Model based process optimisation of an industrial chromatographic process for separation of lactoferrin from bovine milk

Journal articleModel based process development using predictive mechanistic models is a powerful tool for in-silico downstream process development. It allows to obtain a thorough understanding of the process reducing experimental effort. While in pharma industry, mechanistic modeling becomes more common in the last years, it is rarely applied in food industry. This case study investigates risk ranking and possible optimization of the industrial process of purifying lactoferrin from bovine milk using SP Sepharose Big Beads with a resin particle diameter of 200 µm, based on a minimal number of lab-scale experiments combining traditional scale-down experiments with mechanistic modeling. Depending on the location and season, process water pH and the composition of raw milk can vary, posing a challenge for highly efficient process development. A predictive model based on the general rate model with steric mass action binding, extended for pH dependence, was calibrated to describe the elution behavior of lactoferrin and main impurities. The gained model was evaluated against changes in flow rate, step elution conditions, and higher loading and showed excellent agreement with the observed experimental data. The model was then used to investigate the critical process parameters, such as water pH, conductivity of elution steps, and flow rate, on process performance and purity. It was found that the elution behavior of lactoferrin is relatively consistent over the pH range of 5.5 to 7.6, while the elution behavior of the main impurities varies greatly with elution pH. As a result, a significant loss in lactoferrin is unavoidable to achieve desired purities at pH levels below pH 6.0. Optimal process parameters were identified to reduce water and salt consumption and increase purity, depending on water pH and raw milk composition. The optimal conductivity for impurity removal in a low conductivity elution step was found to be 43 mS/cm, while a conductivity of 95 mS/cm leads to the lowest overall salt usage during lactoferrin elution. Further increasing the conductivity during lactoferrin elution can only slightly lower the elution volume thus can also lead to higher total salt usage. Low flow rates during elution of 0.2 column volume per minute are beneficial compared to higher flow rates of 1 column volume per minute. The, on lab-scale, calibrated model allows predicting elution volume and impurity removal for large-scale experiments in a commercial plant processing over 106 liters of milk per day. The successful model extrapolation was possible without recalibration or detailed knowledge of the manufacturing plant. This study therefore provides a possible pathway for rapid process development of chromatographic purification in the food industries combining traditional scale-down experiments with mechanistic modeling.Lukas Gerstweiler, Paulina Schad, Tatjana Trunzer, Lena Enghauser, Max Mayr, Jagan Billakant

Characterisation of the First Enzymes Committed to Lysine Biosynthesis in <em>Arabidopsis thaliana</em>

<div><p>In plants, the lysine biosynthetic pathway is an attractive target for both the development of herbicides and increasing the nutritional value of crops given that lysine is a limiting amino acid in cereals. Dihydrodipicolinate synthase (DHDPS) and dihydrodipicolinate reductase (DHDPR) catalyse the first two committed steps of lysine biosynthesis. Here, we carry out for the first time a comprehensive characterisation of the structure and activity of both DHDPS and DHDPR from <em>Arabidopsis thaliana</em>. The <em>A. thaliana</em> DHDPS enzyme (<em>At</em>-DHDPS2) has similar activity to the bacterial form of the enzyme, but is more strongly allosterically inhibited by (<em>S</em>)-lysine. Structural studies of <em>At</em>-DHDPS2 show (<em>S</em>)-lysine bound at a cleft between two monomers, highlighting the allosteric site; however, unlike previous studies, binding is not accompanied by conformational changes, suggesting that binding may cause changes in protein dynamics rather than large conformation changes. DHDPR from <em>A. thaliana</em> (<em>At</em>-DHDPR2) has similar specificity for both NADH and NADPH during catalysis, and has tighter binding of substrate than has previously been reported. While all known bacterial DHDPR enzymes have a tetrameric structure, analytical ultracentrifugation, and scattering data unequivocally show that <em>At</em>-DHDPR2 exists as a dimer in solution. The exact arrangement of the dimeric protein is as yet unknown, but <em>ab initio</em> modelling of x-ray scattering data is consistent with an elongated structure in solution, which does not correspond to any of the possible dimeric pairings observed in the X-ray crystal structure of DHDPR from other organisms. This increased knowledge of the structure and function of plant lysine biosynthetic enzymes will aid future work aimed at improving primary production.</p> </div

Analysis of DHDPR interface regions using the PDBePISA web server (<i>38</i>).

<p>Analysis of DHDPR interface regions using the PDBePISA web server (<i>38</i>).</p

Crystal structures of unliganded and lysine bound <i>At</i>-DHDPS2.

<p>A) Wall-eyed stereo image of the Cα superposition of <i>At-</i>DHDPS2 with bound lysine (blue Cα trace) and unliganded <i>At-</i>DHDPS2 (gold Cα trace; rmsd = 0.3 Å). The lysine molecules bound at the allosteric site of each monomer of the tetramer are shown in yellow (stick representation). B) The lysine binding site at the monomer-monomer interface of the tight-dimer showing residues in contact with the bound lysine molecules (yellow). Electron density around the bound lysine (grey mesh, contoured at 1.0 sigma) was calculated using refined coordinates omitting the bound lysine molecules. Residues contributed by each monomer of the tight-dimer are shown in different shades of blue, and are indicated by the use of the prime (’) symbol. C) overlay of the lysine binding residues of the tight-dimer from the lysine bound (blue) and unliganded (gold) structures. Lysine molecules are shown in yellow. Residues contributed by each monomer of the tight-dimer are shown in different shades of blue or gold, and are indicated by the use of the prime (’) symbol.</p

X-Ray scattering data of <i>At</i>-DHDPS2.

<p>Data were collected in the absence of ligand, or in the presence of 1 mM (S)-lysine, top panel; curves have been arbitrarily displaced along the logarithmic axis for clarity. Solid lines show the scattering profile from the unliganded crystal structure, calculated using CRYSOL. Distance-distribution functions, <i>p(r)</i> for the unbound and ligand bound <i>At</i>-DHDPS2 were determined using the indirect Fourier tranformation package GNOM (bottom panel).</p

X-Ray scattering of DHDPR.

<p>Data were collected for <i>At</i>-DHDPR2, <i>Ec</i>-DHDPR and <i>Tm</i>-DHDPR (panel A); curves have been arbitrarily displaced along the logarithmic axis for clarity. Data was analysed using GNOM (fitted data shown by red line in panel A) to calculate a distance distribution function for each enzyme (panel B).</p

X-ray data collection and structure refinement statistics for <i>At</i>-DHDPS2 and lysine bound <i>At</i>-DHDPS2.

<p>Values for the highest resolution shells are given in parentheses. The Matthew’s coefficient and estimate of the solvent content are based on 2 molecules of <i>At</i>-DHDPS2, 34 679.5 Da each, in the asymmetric unit.</p>†<p><i>R</i>_sym = ∑<i>_hkl</i>∑<i>_i</i> |<i>I_i</i> (<i>hkl</i>)−〈<i>I(hkl)</i>〉|/∑<i>_hkl</i>∑<i>_iI_i</i> (<i>hkl</i>).</p>‡<p><i>R</i>_p.i.m. = ∑<i>_hkl</i> [1/(<i>N</i>−1)]^½ ∑<i>_i</i> |<i>I_i</i> (<i>hkl</i>)−〈<i>I (hkl)</i>〉|/∑<i>_hkl</i> ∑<i>_i I_i</i> (<i>hkl</i>).</p>§<p><i>R</i>_r.i.m. = ∑<i>_hkl</i> [<i>N</i>/(<i>N</i>−1)]^½ ∑<i>_i</i> |<i>I_i</i> (<i>hkl</i>)−〈<i>I (hkl)</i>〉|/∑<i>_hkl</i> ∑<i>_i I_i</i> (<i>hkl</i>).</p

Analytical ultracentrifugation of <i>At</i>-DHDPS2 and <i>At</i>-DHDPR2.

<p>Sedimentation velocity analysis of <i>At</i>-DHDPS2 and <i>At</i>-DHDPR2. A) Continuous sedimentation coefficient distribution [<i>(c)s</i>] analysis of <i>At-</i>DHDPS2 at a concentration of 0.75 mg.mL⁻¹ (black line). The RMSD and Runs Test Z (RTZ) scores for the fit were 0.008 and 3.2 respectively. B) <i>(c)s</i> analysis of <i>At-</i>DHDPR2 at concentrations of 0.1 mg.mL⁻¹ (black line; RMSD = 0.009, RTZ = 2.4), 0.2 mg.mL⁻¹ (red line; RMSD = 0.010, RTZ = 2.0), 0.4 mg.mL⁻¹ (green line; RMSD = 0.014, RTZ = 8.6) 0.8 mg.mL⁻¹ (pink line; RMSD = 0.013, RTZ = 4.9) and 1.6 mg.mL⁻¹ (blue line; RMSD = 0.015, RTZ = 7.4). Radial absorbance data for the three lower protein concentrations were acquired at a different wavelength to those of the two highest protein concentrations, and the <i>c(s)</i> distributions were scaled accordingly. Residuals for the fits are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040318#pone.0040318.s007" target="_blank">Figure S7</a>.</p

Results of <i>ab initio</i> modeling of <i>At</i>-DHDPR from SAXS data.

<p>Models were generated using GASBOR (left panels) and DAMMIN (right panels). The structural homology model generated by SWISS-MODEL and fitted to the scattering data using CORAL is superimposed for comparison.</p