The class I ribonucleotide reductases (RNRs) are composed of two homodimeric subunits: R1 and R2. R2 houses a diferric-tyrosyl radical (Y•) cofactor. Saccharomyces cerevisiae has two R2s: Y2 (β 2 ) and Y4 (β′ 2 ). Y4 is an unusual R2 because three residues required for iron binding have been mutated. While the heterodimer (ββ′) is thought to be the active form, several rnr4Δ strains are viable. To resolve this paradox, N-terminally epitope-tagged β and β′ were expressed in E. coli or integrated into the yeast genome. In vitro exchange studies reveal that when apo-(His 6 )-β 2 ( His β 2 ) is mixed with β′ 2 , apo-His ββ′ forms quantitatively within 2 min. In contrast, holo-ββ′ fails to exchange with apo-His β 2 to form holo-His ββ and β′ 2 . Isolation of genomically encoded tagged β or β′ from yeast extracts gave a 1:1 complex of β and β′, suggesting that ββ′ is the active form. The catalytic activity, protein concentrations, and Y• content of the rnr4Δ and wild type (wt) strains were compared to clarify the role of β′ in vivo. The Y• content of rnr4Δ is 15-fold less than that of wt, consistent with the observed low activity of rnr4Δ extracts (<0.01 nmol min −1 mg −1 ) versus wt (0.06 ± 0.01 nmol min −1 mg −1 ). FLAG β 2 isolated from the rnr4Δ strain has a specific activity of 2 nmol min −1 mg −1 , similar to that of reconstituted apo-His β 2 (10 nmol min −1 mg −1 ), but significantly less than holo-His ββ′ (~2000 nmol min −1 mg −1 ). These studies together demonstrate that β′ plays a crucial role in cluster assembly in vitro and in vivo and that the active form of the yeast R2 is ββ′.Ribonucleotide reductases (RNRs) 1 catalyze the conversion of ribonucleotides to deoxyribonucleotides, providing the monomeric precursors for DNA replication and repair (1). The class I RNRs are composed of a large subunit, R1, and a small subunit, R2. R1 contains the site of nucleotide reduction and the allosteric effector binding sites that control the rate and the specificity of nucleotide reduction. R2 houses the diferric-tyrosyl radical † D.L.P. and A.D.O. were supported in part by the NIH training grant 5T32 CA 09112-28. J.S. acknowledges support of the NIH (GM29595). M.H. acknowledges support of the NIH (CA095207) and the ACS (0305001GMC).

The class I ribonucleotide reductases catalyze the conversion of nucleotides to deoxynucleotides and are composed of two subunits: R1 and R2. R1 contains the site for nucleotide reduction and the sites that control substrate specificity and the rate of reduction. R2 houses the essential diferric-tyrosyl radical (Y • ) cofactor. In Saccharomyces cerevisiae, two R1s, α n and , have been identified, while R2 is a heterodimer (ββ′). β′ cannot bind iron and generate the Y • ; consequently, the maximum amount of Y • per ββ′ is 1. To determine the cofactor stoichiometry in vivo, a FLAGtagged β ( FLAG β) was constructed and integrated into the genome of Y300 (MHY343). This strain facilitated the rapid isolation of endogenous levels of FLAG ββ′ by immunoaffinity chromatography, which was found to have 0.45 ± 0.08 Y • / FLAG ββ′ and a specific activity of 2.3 ± 0.5 μmol min −1 mg −1 . FLAG ββ′ isolated from MMS-treated MHY343 cells or cells containing a deletion of the transcriptional repressor gene CRT1 also gave a Y • / FLAG ββ′ ratio of 0.5. To determine the Y • /ββ′ ratio without R2 isolation, whole cell EPR and quantitative Western blots of β were performed using different strains and growth conditions. The wild-type (wt) strains gave a Y • /ββ′ ratio of 0.83-0.89. The same strains either treated with MMS or containing a crt1Δ gave ratios between 0.49 and 0.72. Nucleotide reduction assays and quantitative Western blots from the same strains provided an independent measure and confirmation of the Y • /ββ′ ratios. Thus, under normal growth conditions, the cell assembles stoichiometric amounts of Y • and modulation of Y • concentration is not involved in the regulation of RNR activity.

A strategy for isolating each of the four potentially unique heterotropic pairwise allosteric interactions that exist in the homotetramer phosphofructokinase from Bacillus stearothermophilus is described. The strategy involves the construction of hybrid tetramers containing one wild-type subunit and three mutant subunits that have been modified to block binding of both the substrate, fructose 6-phosphate (Fru-6-P), and the allosteric inhibitor, phospho(enol)pyruvate (PEP). Each type of binding site occurs at a subunit interface, and mutations on either side of the interface have been identified that will greatly diminish binding at the respective site. Consequently, four different types of mutant subunits have been created, each containing a different active site and allosteric site modification. The corresponding 1:3 hybrids isolate a different pair of unmodified substrate and allosteric sites with a unique structural disposition located 22, 30, 32, and 45 A apart, respectively. The allosteric inhibition exhibited by the unmodified sites in each of these four hybrids has been quantitatively evaluated in terms of a coupling free energy. Each of the coupling free energies is unique in magnitude, and their relative magnitudes vary with pH. Importantly, the sum of these coupling free energies at each pH is equal to the total heterotropic coupling free energy associated with the tetrameric enzyme. The latter quantity was assessed from the overall inhibition of a control hybrid that removed the homotropic interactions in PEP binding. The results do not agree with either the concerted or sequential models that are often invoked to explain allosteric behavior in oligomeric enzymes.

Chromatographic and non-chromatographic purification of biopharmaceuticals depend on the interactions between protein molecules and a solid-liquid interface. These interactions are dominated by the protein-surface properties, which are a function of protein sequence, structure, and dynamics. In addition, protein-surface properties are critical for in vivo recognition and activation, thus, purification strategies should strive to preserve structural integrity and retain desired pharmacological efficacy. Other factors such as surface diffusion, pore diffusion, and film mass transfer can impact chromatographic separation and resin design. The key factors that impact non-chromatographic separations (e.g., solubility, ligand affinity, charges and hydrophobic clusters, and molecular dynamics) are readily amenable to computational modeling and can enhance the understanding of protein chromatographic. Previously published studies have used computational methods such as quantitative structure-activity relationship (QSAR) or quantitative structure-property relationship (QSPR) to identify and rank order affinity ligands based on their potential to effectively bind and separate a desired biopharmaceutical from host cell protein (HCP) and other impurities. The challenge in the application of such an approach is to discern key yet subtle differences in ligands and proteins that influence biologics purification. Using a relatively small molecular weight protein (insulin), this research overcame limitations of previous modeling efforts by utilizing atomic level detail for the modeling of protein-ligand interactions, effectively leveraging and extending previous research on drug target discovery. These principles were applied to the purification of different commercially available insulin variants. The ability of these computational models to correlate directionally with empirical observation is demonstrated for several insulin systems over a range of purification challenges including resolution of subtle product variants (amino acid misincorporations). Broader application of this methodology in bioprocess development may enhance and speed the development of a robust purification platform.

Tangential flow microfiltration (MF) is a cost-effective and robust bioprocess separation technique, but successful full scale implementation is hindered by the empirical, trial-and-error nature of scale-up. We present an integrated approach leveraging at-line process analytical technology (PAT) and mass balance based modeling to de-risk MF scale-up. Chromatography-based PAT was employed to improve the consistency of an MF step that had been a bottleneck in the process used to manufacture a therapeutic protein. A 10-min reverse phase ultra high performance liquid chromatography (RP-UPLC) assay was developed to provide at-line monitoring of protein concentration. The method was successfully validated and method performance was comparable to previously validated methods. The PAT tool revealed areas of divergence from a mass balance-based model, highlighting specific opportunities for process improvement. Adjustment of appropriate process controls led to improved operability and significantly increased yield, providing a successful example of PAT deployment in the downstream purification of a therapeutic protein. The general approach presented here should be broadly applicable to reduce risk during scale-up of filtration processes and should be suitable for feed-forward and feed-back process control.

Advancement in all disciplines (art, science, education, and engineering) requires a careful balance of disruption and advancement of classical techniques. Often technologies are created with a limited understanding of fundamental principles and are prematurely abandoned. Over time, knowledge improves, new opportunities are identified, and technology is reassessed in a different light leading to a renaissance. Recovery of biological products is currently experiencing such a renaissance. Crystallization is one example of an elegant and ancient technology that has been applied in many fields and was employed to purify insulins from naturally occurring sources. Crystallization can also be utilized to determine protein structures. However, a multitude of parameters can impact protein crystallization and the “hit rate” for identifying protein crystals is relatively low, so much so that the development of a crystallization process is often viewed as a combination of art and science even today. Supplying the worldwide requirement for insulin (and associated variants) requires significant advances in process intensification to support scale of production and to minimize the overall cost to enable broader access. Expanding beyond insulin, the increasing complexity and diversity of biologics agents challenge the current purification methodologies. To harness the full potential of biologics, there is a need to fully explore a broader range of purification technologies, including nonchromatographic approaches. This impetus requires one to challenge and revisit the classical techniques including crystallization, chromatography, and filtration from a different vantage point and with a new set of tools, including molecular modeling. Fortunately, computational biophysics tools now exist to provide insights into mechanisms of protein/ligand interactions and molecular assembly processes (including crystallization) that can be used to support de novo process development. For example, specific regions or motifs of insulins and ligands can be identified and used as targets to support crystallization or purification development. Although the modeling tools have been developed and validated for insulin systems, the same tools can be applied to more complex modalities and to other areas including formulation, where the issue of aggregation and concentration‐dependent oligomerization could be mechanistically modeled. This paper will illustrate a case study juxtaposing historical approaches to insulin downstream processes to a recent production process highlighting the application and evolution of technologies. Insulin production from Escherichia coli via inclusion bodies is an elegant example since it incorporates virtually all the unit operations associated with protein production—recovery of cells, lysis, solubilization, refolding, purification, and crystallization. The case study will include an example of an innovative application of existing membrane technology to combine three‐unit operations into one, significantly reducing solids handling and buffer consumption. Ironically, a new separations technology was developed over the course of the case study that could further simplify and intensify the downstream process, emphasizing and highlighting the ever‐accelerating pace of innovation in downstream processing. Molecular biophysics modeling was also employed to enhance the mechanistic understanding of the crystallization and purification processes.