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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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Ma S, Guo X, Han R, Meng Q, Zhang Y, Quan W, Miao S, Yang Z, Shi X, Wang S. Elucidation of the mechanism of action of ailanthone in the treatment of colorectal cancer: integration of network pharmacology, bioinformatics analysis and experimental validation. Front Pharmacol 2024; 15:1355644. [PMID: 38384287 PMCID: PMC10880095 DOI: 10.3389/fphar.2024.1355644] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 01/29/2024] [Indexed: 02/23/2024] Open
Abstract
Background: Ailanthone, a small compound derived from the bark of Ailanthus altissima (Mill.) Swingle, has several anti-tumour properties. However, the activity and mechanism of ailanthone in colorectal cancer (CRC) remain to be investigated. This study aims to comprehensively investigate the mechanism of ailanthone in the treatment of CRC by employing a combination of network pharmacology, bioinformatics analysis, and molecular biological technique. Methods: The druggability of ailanthone was examined, and its targets were identified using relevant databases. The RNA sequencing data of individuals with CRC obtained from the Cancer Genome Atlas (TCGA) database were analyzed. Utilizing the R programming language, an in-depth investigation of differentially expressed genes was carried out, and the potential target of ailanthone for anti-CRC was found. Through the integration of protein-protein interaction (PPI) network analysis, GO and KEGG enrichment studies to search for the key pathway of the action of Ailanthone. Then, by employing molecular docking verification, flow cytometry, Transwell assays, and Immunofluorescence to corroborate these discoveries. Results: Data regarding pharmacokinetic parameters and 137 target genes for ailanthone were obtained. Leveraging The Cancer Genome Atlas database, information regarding 2,551 differentially expressed genes was extracted. Subsequent analyses, encompassing protein-protein interaction network analysis, survival analysis, functional enrichment analysis, and molecular docking verification, revealed the PI3K/AKT signaling pathway as pivotal mediators of ailanthone against CRC. Additionally, the in vitro experiments indicated that ailanthone substantially affects the cell cycle, induces apoptosis in CRC cells (HCT116 and SW620 cells), and impedes the migration and invasion capabilities of these cells. Immunofluorescence staining showed that ailanthone significantly inhibited the phosphorylation of AKT protein and suppressed the activation of the PI3K/AKT signaling pathway, thereby inhibiting the proliferation and metastasis of CRC cells. Conclusion: Therefore, our findings indicate that Ailanthone exerts anti-CRC effects primarily by inhibiting the activation of the PI3K/AKT pathway. Additionally, we propose that Ailanthone holds potential as a therapeutic agent for the treatment of human CRC.
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Affiliation(s)
- Shanbo Ma
- The College of Life Science, Northwest University, Xi’an, Shaanxi, China
| | - Xiaodi Guo
- The College of Life Science, Northwest University, Xi’an, Shaanxi, China
| | - Ruisi Han
- The College of Life Science, Northwest University, Xi’an, Shaanxi, China
| | - Qian Meng
- The College of Life Science, Northwest University, Xi’an, Shaanxi, China
| | - Yan Zhang
- The College of Life Science, Northwest University, Xi’an, Shaanxi, China
| | - Wei Quan
- Department of Pharmacy, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Shan Miao
- Department of Pharmacy, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, China
| | - Zhao Yang
- Department of Military Medical Psychology, Air Force Military Medical University, Xi’an, Shaanxi, China
| | - Xiaopeng Shi
- Department of Pharmacy, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, China
| | - Siwang Wang
- The College of Life Science, Northwest University, Xi’an, Shaanxi, China
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Iliadis D, De Baets B, Pahikkala T, Waegeman W. A comparison of embedding aggregation strategies in drug-target interaction prediction. BMC Bioinformatics 2024; 25:59. [PMID: 38321386 PMCID: PMC10845509 DOI: 10.1186/s12859-024-05684-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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 01/30/2024] [Indexed: 02/08/2024] Open
Abstract
The prediction of interactions between novel drugs and biological targets is a vital step in the early stage of the drug discovery pipeline. Many deep learning approaches have been proposed over the last decade, with a substantial fraction of them sharing the same underlying two-branch architecture. Their distinction is limited to the use of different types of feature representations and branches (multi-layer perceptrons, convolutional neural networks, graph neural networks and transformers). In contrast, the strategy used to combine the outputs (embeddings) of the branches has remained mostly the same. The same general architecture has also been used extensively in the area of recommender systems, where the choice of an aggregation strategy is still an open question. In this work, we investigate the effectiveness of three different embedding aggregation strategies in the area of drug-target interaction (DTI) prediction. We formally define these strategies and prove their universal approximator capabilities. We then present experiments that compare the different strategies on benchmark datasets from the area of DTI prediction, showcasing conditions under which specific strategies could be the obvious choice.
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Affiliation(s)
- Dimitrios Iliadis
- Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium.
| | - Bernard De Baets
- Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
| | - Tapio Pahikkala
- Department of Computing, University of Turku, 20500, Turku, Finland
| | - Willem Waegeman
- Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
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Ren ZH, Yu CQ, Li LP, You ZH, Li ZW, Zhang SW, Zeng X, Shang YF. SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution. J Chem Inf Model 2024; 64:238-249. [PMID: 38103039 DOI: 10.1021/acs.jcim.3c01665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.
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Affiliation(s)
- Zhong-Hao Ren
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Li-Ping Li
- College of Agriculture and Forestry, Longdong University, Qingyang 745000, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
| | - Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Shan-Wen Zhang
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Yi-Fan Shang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
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Abstract
More and more evidence suggests that circRNA plays a vital role in generating and treating diseases by interacting with miRNA. Therefore, accurate prediction of potential circRNA-miRNA interaction (CMI) has become urgent. However, traditional wet experiments are time-consuming and costly, and the results will be affected by objective factors. In this paper, we propose a computational model BCMCMI, which combines three features to predict CMI. Specifically, BCMCMI utilizes the bidirectional encoding capability of the BERT algorithm to extract sequence features from the semantic information of circRNA and miRNA. Then, a heterogeneous network is constructed based on cosine similarity and known CMI information. The Metapath2vec is employed to conduct random walks following meta-paths in the network to capture topological features, including similarity features. Finally, potential CMIs are predicted using the XGBoost classifier. BCMCMI achieves superior results compared to other state-of-the-art models on two benchmark datasets for CMI prediction. We also utilize t-SNE to visually observe the distribution of the extracted features on a randomly selected dataset. The remarkable prediction results show that BCMCMI can serve as a valuable complement to the wet experiment process.
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Affiliation(s)
- Meng-Meng Wei
- School of Information Engineering, Xijing University, Xi'an, Shaanxi 710123, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an, Shaanxi 710123, China
| | - Li-Ping Li
- College of Agriculture and Forestry, Longdong University, Qingyang, Gansu 745000, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Lei Wang
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning, Guangxi 530007, China
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Wen J, Gan H, Yang Z, Zhou R, Zhao J, Ye Z. Mutual-DTI: A mutual interaction feature-based neural network for drug-target protein interaction prediction. Math Biosci Eng 2023; 20:10610-10625. [PMID: 37322951 DOI: 10.3934/mbe.2023469] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The prediction of drug-target protein interaction (DTI) is a crucial task in the development of new drugs in modern medicine. Accurately identifying DTI through computer simulations can significantly reduce development time and costs. In recent years, many sequence-based DTI prediction methods have been proposed, and introducing attention mechanisms has improved their forecasting performance. However, these methods have some shortcomings. For example, inappropriate dataset partitioning during data preprocessing can lead to overly optimistic prediction results. Additionally, only single non-covalent intermolecular interactions are considered in the DTI simulation, ignoring the complex interactions between their internal atoms and amino acids. In this paper, we propose a network model called Mutual-DTI that predicts DTI based on the interaction properties of sequences and a Transformer model. We use multi-head attention to extract the long-distance interdependent features of the sequence and introduce a module to extract the sequence's mutual interaction features in mining complex reaction processes of atoms and amino acids. We evaluate the experiments on two benchmark datasets, and the results show that Mutual-DTI outperforms the latest baseline significantly. In addition, we conduct ablation experiments on a label-inversion dataset that is split more rigorously. The results show that there is a significant improvement in the evaluation metrics after introducing the extracted sequence interaction feature module. This suggests that Mutual-DTI may contribute to modern medical drug development research. The experimental results show the effectiveness of our approach. The code for Mutual-DTI can be downloaded from https://github.com/a610lab/Mutual-DTI.
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Affiliation(s)
- Jiahui Wen
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
| | - Haitao Gan
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei University, Wuhan 430062, China
| | - Zhi Yang
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei University, Wuhan 430062, China
| | - Ran Zhou
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
| | - Jing Zhao
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei University, Wuhan 430062, China
| | - Zhiwei Ye
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
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