were used to assign a mAb shape by tting to

is the scattering angle. The scattering pattern represented by the form factor is related to the size and shape of the protein in the solution.72 In general, low concen‑ trations of 1–10 mg/mL are used to provide a compromise between the increase in signal‑to‑noise ratio and the decrease in interparticle distances that both accompany increased solution concentration. While the former is always favorable, the latter can inuence the scattering pattern. In practice, proles collected at different con‑ centrations may be merged into a single representative curve. Solutions of mAb2 at [NaCl] = 0, 50, 250 mM and with [Arg] = 250 mM and mAb4 with either 250‑mM NaCl or Arg were all assayed with SAXS, and the resulting 12‑bead CG models with various interaction proles. While each of the CG mod‑ els for a particular mAb and solution condition have identical bead sizes and loca‑ tions, they differ in terms of how many beads experience attractive interactions and where these attractive beads are placed within the CG model. MD simulations were run with 6,000 identical mAb CG representations in a periodic box with a constant number of particles, system volume, and system temperature for each mAb and CG model. They nd that models with only three attractive beads per mAb are frequently detected bound to more than three neighbors. This result appears to invalidate theo‑ retical models that only allow one interaction partner per binding site. low‑up paper, Chowdhury and co‑workers investigated the inuence of short‑range, non‑electrostatic attractive and electrostatic repulsive interactions. Using a similar tting and simulation procedure to Dear etal., Chowdhury and coworkers59manipu‑ lated the parameters within the CG model to best t the experimental structure factor curves under various conditions for mAb2. These simulations found that including uniform VDW interactions between all model beads allows predictions of structure factor curves to become more accurate.

These simulation results highlight the ability of CG simulations to aid in understand‑ ing the difcult inverse question that accompanies spectroscopic assays of biomolecules: what ensemble of structures gives rise to the observed signal? In this case, exploring various CG models and rationalizing which models provide the best predictions for which system provides a wealth of additional information beyond the experiment alone. As computational power increases and model accuracy continues to improve, the use of CG simulations to understand the ne structure of experimental scattering curves may become more commonplace.

In a fol‑

and oligomeric preference of solanezumab

neoantigen (p53 R175H) bound by the same HLA in contact with various TCR, TCRm, and afnity‑enhanced TCRs. The contributions of the peptide, α1 helix, and α2 helix to the total free‑energy change of the pHLA were computed using the molecular mechanic Generalized‑Born surface area approach, a technique related to MMPBSA but using a different representation of the protein for surface area calculations. These calculations revealed that natural and afnity‑enhanced TCRs tend to gain a larger proportion of their binding energy from the peptide than any of the TCRms (Figure8.3d). Further, these calculations suggested that some peptide antigens are easier to bind than others, with WT1 peptides notably all ranking at the bottom in terms of fraction contribution to the total free energy change in the receptor. Similar calculations carried out on the residues in the CDRs (Figure8.3e) indicated that while the CDR[H/B]3 and CDR[L/A]3 predominate across the board, the pattern changes depending on the context. While TCRms tend to gain more energy from CDRH3 than CDRL3, in TCRs, there is no clear preference.