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Predicting disease-associated substitution of a single amino acid by analyzing residue interactions.

ABSTRACT: BACKGROUND: The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues. RESULTS: We found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively. CONCLUSIONS: The satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.

SUBMITTER: Li Y

PROVIDER: S-EPMC3027113 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Predicting disease-associated substitution of a single amino acid by analyzing residue interactions.

Li Yizhou Y Wen Zhining Z Xiao Jiamin J Yin Hui H Yu Lezheng L Yang Li L Li Menglong M

BMC bioinformatics 20110112

<h4>Background</h4>The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.<h4>Results</h4>We found that SAPs can be well c ...[more]

PMID: 21223604

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Project description:The potential avian influenza pandemic remains a threat to public health, as the avian-origin influenza A(H7N9) virus has caused more than 1,560 laboratory-confirmed human infections since 2013, with nearly 40% mortality. Development of low-pathogenic candidate vaccine viruses (CVVs) for vaccine production is essential for pandemic preparedness. However, the suboptimal growth of CVVs in mammalian cells and chicken eggs is often a challenge. By introducing a single adaptive substitution, G218E, into the hemagglutinin (HA), we generated reassortant A(H7N9)-G218E CVVs that were characterized by significantly enhanced growth in both cells and eggs. These G218E CVVs retained the original antigenicity, as determined by a hemagglutination inhibition assay, and effectively protected ferrets from lethal challenge with the highly pathogenic parental virus. We found that the suboptimal replication of the parental H7 CVVs was associated with impeded progeny virus release as a result of strong HA receptor binding relative to weak neuraminidase (NA) cleavage of receptors. In contrast, the G218E-mediated growth improvement was attributed to relatively balanced HA and NA functions, resulted from reduced HA binding to both human- and avian-type receptors, and thus facilitated NA-mediated virus release. Our findings revealed that a single amino acid mutation at residue 218 of the HA improved the growth of A(H7N9) influenza virus by balancing HA and NA functions, shedding light on an alternative approach for optimizing certain influenza CVVs.IMPORTANCE The circulating avian influenza A(H7N9) has caused recurrent epidemic waves with high mortality in China since 2013, in which the alarming fifth wave crossing 2016 and 2017 was highlighted by a large number of human infections and the emergence of highly pathogenic avian influenza (HPAI) A(H7N9) strains in human cases. We generated low-pathogenic reassortant CVVs derived from the emerging A(H7N9) with improved virus replication and protein yield in both MDCK cells and eggs by introducing a single substitution, G218E, into HA, which was associated with reducing HA receptor binding and subsequently balancing HA-NA functions. The in vitro and in vivo experiments demonstrated comparable antigenicity of the G218E CVVs with that of their wild-type (WT) counterparts, and both the WT and the G218E CVVs fully protected ferrets from parental HPAI virus challenge. With high yield traits and the anticipated antigenicity, the G218E CVVs should benefit preparedness against the threat of an A(H7N9) influenza pandemic.

Dataset Information

Predicting disease-associated substitution of a single amino acid by analyzing residue interactions.

Publications

Predicting disease-associated substitution of a single amino acid by analyzing residue interactions.

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