Biology subjects

Zarzar, J.

Publications and source records attributed to Zarzar, J..

3 recordsLinked to original sources

bioRxiv2026

Self-Interaction Nanoparticle Spectroscopy Predicts High-Concentration Viscosity of Therapeutic IgG1 Antibodies

Predicting high-concentration viscosity of monoclonal antibodies such as IgG1 is crucial for their development as therapeutics for subcutaneous delivery. Unfortunately, traditional experimental rheometry methods for assessing viscosity are low-throughput. This study evaluates Self-Interaction Nanoparticle Spectroscopy (SINS) assays--specifically charge-stabilized SINS (CS-SINS) and PEG-stabilized SINS (PS-SINS)--for high-throughput viscosity prediction. We characterized 96 IgG1 antibodies, assessing SINS against in silico descriptors and dynamic light scattering (DLS) data. CS-SINS showed strong correlation with charge, offering limited additional utility. In contrast, PS-SINS provided orthogonal information; integrating it with in silico data and DLS significantly improved random forest model accuracy for binary viscosity classification. PS-SINS measurements in multiple buffers captured complementary information, achieving comparable accuracy without DLS. Importantly, PS-SINS scores exhibited a strong logarithmic relationship (r=0.98) with high-concentration viscosity in Fc variants of clinical antibodies, suggesting a direct mechanistic link. Furthermore, PS-SINS performed reliably with one column purified (protein A) samples, supporting its early-stage application. These findings establish PS-SINS as a high-throughput tool to accelerate the developability assessment of antibody candidates.

biochemistry↗

bioRxiv2025

Rational design of oxidation-resistant antibodies through local electrostatic modulation

Oxidation is a significant degradation pathway in proteins, particularly therapeutic antibodies, that can impair function, efficacy, and stability. Understanding the sequence and structural factors that drive oxidation susceptibility is critical for incorporating chemical stability into early-stage antibody design. Here, we present a machine learning classifier trained on expert-guided structural features to assess tryptophan (Trp) oxidation risks in the complementarity-determining regions (CDR) of 187 antibodies produced internally at Genentech. The model reveals a strong correlation between local negative electrostatic potential and Trp oxidation susceptibility. A simplified two-parameter model derived from these insights achieves 79% accuracy in classifying oxidation risk, compared to 84% accuracy with the full-feature classifier. Beyond our internal dataset, the two-parameter model successfully predicted oxidation risk for all CDR Trp sites in a blind subset of eight clinical-stage antibodies. In addition, we show that modulating the electrostatic potential around the Trp side-chains through distant charge-altering mutations can significantly reduce oxidation rates. In four out of five re-engineered antibodies, oxidation rates decreased by approximately 50%, while half of these maintained binding affinity. Finally, we demonstrate that this approach can guide multi-property optimization by balancing oxidation resistance and affinity in an anti-CD33 antibody. These results establish a strong link between local electrostatic environments and Trp oxidation susceptibility, and provide a practical framework for designing oxidation resistant biotherapeutics.

biochemistry↗

bioRxiv2021

Divining Deamidation and Isomerization in Therapeutic Proteins: Effect of Neighboring Residue

Deamidation of asparagine (ASN) and isomerization of aspartic acid (ASP) residues are among the most commonly observed spontaneous post-translational modifications (PTMs) in proteins. Understanding and predicting a protein sequences propensity for such PTMs can help expedite protein therapeutic discovery and development. In this study, we utilized proton-affinity calculations with semi-empirical quantum mechanics (QM) and {micro}s long equilibrium molecular dynamics (MD) simulations to investigate mechanistic roles of structure and chemical environment in dictating spontaneous degradation of asparagine and aspartic acid residues in 131 clinical-stage therapeutic antibodies. Backbone secondary structure, side-chain rotamer conformation and solvent accessibility were found as three key molecular indicators of ASP isomerization and ASN deamidation. Comparative analysis of backbone dihedral angles along with N-H proton affinity calculations provides a mechanistic explanation for the strong influence of the identity of the n+1 residue on the rate of ASP/ASN degradation. With these findings, we propose a minimalistic physics-based classification model that can be leveraged to predict deamidation and isomerization propensity of therapeutic proteins.

biochemistry↗