Enhancing protein subcellular localization prediction through language model-based knowledge embeddings and machine learning techniques.

Abstract

Determining the subcellular localization of proteins is critical for understanding their functional roles. With the rapid expansion of protein sequence databases, traditional experimental and homology-based approaches for Protein Subcellular Localization (PSCL) prediction are becoming increasingly impractical. Although several computational approaches have been developed, achieving high performance in terms of strict accuracy for multi-label PSCL prediction remains a significant challenge. This study evaluates the effectiveness of embeddings from five Protein Language Models (PLMs), including ProtBERT-BFD, ESM-2, ProtALBERT, ProLLaMA, and ProtGPT-2, as input features for various machine learning classifiers. Through five-fold cross-validation on the Swiss-Prot dataset, the results show that encoder-based PLMs, particularly ESM-2, combined with a Support Vector Machine (SVM) employing a polynomial kernel, consistently achieve the best performance. This configuration demonstrates a modest yet consistent improvement in strict-accuracy metrics compared to other model combinations. The proposed work offers a comprehensive evaluation of different PLM architectures and classifier complexities, achieving a strict accuracy of 0.58 while providing valuable insights into their performance trade-offs. This represents a 3 percentage point improvement over existing state-of-the-art models like DeepLoc 2.0. These results suggest that while current embeddings offer strong performance, further advancements in feature extraction and model architectures are needed to significantly boost strict accuracy.