Prediction of Virus–Receptor Interactions Based on Improving Similarities

2021 ◽  
Author(s):  
Lingzhi Zhu ◽  
Cheng Yan ◽  
Guihua Duan
Keyword(s):  
Virus Receptor ◽  
Receptor Interactions

scholarly journals IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

2019 ◽  
Vol 20 (S23) ◽  
Author(s):  
Cheng Yan ◽  
Guihua Duan ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
Infectious Diseases ◽  
Cross Validation ◽  
Sequence Similarity ◽  
Least Square ◽  
Computational Method ◽  
Receptor Interaction ◽  
Virus Receptor ◽  
Receptor Interactions ◽  
Leave One Out ◽  
Fold Cross Validation

Abstract Background Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited. Result In this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF). Conlusion The experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.

scholarly journals Permissivity of Dipeptidyl Peptidase 4 Orthologs to Middle East Respiratory Syndrome Coronavirus Is Governed by Glycosylation and Other Complex Determinants

2017 ◽  
Vol 91 (19) ◽  
Author(s):  
Kayla M. Peck ◽  
Trevor Scobey ◽  
Jesica Swanstrom ◽  
Kara L. Jensen ◽  
Christina L. Burch ◽  
...  
Keyword(s):  
Middle East ◽  
Guinea Pig ◽  
Host Range ◽  
Dipeptidyl Peptidase ◽  
Glycosylation Site ◽  
Middle East Respiratory Syndrome ◽  
Virus Receptor ◽  
Dipeptidyl Peptidase 4 ◽  
Receptor Interactions ◽  
Species Specific

ABSTRACT Middle East respiratory syndrome coronavirus (MERS-CoV) utilizes dipeptidyl peptidase 4 (DPP4) as an entry receptor. While bat, camel, and human DPP4 support MERS-CoV infection, several DPP4 orthologs, including mouse, ferret, hamster, and guinea pig DPP4, do not. Previous work revealed that glycosylation of mouse DPP4 plays a role in blocking MERS-CoV infection. Here, we tested whether glycosylation also acts as a determinant of permissivity for ferret, hamster, and guinea pig DPP4. We found that, while glycosylation plays an important role in these orthologs, additional sequence and structural determinants impact their ability to act as functional receptors for MERS-CoV. These results provide insight into DPP4 species-specific differences impacting MERS-CoV host range and better inform our understanding of virus-receptor interactions associated with disease emergence and host susceptibility. IMPORTANCE MERS-CoV is a recently emerged zoonotic virus that is still circulating in the human population with an ∼35% mortality rate. With no available vaccines or therapeutics, the study of MERS-CoV pathogenesis is crucial for its control and prevention. However, in vivo studies are limited because MERS-CoV cannot infect wild-type mice due to incompatibilities between the virus spike and the mouse host cell receptor, mouse DPP4 (mDPP4). Specifically, mDPP4 has a nonconserved glycosylation site that acts as a barrier to MERS-CoV infection. Thus, one mouse model strategy has been to modify the mouse genome to remove this glycosylation site. Here, we investigated whether glycosylation acts as a barrier to infection for other nonpermissive small-animal species, namely, ferret, guinea pig, and hamster. Understanding the virus-receptor interactions for these DPP4 orthologs will help in the development of additional animal models while also revealing species-specific differences impacting MERS-CoV host range.

scholarly journals Virus–Receptor Interactions: Structural Insights For Oncolytic Virus Development

2019 ◽  
Vol Volume 8 ◽  
pp. 39-56 ◽  
Author(s):  
Nadishka Jayawardena ◽  
Laura N Burga ◽  
John T Poirier ◽  
Mihnea Bostina
Keyword(s):  
Oncolytic Virus ◽  
Virus Receptor ◽  
Receptor Interactions ◽  
Structural Insights

scholarly journals Dynamics of Virus-Receptor Interactions in Virus Binding, Signaling, and Endocytosis

Viruses ◽  
2015 ◽  
Vol 7 (6) ◽  
pp. 2794-2815 ◽  
Author(s):  
Steeve Boulant ◽  
Megan Stanifer ◽  
Pierre-Yves Lozach
Keyword(s):  
Virus Binding ◽  
Virus Receptor ◽  
Receptor Interactions

Human and simian immunodeficiency viruses: virus-receptor interactions

1993 ◽  
Vol 1 (9) ◽  
pp. 328-333 ◽  
Author(s):  
Nathalie Signoret ◽  
Pascal Poignard ◽  
Dominique Blanc ◽  
Quentin J. Sattentau
Keyword(s):  
Virus Receptor ◽  
Receptor Interactions

scholarly journals Virus–Receptor Interactions and Virus Neutralization: Insights for Oncolytic Virus Development

2020 ◽  
Vol Volume 9 ◽  
pp. 1-15 ◽  
Author(s):  
Nadishka Jayawardena ◽  
John T Poirier ◽  
Laura N Burga ◽  
Mihnea Bostina
Keyword(s):  
Oncolytic Virus ◽  
Virus Neutralization ◽  
Virus Receptor ◽  
Receptor Interactions

scholarly journals Biosynthetic Modulation of Sialic Acid-dependent Virus-Receptor Interactions of Two Primate Polyoma Viruses

1995 ◽  
Vol 270 (3) ◽  
pp. 1308-1314 ◽  
Author(s):  
Oliver T. Keppler ◽  
Peer Stehling ◽  
Markus Herrmann ◽  
Holger Kayser ◽  
Detlef Grunow ◽  
...  
Keyword(s):  
Sialic Acid ◽  
Virus Receptor ◽  
Receptor Interactions
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