  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.
