The ionization of bioactive molecules impacts many ADME-relevant physicochemical properties, in particular, solubility, lipophilicity, and permeability. Ampholytes contain both acidic and basic groups and are distinguished as ordinary ampholytes and zwitterions. An influential review states that zwitterions only exist if the acidic pKa is significantly lower than the basic pKa. Through concordance of measured and calculated pKa and log P, we show that the zwitterionic behavior of several marketed drugs and natural products occurs despite a low or negative ΔpKa. These nonclassical zwitterions are characterized by a weak acidic and basic pKa and conjugation through an extended aromatic system, often including pseudorings via intramolecular hydrogen bonds. In contrast to most classical zwitterions, nonclassical zwitterions can exhibit excellent permeability. As permeability and lipophilicity are typically correlated, the combination of low lipophilicity and high permeability makes nonclassical zwitterions an attractive design principle in medicinal chemistry.

Poorly interpreted structural models of protein-ligand complexes can mislead and undermine structure-based drug design (SBDD). As structural biology methods advance, allowing more complex and previously unattainable structures to be solved, so too does the risk of over interpretation of poorly resolved, low-quality, or incomplete models. In this tutorial-style talk, we’ll explore how to critically evaluate structural data, comparing key differences in model interpretation from X-ray crystallography and cryo-EM. We’ll highlight common challenges and missteps, then walk through computational workflows designed to improve structure quality and reliability—ensuring your models are truly ready for SBDD.

Therapeutic use of soluble TCRs has been validated by the FDA approval of Tebentafusp. These TCR T-Cell engager molecules are TCR/anti-CD3 bispecific fusion proteins that target specific peptide-human leukocyte antigen complexes (pHLA) to activate a highly potent and specific T cell response against cancer cells.

Our TCR phage display discovery platform allows us to generate diverse panels of stable TCRs to any pHLA. This presentation will focus on the in silico structure prediction and interaction property calculations that we deploy to help rank TCRs specific for the pHLA of interest in lead identification. Prioritising those TCRs with binding characteristics amenable to engineering a high affinity TCR with the requisite peptide selectivity is key.

Monoclonal antibodies (mAbs) are the most abundant class of biotherapeutics on the market. Therefore, enhancing our understanding of how these proteins behave from a structural perspective is crucial for gaining insights into their function and improving the quality of the products administered to patients. Due to the lack of experimental data, computational approaches often represent the best choice for predicting the conformational behavior of mAbs. One of the main challenges in simulating these biotherapeutics is the time required to find stable conformations, given the size of the system and the flexibility of these proteins. Accordingly, two studies utilizing accelerated molecular dynamics simulations were carried out. The first study focuses on investigating the role of N-glycosylation, particularly fucosylation, and light chain isotype in modulating the flexibility of classical human IgG1, using two commercial mAbs, adalimumab and avelumab, as case studies. The results indicate that κ and λ light chain isotypes can have different impacts on antibody's dynamics and structural integrity, and that these effects can be combined with the influence of fucosylation. In the second study, we investigate how Fab glycosylation can affect the effector functions of cetuximab, marketed as Erbitux, revealing that Fab glycosylation significantly alters the conformational freedom of the antibody, potentially impairing its interaction with FcγRIIIa and modulating antibody-dependent cell cytotoxicity (ADCC). These works lay the groundwork for future investigations into the modulation of antibody functions through glycosylation and structural engineering.

Antibodies are molecules that exhibit remarkable flexibility, a property that arises from several structural features, including hinge regions, elbow linkers, inter-domain interfaces between Ig-fold domain pairs, and the loops within the paratope. Collectively, these elements contribute to the ability of antibodies to adopt various conformations, thereby enabling them to recognise and bind to a wide array of antigens with high specificity. We demonstrated that, in solution, the binding-competent structure of antibodies is typically the dominant conformation, even though this is often distinct from the structures observed in their unbound state. This suggests that antibodies inherently adopt a structural ensemble in which the binding-competent form is favourable when free in solution. These findings have significant implications for understanding antibody behaviour and designing tools to predict their structures computationally. Specifically, they reveal both opportunities and challenges for AI-driven antibody structure prediction, as accurate modelling must account for the dynamic and flexible nature of these biomolecules. The ability of AI models to incorporate the conformational adaptability of antibodies will be critical for improving predictions, particularly when resolving structures relevant to antigen binding. This work underscores the need for innovative approaches that leverage both experimental data and computational advancements to address the complexity of antibody flexibility in structural biology.