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Li J, Xiong S, Shi H, Cui F, Zhang Z, Wei L. NeuroPred-AIMP: Multimodal Deep Learning for Neuropeptide Prediction via Protein Language Modeling and Temporal Convolutional Networks. J Chem Inf Model 2025; 65:4740-4750. [PMID: 40258183 DOI: 10.1021/acs.jcim.5c00444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2025]
Abstract
Neuropeptides are key signaling molecules that regulate fundamental physiological processes ranging from metabolism to cognitive function. However, accurate identification is a huge challenge due to sequence heterogeneity, obscured functional motifs and limited experimentally validated data. Accurate identification of neuropeptides is critical for advancing neurological disease therapeutics and peptide-based drug design. Existing neuropeptide identification methods rely on manual features combined with traditional machine learning methods, which are difficult to capture the deep patterns of sequences. To address these limitations, we propose NeuroPred-AIMP (adaptive integrated multimodal predictor), an interpretable model that synergizes global semantic representation of the protein language model (ESM) and the multiscale structural features of the temporal convolutional network (TCN). The model introduced the adaptive features fusion mechanism of residual enhancement to dynamically recalibrate feature contributions, to achieve robust integration of evolutionary and local sequence information. The experimental results demonstrated that the proposed model showed excellent comprehensive performance on the independence test set, with an accuracy of 92.3% and the AUROC of 0.974. Simultaneously, the model showed good balance in the ability to identify positive and negative samples, with a sensitivity of 92.6% and a specificity of 92.1%, with a difference of less than 0.5%. The result fully confirms the effectiveness of the multimodal features strategy in the task of neuropeptide recognition.
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Affiliation(s)
- Jinjin Li
- Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao 999078, China
| | - Shuwen Xiong
- Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao 999078, China
| | - Hua Shi
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Leyi Wei
- Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao 999078, China
- School of Software, Shandong University, Jinan 250101, China
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Xiong S, Cai J, Shi H, Cui F, Zhang Z, Wei L. UMPPI: Unveiling Multilevel Protein-Peptide Interaction Prediction via Language Models. J Chem Inf Model 2025; 65:3789-3799. [PMID: 40077987 DOI: 10.1021/acs.jcim.4c02365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2025]
Abstract
Protein-peptide interactions are essential to cellular processes and disease mechanisms. Identifying protein-peptide binding residues is critical for understanding peptide function and advancing drug discovery. However, experimental methods are costly and time-intensive, while existing computational approaches often predict interactions or binding residues separately, lack effective feature integration, or rely heavily on limited high-quality structural data. To address these challenges, we propose UMPPI (Unveiling Multilevel Protein-Peptide Interaction), a multiobjective framework based on the pretrained protein language model ESM2. UMPPI simultaneously predicts binary protein-peptide interactions and binding residues on both peptides and proteins through a multiobjective optimization strategy. By integrating ESM2 to encode sequences and extract latent structural information, UMPPI bridges the gap between sequence-based and structure-based methods. Extensive experiments demonstrated that UMPPI successfully captured binary interactions between peptides and proteins and identified the binding residues on peptides and proteins. UMPPI can serve as a useful tool for protein-peptide interaction prediction and identification of critical binding residues, thereby facilitating the peptide drug discovery process.
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Affiliation(s)
- Shuwen Xiong
- Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao 999078, China
| | - Jiajie Cai
- School of Software, Shandong University, Jinan 250101, China
| | - Hua Shi
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, Xiamen 361005, China
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Leyi Wei
- Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao 999078, China
- School of Software, Shandong University, Jinan 250101, China
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Ahmed Z, Shahzadi K, Jin Y, Li R, Momanyi BM, Zulfiqar H, Ning L, Lin H. Identification of RNA‐dependent liquid‐liquid phase separation proteins using an artificial intelligence strategy. Proteomics 2024; 24:e2400044. [PMID: 38824664 DOI: 10.1002/pmic.202400044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 05/03/2024] [Accepted: 05/21/2024] [Indexed: 06/04/2024]
Abstract
RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these proteins is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis and frontotemporal dementia, making their identification crucial. However, conventional biochemistry-based methods for identifying these proteins are time-consuming and costly. Addressing this challenge, our study developed a robust computational model for their identification. We constructed a comprehensive dataset containing 137 RNA-dependent and 606 non-RNA-dependent LLPS protein sequences, which were then encoded using amino acid composition, composition of K-spaced amino acid pairs, Geary autocorrelation, and conjoined triad methods. Through a combination of correlation analysis, mutual information scoring, and incremental feature selection, we identified an optimal feature subset. This subset was used to train a random forest model, which achieved an accuracy of 90% when tested against an independent dataset. This study demonstrates the potential of computational methods as efficient alternatives for the identification of RNA-dependent LLPS proteins. To enhance the accessibility of the model, a user-centric web server has been established and can be accessed via the link: http://rpp.lin-group.cn.
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Affiliation(s)
- Zahoor Ahmed
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China
| | - Kiran Shahzadi
- Department of Biotechnology, Women University of Azad Jammu and Kashmir Bagh, Bagh, Azad Kashmir, Pakistan
| | - Yanting Jin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Rui Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Biffon Manyura Momanyi
- School of Computer Science and Engineering, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China
| | - Lin Ning
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Hao Lin
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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Qiao B, Wang S, Hou M, Chen H, Zhou Z, Xie X, Pang S, Yang C, Yang F, Zou Q, Sun S. Identifying nucleotide-binding leucine-rich repeat receptor and pathogen effector pairing using transfer-learning and bilinear attention network. Bioinformatics 2024; 40:btae581. [PMID: 39331576 PMCID: PMC11969219 DOI: 10.1093/bioinformatics/btae581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 08/24/2024] [Accepted: 09/25/2024] [Indexed: 09/29/2024] Open
Abstract
MOTIVATION Nucleotide-binding leucine-rich repeat (NLR) family is a class of immune receptors capable of detecting and defending against pathogen invasion. They have been widely used in crop breeding. Notably, the correspondence between NLRs and effectors (CNE) determines the applicability and effectiveness of NLRs. Unfortunately, CNE data is very scarce. In fact, we've found a substantial 91 291 NLRs confirmed via wet experiments and bioinformatics methods but only 387 CNEs are recognized, which greatly restricts the potential application of NLRs. RESULTS We propose a deep learning algorithm called ProNEP to identify NLR-effector pairs in a high-throughput manner. Specifically, we conceptualized the CNE prediction task as a protein-protein interaction (PPI) prediction task. Then, ProNEP predicts the interaction between NLRs and effectors by combining the transfer learning with a bilinear attention network. ProNEP achieves superior performance against state-of-the-art models designed for PPI predictions. Based on ProNEP, we conduct extensive identification of potential CNEs for 91 291 NLRs. With the rapid accumulation of genomic data, we expect that this tool will be widely used to predict CNEs in new species, advancing biology, immunology, and breeding. AVAILABILITY AND IMPLEMENTATION The ProNEP is available at http://nerrd.cn/#/prediction. The project code is available at https://github.com/QiaoYJYJ/ProNEP.
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Affiliation(s)
- Baixue Qiao
- Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), Harbin 150001, China
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150001, China
| | - Shuda Wang
- Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), Harbin 150001, China
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150001, China
| | - Mingjun Hou
- Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), Harbin 150001, China
| | - Haodi Chen
- Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), Harbin 150001, China
| | - Zhengwenyang Zhou
- Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), Harbin 150001, China
| | - Xueying Xie
- Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), Harbin 150001, China
| | - Shaozi Pang
- Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), Harbin 150001, China
| | - Chunxue Yang
- College of Landscape Architecture, Northeast Forestry University, Harbin 150001, China
| | - Fenglong Yang
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350122, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Shanwen Sun
- Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), Harbin 150001, China
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150001, China
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Lin P, Li H, Huang SY. Deep learning in modeling protein complex structures: From contact prediction to end-to-end approaches. Curr Opin Struct Biol 2024; 85:102789. [PMID: 38402744 DOI: 10.1016/j.sbi.2024.102789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/16/2024] [Accepted: 02/06/2024] [Indexed: 02/27/2024]
Abstract
Protein-protein interactions play crucial roles in many biological processes. Traditionally, protein complex structures are normally built by protein-protein docking. With the rapid development of artificial intelligence and its great success in monomer protein structure prediction, deep learning has widely been applied to modeling protein-protein complex structures through inter-protein contact prediction and end-to-end approaches in the past few years. This article reviews the recent advances of deep-learning-based approaches in modeling protein-protein complex structures as well as their advantages and limitations. Challenges and possible future directions are also briefly discussed in applying deep learning for the prediction of protein complex structures.
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Affiliation(s)
- Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China.
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Qi R, Zhang Z, Wu J, Dou L, Xu L, Cheng Y. A new method for handling heterogeneous data in bioinformatics. Comput Biol Med 2024; 170:107937. [PMID: 38217975 DOI: 10.1016/j.compbiomed.2024.107937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 12/25/2023] [Accepted: 01/01/2024] [Indexed: 01/15/2024]
Abstract
Heterogeneous data, especially a mixture of numerical and categorical data, widely exist in bioinformatics. Most of works focus on defining new distance metrics rather than learning discriminative metrics for mixed data. Here, we create a new support vector heterogeneous metric learning framework for mixed data. A heterogeneous sample pair kernel is defined for mixed data and metric learning is then converted to a sample pair classification problem. The suggested approach lends itself well to effective resolution through conventional support vector machine solvers. Empirical assessments conducted on mixed data benchmarks and cancer datasets affirm the exceptional efficacy demonstrated by the proposed modeling technique.
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Affiliation(s)
- Ren Qi
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zehua Zhang
- Scientific and Technological Innovation Center, Beijing, China
| | - Jin Wu
- School of Management, Shenzhen Polytechnic University, Shenzhen, China
| | - Lijun Dou
- School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, China
| | - Yue Cheng
- School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, China.
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Pang Y, Liu B. DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model. BMC Biol 2024; 22:3. [PMID: 38166858 PMCID: PMC10762911 DOI: 10.1186/s12915-023-01803-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 12/15/2023] [Indexed: 01/05/2024] Open
Abstract
Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions. A disordered region interacts with different partners to perform multiple functions, and these disordered functions exhibit different dependencies and correlations. In this study, we introduce DisoFLAG, a computational method that leverages a graph-based interaction protein language model (GiPLM) for jointly predicting disorder and its multiple potential functions. GiPLM integrates protein semantic information based on pre-trained protein language models into graph-based interaction units to enhance the correlation of the semantic representation of multiple disordered functions. The DisoFLAG predictor takes amino acid sequences as the only inputs and provides predictions of intrinsic disorder and six disordered functions for proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linker. We evaluated the predictive performance of DisoFLAG following the Critical Assessment of protein Intrinsic Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. The standalone package and web server of DisoFLAG have been established to provide accurate prediction tools for intrinsic disorders and their associated functions.
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Affiliation(s)
- Yihe Pang
- School of Computer Science and Technology, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Beijing, Haidian District, 100081, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Beijing, Haidian District, 100081, China.
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Beijing, Haidian District, 100081, China.
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Liu Z, Zhu YH, Shen LC, Xiao X, Qiu WR, Yu DJ. Integrating unsupervised language model with multi-view multiple sequence alignments for high-accuracy inter-chain contact prediction. Comput Biol Med 2023; 166:107529. [PMID: 37748220 DOI: 10.1016/j.compbiomed.2023.107529] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/30/2023] [Accepted: 09/19/2023] [Indexed: 09/27/2023]
Abstract
Accurate identification of inter-chain contacts in the protein complex is critical to determine the corresponding 3D structures and understand the biological functions. We proposed a new deep learning method, ICCPred, to deduce the inter-chain contacts from the amino acid sequences of the protein complex. This pipeline was built on the designed deep residual network architecture, integrating the pre-trained language model with three multiple sequence alignments (MSAs) from different biological views. Experimental results on 709 non-redundant benchmarking protein complexes showed that the proposed ICCPred significantly increased inter-chain contact prediction accuracy compared to the state-of-the-art approaches. Detailed data analyses showed that the significant advantage of ICCPred lies in the utilization of pre-trained transformer language models which can effectively extract the complementary co-evolution diversity from three MSAs. Meanwhile, the designed deep residual network enhances the correlation between the co-evolution diversity and the patterns of inter-chain contacts. These results demonstrated a new avenue for high-accuracy deep-learning inter-chain contact prediction that is applicable to large-scale protein-protein interaction annotations from sequence alone.
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Affiliation(s)
- Zi Liu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, China; Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 , China
| | - Yi-Heng Zhu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210095 , China
| | - Long-Chen Shen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, China
| | - Xuan Xiao
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 , China
| | - Wang-Ren Qiu
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 , China.
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, China.
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Lee M. Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review. Molecules 2023; 28:5169. [PMID: 37446831 DOI: 10.3390/molecules28135169] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.
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Affiliation(s)
- Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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