Reliable in silico ranking of engineered therapeutic TCR binding affinities with MMPB/GBSA

Accurate and efficient in silico ranking of proteinprotein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson-Boltzmann/generalized Born surface area (MMPB/ GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (<= 4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5-10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development

Peptide cargo tunes a network of correlated motions in human leukocyte antigens

Most biomolecular interactions are typically thought to increase the (local) rigidity of a complex, for example, in drug‐target binding. However, detailed analysis of specific biomolecular complexes can reveal a more subtle interplay between binding and rigidity. Here, we focussed on the human leucocyte antigen (HLA), which plays a crucial role in the adaptive immune system by presenting peptides for recognition by the αβ T‐cell receptor (TCR). The role that the peptide plays in tuning HLA flexibility during TCR recognition is potentially crucial in determining the functional outcome of an immune response, with obvious relevance to the growing list of immunotherapies that target the T‐cell compartment. We have applied high‐pressure/temperature perturbation experiments, combined with molecular dynamics simulations, to explore the drivers that affect molecular flexibility for a series of different peptide–HLA complexes. We find that different peptide sequences affect peptide–HLA flexibility in different ways, with the peptide cargo tuning a network of correlated motions throughout the pHLA complex, including in areas remote from the peptide‐binding interface, in a manner that could influence T‐cell antigen discrimination

Exposing the Interplay Between Enzyme Turnover, Protein Dynamics and the Membrane Environment in Monoamine Oxidase B

There is an increasing realization that structure-based drug design may show improved success rates by understanding the ensemble of conformations and sub-states accessible to an enzyme and how the environment affects this ensemble. Human monoamine oxidase B (MAO-B) catalyzes the oxidation of amines and is inhibited for the treatment of both Parkinson’s disease and depression. Despite its clinical importance, its catalytic mechanism remains unclear and routes to drugging this target would be valuable and relevant. Evidence of a radical in either the transition state or resting state of MAO-B is present throughout the literature, and is suggested to be a flavin semiquinone, a tyrosyl radical or both. Here we see evidence of a resting state flavin semiquinone, via absorption redox studies and electron paramagnetic resonance, suggesting that the anionic semiquinone is biologically relevant. Based on enzyme kinetic studies, enzyme variants and molecular dynamics simulations we find evidence for the crucial importance of the membrane environment in mediating the activity of MAO-B and that this mediation is related to effects on the protein dynamics of MAO-B. Further, our MD simulations identify a hitherto undescribed entrance for substrate binding, membrane modulated substrate access, and indications for half-site reactivity: only one active site is accessible to binding at a time. Our study combines both experimental and computational evidence to illustrate the subtle interplay between enzyme activity, protein dynamics and the immediate membrane environment. Understanding key biomedical enzymes to this level of detail will be crucial to inform strategies (and binding sites) for rational drug design for these drug targets

Specificity of bispecific T cell receptors and antibodies targeting peptide-HLA

Tumor-associated peptide–human leukocyte antigen complexes (pHLAs) represent the largest pool of cell surface–expressed cancer-specific epitopes, making them attractive targets for cancer therapies. Soluble bispecific molecules that incorporate an anti-CD3 effector function are being developed to redirect T cells against these targets using 2 different approaches. The first achieves pHLA recognition via affinity-enhanced versions of natural TCRs (e.g., immune-mobilizing monoclonal T cell receptors against cancer [ImmTAC] molecules), whereas the second harnesses an antibody-based format (TCR-mimic antibodies). For both classes of reagent, target specificity is vital, considering the vast universe of potential pHLA molecules that can be presented on healthy cells. Here, we made use of structural, biochemical, and computational approaches to investigate the molecular rules underpinning the reactivity patterns of pHLA-targeting bispecifics. We demonstrate that affinity-enhanced TCRs engage pHLA using a comparatively broad and balanced energetic footprint, with interactions distributed over several HLA and peptide side chains. As ImmTAC molecules, these TCRs also retained a greater degree of pHLA selectivity, with less off-target activity in cellular assays. Conversely, TCR-mimic antibodies tended to exhibit binding modes focused more toward hot spots on the HLA surface and exhibited a greater degree of crossreactivity. Our findings extend our understanding of the basic principles that underpin pHLA selectivity and exemplify a number of molecular approaches that can be used to probe the specificity of pHLA-targeting molecules, aiding the development of future reagents

Harnessing Conformational Plasticity to Generate Designer Enzymes

Recent years have witnessed an explosion of interest in understanding the role of conformational dynamics both in the evolution of new enzymatic activities from existing enzymes and in facilitating the emergence of enzymatic activity de novo on scaffolds that were previously non-catalytic. There are also an increasing number of examples in the literature of targeted engineering of conformational dynamics being successfully used to alter enzyme selectivity and activity. Despite the obvious importance of conformational dynamics to both enzyme function and evolvability, many (although not all) computational design approaches still focus either on pure sequence-based approaches or on using structures with limited flexibility to guide the design. However, there exist a wide variety of computational approaches that can be (re)purposed to introduce conformational dynamics as a key consideration in the design process. Coupled with laboratory evolution and more conventional existing sequence- and structure-based approaches, these techniques provide powerful tools for greatly expanding the protein engineering toolkit. This Perspective provides an overview of evolutionary studies that have dissected the role of conformational dynamics in facilitating the emergence of novel enzymes, as well as advances in computational approaches that allow one to target conformational dynamics as part of enzyme design. Harnessing conformational dynamics in engineering studies is a powerful paradigm with which to engineer the next generation of designer biocatalysts