Machine Learning-Based Classification of Single-Molecule Nanopipette Translocation Signals for Label-Free Discrimination of Proteins

Nanopipette-based single-molecule sensing offers a label-free route to protein analysis, but distinguishing structurally different proteins from ionic current traces remains difficult. Here, we classified three clinically important proteins (CCL5, MMP1 and TIMP1) from their nanopipette translocation signals. Twelve biophysical features were extracted from a balanced subset of 846 events (282 per protein) drawn from 1317 recorded events (one experiment per protein). Random Forest, SVM and gradient boosting were compared by five-fold cross-validation; gradient boosting performed best (accuracy 92.1 ± 2.0%, macro ROC-AUC 0.982). Absolute current drop and relative blockage (ΔI/I0) were the most important features (about 37% of importance), and PCA and clustering separated TIMP1 but not CCL5 and MMP1. However, with a histogram-based gradient boosting classifier on a pool of 28 experiments (7095 events), balanced accuracy fell from 90.0% under event-level cross-validation to 73.5% when whole experiments recorded with other nanopipettes were held out (mean CCL5 recall 30%), and to 47–63% without baseline-related features, whereas open-pore current alone gave 92.0% and 82.6%, indicating pipette-specific contributions. These proof-of-concept findings indicate that, within the present dataset, nanopipette signals can distinguish the three proteins, whereas generalization to independent nanopipettes, particularly for CCL5, remains to be demonstrated.