Identifying Key Residues That Drive Strong Electrostatic Attractions between Therapeutic Antibodies

Area of Science:

• Biochemistry
• Computational Biology
• Protein Science

Background:

• Electrostatic interactions are crucial for protein-protein interactions (PPI).
• Predicting PPI based on charge distribution is challenging without considering pairwise interactions.
• Previous studies identified monoclonal antibodies (MAbs) with varying electrostatic interaction behaviors.

Purpose of the Study:

• To develop a systematic computational method for identifying influential amino acids and interaction patterns in diverse PPI.
• To pinpoint key residues and pairwise amino acid interaction maps responsible for distinct electrostatic interaction behaviors.
• To propose a computationally efficient approach for identifying critical amino acids using Mayer-weighted interaction energies.

Main Methods:

• Systematic computational elimination of charges on individual amino acid residues in wild-type protein sequences.
• Prediction of changes in the second osmotic virial coefficient to assess the impact of charge modifications.
• Analysis of pairwise amino acid interaction maps to understand diverse PPI behaviors.

Main Results:

• Identified specific interaction "maps" correlating with qualitatively different net electrostatic PPI for different MAbs and solution conditions.
• Highlighted key sets of amino acids that significantly contribute to strongly attractive PPI.
• Demonstrated that diverse electrostatic PPI behaviors arise from distinct patterns of residue interactions.

Conclusions:

• The computational approach effectively identifies the origin of diverse electrostatic PPI by analyzing residue-level contributions.
• Key amino acid sets and their interaction patterns are critical determinants of attractive or repulsive electrostatic interactions.
• A more efficient method using Mayer-weighted interaction energies can aid in identifying influential amino acids for PPI studies.