iNClassSec-ESM: Non-Classical secreted protein predictor based on multimodal protein language model

Introduction

Non-classical secreted proteins (NCSPs) are a class of proteins lacking signal peptides, secreted by Gram-positive bacteria through non-classical secretion pathways. With the increasing demand for highly secreted proteins in recent years, non-classical secretion pathways have received more attention due to their advantages over classical secretion pathways (Sec/Tat). However, because the mechanisms of non-classical secretion pathways are not yet clear, identifying NCSPs through biological experiments is expensive and time-consuming, making it imperative to develop computational methods to address this issue. Existing NCSP prediction methods mainly use traditional handcrafted features to represent proteins from sequence information, which limits the models' ability to capture complex protein characteristics. In this study, we proposed a novel NCSP predictor, iNClassSec-ESM, which combined deep learning with traditional classifiers to enhance prediction performance. iNClassSec-ESM integrates an XGBoost model trained on comprehensive handcrafted features and a Deep Neural Network (DNN) trained on hidden layer embeddings from the protein language model (PLM) ESM3. The ESM3 is the recently proposed multimodal PLM and has not yet been fully explored in terms of protein representation. In this study, we extracted hidden layer embeddings from ESM3 as inputs for multiple classifiers and deep learning networks, and compared them with existing PLMs. Benchmark experiments indicate that iNClassSec-ESM outperforms most of existing methods across multiple performance metrics and could serve as an effective tool for discovering potential NCSPs. Additionally, the ESM3 hidden layer embeddings, as an innovative protein representation method, show great potential for the application in broader protein-related classification tasks.

Benchmark experiments indicate that iNClassSec-ESM outperforms most of existing methods across multiple performance metrics and could serve as an effective tool for discovering potential NCSPs. Additionally, the ESM3 hidden layer embeddings, as an innovative protein representation method, show great potential for the application in broader protein-related classification tasks.

Dependencies

• Python Version: 3.10
• Required Packages: See requirements.txt for a complete list of dependencies.

Dataset

dataset/ directory contains data compiled from previous experimental summaries. This includes various protein sequences and related experimental results that serve as the foundation for our analysis.

result/ directory holds the processed data generated using the POSSUM software. Specifically, it includes:

• Segmented Protein Sequences: Individual protein sequences that have been partitioned by the POSSUM tool for detailed analysis.
• PSSM Matrices: Corresponding Position-Specific Scoring Matrices (PSSM) calculated for each segmented protein sequence.

Usage

• Evaluation the Model stack_5cv.py

Run this script to produce final ensemble model performance based on the 5-fold cross-validation and plot the ROC curve.

• Train the Model train.py

This script performs hyperparameter tuning and model training on your dataset using 5-fold cross-validation. The trained model parameters are saved to a file, which can then be used for making predictions.

• Make Predictions prediction.py

This script loads the trained model and makes predictions on a new dataset.

**Please ensure that the feature descriptors for the sequences to be predicted are correctly generated and saved in the feature/ directory.