and Tm values. The 321 variants were statistically analysed and mapped to protein structure. Mutations detrimental to total activity and stability clustered around the active site. By contrast, variants with increased total activity tended to combine stabilising mutations that did not disrupt the active site.

NMR Reveals Functionally Relevant Thermally Induced Structural Changes within the Native Ensemble of G-CSF.

Structure-function relationships in proteins refer to a trade-off between stability and bioactivity, molded by evolution of the molecule. Identifying which protein amino acid residues jeopardize global or local stability for the benefit of bioactivity would reveal residues pivotal to this structure-function trade-off. Here, we use 15N-1H heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectroscopy to probe the microenvironment and dynamics of residues in granulocyte colony-stimulating factor (G-CSF) through thermal perturbation. From this analysis, we identified four residues (G4, A6, T133, and Q134) that we classed as significant to global stability, given that they all experienced large environmental and dynamic changes and were closely correlated to each other in their NMR characteristics. Additionally, we observe that roughly four structural clusters are subject to localized conformational changes or partial unfolding prior to global unfolding at higher temperature. Combining NMR observables with structure relaxation methods reveals that these structural clusters concentrate around loop AB (binding site III inclusive). This loop has been previously implicated in conformational changes that result in an aggregation prone state of G-CSF. Residues H43, V48, and S63 appear to be pivotal to an opening motion of loop AB, a change that is possibly also important for function. Hence, we present here an approach to profiling residues in order to highlight their potential roles in the two vital characteristics of proteins: stability and bioactivity.

Engineering of enzymes using non-natural amino acids.

In enzyme engineering, the main targets for enhancing properties are enzyme activity, stereoselective specificity, stability, substrate range, and the development of unique functions. With the advent of genetic code extension technology, non-natural amino acids (nnAAs) are able to be incorporated into proteins in a site-specific or residue-specific manner, which breaks the limit of 20 natural amino acids for protein engineering. Benefitting from this approach, numerous enzymes have been engineered with nnAAs for improved properties or extended functionality. In the present review, we focus on applications and strategies for using nnAAs in enzyme engineering. Notably, approaches to computational modelling of enzymes with nnAAs are also addressed. Finally, we discuss the bottlenecks that currently need to be addressed in order to realise the broader prospects of this genetic code extension technique.

Elucidation of critical pH-dependent structural changes in Botulinum Neurotoxin E.

Botulinum Neurotoxins (BoNT) are the most potent toxins currently known. However, they also have therapeutic applications for an increasing number of motor related conditions due to their specificity, and low diffusion into the system. Although the start- and end- points for the BoNT mechanism of action are well-studied, a critical step remains poorly understood. It is theorised that BoNTs undergo a pH-triggered conformational shift, activating the neurotoxin by priming it to form a transmembrane (TM) channel. To test this hypothesis, we combined molecular dynamics (MD) simulations and small-angle x-ray scattering (SAXS), revealing a new conformation of serotype E (BoNT/E). This conformation was exclusively observed in simulations below pH 5.5, as determined by principal component analysis (PCA), and its theoretical SAXS profile matched an experimental SAXS profile obtained at pH 4. Additionally, a localised secondary structural change was observed in MD simulations below pH 5.5, in a region previously identified as instrumental for membrane insertion for serotype A (BoNT/A). These changes were found at a critical pH value for BoNTs in vivo, and may be relevant for their therapeutic use.

Hot spots-making directed evolution easier.

Directed evolution has emerged as a powerful strategy to engineer various properties of proteins. Traditional methods to construct libraries such as error-prone PCR and DNA shuffling commonly produce large, relatively inefficient libraries. In the absence of a high-throughput screening method, searching such libraries is time-consuming, laborious and costly. On the other hand, targeted mutagenesis guided by structure or sequence information has become a popular way to produce so-called smart libraries. With an increased ratio of advantageous to deleterious mutations, smart libraries increase the efficiency of directed evolution, provided that target site prediction is reliable. Mutation target site or hot spot prediction is critical to the quality of libraries and the performance of directed evolution. Appropriate selection of hot spots enables the generation of proteins with desired properties efficiently and rationally. Here, we give an overview of seven kinds of hot spots that are divided into two categories: sequence-based hot spots including CbD (conserved but different) sites and coevolving residues, and then 3D structure-based hot spots including active-site residues, access tunnel sites, flexible sites, distal sites coupled to active center, and interface sites. This review also covers the latest advances in computational tools for identifying these hot spots and many successful cases using them for enzyme engineering.

Protein Engineering and HDX Identify Structural Regions of G-CSF Critical to Its Stability and Aggregation.

The protein engineering and formulation of therapeutic proteins for prolonged shelf-life remain a major challenge in the biopharmaceutical industry. Understanding the influence of mutations and formulations on the protein structure and dynamics could lead to more predictive approaches to their improvement. Previous intrinsic fluorescence analysis of the chemically denatured granulocyte colony-stimulating factor (G-CSF) suggested that loop AB could subtly reorganize to form an aggregation-prone intermediate state. Hydrogen deuterium exchange mass spectrometry (HDX-MS) has also revealed that excipient binding increased the thermal unfolding transition midpoint (Tm) by stabilizing loop AB. Here, we have combined protein engineering with biophysical analyses and HDX-MS to reveal that increased exchange in a core region of the G-CSF comprising loop AB (ABI, a small helix, ABII) and loop CD packed onto helix B and the beginning of loop BC leads to a decrease in Tm and higher aggregation rates. Furthermore, some mutations can increase the population of the aggregation-prone conformation within the native ensemble, as measured by the greater local exchange within this core region.

Biophysical characterization of the inactivation of E. coli transketolase by aqueous co-solvents.

Transketolase (TK) has been previously engineered, using semi-rational directed evolution and substrate walking, to accept increasingly aliphatic, cyclic, and then aromatic substrates. This has ultimately led to the poor water solubility of new substrates, as a potential bottleneck to further exploitation of this enzyme in biocatalysis. Here we used a range of biophysical studies to characterise the response of both E. coli apo- and holo-TK activity and structure to a range of polar organic co-solvents: acetonitrile (AcCN), n-butanol (nBuOH), ethyl acetate (EtOAc), isopropanol (iPrOH), and tetrahydrofuran (THF). The mechanism of enzyme deactivation was found to be predominantly via solvent-induced local unfolding. Holo-TK is thermodynamically more stable than apo-TK and yet for four of the five co-solvents it retained less activity than apo-TK after exposure to organic solvents, indicating that solvent tolerance was not simply correlated to global conformational stability. The co-solvent concentrations required for complete enzyme inactivation was inversely proportional to co-solvent log(P), while the unfolding rate was directly proportional, indicating that the solvents interact with and partially unfold the enzyme through hydrophobic contacts. Small amounts of aggregate formed in some cases, but this was not sufficient to explain the enzyme inactivation. TK was found to be tolerant to 15% (v/v) iPrOH, 10% (v/v) AcCN, or 6% (v/v) nBuOH over 3 h. This work indicates that future attempts to engineer the enzyme to better tolerate co-solvents should focus on increasing the stability of the protein to local unfolding, particularly in and around the cofactor-binding loops.

Engineering improved ethylene production: Leveraging systems biology and adaptive laboratory evolution.

Ethylene is a small hydrocarbon gas widely used in the chemical industry. Annual worldwide production currently exceeds 150 million tons, producing considerable amounts of CO2 contributing to climate change. The need for a sustainable alternative is therefore imperative. Ethylene is natively produced by several different microorganisms, including Pseudomonas syringae pv. phaseolicola via a process catalyzed by the ethylene-forming enzyme (EFE), subsequent heterologous expression of EFE has led to ethylene production in non-native bacterial hosts including Escherichia coli and cyanobacteria. However, solubility of EFE and substrate availability remain rate-limiting steps in biological ethylene production. We employed a combination of genome-scale metabolic modelling, continuous fermentation, and protein evolution to enable the accelerated development of a high efficiency ethylene producing E. coli strain, yielding a 49-fold increase in production, the most significant improvement reported to date. Furthermore, we have clearly demonstrated that this increased yield resulted from metabolic adaptations that were uniquely linked to EFE (wild type versus mutant). Our findings provide a novel solution to deregulate metabolic bottlenecks in key pathways, which can be readily applied to address other engineering challenges.