NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores - Palistha Shrestha; Chandana S. Talwar; Jeevan Kandel; Kwang-Hyun Park; Kil To Chong; Eui-Jeon Woo; Hilal Tayara (Journal of Cheminformatics)
Abstract Nanobodies offer significant therapeutic potential due to their small size, stability, and versatility. Although advancements in computational protein design have made designing de novo nanobodies increasingly feasible, there are limited tools specifically tailored for this purpose. Rosetta with its specialized protocols, is a prominent tool for nanobody design but is limited by a high false-negative rate, necessitating extensive high-throughput screening. This results in increased costs, time, and labor due to the need for large-scale experimentation and detailed structural analysis. To address current challenges in nanobody design, we introduce NanoBinder, an interpretable machine learning model that predicts nanobody-antigen binding using Rosetta energy scores. NanoBinder utilizes a Random Forest model trained on experimentally validated complexes and can be seamlessly integrated into the Rosetta software. It employs SHAP summary plots for interpretability, which helps identify key features influencing binding interactions. Experimentally validated on forty-nine diverse nanobodies, NanoBinder accurately predicts non-binders and shows reasonable performance in identifying binders. This approach significantly enhances predictive accuracy, reduces the need for extensive experimental assays, and accelerates nanobody development, thereby offering a powerful tool to mitigate the costs, time, and labor associated with high-throughput screening. Scientific contribution This study introduces NanoBinder, a machine learning framework for predicting nanobody-antigen binding using Rosetta-derived energy features. Through rigorous experimental validation across diverse nanobody sets, NanoBinder enhances nanobody screening workflows by reducing false positives and minimizing reliance on extensive wet-lab assays. The approach bridges the gap between physics-based modeling and data-driven prediction in nanobody design.