Sequence-Based Nanobody-Antigen Binding Prediction

TL;DR

This paper introduces a sequence-based machine learning method for predicting nanobody-antigen binding with high accuracy, bypassing the need for 3D structural data and enabling faster screening of potential nanobodies.

Contribution

The study presents a novel gapped k-mer embedding approach combined with machine learning to predict nanobody-antigen interactions solely from sequence data, improving efficiency over traditional methods.

Findings

Achieves up to 90% accuracy in binding prediction.

Significantly faster than computational docking techniques.

Provides a practical tool for nanobody screening without structural data.

Abstract

Nanobodies (Nb) are monomeric heavy-chain fragments derived from heavy-chain only antibodies naturally found in Camelids and Sharks. Their considerably small size (~3-4 nm; 13 kDa) and favorable biophysical properties make them attractive targets for recombinant production. Furthermore, their unique ability to bind selectively to specific antigens, such as toxins, chemicals, bacteria, and viruses, makes them powerful tools in cell biology, structural biology, medical diagnostics, and future therapeutic agents in treating cancer and other serious illnesses. However, a critical challenge in nanobodies production is the unavailability of nanobodies for a majority of antigens. Although some computational methods have been proposed to screen potential nanobodies for given target antigens, their practical application is highly restricted due to their reliance on 3D structures. Moreover, predicting nanobodyantigen interactions (binding) is a time-consuming and labor-intensive task. This study aims to develop a machine-learning method to predict Nanobody-Antigen binding solely based on the sequence data. We curated a comprehensive dataset of Nanobody-Antigen binding and nonbinding data and devised an embedding method based on gapped k-mers to predict binding based only on sequences of nanobody and antigen. Our approach achieves up to 90% accuracy in binding prediction and is significantly more efficient compared to the widely-used computational docking technique.