AbstractThe loading of the bacterial replicative helicase is an essential step for genome replication and depends on the assistance of accessory proteins. Several of these proteins have been identified across the bacterial phyla. DciA is the most common loading protein in bacteria, yet the one whose mechanism is the least understood. We have previously shown thatVcDciA fromVibrio cholerae,composed of a globular KH-like domain followed by an unfolded extension, has a strong affinity for DNA. Here, we characterized the droplets formed byVcDciA upon interaction with a short single-stranded substrate. We demonstrate the fluidity of these droplets using light microscopy and address their network organization through electron microscopy, thereby bridging events to conclude on a liquid-liquid phase separation behavior. Additionally, we observe the recruitment ofVcDnaB inside theVcDciA-DNA droplets. We show that DnaC fromEscherichia coliis also competent to form these condensate structures in the presence of ssDNA. Our data open up possibilities for the involvement of DciA in the formation of non-membrane compartments within the bacterium, facilitating the assembly of replication players with the chromosomal DNA.

L-Lipoic acid (LA) is an important antioxidant with various industrial applications as a nutraceutical and therapeutic. Currently, LA is produced by chemical synthesis. Cell factory development is complex as LA and its direct precursors only occur naturally in protein-bound forms. Here we report a rationally engineered LA cell factory and demonstrate de novo free LA production from glucose for the first time in E. coli. The pathway represents a significant challenge as the three key enzymes, native Octanoyltransferase (LipB) and Lipoyl Synthase (LipA), and heterologous Lipoamidase (LpA), are all toxic to overexpress in E. coli. To overcome the toxicity of LipB, functional metagenomic selection was used to identify a highly active and non-toxic LipB and LipA from S. liquefaciens. Using high throughput screening, we balanced translation initiation rates and dual, orthogonal induction systems for the toxic genes, LipA and LpA. The optimized strain yielded 2.5 mg free LA per gram of glucose in minimal media, expressing carefully balanced LipB and LipA, Enterococcus faecalis LpA, and a truncated, native, Dihydrolipoyllysine-residue acetyltransferase (AceF) lipoylation domain. When the optimized cell factory strain was cultivated in a fed-batch fermentation, a titer of 87 mg/L free LA in the supernatant was reached after 48 h. This titer is ∼3000-fold higher than previously reported free LA titer and ∼8-fold higher than the previous best total, protein-bound LA titer. The strategies presented here could be helpful in designing, constructing and balancing biosynthetic pathways that harbor toxic enzymes with protein-bound intermediates or products.

In the development of therapeutic proteins, analytical assessment of structural stability and integrity constitutes an important activity, as protein stability and integrity influence drug efficacy, and ultimately patient safety. Existing analytical methodologies solely rely on relative changes in optical properties such as fluorescence or scattering upon thermal or chemical perturbation. Here, we present an absolute analytical method for assessing protein stability, structure, and unfolding utilizing Taylor dispersion analysis (TDA) and LED-UV fluorescence detection. The developed TDA method measures the change in size (hydrodynamic radius) and intrinsic fluorescence of a protein during in-line denaturation with guanidinium hydrochloride (GuHCl). The conformational stability of the therapeutic antibody adalimumab and human serum albumin were characterized as a function of pH. The simple workflow and low sample consumption (40 ng protein per data point) of the methodology make it ideal for assessing protein characteristics related to stability in early drug development or when having a scarce amount of sample available.

Biopharmaceuticals have revolutionized the treatment of many diseases such as diabetes, cancer, and autoimmune disorders. These complex entities provide unique advantages like high specificity towards their target. Unfortunately, biopharmaceuticals are also prone to elicit undesired immunogenic responses (immunogenicity), compromising treatment efficacy as well as patient safety due to severe adverse effects including life threatening conditions. Current immunogenicity assays are hampered by immobilization procedures, complicated sample pre-treatment, or rely on cell-based methods which all prevent reliable and continuous monitoring of patients. In this work, we present Flow Induced Dispersion Analysis (FIDA) for assessment of immunogenicity and drug activity in serum samples from arthritis patients receiving adalimumab. FIDA is a first principle technique for size-based characterization of biomolecules and their complexes under biologically relevant conditions. The FIDA methodology rely on an absolute and quantitative readout (hydrodynamic radius) thus reducing the need for positive and negative controls. Here, FIDA is applied for evaluating active adalimumab in serum by studying the interaction with its target tumor necrosis factor alpha (TNF-α). We report proof of principle for a quantitative approach for stratifying patients exhibiting presence of neutralizing and non-neutralizing antibodies based on their individual drug activity pattern. Further, it can be applied to any biopharmaceutical having soluble drug targets and it holds potential in a companion diagnostics setting.

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

Background and aimGenetic markers are established as predictive of type 1 diabetes, but unknown early life environment is believed to be involved. Umbilical cord blood may reflect perinatal metabolism and exposures. We studied whether selected polar metabolites in cord blood contribute to prediction of type 1 diabetes.MethodsUsing a targeted UHPLC-QQQ-MS platform, we quantified 27 low-molecular-weight metabolites (including amino acids, small organic acids, and bile acids) in 166 children, who later developed type 1 diabetes, and 177 random control children in the Norwegian Mother, Father, and Child cohort. We analyzed the data using logistic regression (estimating odds ratios per SD [adjusted odds ratio (aOR)]), area under the receiver operating characteristic curve (AUC), and k-means clustering. Metabolites were compared to a genetic risk score based on 51 established non-HLA single-nucleotide polymorphisms, and a 4-category HLA risk group.ResultsThe strongest associations for metabolites were aminoadipic acid (aOR = 1.23; 95% CI, 0.97-1.55), indoxyl sulfate (aOR = 1.15; 95% CI, 0.87-1.51), and tryptophan (aOR = 0.84; 95% CI, 0.65-1.10), with other aORs close to 1.0, and none significantly associated with type 1 diabetes. K-means clustering identified 6 clusters, none of which were associated with type 1 diabetes. Cross-validated AUC showed no predictive value of metabolites (AUC 0.49), whereas the non-HLA genetic risk score AUC was 0.56 and the HLA risk group AUC was 0.78.ConclusionsIn this large study, we found no support of a predictive role of cord blood concentrations of selected bile acids and other small polar metabolites in the development of type 1 diabetes.

Metabolically engineered cyanobacteria have the potential to mitigate anthropogenic CO2 emissions by converting CO2 into renewable fuels and chemicals. Yet, better understanding of metabolic regulation in cyanobacteria is required to develop more productive strains that can make industrial scale-up economically feasible. The aim of this study was to find the cause for the previously reported inconsistency between oscillating transcription and constant protein levels under day-night growth conditions. To determine whether translational regulation counteracts transcriptional changes, Synechocystis sp. PCC 6803 was cultivated in an artificial day-night setting and the level of transcription, translation and protein was measured across the genome at different time points using mRNA sequencing, ribosome profiling and quantitative proteomics. Furthermore, the effect of protein turnover on the amplitude of protein oscillations was investigated through in silico simulations using a protein mass balance model. Our experimental analysis revealed that protein oscillations were not dampened by translational regulation, as evidenced by high correlation between translational and transcriptional oscillations (r = 0.88) and unchanged protein levels. Instead, model simulations showed that these observations can be attributed to a slow protein turnover, which reduces the effect of protein synthesis oscillations on the protein level. In conclusion, these results suggest that cyanobacteria have evolved to govern diurnal metabolic shifts through allosteric regulatory mechanisms in order to avoid the energy burden of replacing the proteome on a daily basis. Identification and manipulation of such mechanisms could be part of a metabolic engineering strategy for overproduction of chemicals.

Microfluidic systems under laminar flow conditions provide in-solution information about species size and binding affinities at very modest sample costs. Flow-induced dispersion analysis directly measures the spread of the analyte profile using Taylor dispersion analysis, whereas microfluidic diffusional sizing quantifies the transfer of analyte from one phase to another. Species of sizes between 0.5 and 1000 nm can be analyzed, and different populations resolved. Both techniques also allow analysis in complex media and medium throughput analysis. These properties make them valuable complements to existing approaches to measure biomolecular interactions.

Multidimensional fitness landscapes provide insights into the molecular basis of laboratory and natural evolution. To date, such efforts usually focus on limited protein families and a single enzyme trait, with little concern about the relationship between protein epistasis and conformational dynamics. Here, we report a multiparametric fitness landscape for a cytochrome P450 monooxygenase that was engineered for the regio- and stereoselective hydroxylation of a steroid. We develop a computational program to automatically quantify non-additive effects among all possible mutational pathways, finding pervasive cooperative signs and magnitude epistasis on multiple catalytic traits. By using quantum mechanics and molecular dynamics simulations, we show that these effects are modulated by long-range interactions in loops, helices and β-strands that gate the substrate access channel allowing for optimal catalysis. Our work highlights the importance of conformational dynamics on epistasis in an enzyme involved in secondary metabolism and offers insights for engineering P450s.