Machine learning (ML) has the potential to revolutionize antibody design and selection, but its success depends on access to well-curated datasets of antibody-antigen interactions. We developed a synthetic Fab yeast display library optimized for seamless integration with ML processes, focusing on sequence diversity within the complementary determining region heavy chain CDRH3 loop. The library incorporates key sequence features derived from human B cell repertoires captured in a compact antigen recognition module (ARM) format. Built with the VH1-69 heavy chain and four light chains, the library was evaluated against ten human and murine cell surface antigens, including programmed cell death ligand 1 (PD-L1), T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT), and roundabout guidance receptor 1 (ROBO1). This approach yielded hundreds of antibodies with robust biophysical properties, some of which were validated by flow cytometry and immunohistochemistry. Furthermore, ML analysis identified additional antibodies for ROBO2 and PD-L2 from the aggregate sequencing data. The publicly available dataset establishes an ML-compatible framework designed to accelerate and streamline antibody discovery and development. A record of this paper’s transparent peer review process is included in the supplemental information.
Efficient production of human proteins for the development of tool compounds and biologics depends on a detailed understanding of the protein expression machinery in mammalian cells. Codon optimization is widely believed to enhance protein yield, yet its impact in homologous mammalian systems remains poorly defined. Here, we systematically compare five codon usage strategies reflecting common assumptions about rare codons, RNA stability, and synthesis efficiency. We developed pTipi, an efficient open-source mammalian expression vector, and evaluated its performance in antibody production. We generated plasmids for common epitope tag antibodies such as V5, anti-biotin and anti-His for distribution by Addgene. To compare codon usage schemes, we performed a bake-off of 18 human and murine Wnt pathway glycoproteins in mammalian cells. Small-scale expression screens revealed that codon optimization did not provide a general advantage over native coding sequences, while strategies prioritizing RNA stability consistently reduced expression. Interestingly, a skewed codon scheme using the most abundant codons produced yields comparable to native sequences and occasionally enhanced protein output. To enable flexible evaluation of codon strategies, we implemented a Golden Gate–compatible pTipi platform for efficient synthetic gene incorporation. We conclude that native codons are sufficient for robust homologous mammalian expression of glycoproteins, while selective codon skewing can be beneficial for some targets. ### Competing Interest Statement The authors have declared no competing interest.
Antibody developability is increasingly predictable from sequence, yet software and trained models are rarely made available. We present DELPHI, open software for training developability predictors from labelled antibody assay data, together with ready-to-run, retrainable models. DELPHI compares 25 language-model and classifier combinations under CDR H3-cluster cross-validation that reduces sequence-similarity leakage, measures how performance changes with labelled training-set size, and reports residue-level model attributions. Applied to in-house polyreactivity and size-exclusion (SEC) data, it reaches mean AUC 0.959 and 0.933. Trained on those data alone, it transfers to a 246,293-antibody public library (AUC 0.950 with our deployed model) and ranks polyreactivity at a level similar to the best reported Ginkgo competition point estimate, without training on its data. Any laboratory can screen candidates before running assays, generate residue-level engineering hypotheses, and retrain DELPHI for a new assay. ### Competing Interest Statement The authors have declared no competing interest. Institute for Protein Innovation
Complex neuronal circuits arise from a small set of cell-surface receptors that position neurons, promote axon extension, and define synaptic connections. A central receptor is Deleted in Colon Cancer (DCC), which mediates both short- and long-range axon guidance and confines migrating neurons to the central nervous system. DCC's versatility reflects its ability to interact at distinct sites of its extracellular domain with two ligands, Netrin-1 and Draxin, which also bind to each other. Alternative splicing further alters the Netrin-1 binding site and modulates affinity. By generating two mouse lines with mutations that selectively impair DCC binding to Netrin-1 and/or Draxin, we show that molecular modularity within the DCC extracellular domain is essential for precise circuit assembly. An eight-amino acid insertion in the DCC long isoform is required for Netrin-1-dependent long-range commissural axon guidance in the spinal cord. Conversely, isoleucine 372 in the Draxin binding site enables DCC clustering and is necessary for all known DCC functions, including axon guidance in the spinal cord and retina and neuronal migration in the brainstem. Draxin also supports long-range commissural guidance, but mutations in its binding site cause stronger defects. These results underscore how DCC's distinct modules drive specific developmental responses.
Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride towards achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.
