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Sonmez, K.

Publications and source records attributed to Sonmez, K..

2 recordsLinked to original sources
bioRxiv2026

Agent-Guided De Novo Design of Nanobody Binders Against a Novel Cancer Target

Therapeutic antibody discovery remains slow and resource-intensive, with traditional methods providing limited control over epitope selection. We present a workflow for de novo nanobody design applied to a novel Desmoplastic Small Round Cell Tumor target encompassing four stages: (1) epitope identification guided by our hotspot recommendation agent using physical chemistry-based structure and sequence analysis tools with two curated databases (IEDB, PFAM), (2) de novo nanobody generation using three independent methods (RFantibody, IgGM, mBER) across multiple predicted antigen structures and nanobody frameworks, (3) multi-metric scoring including structural metrics from folding models, and in silico binding affinity from our sequence-based predictor, (4) high-throughput yeast surface display (YSD) screening followed by surface plasmon resonance (SPR) characterization of the specific binders. We generated 288,000 nanobody designs spanning eight target epitope regions and three variable domains of heavy chain-only antibody (VHH) frameworks. Multi-objective Pareto filtering with our candidate selection agent yielded 100,000 candidates for YSD screening with fluorescence-activated cell sorting (FACS). Of 116 enriched candidates advanced to SPR characterization, 46/116 (39.7%) produced reliable kinetic fits with Rmax [≥] 30 RU, yielding KD values from 0.66 nM to 305 nM (median 31.7 nM). These results show that an agent-guided computational workflow can design nanomolar to sub-nanomolar nanobody binders against a novel target without experimental structure or prior antibody information.

bioinformatics↗
bioRxiv2026

Context-aware Multi-Property Antibody Predictor: a Novel Framework Integrating Text and Protein Language Models

Recent advances in Machine Learning have transformed antibody development through in-silico models, accelerating therapeutic candidate identification. However, challenges persist: rapid adaptation of property predictors to laboratory-specific assays with incomplete datasets; batch effects introducing systematic bias; assay costs necessitating efficient unseen property prediction. We introduce a novel multi-modal architecture featuring specialized tokenization and embedding projection that integrates text and protein language models (pLM) and a learning strategy to enable in-context learning for multi-property prediction without learning shortcuts. Our framework enables prompting without dictionary merging across modalities, creating a compact model capable of in-context learning for multi-property prediction. The orchestrating model avoids pLM-to-text projection while enabling inference-time adaptation without retraining. Using 876,898 antibodies with batch effect simulation, our architecture achieved Spearmans {rho}>0.8 across multiple developability properties, significantly outperforming fine-tuned multimodal-LLMs and showed the ability of leveraging correlation between properties for prediction. This approach has the potential to address critical antibody development challenges.

bioinformatics↗