Understanding the evolutionary trend of intrinsically structural disorders in cancer relevant proteins as probed by Shannon entropy scoring and structure network analysis

• Published in: BMC bioinformatics Vol. 19; no. Suppl 13; pp. 549 - 242
• Main Authors: Sen, Sagnik, Dey, Ashmita, Chowdhury, Sourav, Maulik, Ujjwal, Chattopadhyay, Krishnananda
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 04-02-2019; BioMed Central; BMC
• Subjects: Acute lymphoblastic leukemia; Acute lymphocytic leukemia; Animals; Apoptosis; Biological activity; Breast cancer; Cancer; Cancer cells; Cell cycle; Cellular structure; Diseases; Entropy; Entropy (Information theory); Entropy (Thermodynamics); Evolution, Molecular; Genomes; Health aspects; Health care; Health risks; Humans; Hydrophobic and Hydrophilic Interactions; Hydrophobicity index; Hydrotherapy; Intrinsically Disordered Proteins - chemistry; Leukemia; Lymphatic leukemia; Lymphocytic leukemia; Lymphoma; Lymphomas; Multiple sequence alignment; Mutation; Neoplasms - metabolism; Network analysis; Non-Hodgkin's lymphomas; Physiological aspects; Prostate cancer; Protein families; Protein structure; Proteins; Shannon entropy scoring; Structure network model; Trends; Tumors; India; Shannon entropy scoring; Multiple sequence alignment; Structure network model; Hydrophobicity index
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2552-0

Malignant diseases have become a threat for health care system. A panoply of biological processes is involved as the cause of these diseases. In order to unveil the mechanistic details of these diseased states, we analyzed protein families relevant to these diseases.
Our present study pivots around four apparently unrelated cancer types among which two are commonly occurring viz. Prostate Cancer, Breast Cancer and two relatively less frequent viz. Acute Lymphoblastic Leukemia and Lymphoma. Eight protein families were found to have implications for these cancer types. Our results strikingly reveal that some of the proteins with implications in the cancerous cellular states were showing the structural organization disparate from the signature of the family it constitutes. The sequences were further mapped onto respective structures and compared with the entropic profile. The structures reveal that entropic scores were able to reveal the inherent structural bias of these proteins with quantitative precision, otherwise unseen from other analysis. Subsequently, the betweenness centrality scoring of each residue from the structure network models was resorted to explore the changes in dependencies on residue owing to structural disorder.
These observations help to obtain the mechanistic changes resulting from the structural orchestration of protein structures. Finally, the hydropathy indexes were obtained to validate the sequence space observations using Shannon entropy and in-turn establishing the compatibility.