LocPro: A deep learning-based prediction of protein subcellular localization for promoting multi-directional pharmaceutical research
Yintao Zhang, Lingyan Zheng, Nanxin You, Wei Hu, Wanghao Jiang, Mingkun Lu, Hangwei Xu, Haibin Dai, Tingting Fu, Ying Zhou
Abstract
Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in ( a ) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, ( b ) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected layer (FC), and bidirectional long short-term memory (BiLSTM) blocks, and ( c ) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/ • A dual-channel protein representation approach combining ESM2 and expert-driven features • A hybrid deep neural network architecture combining CNN, FC, and BiLSTM blocks • A multi-label framework for predicting protein subcellular localization at multiple granularity levels