Integrating AlphaFold2 with physics-based ensemble docking for high-efficiency nanobody discovery

Analysing open-source protein folding models for nanobody binding prediction

These findings provide practical guidance for integrating open-source protein structure prediction models into AI-driven nanobody discovery pipelines while highlighting the need for improved generalization across antigens.

Comprehensive Mapping of Immune Nanobody Repertoires with NanoMAP 2267451

Nanobodies have recently emerged as an alternative to classical antibodies in therapeutic and diagnostic contexts, promising improved stability and simpler manufacturing. However, many labs still rely on low throughput conventional screening methods for nanobody discovery. Here we report streamlined experimental and computational tools for discovery of nanobodies, permitting deep characterization of the binding properties of immune repertoires. To improve nanobody discovery, we developed NanoMAP, an integrated experimental and computational pipeline for nanobody discovery. We immunized alpacas with a pool of antigens, and created a phage display library from circulating B-cells. We then panned this phage display library on each antigen separately, and used competitors or antigen variants to assess complex binding phenotypes of the immune repertoire. Finally, we sequenced the panned libraries and developed a clustering method that allows data to be aggregated within B-cell clonal families, improving signal-to-noise ratios and reducing the complexity of the repertoire. We tested NanoMAP on three distinct pools of targets, collecting data on close to 1M unique nanobody sequences. We found that our specialized clustering method outperformed standard sequence clustering, producing clonal families that are coherent in sequence and function. By aggregating sequencing data within clonal families, NanoMAP produced reliable and rich data on binding phenotypes for each antigen. Using this information, we discovered nanobodies recognizing functionally relevant, and evolutionarily conserved sites on each antigen, demonstrating the broad utility of our methods. NIAID R01AI25704, NIGMS 5K12GM133314-07 Computational and Systems Immunology (COMP)

A Picomolar MBP-Binding Nanobody for Target Enrichment and Modular Complex Engineering

The variable domains of heavy chain only antibodies, also called nanobodies, have provided powerful new tools for biomedical research. Their rapid development across multiple fields has helped address emerging scientific and clinical challenges, establishing single-domain antibodies (sdAbs) as highly adaptable platforms for a wide range of biotechnological and therapeutic applications. In this study, we identified a nanobody from an alpaca immune library, sdAbCM1, that binds to the maltose-maltodextrin binding protein, MBP, with picomolar affinity. Biophysical investigations showed that sdAbCM1 recognizes a yet unreported epitope and interacts with MBP in its closed conformation when maltose is present. The high affinity, epitope specificity, and ability to tolerate the ligand-bound state render sdAbCM1 a valuable and versatile biotechnological tool with potential applications such as detection, localization, and purification of MBP fusion proteins and protein complex engineering for single-particle microscopy.

Tetrapeptide inhibitors of BACE-1 revealed by combined data-driven screening and physics-based free-energy refinement.

assemblies by information-driven docking: Introducing the

AI-enabled discovery and biochemical optimization of minibinders targeting cancer cell-surface proteins

Experimental validation and functional optimization remain bottlenecks in AI-based protein design. We present a scalable workflow for developing AI-designed minibinders against cancer-associated surface proteins. Screening thousands of designs using mammalian cell-surface display identifies several high-affinity PD-L1 minibinders but far fewer for CD276 (B7-H3) and VTCN1 (B7-H4), highlighting substantial target dependence. Interface predicted template modeling (ipTM) scores generated by Chai-1 with ESM embeddings correlate with binding success and capture deleterious effects of interface mutations. Fluorophore-labeled AI-minibinders enable flow-cytometric staining comparable to conventional antibodies. However, when incorporated into chimeric antigen receptors (CAR), some show poor cell-surface trafficking and limited functionality. Redesign through a genetic algorithm-based diversification strategy that preserves the binding interface while changing non-binding surfaces experimentally reveals an isoelectric point (pI) window that improves CAR expression and enhances target-selective tumor cell killing. Our findings identify biochemical optimization beyond the binding interface as a critical requirement for translating AI-minibinders into functional applications. In this work the authors present a scalable mammalian cell-display workflow to identify AI-designed minibinders against cancer surface targets. AI-guided optimization beyond the binding interface improves their expression as chimeric antigen receptors and target-selective killing.