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Geng G, Wang L, Xu Y, Wang T, Ma W, Duan H, Zhang J, Mao A. MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction. Methods 2024; 228:22-29. [PMID: 38754712 DOI: 10.1016/j.ymeth.2024.05.010] [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: 01/28/2024] [Revised: 05/09/2024] [Accepted: 05/12/2024] [Indexed: 05/18/2024] Open
Abstract
Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interactions, limitations persist. Most methods rely on handcrafted features, restricting their applicability. They predominantly extract information from individual drugs, neglecting the importance of interaction details between drug pairs. To address these issues, we propose MGDDI, a graph neural network-based model for predicting potential adverse drug interactions. Notably, we use a multiscale graph neural network (MGNN) to learn drug molecule representations, addressing substructure size variations and preventing gradient issues. For capturing interaction details between drug pairs, we integrate a substructure interaction learning module based on attention mechanisms. Our experimental results demonstrate MGDDI's superiority in predicting adverse drug interactions, offering a solution to current methodological limitations.
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Affiliation(s)
- Guannan Geng
- Department of Endocrinology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lizhuang Wang
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Yanwei Xu
- Beidahuang Group Neuropsychiatric Hospital, Jiamusi, China; Department of Stomatology and Dental Hygiene, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Tianshuo Wang
- School of Software, Shandong University, Jinan, China
| | - Wei Ma
- Department of Stomatology and Dental Hygiene, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
| | - Jiahui Zhang
- Department of Stomatology and Dental Hygiene, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China.
| | - Anqiong Mao
- The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Department of Anesthesiology, Luzhou, China.
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Zhang Y, Li J, Lin S, Zhao J, Xiong Y, Wei DQ. An end-to-end method for predicting compound-protein interactions based on simplified homogeneous graph convolutional network and pre-trained language model. J Cheminform 2024; 16:67. [PMID: 38849874 PMCID: PMC11162000 DOI: 10.1186/s13321-024-00862-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 05/19/2024] [Indexed: 06/09/2024] Open
Abstract
Identification of interactions between chemical compounds and proteins is crucial for various applications, including drug discovery, target identification, network pharmacology, and elucidation of protein functions. Deep neural network-based approaches are becoming increasingly popular in efficiently identifying compound-protein interactions with high-throughput capabilities, narrowing down the scope of candidates for traditional labor-intensive, time-consuming and expensive experimental techniques. In this study, we proposed an end-to-end approach termed SPVec-SGCN-CPI, which utilized simplified graph convolutional network (SGCN) model with low-dimensional and continuous features generated from our previously developed model SPVec and graph topology information to predict compound-protein interactions. The SGCN technique, dividing the local neighborhood aggregation and nonlinearity layer-wise propagation steps, effectively aggregates K-order neighbor information while avoiding neighbor explosion and expediting training. The performance of the SPVec-SGCN-CPI method was assessed across three datasets and compared against four machine learning- and deep learning-based methods, as well as six state-of-the-art methods. Experimental results revealed that SPVec-SGCN-CPI outperformed all these competing methods, particularly excelling in unbalanced data scenarios. By propagating node features and topological information to the feature space, SPVec-SGCN-CPI effectively incorporates interactions between compounds and proteins, enabling the fusion of heterogeneity. Furthermore, our method scored all unlabeled data in ChEMBL, confirming the top five ranked compound-protein interactions through molecular docking and existing evidence. These findings suggest that our model can reliably uncover compound-protein interactions within unlabeled compound-protein pairs, carrying substantial implications for drug re-profiling and discovery. In summary, SPVec-SGCN demonstrates its efficacy in accurately predicting compound-protein interactions, showcasing potential to enhance target identification and streamline drug discovery processes.Scientific contributionsThe methodology presented in this work not only enables the comparatively accurate prediction of compound-protein interactions but also, for the first time, take sample imbalance which is very common in real world and computation efficiency into consideration simultaneously, accelerating the target identification and drug discovery process.
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Affiliation(s)
- Yufang Zhang
- School of Mathematical Sciences and SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, 200240, China
- Peng Cheng Laboratory, Shenzhen, 518055, Guangdong, China
- Zhongjing Research and Industrialization, Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, 473006, Henan, China
| | - Jiayi Li
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai JiaoTong University, Shanghai, China
| | - Shenggeng Lin
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai JiaoTong University, Shanghai, China
| | - Jianwei Zhao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai JiaoTong University, Shanghai, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai JiaoTong University, Shanghai, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
| | - Dong-Qing Wei
- Peng Cheng Laboratory, Shenzhen, 518055, Guangdong, China.
- Zhongjing Research and Industrialization, Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, 473006, Henan, China.
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai JiaoTong University, Shanghai, 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:e2400044. [PMID: 38824664 DOI: 10.1002/pmic.202400044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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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Feng C, Wei H, Li X, Feng B, Xu C, Zhu X, Liu R. A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features. Comput Biol Med 2024; 176:108534. [PMID: 38754217 DOI: 10.1016/j.compbiomed.2024.108534] [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: 02/04/2024] [Revised: 04/03/2024] [Accepted: 04/28/2024] [Indexed: 05/18/2024]
Abstract
Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.
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Affiliation(s)
- Changli Feng
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Haiyan Wei
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Xin Li
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Bin Feng
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Chugui Xu
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Xiaorong Zhu
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Ruijun Liu
- School of Software, Beihang University, Beijing, 100191, China.
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Jiao S, Ye X, Sakurai T, Zou Q, Liu R. Integrated convolution and self-attention for improving peptide toxicity prediction. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae297. [PMID: 38696758 DOI: 10.1093/bioinformatics/btae297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/02/2024] [Accepted: 04/30/2024] [Indexed: 05/04/2024]
Abstract
MOTIVATION Peptides are promising agents for the treatment of a variety of diseases due to their specificity and efficacy. However, the development of peptide-based drugs is often hindered by the potential toxicity of peptides, which poses a significant barrier to their clinical application. Traditional experimental methods for evaluating peptide toxicity are time-consuming and costly, making the development process inefficient. Therefore, there is an urgent need for computational tools specifically designed to predict peptide toxicity accurately and rapidly, facilitating the identification of safe peptide candidates for drug development. RESULTS We provide here a novel computational approach, CAPTP, which leverages the power of convolutional and self-attention to enhance the prediction of peptide toxicity from amino acid sequences. CAPTP demonstrates outstanding performance, achieving a Matthews correlation coefficient of approximately 0.82 in both cross-validation settings and on independent test datasets. This performance surpasses that of existing state-of-the-art peptide toxicity predictors. Importantly, CAPTP maintains its robustness and generalizability even when dealing with data imbalances. Further analysis by CAPTP reveals that certain sequential patterns, particularly in the head and central regions of peptides, are crucial in determining their toxicity. This insight can significantly inform and guide the design of safer peptide drugs. AVAILABILITY AND IMPLEMENTATION The source code for CAPTP is freely available at https://github.com/jiaoshihu/CAPTP.
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Affiliation(s)
- Shihu Jiao
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Ruijun Liu
- School of Software, Beihang University, Beijing 100191, China
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Gu X, Liu J, Yu Y, Xiao P, Ding Y. MFD-GDrug: multimodal feature fusion-based deep learning for GPCR-drug interaction prediction. Methods 2024; 223:75-82. [PMID: 38286333 DOI: 10.1016/j.ymeth.2024.01.017] [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: 12/03/2023] [Revised: 01/14/2024] [Accepted: 01/26/2024] [Indexed: 01/31/2024] Open
Abstract
The accurate identification of drug-protein interactions (DPIs) is crucial in drug development, especially concerning G protein-coupled receptors (GPCRs), which are vital targets in drug discovery. However, experimental validation of GPCR-drug pairings is costly, prompting the need for accurate predictive methods. To address this, we propose MFD-GDrug, a multimodal deep learning model. Leveraging the ESM pretrained model, we extract protein features and employ a CNN for protein feature representation. For drugs, we integrated multimodal features of drug molecular structures, including three-dimensional features derived from Mol2vec and the topological information of drug graph structures extracted through Graph Convolutional Neural Networks (GCN). By combining structural characterizations and pretrained embeddings, our model effectively captures GPCR-drug interactions. Our tests on leading GPCR-drug interaction datasets show that MFD-GDrug outperforms other methods, demonstrating superior predictive accuracy.
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Affiliation(s)
- Xingyue Gu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Junkai Liu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Yue Yu
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
| | - Pengfeng Xiao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324003, China; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611730, China.
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