Chapter Seven Sortase Transpeptidases: Structural Biology and Catalytic Mechanism

Gram-positive bacteria use sortase cysteine transpeptidase enzymes to covalently attach proteins to their cell wall and to assemble pili. In pathogenic bacteria sortases are potential drug targets, as many of the proteins that they display on the microbial surface play key roles in the infection process. Moreover, the Staphylococcus aureus Sortase A (SaSrtA) enzyme has been developed into a valuable biochemical reagent because of its ability to ligate biomolecules together in vitro via a covalent peptide bond. Here we review what is known about the structures and catalytic mechanism of sortase enzymes. Based on their primary sequences, most sortase homologs can be classified into six distinct subfamilies, called class A-F enzymes. Atomic structures reveal unique, class-specific variations that support alternate substrate specificities, while structures of sortase enzymes bound to sorting signal mimics shed light onto the molecular basis of substrate recognition. The results of computational studies are reviewed that provide insight into how key reaction intermediates are stabilized during catalysis, as well as the mechanism and dynamics of substrate recognition. Lastly, the reported in vitro activities of sortases are compared, revealing that the transpeptidation activity of SaSrtA is at least 20-fold faster than other sortases that have thus far been characterized. Together, the results of the structural, computational, and biochemical studies discussed in this review begin to reveal how sortases decorate the microbial surface with proteins and pili, and may facilitate ongoing efforts to discover therapeutically useful small molecule inhibitors

Rapid addition of unlabeled silent solubility tags to proteins using a new substrate-fused sortase reagent

Many proteins can't be studied using solution NMR methods because they have limited solubility. To overcome this problem, recalcitrant proteins can be fused to a more soluble protein that functions as a solubility tag. However, signals arising from the solubility tag hinder data analysis because they increase spectral complexity. We report a new method to rapidly and efficiently add a non-isotopically labeled Small Ubiquitin-like Modifier protein (SUMO) solubility tag to an isotopically labeled protein. The method makes use of a newly developed SUMO-Sortase tagging reagent in which SUMO and the Sortase A (SrtA) enzyme are present within the same polypeptide. The SUMO-Sortase reagent rapidly attaches SUMO to any protein that contains the sequence LPXTG at its C-terminus. It modifies proteins at least 15-times faster than previously described approaches, and does not require active dialysis or centrifugation during the reaction to increase product yields. In addition, silently tagged proteins are readily purified using the well-established SUMO expression and purification system. The utility of the SUMO-Sortase tagging reagent is demonstrated using PhoP and green fluorescent proteins, which are ~90% modified with SUMO at room temperature within four hours. SrtA is widely used as a tool to construct bioconjugates. Significant rate enhancements in these procedures may also be achieved by fusing the sortase enzyme to its nucleophile substrate

Solution Structure of the Sortase Required for Efficient Production of Infectious <i>Bacillus anthracis</i> Spores

<i>Bacillus anthracis</i> forms metabolically dormant endospores that upon germination can cause lethal anthrax disease in humans. Efficient sporulation requires the activity of the SrtC sortase (BaSrtC), a cysteine transpeptidase that covalently attaches the BasH and BasI proteins to the peptidoglycan of the forespore and predivisional cell, respectively. To gain insight into the molecular basis of protein display, we used nuclear magnetic resonance to determine the structure and backbone dynamics of the catalytic domain of BaSrtC (residues Ser⁵⁶–Lys¹⁹⁸). The backbone and heavy atom coordinates of structurally ordered amino acids have coordinate precision of 0.42 ± 0.07 and 0.82 ± 0.05 Å, respectively. BaSrtC_Δ55 adopts an eight-stranded β-barrel fold that contains two short helices positioned on opposite sides of the protein. Surprisingly, the protein dimerizes and contains an extensive, structurally disordered surface that is positioned adjacent to the active site. The surface is formed by two loops (β2−β3 and β4<i>–</i>H1 loops) that surround the active site histidine, suggesting that they may play a key role in associating BaSrtC with its lipid II substrate. BaSrtC anchors proteins bearing a noncanonical LPNTA sorting signal. Modeling studies suggest that the enzyme recognizes this substrate using a rigid binding pocket and reveals the presence of a conserved subsite for the signal. This first structure of a class D member of the sortase superfamily unveils class-specific features that may facilitate ongoing efforts to discover sortase inhibitors for the treatment of bacterial infections

Structural and computational studies of the Staphylococcus aureus sortase B-substrate complex reveal a substrate-stabilized oxyanion hole.

Sortase cysteine transpeptidases covalently attach proteins to the bacterial cell wall or assemble fiber-like pili that promote bacterial adhesion. Members of this enzyme superfamily are widely distributed in Gram-positive bacteria that frequently utilize multiple sortases to elaborate their peptidoglycan. Sortases catalyze transpeptidation using a conserved active site His-Cys-Arg triad that joins a sorting signal located at the C terminus of their protein substrate to an amino nucleophile located on the cell surface. However, despite extensive study, the catalytic mechanism and molecular basis of substrate recognition remains poorly understood. Here we report the crystal structure of the Staphylococcus aureus sortase B enzyme in a covalent complex with an analog of its NPQTN sorting signal substrate, revealing the structural basis through which it displays the IsdC protein involved in heme-iron scavenging from human hemoglobin. The results of computational modeling, molecular dynamics simulations, and targeted amino acid mutagenesis indicate that the backbone amide of Glu(224) and the side chain of Arg(233) form an oxyanion hole in sortase B that stabilizes high energy tetrahedral catalytic intermediates. Surprisingly, a highly conserved threonine residue within the bound sorting signal substrate facilitates construction of the oxyanion hole by stabilizing the position of the active site arginine residue via hydrogen bonding. Molecular dynamics simulations and primary sequence conservation suggest that the sorting signal-stabilized oxyanion hole is a universal feature of enzymes within the sortase superfamily

Utility of physiologically based pharmacokinetic modeling to predict inter-antibody variability in monoclonal antibody pharmacokinetics in mice

ABSTRACTIn this investigation, we tested the hypothesis that a physiologically based pharmacokinetic (PBPK) model incorporating measured in vitro metrics of off-target binding can largely explain the inter-antibody variability in monoclonal antibody (mAb) pharmacokinetics (PK). A diverse panel of 83 mAbs was evaluated for PK in wild-type mice and subjected to 10 in vitro assays to measure major physiochemical attributes. After excluding for target-mediated elimination and immunogenicity, 56 of the remaining mAbs with an eight-fold variability in the area under the curve ([Formula: see text]: 1.74 × 106 −1.38 × 107 ng∙h/mL) and 10-fold difference in clearance (2.55–26.4 mL/day/kg) formed the training set for this investigation. Using a PBPK framework, mAb-dependent coefficients F1 and F2 modulating pinocytosis rate and convective transport, respectively, were estimated for each mAb with mostly good precision (coefficient of variation (CV%)  1. The predictive utility of the developed PBPK model was evaluated against a separate panel of 14 mAbs biased toward high clearance, among which area under the curve of PK data of 12 mAbs was predicted within 2.5-fold error, and the positive and negative predictive values for clearance prediction were 85% and 100%, respectively. MAb heparin chromatography assay output allowed a priori identification of mAb candidates with unfavorable PK

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

ABSTRACTBiologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration–time curve (AUC0–672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL