NanoSSL: attention mechanism-based self-supervised learning method for protein identification using nanopores

TL;DR

NanoSSL is a new machine learning method that improves protein identification from nanopore data using self-supervised learning and attention mechanisms.

Contribution

NanoSSL introduces a novel self-supervised learning framework with attention mechanisms for protein identification using nanopores.

Findings

NanoSSL achieved high performance in classifying mutated Aβ1-42 proteins using four metrics: accuracy, precision, recall, and F1 score.

The method leverages a masked autoencoder and attention mechanisms to learn useful representations from fragmented nanopore signals.

Self-supervised pretraining combined with supervised fine-tuning improves molecular identification in nanopore-based proteomics.

Abstract

Nanopores are cutting-edge interdisciplinary tools that can analyze biomolecules at the single-molecule level for many applications, e.g. DNA sequencing. Efforts are underway to extend nanopores to proteomics, including the development of machine learning algorithms for protein sequencing and identification. However, single-molecule data are intrinsically noisy and hard to process. Moreover, the development and performance of machine learning for nanopore is jeopardized by data scarcity. Self-supervised learning is an emerging method that may yield advantages in nanopore scenarios.

We propose and experimentally validate Nanopore analysis using Self-Supervised Learning (NanoSSL), a generative self-supervised learning framework based on attention mechanisms for the identification of protein signals from nanopores. Leveraging a two-step approach consisting of self-supervised pre-training and supervised fine-tuning, NanoSSL learns useful feature representations from empirical data to facilitate downstream classification tasks. Inspired by the concept of fragmentation in conventional protein sequencing technologies, during pretraining each translocation event is split into multiple non-overlapping fragments of equal size, some of which are randomly masked and reconstructed using a masked autoencoder. Learning the feature representations of the reconstructed nanopore events facilitates molecular identification in fine-tuning. In this study, we retested a publicly available nanopore multiplexed protein sensing dataset for model iteration, and subsequently measured Alzheimer’s disease biomarker Aβ1-42 using homemade solid-state nanopores. Empirical results indicated NanoSSL achieved an unprecedented performance across four metrics: accuracy, precision, recall, and F1 score, when classifying two mutated Aβ1-42, E22G and G37R. The self-supervised learning and attention mechanism were verified as the source of performance gains.

The main program is available at https://doi.org/10.5281/zenodo.17172822.

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