Structure-based affinity maturation of a chimeric anti-ricin antibody C4C13

Research on the Mechanism of Action of a Citrinin and Anti-Citrinin Antibody Based on Mimotope X27

Immunoassays are developed based on antigen–antibody interactions. A mimotope is an effective recognition receptor used to study the mechanism of action of antigens and antibodies, and is used for improving the sensitivity of the antibody. In this study, we built a 3D structure of the citrinin (CIT) mimotope X27 and anti-CIT single-chain antibody fragment (ScFv) through a “homologous modeling” strategy. Then, CIT and X27 were respectively docked to anti-CIT ScFv by using the “molecular docking” program. Finally, T28, F29, N30, R31, and Y32 were confirmed as the key binding sites in X27. Furthermore, the result of the phage-ELISA showed that the mutational phage lost the binding activity to the anti-CIT ScFv when the five amino acids were mutated to “alanine”, thereby proving the correctness of the molecular docking model. Lastly, a site-directed saturation strategy was adopted for the sites (T28, F29, N30, R31, and Y32). Eighteen different amino acids were introduced to each site on average. The activities of all mutants were identified by indirect competitive ELISA. The sensitivities of mutants T28F, T28I, F29I, F29V, N30T, and N30V were 1.83-, 1.37-, 1.70-, 2.96-, 1.31-, and 2.01-fold higher than that of the wild-type, respectively. In conclusion, the binding model between the CIT and antibody was elaborated for the first time based on the mimotope method, thereby presenting another strategy for improving the sensitivity of citrinin detection in immunoassays.

Using homology modeling to interrogate binding affinity in neutralization of ricin toxin by a family of single domain antibodies

In this report we investigated, within a group of closely related single domain camelid antibodies (V_H Hs), the relationship between binding affinity and neutralizing activity as it pertains to ricin, a fast-acting toxin and biothreat agent. The V1C7-like V_H Hs (V1C7, V2B9, V2E8, and V5C1) are similar in amino acid sequence, but differ in their binding affinities and toxin-neutralizing activities. Using the X-ray crystal structure of V1C7 in complex with ricin's enzymatic subunit (RTA) as a template, Rosetta-based homology modeling coupled with energetic decomposition led us to predict that a single pairwise interaction between Arg29 on V5C1 and Glu67 on RTA was responsible for the difference in ricin toxin binding affinity between V1C7, a weak neutralizer, and V5C1, a moderate neutralizer. This prediction was borne out experimentally: substitution of Arg for Gly at position 29 enhanced V1C7's binding affinity for ricin, whereas the reverse (ie, Gly for Arg at position 29) diminished V5C1's binding affinity by >10 fold. As expected, the V5C1_R29G mutant was largely devoid of toxin-neutralizing activity (TNA). However, the TNA of the V1C7_G29R mutant was not correspondingly improved, indicating that in the V1C7 family binding affinity alone does not account for differences in antibody function. V1C7 and V5C1, as well as their respective point mutants, recognized indistinguishable epitopes on RTA, at least at the level of sensitivity afforded by hydrogen-deuterium mass spectrometry. The results of this study have implications for engineering therapeutic antibodies because they demonstrate that even subtle differences in epitope specificity can account for important differences in antibody function.

Unraveling the Roots of Selectivity of Peptide Affinity Reagents for Structurally Similar Ribosomal Inactivating Protein Derivatives

Peptide capture agents have become increasingly useful tools for a variety of sensing applications due to their ease of discovery, stability, and robustness. Despite the ability to rapidly discover candidates through biopanning bacterial display libraries and easily mature them to Protein Catalyzed Capture (PCC) agents with even higher affinity and selectivity, an ongoing challenge and critical selection criteria is that the peptide candidates and final reagent be selective enough to replace antibodies, the gold-standard across immunoassay platforms. Here, we have discovered peptide affinity reagents against abrax, a derivative of abrin with reduced toxicity. Using on-cell Fluorescence Activated Cell Sorting (FACS) assays, we show that the peptides are highly selective for abrax over RiVax, a similar derivative of ricin originally designed as a vaccine, with significant structural homology to abrax. We rank the newly discovered peptides for strongest affinity and analyze three observed consensus sequences with varying affinity and specificity. The strongest (Tier 1) consensus was FWDTWF, which is highly aromatic and hydrophobic. To better understand the observed selectivity, we use the XPairIt peptide-protein docking protocol to analyze binding location predictions of the individual Tier 1 peptides and consensus on abrax and RiVax. The binding location profiles on the two proteins are quite distinct, which we determine is due to differences in pocket size, pocket environment (including hydrophobicity and electronegativity), and steric hindrance. This study provides a model system to show that peptide capture candidates can be quite selective for a structurally similar protein system, even without further maturation, and offers an in silico method of analysis for understanding binding and down-selecting candidates.

General, Label-Free Method for Determining K(d) and Ligand Concentration Simultaneously

Some of the most commonly used affinity reagents (e.g., antibodies) are often developed and used in conditions where their input concentrations ([L]0) and affinities (K(d)) are not known. Here, we have developed a general approach to determine both [L]0 and K(d) values simultaneously for affinity reagents (small molecules, proteins, and antibodies). To do this, we perform quantitative equilibrium exclusion immunoassays with two different concentrations of target and fit the data simultaneously to determine K(d) and [L]0. The results give accurate and reproducible measures of both values compared to established methods. By performing detailed error analysis, we demonstrate that our fitting gives unique solutions and indicates where K(d) and [L]0 measures are reliable. Furthermore, we found that a divalent model of antibody binding gives accurate K(d) and [L]0 values in both the forward (antibody immobilized) and the reverse (target immobilized) assays-addressing the long-term problem of obtaining quantitative data from reverse assays.