Proteins: Structure, Function, and Bioinformatics

Crystal structure of a human MUC16 SEA domain reveals insight into the nature of the CA125 tumor marker

Abstract MUC16 is a membrane bound glycoprotein involved in the progression and metastasis of pancreatic and ovarian cancer. The protein is shed into the serum and the resulting cancer antigen 125 (CA125) can be detected by immunoassays. The CA125 epitope is used for monitoring ovarian cancer treatment progression, and has emerged as a potential target for antibody mediated immunotherapy. The extracellular tandem repeat domain of the protein is composed of repeating segments of heavily glycosylated sequence intermixed with homologous SEA (Sperm protein, Enterokinase and Agrin) domains. Here we report the purification and the first X‐ray structure of a human MUC16 SEA domain. The structure was solved by molecular replacement using a Rosetta generated structure as a search model. The SEA domain reacted with three different MUC16 therapeutic antibodies, confirming that the CA125 epitope is localized to the SEA domain. The structure revealed a canonical ferredoxin‐like fold, and contained a conserved disulfide bond. Analysis of the relative solvent accessibility of side chains within the SEA domain clarified the assignment of N‐linked and O‐linked glycosylation sites within the domain. A model of the glycosylated SEA domain revealed two major accessible faces, which likely represent the binding sites of CA125 specific antibodies. The results presented here will serve to accelerate future work to understand the functional role of MUC16 SEA domains and antibody recognition of the CA125 epitope.

Prioritizing the candidate genes related to cervical cancer using the moment of inertia tensor

AbstractIt is well known that cervical cancer poses the fourth most malignancy threat to women worldwide among all cancer types. There is a tremendous improvement in realizing the underlying molecular associations in cervical cancer. Several studies reported pieces of evidence for the involvement of various genes in the disease progression. However, with the ever‐evolving bioinformatics tools, there has been an upsurge in predicting numerous genes responsible for cervical cancer progression and making it highly complex to target the genes for further evaluation. In this article, we prioritized the candidate genes based on the sequence similarity analysis with known cancer genes. For this purpose, we used the concept of the moment of inertia tensor, which reveals the similarities between the protein sequences more efficiently. Tensor for moment of inertia explores the similarity of the protein sequences based on the physicochemical properties of amino acids. From our analysis, we obtained 14 candidate cervical cancer genes, which are highly similar to known cervical cancer genes. Further, we analyzed the GO terms and prioritized these genes based on the number of hits with biological process, molecular functions, and their involvement in KEGG pathways. We also discussed the evidence‐based involvement of the prioritized genes in other cancers and listed the available drugs for those genes.