Biology subjects

Frick, R.

Publications and source records attributed to Frick, R..

4 recordsLinked to original sources

bioRxiv2025

Adapting ProteinMPNN for antibody design without retraining

The neural network ProteinMPNN designs protein sequences capable of folding into predefined tertiary structures and quaternary assemblies. It has become widely used due to its high success rates when working with synthetic topologies rich in secondary structure. Here, we show degraded performance on the complementarity-determining regions (CDRs) of antibodies, with designs frequently failing to resemble native antibodies or failing to refold into the designed conformations. We also show that this underperformance can be rescued by ensembling its predictions with those from the antibody-specific protein language model AbLang, which designs exclusively using sequence information learned from large databases of antibody sequences. Finally, we tested 96 trastuzumab variants with CDRH3 loops redesigned by the ensembled ProteinMPNN+AbLang method and found that it generated thirty-six HER2 binders, compared to three out of 96 designs generated by ProteinMPNN alone. The data highlight the value of incorporating additional restraints derived from language models during structure-based sequence design of antibodies.

bioengineering↗

bioRxiv2022

Hallucinating structure-conditioned antibody libraries for target-specific binders

Antibodies are widely developed and used as therapeutics to treat cancer, infectious disease, and inflammation. During development, initial leads routinely undergo additional engineering to increase their target affinity. Experimental methods for affinity maturation are expensive, laborious, and time-consuming and rarely allow the efficient exploration of the relevant design space. Deep learning (DL) models are transforming the field of protein engineering and design. While several DL-based protein design methods have shown promise, the antibody design problem is distinct, and specialized models for antibody design are desirable. Inspired by hallucination frameworks that leverage accurate structure prediction DL models, we propose the FvHallucinator for designing antibody sequences, especially the CDR loops, conditioned on an antibody structure. Such a strategy generates targeted CDR libraries that retain the conformation of the binder and thereby the mode of binding to the epitope on the antigen. On a benchmark set of 60 antibodies, FvHallucinator generates sequences resembling natural CDRs and recapitulates perplexity of canonical CDR clusters. Furthermore, the FvHallucinator designs amino acid substitutions at the VH-VL interface that are enriched in human antibody repertoires and therapeutic antibodies. We propose a pipeline that screens FvHallucinator designs to obtain a library enriched in binders for an antigen of interest. We apply this pipeline to the CDR H3 of the Trastuzumab-HER2 complex to generate in silico designs improving upon the binding affinity and interfacial properties of the original antibody. Thus, the FvHallucinator pipeline enables generation of inexpensive, diverse, and targeted antibody libraries enriched in binders for antibody affinity maturation.

bioinformatics↗

bioRxiv2020

Robustification of RosettaAntibody and Rosetta SnugDock

In recent years, the observed antibody sequence space has grown exponentially due to advances in high-throughput sequencing of immune receptors. The rise in sequences has not been mirrored by a rise in structures, as experimental structure determination techniques have remained low-throughput. Computational modeling, however, has the potential to close the sequence-structure gap. To achieve this goal, computational methods must be robust, fast, easy to use, and accurate. Here we report on the latest advances made in RosettaAntibody and Rosetta SnugDock--methods for antibody structure prediction and antibody-antigen docking. We simplified the user interface, expanded and automated the template database, generalized the kinematics of antibody-antigen docking (which enabled modeling of single-domain antibodies) and incorporated new loop modeling techniques. To evaluate the effects of our updates on modeling accuracy, we developed rigorous tests under a new scientific benchmarking framework within Rosetta. Benchmarking revealed that more structurally similar templates could be identified in the updated database and that SnugDock broadened its applicability without losing accuracy. However, there are further advances to be made, including increasing the accuracy and speed of CDR-H3 loop modeling, before computational approaches can accurately model any antibody.

biophysics↗

bioRxiv2019

Affinity-engineered human antibodies detect celiac disease gluten pMHC complexes and inhibit T-cell activation

Antibodies specific for antigenic peptides bound to major histocompatibility complex (MHC) molecules are valuable tools for studies of antigen presentation. Such T-cell receptor (TCR)-like antibodies may also have therapeutic potential in human disease due to their ability to target disease-associated antigens with high specificity. We previously generated celiac disease (CeD) relevant TCR-like antibodies that recognize the prevalent gluten epitope DQ2.5-glia-1a in complex with HLA-DQ2.5. Here, we report on second-generation high-affinity antibodies towards this epitope as well as a panel of novel TCR-like antibodies to another immunodominant gliadin epitope, DQ2.5-glia-2. The strategy for affinity engineering was based on Rosetta modeling combined with pIX phage display and is applicable to similar protein engineering efforts. We isolated picomolar affinity binders and validated them in Fab and IgG format. Flow cytometry experiments with CeD biopsy material confirm the unique disease specificity of these TCR-like antibodies and reinforce the notion that B cells and plasma cells have a dominant role in gluten antigen presentation in the inflamed CeD gut. Further, the lead candidate 3.C11 potently inhibited CD4+ T-cell activation and proliferation in vitro in an HLA and epitope specific manner, pointing to a potential for targeted disease interception without compromising systemic immunity. Significance StatementConsumption of gluten-containing food drives celiac disease in genetically predisposed individuals. The underlying disease mechanism is not fully understood, but it is strictly dependent on activation of pathogenic T cells. We have engineered high-affinity human antibodies recognizing the T-cell target HLA-DQ2.5 in complex with gluten epitopes and studied cell-specific antigen presentation in patients, which shows that plasma cells and not dendritic cells dominate the inflamed tissue. The only available treatment is lifelong adherence to a gluten-free diet, which is difficult and not effective in all cases. We show that at least one of our antibodies can specifically inhibit activation of pathogenic T-cells in vitro and therefore shows promise for therapy.

immunology↗