Molecular basis of a million-fold affinity maturation process in a protein-protein interaction

Microcystin-LR nanobody screening from an alpaca phage display nanobody library and its expression and application

Microcystin-LR (MC-LR) is a type of biotoxin that pollutes the ecological environment and food. The study aimed to obtain new nanobodies from phage nanobody library for determination of MC-LR. The toxin was conjugated to keyhole limpet haemocyanin (KLH) and bovine serum albumin (BSA), respectively, then the conjugates were used as coated antigens for enrichment (coated MC-LR-KLH) and screening (coated MC-LR-BSA) of MC-LR phage nanobodies from an alpaca phage display nanobody library. The antigen-specific phage particles were enriched effectively with four rounds of biopanning. At the last round of enrichment, total 20 positive monoclonal phage nanobodies were obtained from the library, which were analyzed after monoclonal phage enzyme linked immunosorbent assay (ELISA), colony PCR and DNA sequencing. The most three positive nanobody genes, ANAb12, ANAb9 and ANAb7 were cloned into pET26b vector, then the nanobodies were expressed in Escherichia coli BL21 respectively. After being purified, the molecular weight (M.W.) of all nanobodies were approximate 15kDa with sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The purified nanobodies, ANAb12, ANAb9 and ANAb7 were used to establish the indirect competitive ELISA (IC-ELISA) for MC-LR, and their half-maximum inhibition concentrations (IC₅₀) were 0.87, 1.17 and 1.47μg/L, their detection limits (IC₁₀) were 0.06, 0.08 and 0.12μg/L, respectively. All of them showed strong cross-reactivity (CRs) of 82.7-116.9% for MC-RR, MC-YR and MC-WR, and weak CRs of less than 4.56% for MC-LW, less than 0.1% for MC-LY and MC-LF. It was found that all the IC-ELISAs for MC-LR spiked in tap water samples detection were with good accuracy, stability and repeatability, their recoveries were 84.0-106.5%, coefficient of variations (CVs) were 3.4-10.6%. These results showed that IC-ELISA based on the nanobodies from the alpaca phage display antibody library were promising for high sensitive determination of multiple MCs.

Soluble T cell receptor Vβ domains engineered for high-affinity binding to staphylococcal or streptococcal superantigens

Staphylococcus aureus and group A Streptococcus secrete a collection of toxins called superantigens (SAgs), so-called because they stimulate a large fraction of an individual’s T cells. One consequence of this hyperactivity is massive cytokine release leading to severe tissue inflammation and, in some cases, systemic organ failure and death. The molecular basis of action involves the binding of the SAg to both a T cell receptor (TCR) on a T cell and a class II product of the major histocompatibility complex (MHC) on an antigen presenting cell. This cross-linking leads to aggregation of the TCR complex and signaling. A common feature of SAgs is that they bind with relatively low affinity to the variable region (V) of the beta chain of the TCR. Despite this low affinity binding, SAgs are very potent, as each T cell requires only a small fraction of their receptors to be bound in order to trigger cytokine release. To develop high-affinity agents that could neutralize the activity of SAgs, and facilitate the development of detection assays, soluble forms of the Vβ regions have been engineered to affinities that are up to 3 million-fold higher for the SAg. Over the past decade, six different Vβ regions against SAgs from S. aureus (SEA, SEB, SEC3, TSST-1) or S. pyogenes (SpeA and SpeC) have been engineered for high-affinity using yeast display and directed evolution. Here we review the engineering of these high-affinity Vβ proteins, structural features of the six different SAgs and the Vβ proteins, and the specific properties of the engineered Vβ regions that confer high-affinity and specificity for their SAg ligands.

Characterization of the Staphylococcal enterotoxin A: Vβ receptor interaction using human receptor fragments engineered for high affinity

Staphylococcal food poisoning is a gastrointestinal disorder caused by the consumption of food containing Staphylococcal enterotoxins. Staphylococcal enterotoxin A (SEA) is the most common enterotoxin recovered from food poisoning outbreaks in the USA. In addition to its enteric activity, SEA also acts as a potent superantigen through stimulation of T cells, although less is known about its interactions than the superantigens SEB, SEC and toxic shock syndrome toxin-1. To understand more about SEA:receptor interactions, and to develop toxin-detection systems for use in food testing, we engineered various SEA-binding receptor mutants. The extracellular domain of the receptor, a variable region of the beta chain (Vβ22) of the T-cell receptor, was engineered for stability as a soluble protein and for high affinity, using yeast-display technology. The highest affinity mutant was shown to bind SEA with a Kd value of 4 nM. This was a 25 000-fold improvement in affinity compared with the wild-type receptor, which bound to SEA with low affinity (Kd value of 100 µM), similar to other superantigen:Vβ interactions. The SEA:Vβ interface was centered around residues within the complementarity determining region 2 loop. The engineered receptor was specific for SEA, in that it did not bind to two other closely related enterotoxins SEE or SED, providing information on the SEA residues possibly involved in the interaction. The specificity and affinity of these high-affinity Vβ proteins also provide useful agents for the design of more sensitive and specific systems for SEA detection.

Engineering a soluble high-affinity receptor domain that neutralizes staphylococcal enterotoxin C in rabbit models of disease

Superantigens (SAgs) are a class of immunostimulatory exotoxins that activate large numbers of T cells, leading to overproduction of cytokines and subsequent inflammatory reactions and systemic toxicity. Staphylococcal enterotoxin C (SEC), a SAg secreted by Staphylococcus aureus, has been implicated in various illnesses including non-menstrual toxic shock syndrome (TSS) and necrotizing pneumonia. SEC has been shown to cause TSS illness in rabbits and the toxin contributes to lethality associated with methicillin-resistant S.aureus (MRSA) in a rabbit model of pneumonia. With the goal of reducing morbidity and mortality associated with SEC, a high-affinity variant of the extracellular variable domain of the T-cell receptor beta-chain for SEC (~14 kDa) was generated by directed evolution using yeast display. This protein was characterized biochemically and shown to cross-react with the homologous (65% identical) SAg staphylococcal enterotoxin B (SEB). The soluble, high-affinity T-cell receptor protein neutralized SEC and SEB in vitro and also significantly reduced the bacterial burden of an SEC-positive strain of MRSA (USA400 MW2) in an infective endocarditis model. The neutralizing agent also prevented lethality due to MW2 in a necrotizing pneumonia rabbit model. These studies characterize a soluble high-affinity neutralizing agent against SEC, which is cross-reactive with SEB, and that has potential to be used intravenously with antibiotics to manage staphylococcal diseases that involve these SAgs.

Combining different design strategies for rational affinity maturation of the MICA-NKG2D interface

We redesigned residues on the surface of MICA, a protein that binds the homodimeric immunoreceptor NKG2D, to increase binding affinity with a series of rational, incremental changes. A fixed-backbone RosettaDesign protocol scored a set of initial mutations, which we tested by surface plasmon resonance for thermodynamics and kinetics of NKG2D binding, both singly and in combination. We combined the best four mutations at the surface with three affinity-enhancing mutations below the binding interface found with a previous design strategy. After curating design scores with three cross-validated tests, we found a linear relationship between free energy of binding and design score, and to a lesser extent, enthalpy and design score. Multiple mutants bound with substantial subadditivity, but in at least one case full additivity was observed when combining distant mutations. Altogether, combining the best mutations from the two strategies into a septuple mutant enhanced affinity by 50-fold, to 50 nM, demonstrating a simple, effective protocol for affinity enhancement.

Partner-aware prediction of interacting residues in protein-protein complexes from sequence data

Computational prediction of residues that participate in protein-protein interactions is a difficult task, and state of the art methods have shown only limited success in this arena. One possible problem with these methods is that they try to predict interacting residues without incorporating information about the partner protein, although it is unclear how much partner information could enhance prediction performance. To address this issue, the two following comparisons are of crucial significance: (a) comparison between the predictability of inter-protein residue pairs, i.e., predicting exactly which residue pairs interact with each other given two protein sequences; this can be achieved by either combining conventional single-protein predictions or making predictions using a new model trained directly on the residue pairs, and the performance of these two approaches may be compared: (b) comparison between the predictability of the interacting residues in a single protein (irrespective of the partner residue or protein) from conventional methods and predictions converted from the pair-wise trained model. Using these two streams of training and validation procedures and employing similar two-stage neural networks, we showed that the models trained on pair-wise contacts outperformed the partner-unaware models in predicting both interacting pairs and interacting single-protein residues. Prediction performance decreased with the size of the conformational change upon complex formation; this trend is similar to docking, even though no structural information was used in our prediction. An example application that predicts two partner-specific interfaces of a protein was shown to be effective, highlighting the potential of the proposed approach. Finally, a preliminary attempt was made to score docking decoy poses using prediction of interacting residue pairs; this analysis produced an encouraging result.