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

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.