Analysis of core-periphery organization in protein contact networks reveals groups of structurally and functionally critical residues

Data-driven evaluation of suitable immunogens for improved antibody selection

Antibodies are indispensable in laboratory and clinical applications due to their high specificity and affinity for protein antigens. However, selecting the right protein fragments as immunogens for antibody production remains challenging. Leveraging the Human Protein Atlas, this study systematically evaluates immunogen properties aiming to identify key factors that influence their suitability. Antibodies were classified as successful or unsuccessful based on standardized validation experiments, and the structural and functional properties of their immunogens were analyzed. Results indicated that longer immunogens often resulted in more successful but less specific antibodies. Shorter immunogens (50 residues or fewer) with disordered or unfolded regions at the N- or C-terminus and long coil stretches were more likely to generate successful antibodies. Conversely, immunogens with high beta sheet content, transmembrane regions, or disulfide bridges were associated with poorer antibody performance. Post-translational modification sites within immunogens appeared to mark beneficial regions for antibody generation. To support antibody selection, a novel R package, immunogenViewer, was developed, enabling researchers to easily apply these insights when immunogen sequences are disclosed. By providing a deeper understanding of immunogen suitability, this study promotes the development of more effective antibodies, ultimately addressing issues of reproducibility and reliability in antibody-based research. The findings are highly relevant to the research community, as end users often lack control over the immunogen selection process in antibody production. The R package is freely available as part of Bioconductor: https://bioconductor.org/packages/release/bioc/html/immunogenViewer.html.

Hierarchical closeness-based properties reveal cancer survivability and biomarker genes in molecular signaling networks

Specific molecular signaling networks underlie different cancer types and quantitative analyses on those cancer networks can provide useful information about cancer treatments. Their structural metrics can reveal survivability of cancer patients and be used to identify biomarker genes for early cancer detection. In this study, we devised a novel structural metric called hierarchical closeness (HC) entropy and found that it was negatively correlated with 5-year survival rates. We also made an interesting observation that a network of higher HC entropy was likely to be more robust against mutations. This finding suggested that cancers of high HC entropy tend to be incurable because their signaling networks are robust to perturbations caused by treatment. We also proposed a novel core identification method based on the reachability factor in the HC measure. The cores were permitted to decompose such that the negative relationship between HC entropy and cancer survival rate was consistently conserved in every core level. Interestingly, we observed that many promising biomarker genes for early cancer detection reside in the innermost core of a signaling network. Taken together, the proposed analyses of the hierarchical structure of cancer signaling networks may be useful in developing future novel cancer treatments.