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Cong H, Liu H, Cao Y, Liang C, Chen Y. Protein-protein interaction site prediction by model ensembling with hybrid feature and self-attention. BMC Bioinformatics 2023; 24:456. [PMID: 38053020 DOI: 10.1186/s12859-023-05592-7] [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: 12/18/2022] [Accepted: 11/30/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Protein-protein interactions (PPIs) are crucial in various biological functions and cellular processes. Thus, many computational approaches have been proposed to predict PPI sites. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in sequences. Many feature extraction methods rely on the sliding window technique, which simply merges all the features of residues into a vector. The importance of some key residues may be weakened in the feature vector, leading to poor performance. RESULTS We propose a novel sequence-based method for PPI sites prediction. The new network model, PPINet, contains multiple feature processing paths. For a residue, the PPINet extracts the features of the targeted residue and its context separately. These two types of features are processed by two paths in the network and combined to form a protein representation, where the two types of features are of relatively equal importance. The model ensembling technique is applied to make use of more features. The base models are trained with different features and then ensembled via stacking. In addition, a data balancing strategy is presented, by which our model can get significant improvement on highly unbalanced data. CONCLUSION The proposed method is evaluated on a fused dataset constructed from Dset186, Dset_72, and PDBset_164, as well as the public Dset_448 dataset. Compared with current state-of-the-art methods, the performance of our method is better than the others. In the most important metrics, such as AUPRC and recall, it surpasses the second-best programmer on the latter dataset by 6.9% and 4.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model, especially, the hybrid feature. We share our code for reproducibility and future research at https://github.com/CandiceCong/StackingPPINet .
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Affiliation(s)
- Hanhan Cong
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
- Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan, China
| | - Hong Liu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China.
- Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan, China.
| | - Yi Cao
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China
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Zheng Z, Jiao Y, You H, An J, Sun Y. Fast end-to-end surface interpretation of SARS-CoV-2 variants by differentiable molecular surface interaction fingerprinting method. Comput Struct Biotechnol J 2023; 21:4816-4824. [PMID: 37841329 PMCID: PMC10570951 DOI: 10.1016/j.csbj.2023.09.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/14/2023] [Accepted: 09/26/2023] [Indexed: 10/17/2023] Open
Abstract
Confronting the challenge of persistent mutations of SARS-CoV-2, researchers have turned to deep learning methods to predict the mutated structures of spike proteins and to hypothesize potential changes in their structures and drug efficacies. However, limited works are focused on the surface learning of spike proteins even though their biological functions are usually defined by the geometric and chemical features of 3D molecular surfaces. In addition, the current used geometric deep learning methods are based on mesh representations of proteins to identify potential binding targets for drugs. However, the use of meshes has limitations and is not applicable for many important tasks in molecular biology. To address these limitations, we adopt the differentiable molecular surface interaction fingerprinting (dMaSIF) method which is based on the 3D point clouds and a novel efficient geometric convolutional layer to fast predict the interaction sites on the protein surface. The different binding site patterns for Delta, Omicron and its subvariants are clearly visualized. We find that Delta and Omicron show the similar surface binding patterns while BA.2, BA.2.13, BA.3 and BA.4 present similar ones. BA.4 possesses higher positive interaction site ratio than the others which may account for its higher transmission and infection among humans. In addition, the positive interaction site ratios of BA.2, BA.2.13, BA.3 are higher than Delta and Omicron, which are accordant with their transmission and infection rates. Hopefully our work offers a new effective route to analyze the protein-protein interaction for the SARS-CoV-2 variants.
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Affiliation(s)
- Ziyang Zheng
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yanqi Jiao
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Haixin You
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Junfeng An
- Shen Zhi Xing (Shenzhen) Technology, Shenzhen, Guangdong 518055, China
| | - Yao Sun
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
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Li K, Quan L, Jiang Y, Li Y, Zhou Y, Wu T, Lyu Q. ctP 2ISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:297-306. [PMID: 35213314 DOI: 10.1109/tcbb.2022.3154413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP2ISP to improve the prediction of protein-protein interaction sites. ctP2ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify protein-protein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP2ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP2ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights. The source code and data can be obtained from https://github.com/lennylv/ctP2ISP.
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Soleymani F, Paquet E, Viktor H, Michalowski W, Spinello D. Protein-protein interaction prediction with deep learning: A comprehensive review. Comput Struct Biotechnol J 2022; 20:5316-5341. [PMID: 36212542 PMCID: PMC9520216 DOI: 10.1016/j.csbj.2022.08.070] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/15/2022] Open
Abstract
Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.
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Affiliation(s)
- Farzan Soleymani
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON, Canada
| | - Eric Paquet
- National Research Council, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada
| | - Herna Viktor
- School of Electrical Engineering and Computer Science, University of Ottawa, ON, Canada
| | | | - Davide Spinello
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON, Canada
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- *Correspondence: Arnaud Droit,
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Paiva VDA, Gomes IDS, Monteiro CR, Mendonça MV, Martins PM, Santana CA, Gonçalves-Almeida V, Izidoro SC, Melo-Minardi RCD, Silveira SDA. Protein structural bioinformatics: An overview. Comput Biol Med 2022; 147:105695. [DOI: 10.1016/j.compbiomed.2022.105695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 11/27/2022]
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Casadio R, Martelli PL, Savojardo C. Machine learning solutions for predicting protein–protein interactions. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Rita Casadio
- Biocomputing Group University of Bologna Bologna Italy
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Wei J, Chen S, Zong L, Gao X, Li Y. Protein-RNA interaction prediction with deep learning: structure matters. Brief Bioinform 2022; 23:bbab540. [PMID: 34929730 PMCID: PMC8790951 DOI: 10.1093/bib/bbab540] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/14/2021] [Accepted: 11/22/2021] [Indexed: 12/11/2022] Open
Abstract
Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Because of the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RNA-binding protein-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.
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Affiliation(s)
- Junkang Wei
- Department of Computer Science and Engineering (CSE), The Chinese
University of Hong Kong (CUHK), 999077, Hong Kong SAR, China
| | - Siyuan Chen
- Computational Bioscience Research Center (CBRC),
King Abdullah University of Science and Technology (KAUST),
23955-6900, Thuwal, Saudi Arabia
| | - Licheng Zong
- Department of Computer Science and Engineering (CSE), The Chinese
University of Hong Kong (CUHK), 999077, Hong Kong SAR, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC),
King Abdullah University of Science and Technology (KAUST),
23955-6900, Thuwal, Saudi Arabia
| | - Yu Li
- Department of Computer Science and Engineering (CSE), The Chinese
University of Hong Kong (CUHK), 999077, Hong Kong SAR, China
- The CUHK Shenzhen Research Institute, Hi-Tech Park, 518057,
Shenzhen, China
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