Identifying Key Residues That Drive Strong Electrostatic Attractions between Therapeutic Antibodies

Abstract

Attractive electrostatic protein–protein interactions (PPI) necessarily involve identifying oppositely charged regions of the protein surface that interact favorably. This cannot be done reliably if one only considers a single protein in isolation unless there are obvious charge “patches” that result in extreme molecular dipoles. Prior work [J. Pharm. Sci.2019108120132] identified three monoclonal antibodies (MAbs) that displayed experimental behavior ranging from net repulsive to strongly attractive electrostatic interactions. The present work provides a systematic computational approach for identifying the origin of diverse PPI, in terms of which sets of amino acids or individual amino acids are most influential, and determining if there are different patterns of pairwise amino acid interaction “maps” that result in different behaviors. The charge was eliminated computationally, one by one, for each charged residue in the wild-type sequences, which resulted in predicted changes in the second osmotic virial coefficient. The results highlight interaction “maps” that correspond to cases with qualitatively different net electrostatic PPI for the different MAbs and solution conditions, as well as key sets of residues that contribute to strongly attractive PPI. A more computationally efficient method is also proposed to identify key amino acids based on Mayer-weighted interaction energies.

Toward a Suite of Coarse-Grained Models for Molecular Simulation of Monoclonal Antibodies and Therapeutic Proteins

“…12−33 Protein−protein self-interactions are highlighted in this report, as net selfinteractions are readily measured experimentally 4,17−20,27,34−46 and can be quantified through molecular simulation. [16][17][18][19][20][21][22][23]27,31,32,47 Protein−protein self-interactions are quantified in the present work via osmotic virial coefficients 18,20,27 and are a function of steric repulsions, electrostatic repulsions and attractions, and short-ranged nonelectrostatic attractions (e.g., van der Waals forces as well as hydrophobic and hydration effects) between protein molecules. 16,48 While experimental measures of self-interactions are related to a number of problematic behaviors in therapeutic protein formulations, they do not necessarily quantitatively predict these behaviors.…”

Computational models for studying physical instabilities in high concentration biotherapeutic formulations

“… 14 1 bead per residue Protein self-assembly; 48 Protein-protein interactions. 49 , 50 CABS 51 Up to 4 beads per residue Prediction of aggregation-prone regions as part of AGGRESCAN-3D; 52 Protein-peptide docking; 53 Protein folding. 54 Kim and Hummer 55 1 bead per residue Structure refinement from SAXS data; 56 Protein phase behavior; 57 Protein-protein interactions.…”

“… 158 , 159 Other low-resolution CG models based on rigid representations of proteins have been used to understand the role of surface residues and solution conditions of protein self-association. 49 , 50 , 62 For instance, Blanco et al . 14 used a 1-bead per residue protein model for γD-crystallin to evaluate the effect of ionic strength on modulating preferential protein orientations during self-association, enabling the identification of key mutations to reduce the aggregation rate.…”

Designing Feature-Controlled Humanoid Antibody Discovery Libraries Using Generative Adversarial Networks

“…Large surface patches in antibody therapeutics have been linked to developability issues such as aggregation, thermal instability, elevated viscosity, and increased clearance rate, 23,33,37 but also to improvement of specificity, 31 particularly when the patches are related to charge. As such, biasing a library towards larger or smaller patches might have beneficial effects.…”

“…While many papers have been published trying to develop a predictable connection between an antibody's sequence and/or computed molecular structure and the molecule's various physical characteristics, the connection is elusive as it involves complex nonlinear interactions between the constituent amino acid residues. 11,[22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] Frequently, such work involves an exceptionally small number of molecules, frequently under 200 and often under 50, from a non-diverse set of sequences -a small number of parental sequences, several parents with a small number of highly-related sequence variants, or a single antibody with mutational scanning. Such approaches give information on an individual antibody or small group, but are highly unlikely to generalize the complexity of residue interactions to other antibodies.…”