LocPro: A deep learning-based prediction of protein subcellular localization for promoting multi-directional pharmaceutical research.
10.1016/j.jpha.2025.101255
- Author:
Yintao ZHANG
1
;
Lingyan ZHENG
1
;
Nanxin YOU
2
;
Wei HU
1
;
Wanghao JIANG
2
;
Mingkun LU
2
;
Hangwei XU
2
;
Haibin DAI
1
;
Tingting FU
1
;
Ying ZHOU
1
Author Information
1. Department of Pharmacy, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.
2. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- Publication Type:Journal Article
- Keywords:
Multi-label prediction;
Pharmaceutical research;
Protein large language model;
Protein subcellular location
- From:
Journal of Pharmaceutical Analysis
2025;15(8):101255-101255
- CountryChina
- Language:English
-
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 (FC) layer, 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/.
