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Zeng X, Meng FF, Wen ML, Li SJ, Li Y. GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs. BMC Genomics 2024; 25:406. [PMID: 38724906 PMCID: PMC11080243 DOI: 10.1186/s12864-024-10299-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/10/2024] [Indexed: 05/13/2024] Open
Abstract
Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.
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Affiliation(s)
- Xin Zeng
- College of Mathematics and Computer Science, Dali University, 671003, Dali, China
| | - Fan-Fang Meng
- College of Mathematics and Computer Science, Dali University, 671003, Dali, China
| | - Meng-Liang Wen
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, 650000, Kunming, China
| | - Shu-Juan Li
- Yunnan Institute of Endemic Diseases Control & Prevention, 671000, Dali, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, 671003, Dali, China.
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Hu L, Zhang M, Hu P, Zhang J, Niu C, Lu X, Jiang X, Ma Y. Dual-channel hypergraph convolutional network for predicting herb-disease associations. Brief Bioinform 2024; 25:bbae067. [PMID: 38426326 PMCID: PMC10939431 DOI: 10.1093/bib/bbae067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/26/2024] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.
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Affiliation(s)
- Lun Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Menglong Zhang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Pengwei Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Jun Zhang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Chao Niu
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory Basis of Xinjiang Indigenous Medicinal Plants Resource Utilization, Key Laboratory of Chemistry of Plant Resources in Arid Regions, Xinjiang Technical Institute of Physicsand Chemistry,Chinese Academy of Sciences Urumqi, China
| | - Xueying Lu
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory Basis of Xinjiang Indigenous Medicinal Plants Resource Utilization, Key Laboratory of Chemistry of Plant Resources in Arid Regions, Xinjiang Technical Institute of Physicsand Chemistry,Chinese Academy of Sciences Urumqi, China
| | - Xiangrui Jiang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica,Chinese Academy of Sciences Shanghai, China
| | - Yupeng Ma
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
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Wang J, Chen C, Yao G, Ding J, Wang L, Jiang H. Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review. Molecules 2023; 28:7865. [PMID: 38067593 PMCID: PMC10707872 DOI: 10.3390/molecules28237865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/13/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.
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Affiliation(s)
| | | | | | - Junjie Ding
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (J.W.); (C.C.); (G.Y.)
| | - Liangliang Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (J.W.); (C.C.); (G.Y.)
| | - Hui Jiang
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (J.W.); (C.C.); (G.Y.)
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Wang X, Yang W, Yang Y, He Y, Zhang J, Wang L, Hu L. PPISB: A Novel Network-Based Algorithm of Predicting Protein-Protein Interactions With Mixed Membership Stochastic Blockmodel. IEEE/ACM Trans Comput Biol Bioinform 2023; 20:1606-1612. [PMID: 35939453 DOI: 10.1109/tcbb.2022.3196336] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Protein-protein interactions (PPIs) play an essential role for most of biological processes in cells. Many computational algorithms have thus been proposed to predict PPIs. However, most of them heavily rest on the biological information of proteins while ignoring the latent structural features of proteins presented in a PPI network. In this paper, we propose an efficient network-based prediction algorithm, namely PPISB, based on a mixed membership stochastic blockmodel. By simulating the generative process of a PPI network, PPISB is able to capture the latent community structures. The inference procedure adopted by PPISB further optimizes the membership distributions of proteins over different complexes. After that, a distance measure is designed to compute the similarity between two proteins in terms of their likelihoods of being in the same complex, thus verifying whether they interact with each other or not. To evaluate the performance of PPISB, a series of extensive experiments have been conducted with five PPI networks collected from different species and the results demonstrate that PPISB has a promising performance when applied to predict PPIs in terms of several evaluation metrics. Hence, we reason that PPISB is preferred over state-of-the-art network-based prediction algorithms especially for predicting potential PPIs.
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Cho SB. Special Issue on “Biological Network Approaches and Applications”. Processes (Basel) 2023; 11:307. [DOI: 10.3390/pr11020307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Biological phenomena comprise various interactions between genes and molecules [...]
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Zhang ML, Zhao BW, Su XR, He YZ, Yang Y, Hu L. RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction. BMC Bioinformatics 2022; 23:516. [PMID: 36456957 PMCID: PMC9713188 DOI: 10.1186/s12859-022-05069-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Drug repositioning is a very important task that provides critical information for exploring the potential efficacy of drugs. Yet developing computational models that can effectively predict drug-disease associations (DDAs) is still a challenging task. Previous studies suggest that the accuracy of DDA prediction can be improved by integrating different types of biological features. But how to conduct an effective integration remains a challenging problem for accurately discovering new indications for approved drugs. METHODS In this paper, we propose a novel meta-path based graph representation learning model, namely RLFDDA, to predict potential DDAs on heterogeneous biological networks. RLFDDA first calculates drug-drug similarities and disease-disease similarities as the intrinsic biological features of drugs and diseases. A heterogeneous network is then constructed by integrating DDAs, disease-protein associations and drug-protein associations. With such a network, RLFDDA adopts a meta-path random walk model to learn the latent representations of drugs and diseases, which are concatenated to construct joint representations of drug-disease associations. As the last step, we employ the random forest classifier to predict potential DDAs with their joint representations. RESULTS To demonstrate the effectiveness of RLFDDA, we have conducted a series of experiments on two benchmark datasets by following a ten-fold cross-validation scheme. The results show that RLFDDA yields the best performance in terms of AUC and F1-score when compared with several state-of-the-art DDAs prediction models. We have also conducted a case study on two common diseases, i.e., paclitaxel and lung tumors, and found that 7 out of top-10 diseases and 8 out of top-10 drugs have already been validated for paclitaxel and lung tumors respectively with literature evidence. Hence, the promising performance of RLFDDA may provide a new perspective for novel DDAs discovery over heterogeneous networks.
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Affiliation(s)
- Meng-Long Zhang
- grid.9227.e0000000119573309The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, China ,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Bo-Wei Zhao
- grid.9227.e0000000119573309The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, China ,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Xiao-Rui Su
- grid.9227.e0000000119573309The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, China ,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Yi-Zhou He
- grid.162110.50000 0000 9291 3229School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Yue Yang
- grid.162110.50000 0000 9291 3229School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Lun Hu
- grid.9227.e0000000119573309The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, China ,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
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Hu L, Li Z, Tang Z, Zhao C, Zhou X, Hu P. Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach. BMC Bioinformatics 2022; 23:447. [PMID: 36303135 PMCID: PMC9608884 DOI: 10.1186/s12859-022-04999-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 10/13/2022] [Indexed: 11/10/2022] Open
Abstract
Background The site information of substrates that can be cleaved by human immunodeficiency virus 1 proteases (HIV-1 PRs) is of great significance for designing effective inhibitors against HIV-1 viruses. A variety of machine learning-based algorithms have been developed to predict HIV-1 PR cleavage sites by extracting relevant features from substrate sequences. However, only relying on the sequence information is not sufficient to ensure a promising performance due to the uncertainty in the way of separating the datasets used for training and testing. Moreover, the existence of noisy data, i.e., false positive and false negative cleavage sites, could negatively influence the accuracy performance. Results In this work, an ensemble learning algorithm for predicting HIV-1 PR cleavage sites, namely EM-HIV, is proposed by training a set of weak learners, i.e., biased support vector machine classifiers, with the asymmetric bagging strategy. By doing so, the impact of data imbalance and noisy data can thus be alleviated. Besides, in order to make full use of substrate sequences, the features used by EM-HIV are collected from three different coding schemes, including amino acid identities, chemical properties and variable-length coevolutionary patterns, for the purpose of constructing more relevant feature vectors of octamers. Experiment results on three independent benchmark datasets demonstrate that EM-HIV outperforms state-of-the-art prediction algorithm in terms of several evaluation metrics. Hence, EM-HIV can be regarded as a useful tool to accurately predict HIV-1 PR cleavage sites.
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Affiliation(s)
- Lun Hu
- grid.9227.e0000000119573309Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Zhenfeng Li
- grid.162110.50000 0000 9291 3229School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Zehai Tang
- grid.162110.50000 0000 9291 3229School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Cheng Zhao
- grid.162110.50000 0000 9291 3229School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Xi Zhou
- grid.9227.e0000000119573309Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Pengwei Hu
- grid.9227.e0000000119573309Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
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