scholarly journals Link prediction of viral spike proteins and cell receptors using structural perturbation method

2021 ◽  
Author(s):  
Iris Zhou
Keyword(s):  
Perturbation Method ◽  
Amino Acid Sequences ◽  
New Host ◽  
Major Barrier ◽  
Similarity Network ◽  
Cell Receptors ◽  
Structural Perturbation ◽  
Protein Receptors ◽  
Human Viruses ◽  
Spike Proteins

Abstract Many protein receptors for animal and human viruses have been discovered in decades of studies. The main determinant of virus entry is the binding of the viral spike protein to host cell receptors, which mediates membrane fusion. In this work, a bilayer network is constructed by integrating the similarity network of the viral spike proteins, the similarity network of host receptors, and the association network between viruses and receptors. The structural perturbation method (SPM) is used to predict possible emerging infection of a virus in potential new host organisms. The reliability of this method is based on the hypothesis that the major barrier to virus infection is the differences in the compatibility of spike proteins and cell receptors, which is determined by the amino acid sequences among species.

scholarly journals Link prediction of viral spike proteins and cell receptors using structural perturbation method

2021 ◽  
Author(s):  
Iris Zhou
Keyword(s):  
Perturbation Method ◽  
Amino Acid Sequences ◽  
New Host ◽  
Major Barrier ◽  
Similarity Network ◽  
Cell Receptors ◽  
Structural Perturbation ◽  
Protein Receptors ◽  
Human Viruses ◽  
Spike Proteins

Abstract Many protein receptors for animal and human viruses have been discovered in decades of studies. The main determinant of virus entry is the binding of the viral spike protein to host cell receptors, which mediates membrane fusion.In this work, a bilayer network is constructed by integrating the similarity network of the viral spike proteins, the similarity network of host receptors, and the association network between viruses and receptors. The structural perturbation method (SPM) is used to predict possible emerging infection of a virus in potential new host organisms. The reliability of this method is based on the hypothesis that the major barrier to virus infection is the differences in the compatibility of spike proteins and cell receptors, which is determined by the amino acid sequences among species.

scholarly journals SPRDA: a matrix completion approach based on the structural perturbation to infer disease-associated Piwi-Interacting RNAs

2020 ◽  
Author(s):  
Kai Zheng ◽  
Zhu-Hong You ◽  
Lei Wang ◽  
Leon Wong ◽  
Zhao-hui Zhan
Keyword(s):  
Perturbation Method ◽  
High Performance ◽  
Large Scale ◽  
Matrix Completion ◽  
Disease Diagnosis ◽  
Diagnosis And Treatment ◽  
Biological Research ◽  
Sequence Information ◽  
Similarity Network ◽  
Structural Perturbation

AbstractEmerging evidence suggests that PIWI-interacting RNAs (piRNAs) are one of the most influential small non-coding RNAs (ncRNAs) that regulate RNA silencing. piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. The main motivation of this work is tantamount to fill the gap in research on large-scale prediction of disease-related piRNAs. In this study, a novel computational model based on the structural perturbation method is proposed, called SPRDA. In detail, the duplex network is constructed based on the piRNA similarity network and disease similarity network extracted from piRNA sequence information, Gaussian interaction profile kernel similarity information and gene-disease association information. The structural perturbation method is then used to predict the potential associations on the duplex network, which is more predictive than other network structures in terms of structural consistency. In the five-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.

scholarly journals SPMLMI: predicting lncRNA–miRNA interactions in humans using a structural perturbation method

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11426
Author(s):  
Mingmin Xu ◽  
Yuanyuan Chen ◽  
Wei Lu ◽  
Lingpeng Kong ◽  
Jingya Fang ◽  
...  
Keyword(s):  
Perturbation Method ◽  
Model Performance ◽  
Similarity Network ◽  
Structural Perturbation ◽  
Non Coding Rna ◽  
Structural Consistency ◽  
Long Non Coding Rna ◽  
Important Basis ◽  
Lncrna Similarity Network ◽  
Better Than

Long non-coding RNA (lncRNA)–microRNA (miRNA) interactions are quickly emerging as important mechanisms underlying the functions of non-coding RNAs. Accordingly, predicting lncRNA–miRNA interactions provides an important basis for understanding the mechanisms of action of ncRNAs. However, the accuracy of the established prediction methods is still limited. In this study, we used structural consistency to measure the predictability of interactive links based on a bilayer network by integrating information for known lncRNA–miRNA interactions, an lncRNA similarity network, and an miRNA similarity network. In particular, by using the structural perturbation method, we proposed a framework called SPMLMI to predict potential lncRNA–miRNA interactions based on the bilayer network. We found that the structural consistency of the bilayer network was higher than that of any single network, supporting the utility of bilayer network construction for the prediction of lncRNA–miRNA interactions. Applying SPMLMI to three real datasets, we obtained areas under the curves of 0.9512 ± 0.0034, 0.8767 ± 0.0033, and 0.8653 ± 0.0021 based on 5-fold cross-validation, suggesting good model performance. In addition, the generalizability of SPMLMI was better than that of the previously established methods. Case studies of two lncRNAs (i.e., SNHG14 and MALAT1) further demonstrated the feasibility and effectiveness of the method. Therefore, SPMLMI is a feasible approach to identify novel lncRNA–miRNA interactions underlying complex biological processes.

scholarly journals Prediction of potential disease-associated microRNAs using structural perturbation method

2017 ◽  
Author(s):  
Xiangxiang Zeng ◽  
Li Liu ◽  
Linyuan Lü ◽  
Quan Zou
Keyword(s):  
Perturbation Method ◽  
Experimental Studies ◽  
Computational Method ◽  
Identification Accuracy ◽  
Supplementary Information ◽  
Disease Network ◽  
Similarity Network ◽  
Structural Perturbation ◽  
Disease Associations ◽  
Reliable Performance

AbstractMotivationThe identification of disease-related microRNAs(miRNAs) is an essential but challenging task in bioinformatics research. Similarity-based link prediction methods are often used to predict potential associations between miRNAs and diseases. In these methods, all unobserved associations are ranked by their similarity scores. Higher score indicates higher probability of existence. However, most previous studies mainly focus on designing advanced methods to improve the prediction accuracy while neglect to investigate the link predictability of the networks that present the miRNAs and diseases associations. In this work, we construct a bilayer network by integrating the miRNA–disease network, the miRNA similarity network and the disease similarity network. We use structural consistency as an indicator to estimate the link predictability of the related networks. On the basis of the indicator, a derivative algorithm, called structural perturbation method (SPM), is applied to predict potential associations between miRNAs and diseases.ResultsThe link predictability of bilayer network is higher than that of miRNA–disease network, indicating that the prediction of potential miRNAs-diseases associations on bilayer network can achieve higher accuracy than based merely on the miRNA–disease network. A comparison between the SPM and other algorithms reveals the reliable performance of SPM which performed well in a 5-fold cross-validation. We test fifteen networks. The AUC values of SPM are higher than some well-known methods, indicating that SPM could serve as a useful computational method for improving the identification accuracy of miRNA-disease associations. Moreover, in a case study on breast neoplasm, 80% of the top-20 predicted miRNAs have been manually confirmed by previous experimental studies.Availability and Implementationhttps://github.com/lecea/[email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Prediction of potential disease-associated microRNAs using structural perturbation method

2018 ◽  
Vol 34 (14) ◽  
pp. 2425-2432 ◽  
Author(s):  
Xiangxiang Zeng ◽  
Li Liu ◽  
Linyuan Lü ◽  
Quan Zou
Keyword(s):  
Perturbation Method ◽  
Structural Perturbation

scholarly journals Predicting recognition between T cell receptors and epitopes with TCRGP

2021 ◽  
Vol 17 (3) ◽  
pp. e1008814
Author(s):  
Emmi Jokinen ◽  
Jani Huuhtanen ◽  
Satu Mustjoki ◽  
Markus Heinonen ◽  
Harri Lähdesmäki
Keyword(s):  
T Cell ◽  
T Cell Receptors ◽  
Comprehensive Evaluation ◽  
Adaptive Immune System ◽  
Amino Acid Sequences ◽  
Sequencing Data ◽  
Cell Receptors ◽  
Adaptive Immune ◽  
Complementarity Determining Regions ◽  
Process Method

Adaptive immune system uses T cell receptors (TCRs) to recognize pathogens and to consequently initiate immune responses. TCRs can be sequenced from individuals and methods analyzing the specificity of the TCRs can help us better understand individuals’ immune status in different disorders. For this task, we have developed TCRGP, a novel Gaussian process method that predicts if TCRs recognize specified epitopes. TCRGP can utilize the amino acid sequences of the complementarity determining regions (CDRs) from TCRα and TCRβ chains and learn which CDRs are important in recognizing different epitopes. Our comprehensive evaluation with epitope-specific TCR sequencing data shows that TCRGP achieves on average higher prediction accuracy in terms of AUROC score than existing state-of-the-art methods in epitope-specificity predictions. We also propose a novel analysis approach for combined single-cell RNA and TCRαβ (scRNA+TCRαβ) sequencing data by quantifying epitope-specific TCRs with TCRGP and identify HBV-epitope specific T cells and their transcriptomic states in hepatocellular carcinoma patients.

scholarly journals Repertoire Builder: high-throughput structural modeling of B and T cell receptors

2019 ◽  
Vol 4 (4) ◽  
pp. 761-768 ◽  
Author(s):  
Dimitri Schritt ◽  
Songling Li ◽  
John Rozewicki ◽  
Kazutaka Katoh ◽  
Kazuo Yamashita ◽  
...  
Keyword(s):  
Amino Acid ◽  
T Cell ◽  
High Throughput ◽  
T Cell Receptors ◽  
Three Dimensional ◽  
Amino Acid Sequences ◽  
Atomic Resolution ◽  
B Cell Receptors ◽  
Cell Receptors ◽  
Three Dimensional Models

Repertoire Builder (https://sysimm.org/rep_builder/) is a method for generating atomic-resolution, three-dimensional models of B cell receptors (BCRs) or T cell receptors (TCRs) from their amino acid sequences.

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