In response to infections and irritants, the respiratory epithelium releases the alarmin interleukin (IL)-33 to elicit a rapid immune response. However, little is known about the regulation of IL-33 following its release. Here we report that the biological activity of IL-33 at its receptor ST2 is rapidly terminated in the extracellular environment by the formation of two disulphide bridges, resulting in an extensive conformational change that disrupts the ST2 binding site. Both reduced (active) and disulphide bonded (inactive) forms of IL-33 can be detected in lung lavage samples from mice challenged with Alternaria extract and in sputum from patients with moderate–severe asthma. We propose that this mechanism for the rapid inactivation of secreted IL-33 constitutes a ‘molecular clock' that limits the range and duration of ST2-dependent immunological responses to airway stimuli. Other IL-1 family members are also susceptible to cysteine oxidation changes that could regulate their activity and systemic exposure through a similar mechanism.

Uncontrolled self-association is a major challenge in the exploitation of proteins as therapeutics. Here we describe the development of a structural proteomics approach to identify the amino acids responsible for aberrant self-association of monoclonal antibodies and the design of a variant with reduced aggregation and increased serum persistence in vivo. We show that the human monoclonal antibody, MEDI1912, selected against nerve growth factor binds with picomolar affinity, but undergoes reversible self-association and has a poor pharmacokinetic profile in both rat and cynomolgus monkeys. Using hydrogen/deuterium exchange and cross-linking-mass spectrometry we map the residues responsible for self-association of MEDI1912 and show that disruption of the self-interaction interface by three mutations enhances its biophysical properties and serum persistence, whilst maintaining high affinity and potency. Immunohistochemistry suggests that this is achieved via reduction of non-specific tissue binding. The strategy developed represents a powerful and generic approach to improve the properties of therapeutic proteins.

A system is reported for the recombinant expression of individual ketoreductase (KR) domains from modular polyketide synthases (PKSs) and scrutiny of their intrinsic specificity and stereospecificity toward surrogate diketide substrates. The eryKR(1) and the tylKR(1) domains, derived from the first extension module of the erythromycin PKS and the tylosin PKS, respectively, both catalyzed reduction of (2R, S)-2-methyl-3-oxopentanoic acid N-acetylcysteamine thioester, with complete stereoselectivity and stereospecificity, even though the substrate is not tethered to an acyl carrier protein or an intact PKS multienzyme. In contrast, and to varying degrees, the isolated enzymes eryKR(2), eryKR(5), and eryKR(6) exercised poorer control over substrate selection and the stereochemical course of ketoreduction. These data, together with modeling of diketide binding to KR(1) and KR(2), demonstrate the fine energetic balance between alternative modes of presentation of ketoacylthioester substrates to KR active sites.

SAbPred is a server that makes predictions of the properties of antibodies focusing on their structures. Antibody informatics tools can help improve our understanding of immune responses to disease and aid in the design and engineering of therapeutic molecules. SAbPred is a single platform containing multiple applications which can: number and align sequences; automatically generate antibody variable fragment homology models; annotate such models with estimated accuracy alongside sequence and structural properties including potential developability issues; predict paratope residues; and predict epitope patches on protein antigens. The server is available at http://opig.stats.ox.ac.uk/webapps/sabpred.

Antibodies represent essential tools in research and diagnostics and are rapidly growing in importance as therapeutics. Commonly used methods to obtain novel antibodies typically yield several candidates capable of engaging a given target. The development steps that follow, however, are usually performed with only one or few candidates since they can be resource demanding, thereby increasing the risk of failure of the overall antibody discovery program. In particular, insufficient solubility, which may lead to aggregation under typical storage conditions, often hinders the ability of a candidate antibody to be developed and manufactured. Here we show that the selection of soluble lead antibodies from an initial library screening can be greatly facilitated by a fast computational prediction of solubility that requires only the amino acid sequence as input. We quantitatively validate this approach on a panel of nine distinct monoclonal antibodies targeting nerve growth factor (NGF), for which we compare the predicted and measured solubilities finding a very close match, and we further benchmark our predictions with published experimental data on aggregation hotspots and solubility of mutational variants of one of these antibodies.