Biology subjects

Felbinger, N. R.

Publications and source records attributed to Felbinger, N. R..

2 recordsLinked to original sources

bioRxiv2025

Engineering HIV antibodies with enhanced breadth and potency of neutralization through multistate affinity maturation

Broadly neutralizing antibodies (bnAbs) against HIV hold promise as therapeutic and prophylactic agents, but realizing this potential requires antibodies that function across the antigenic heterogeneity of the HIV envelope glycoprotein (Env). Although numerous bnAbs have been isolated from infected individuals, their breadth and potency may not be sufficient to tackle global viral diversity, motivating efforts to further improve their neutralization capacity. Here, we address this challenge using a multistate antibody engineering approach integrating deep mutational scanning with combinatorial library screening across diverse Env variants. This strategy enables identification of mutation patterns that confer improved binding across antigenically distinct targets. Starting from one of the best-in-class CD4-binding site bnAbs, we performed iterative optimization to increase binding affinity across diverse Env variants. The resulting lead candidate exhibited improved breadth and up to 100-fold higher potency against pseudoviruses from large cross-clade historical and contemporary panels while maintaining biophysical and pharmacokinetic profiles conducive to clinical development. Structural and molecular dynamics analyses revealed a unique tri-tyrosine aromatic triad and reinforced electrostatic contacts that stabilized the bnAb/Env interface. These findings demonstrate that systematic in vitro engineering can generate bnAbs with enhanced breadth and potency, providing a generalizable strategy for developing therapeutic antibodies against highly diverse pathogens.

bioengineering↗

bioRxiv2024

Exploring the Potential of Structure-Based Deep Learning Approaches for T cell Receptor Design

Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optimization of existing ones designed for specific functions, such as binding a target protein. Despite the demonstrated potential of such approaches in designing general protein binders, their application in designing immunotherapeutics remains relatively unexplored. A relevant application is the design of T cell receptors (TCRs). Given the crucial role of T cells in mediating immune responses, redirecting these cells to tumor or infected target cells through the engineering of TCRs has shown promising results in treating diseases, especially cancer. However, the computational design of TCR interactions presents challenges for current physics-based methods, particularly due to the unique natural characteristics of these interfaces, such as low affinity and cross-reactivity. For this reason, in this study, we explored the potential of two structure-based deep learning protein design methods, ProteinMPNN and ESM-IF, in designing fixed-backbone TCRs for binding target antigenic peptides presented by the MHC through different design scenarios. To evaluate TCR designs, we employed a comprehensive set of sequence- and structure-based metrics, highlighting the benefits of these methods in comparison to classical physics-based design methods and identifying deficiencies for improvement.

bioinformatics↗