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Cai W, Liu P, Wang Z, Jiang H, Liu C, Fei Z, Yang Z. Link prediction in protein-protein interaction network: A similarity multiplied similarity algorithm with paths of length three. J Theor Biol 2024; 589:111850. [PMID: 38740126 DOI: 10.1016/j.jtbi.2024.111850] [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: 11/08/2023] [Revised: 03/26/2024] [Accepted: 05/03/2024] [Indexed: 05/16/2024]
Abstract
Protein-protein interactions (PPIs) are crucial for various biological processes, and predicting PPIs is a major challenge. To solve this issue, the most common method is link prediction. Currently, the link prediction methods based on network Paths of Length Three (L3) have been proven to be highly effective. In this paper, we propose a novel link prediction algorithm, named SMS, which is based on L3 and protein similarities. We first design a mixed similarity that combines the topological structure and attribute features of nodes. Then, we compute the predicted value by summing the product of all similarities along the L3. Furthermore, we propose the Max Similarity Multiplied Similarity (maxSMS) algorithm from the perspective of maximum impact. Our computational prediction results show that on six datasets, including S. cerevisiae, H. sapiens, and others, the maxSMS algorithm improves the precision of the top 500, area under the precision-recall curve, and normalized discounted cumulative gain by an average of 26.99%, 53.67%, and 6.7%, respectively, compared to other optimal methods.
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Affiliation(s)
- Wangmin Cai
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Peiqiang Liu
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
| | - Zunfang Wang
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Hong Jiang
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Chang Liu
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Zhaojie Fei
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Zhuang Yang
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
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Sojka J, Šamajová O, Šamaj J. Gene-edited protein kinases and phosphatases in molecular plant breeding. TRENDS IN PLANT SCIENCE 2024; 29:694-710. [PMID: 38151445 DOI: 10.1016/j.tplants.2023.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 11/07/2023] [Accepted: 11/29/2023] [Indexed: 12/29/2023]
Abstract
Protein phosphorylation, the most common and essential post-translational modification, belongs to crucial regulatory mechanisms in plants, affecting their metabolism, intracellular transport, cytoarchitecture, cell division, growth, development, and interactions with the environment. Protein kinases and phosphatases, two important families of enzymes optimally regulating phosphorylation, have now become important targets for gene editing in crops. We review progress on gene-edited protein kinases and phosphatases in crops using clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9). We also provide guidance for computational prediction of alterations and/or changes in function, activity, and binding of protein kinases and phosphatases as consequences of CRISPR/Cas9-based gene editing with its possible application in modern crop molecular breeding towards sustainable agriculture.
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Affiliation(s)
- Jiří Sojka
- Department of Biotechnology, Faculty of Science, Palacký University Olomouc, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
| | - Olga Šamajová
- Department of Biotechnology, Faculty of Science, Palacký University Olomouc, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
| | - Jozef Šamaj
- Department of Biotechnology, Faculty of Science, Palacký University Olomouc, Šlechtitelů 27, 783 71 Olomouc, Czech Republic.
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Mischley V, Maier J, Chen J, Karanicolas J. PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.16.585347. [PMID: 38559274 PMCID: PMC10979958 DOI: 10.1101/2024.03.16.585347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Protein-protein interactions underlie nearly all cellular processes. With the advent of protein structure prediction methods such as AlphaFold2 (AF2), models of specific protein pairs can be built extremely accurately in most cases. However, determining the relevance of a given protein pair remains an open question. It is presently unclear how to use best structure-based tools to infer whether a pair of candidate proteins indeed interact with one another: ideally, one might even use such information to screen amongst candidate pairings to build up protein interaction networks. Whereas methods for evaluating quality of modeled protein complexes have been co-opted for determining which pairings interact (e.g., pDockQ and iPTM), there have been no rigorously benchmarked methods for this task. Here we introduce PPIscreenML, a classification model trained to distinguish AF2 models of interacting protein pairs from AF2 models of compelling decoy pairings. We find that PPIscreenML out-performs methods such as pDockQ and iPTM for this task, and further that PPIscreenML exhibits impressive performance when identifying which ligand/receptor pairings engage one another across the structurally conserved tumor necrosis factor superfamily (TNFSF). Analysis of benchmark results using complexes not seen in PPIscreenML development strongly suggest that the model generalizes beyond training data, making it broadly applicable for identifying new protein complexes based on structural models built with AF2.
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Affiliation(s)
- Victoria Mischley
- Cancer Signaling & Microenvironment Program, Fox Chase Cancer Center, Philadelphia PA 19111
- Molecular Cell Biology and Genetics, Drexel University, Philadelphia PA 19102
| | | | | | - John Karanicolas
- Cancer Signaling & Microenvironment Program, Fox Chase Cancer Center, Philadelphia PA 19111
- Moulder Center for Drug Discovery Research, Temple University School of Pharmacy, Philadelphia PA 19140
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Hou Y, Xie T, He L, Tao L, Huang J. Topological links in predicted protein complex structures reveal limitations of AlphaFold. Commun Biol 2023; 6:1098. [PMID: 37898666 PMCID: PMC10613300 DOI: 10.1038/s42003-023-05489-4] [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: 02/21/2023] [Accepted: 10/19/2023] [Indexed: 10/30/2023] Open
Abstract
AlphaFold is making great progress in protein structure prediction, not only for single-chain proteins but also for multi-chain protein complexes. When using AlphaFold-Multimer to predict protein‒protein complexes, we observed some unusual structures in which chains are looped around each other to form topologically intertwining links at the interface. Based on physical principles, such topological links should generally not exist in native protein complex structures unless covalent modifications of residues are involved. Although it is well known and has been well studied that protein structures may have topologically complex shapes such as knots and links, existing methods are hampered by the chain closure problem and show poor performance in identifying topologically linked structures in protein‒protein complexes. Therefore, we address the chain closure problem by using sliding windows from a local perspective and propose an algorithm to measure the topological-geometric features that can be used to identify topologically linked structures. An application of the method to AlphaFold-Multimer-predicted protein complex structures finds that approximately 1.72% of the predicted structures contain topological links. The method presented in this work will facilitate the computational study of protein‒protein interactions and help further improve the structural prediction of multi-chain protein complexes.
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Affiliation(s)
- Yingnan Hou
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
| | - Tengyu Xie
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
| | - Liuqing He
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
| | - Liang Tao
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
| | - Jing Huang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China.
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China.
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Shen L, Feng H, Qiu Y, Wei GW. SVSBI: sequence-based virtual screening of biomolecular interactions. Commun Biol 2023; 6:536. [PMID: 37202415 PMCID: PMC10195826 DOI: 10.1038/s42003-023-04866-3] [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: 01/10/2023] [Accepted: 04/24/2023] [Indexed: 05/20/2023] Open
Abstract
Virtual screening (VS) is a critical technique in understanding biomolecular interactions, particularly in drug design and discovery. However, the accuracy of current VS models heavily relies on three-dimensional (3D) structures obtained through molecular docking, which is often unreliable due to the low accuracy. To address this issue, we introduce a sequence-based virtual screening (SVS) as another generation of VS models that utilize advanced natural language processing (NLP) algorithms and optimized deep K-embedding strategies to encode biomolecular interactions without relying on 3D structure-based docking. We demonstrate that SVS outperforms state-of-the-art performance for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for protein-protein interactions in five biological species. SVS has the potential to transform current practices in drug discovery and protein engineering.
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Affiliation(s)
- Li Shen
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA
| | - Hongsong Feng
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA
| | - Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA.
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Lyu Y, He R, Hu J, Wang C, Gong X. Prediction of the tetramer protein complex interaction based on CNN and SVM. Front Genet 2023; 14:1076904. [PMID: 36777731 PMCID: PMC9909274 DOI: 10.3389/fgene.2023.1076904] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 01/16/2023] [Indexed: 01/27/2023] Open
Abstract
Protein-protein interactions play an important role in life activities. The study of protein-protein interactions helps to better understand the mechanism of protein complex interaction, which is crucial for drug design, protein function annotation and three-dimensional structure prediction of protein complexes. In this paper, we study the tetramer protein complex interaction. The research has two parts: The first part is to predict the interaction between chains of the tetramer protein complex. In this part, we proposed a feature map to represent a sample generated by two chains of the tetramer protein complex, and constructed a Convolutional Neural Network (CNN) model to predict the interaction between chains of the tetramer protein complex. The AUC value of testing set is 0.6263, which indicates that our model can be used to predict the interaction between chains of the tetramer protein complex. The second part is to predict the tetramer protein complex interface residue pairs. In this part, we proposed a Support Vector Machine (SVM) ensemble method based on under-sampling and ensemble method to predict the tetramer protein complex interface residue pairs. In the top 10 predictions, when at least one protein-protein interaction interface is correctly predicted, the accuracy of our method is 82.14%. The result shows that our method is effective for the prediction of the tetramer protein complex interface residue pairs.
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Affiliation(s)
- Yanfen Lyu
- Department of Mathematics and PhysicsScience and Engineering, Hebei University of Engineering, Handan, China
| | - Ruonan He
- School of Information, Renmin University of China, Beijing, China
| | - Jingjing Hu
- Department of Mathematics and PhysicsScience and Engineering, Hebei University of Engineering, Handan, China
| | - Chunxia Wang
- School of Landscape and Ecological Engineering, Hebei University of Engineering, Handan, China,*Correspondence: Chunxia Wang, ; Xinqi Gong,
| | - Xinqi Gong
- Mathematical Intelligence Application Lab, Institute for Mathematical Sciences, School of Math, Renmin University of China, Beijing, China,Beijing Academy of Artificial Intelligence, Beijing, China,*Correspondence: Chunxia Wang, ; Xinqi Gong,
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