Welcome to the Recombinant Antibody Network

@article{pmid42178617,

title = {Dual targeting of inhibitory EGFR epitopes with synthetic antibodies in therapeutic-resistant cancers},

author = {Jarrett J Adams and Evan Mallette and Max London and Ryan J Liang and Dewald van Dyk and Zvezdan Pavlovic and Isabelle Pot and C Ronald Geyer and Heather A Bruce and Levi L Blazer and Craig A Hokanson and Michael D L Suits and Alexander U Singer and Sachdev S Sidhu},

doi = {10.1002/pro.70645},

issn = {1469-896X},

year  = {2026},

date = {2026-06-01},

urldate = {2026-06-01},

journal = {Protein Sci},

volume = {35},

number = {6},

pages = {e70645},

abstract = {Therapeutic antibodies that inhibit the epidermal growth factor receptor (EGFR) are limited to a subset of EGFR-driven cancers. This is in part due to resistance mechanisms that attenuate efficacy. All approved therapeutic antibodies target the closed form of EGFR and compete with the ligand. However, tumors can be desensitized to these antibodies by upregulation of EGFR ligands or through EGFR mutations that uncouple kinase activity from ligand binding. We sought to overcome these resistance mechanisms by developing antibodies targeting alternative epitopes of EGFR. Using phage-displayed libraries, we developed two distinct antibodies, one that competed with EGF and another that did not. Crystal structures revealed that the competitive antibody bound to a site that overlapped the EGF-binding site, whereas the other antibody bound to the arm that induces receptor dimerization. Because the libraries used a common light chain, we were able to assemble a biparatopic antibody that incorporated both antigen-binding sites and thus targeted both epitopes on EGFR. We showed that the antibody that targeted the dimerization arm inhibited the growth of cancer cell lines that were resistant to the antibody that targeted the EGF-binding site. Moreover, the biparatopic antibody was more potent than the monoparatopic antibodies. Our results suggest that antibodies that target the dimerization arm of EGFR may be effective across a broader range of cancers than antibodies that target the EGF-binding site, and that a biparatopic antibody targeting both epitopes may be the most effective therapeutic for inhibiting aberrant EGFR signaling in cancer.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid42165126,

title = {A universal Fab targeting a conserved U1A-RNA epitope for RNA structure determination by cryo-EM},

author = {Ekaterina V Filippova and Daniel Krochmal and Somnath Mukherjee and Joseph A Piccirilli and Anthony A Kossiakoff},

doi = {10.1093/nar/gkag502},

issn = {1362-4962},

year  = {2026},

date = {2026-05-01},

urldate = {2026-05-01},

journal = {Nucleic Acids Res},

volume = {54},

number = {10},

abstract = {Recent advances in cryo-electron microscopy (cryo-EM) have made antigen-binding fragments (Fabs) essential tools in the field of structural biology. Fabs facilitate image alignment, thereby enhancing three-dimensional (3D) reconstruction, and increase the effective size of proteins, aiding in their structural elucidation. In this study, we sought to broaden the use of Fabs as fiducial markers to elucidate the structures of RNA molecules. Identifying an appropriate Fab for a specific RNA target can be particularly challenging due to RNA's inherent flexibility and tendency to assume multiple conformations, which complicate the process and prolong the structure determination timeline. To address this challenge, we designed a universal Fab that specifically recognizes a U1A-RNA epitope, thereby reducing the need for Fab selection tailored to each individual RNA target. We determined the cryo-EM structure of the class I ligase ribozyme complexed with a portable U1hpII loop bound to the U1A protein and the Fab. The resulting structure revealed that the Fab interacts with a conserved U1A-RNA binding region, which can be engineered into other RNA molecules. This strategy presents significant potential for streamlining the structural determination of various RNAs, which are essential for biological and biomedical research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41959119,

title = {Structural Basis of Serine Protease Inhibition by Antibodies from Biased Fab Phage-Display Libraries},

author = {Kyle J Anderson and Melody S Lee and Natalia Sevillano and Gang Chen and Michael J Hornsby and Sachdev S Sidhu and Charles S Craik},

doi = {10.64898/2026.03.12.711446},

issn = {2692-8205},

year  = {2026},

date = {2026-03-01},

urldate = {2026-03-01},

journal = {bioRxiv},

abstract = {Biased Fab phage-display libraries were designed to determine whether inhibitory CDR H3 motifs from potent anti-matriptase antibodies could be transferred to target homologous serine proteases. Using reverse-binding and substrate-like H3 motifs from parental clones A11 and E2 as templates, six synthetic libraries with 10 diversity were constructed. Selection against matriptase identified sixteen inhibitors with sub-100 nM potency, representing 100,000-fold improvement over circularized H3 loops alone. Selection against TMPRSS2, a serine protease implicated in viral entry and prostate cancer with 43% sequence identity to matriptase, yielded binders with micromolar inhibitory potency. Selection against urokinase plasminogen activator (uPA, 35% identity) identified binders that adopted a substrate-like CDR H3 binding mode in our structural models. Across all reference structures, including the separately identified uPA inhibitor AB2 (PDB: 9PYF, deposited with this work), benchmarking of five co-folding methods and rigid-body docking showed that co-folding consistently achieved acceptable to high quality DockQ scores, outperforming traditional docking and capturing the recognition of key active site determinants. Ensemble predictions of mutational binding energy changes (ΔΔ) using these models identified key paratope-epitope interactions, with predictions validated through mutagenesis. This work establishes a framework integrating biased antibody libraries with computational structure prediction and analysis for targeting conserved protease epitopes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}