Antibodies represent essential tools in research and diagnostics and are rapidly growing in importance as therapeutics. Commonly used methods to obtain novel antibodies typically yield several candidates capable of engaging a given target. The development steps that follow, however, are usually performed with only one or few candidates since they can be resource demanding, thereby increasing the risk of failure of the overall antibody discovery program. In particular, insufficient solubility, which may lead to aggregation under typical storage conditions, often hinders the ability of a candidate antibody to be developed and manufactured. Here we show that the selection of soluble lead antibodies from an initial library screening can be greatly facilitated by a fast computational prediction of solubility that requires only the amino acid sequence as input. We quantitatively validate this approach on a panel of nine distinct monoclonal antibodies targeting nerve growth factor (NGF), for which we compare the predicted and measured solubilities finding a very close match, and we further benchmark our predictions with published experimental data on aggregation hotspots and solubility of mutational variants of one of these antibodies.

Predicting the concentrated solution behavior for monoclonal antibodies requires developing and using minimal models to describe their shape and interaction potential. Toward this end, the small-angle X-ray scattering (SAXS) profiles for a monoclonal antibody (COE-03) have been measured under solution conditions chosen to produce weak self-association. The experiments are complemented with molecular simulations of a three-bead antibody model with and without interbead attraction. The scattering profile is extracted directly from the molecular simulation to avoid using the decoupling approximation. We examine the ability of the three-bead model to capture features of the scattering profile and the dependence of compressibilty on protein concentration. The three-bead model is able to reproduce generic features of the experimental structure factor as a function of wave vector S(k) including a well-defined shoulder, which is a consequence of the planar structure of the antibody, and a well-defined minimum in S(k) at k ∼ 0.025 Å. We also show the decoupling approximation is incapable of accounting for highly anisotropic shapes. The best-fit parameters obtained from matching spherical models to simulated scattering profiles are protein concentration dependent, which limits their applicability for predicting thermodynamic properties. Nevertheless, the experimental compressibility curves can be accurately reproduced by an appropriate parametrization of the Baxter adhesive model, indicating the model provides a semiempirical equation of state for the antibody. The results provide insights into how equations of state can be improved for antibodies by accounting for their anisotropic shapes.

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