Interaction Mechanism between α-Lactalbumin and Caffeic Acid: A Multispectroscopic and Molecular Docking Study.

Caffeic acid (CA) is a phenolic acid found in a variety of foods. In this study, the interaction mechanism between α-lactalbumin (ALA) and CA was explored with the use of spectroscopic and computational techniques. The Stern-Volmer quenching constant data suggest a static mode of quenching between CA and ALA, depicting a gradual decrease in quenching constants with temperature rise. The binding constant, Gibbs free energy, enthalpy, and entropy values at 288, 298, and 310 K were calculated, and the obtained values suggest that the reaction is spontaneous and exothermic. Both in vitro and in silico studies show that hydrogen bonding is the dominant force in the CA-ALA interaction. Ser112 and Lys108 of ALA are predicted to form three hydrogen bonds with CA. The UV-visible spectroscopy measurements demonstrated that the absorbance peak A280nm increased after addition of CA due to conformational change. The secondary structure of ALA was also slightly modified due to CA interaction. The circular dichroism (CD) studies showed that ALA gains more α-helical structure in response to increasing concentration of CA. The surface hydrophobicity of ALA is not changed in the presence of ethanol and CA. The present findings shown herein are helpful in understanding the binding mechanism of CA with whey proteins for the dairy processing industry and food nutrition security.

K-core decomposition of a protein domain co-occurrence network reveals lower cancer mutation rates for interior cores.

BACKGROUND: Network biology currently focuses primarily on metabolic pathways, gene regulatory, and protein-protein interaction networks. While these approaches have yielded critical information, alternative methods to network analysis will offer new perspectives on biological information. A little explored area is the interactions between domains that can be captured using domain co-occurrence networks (DCN). A DCN can be used to study the function and interaction of proteins by representing protein domains and their co-existence in genes and by mapping cancer mutations to the individual protein domains to identify signals. RESULTS: The domain co-occurrence network was constructed for the human proteome based on PFAM domains in proteins. Highly connected domains in the central cores were identified using the k-core decomposition technique. Here we show that these domains were found to be more evolutionarily conserved than the peripheral domains. The somatic mutations for ovarian, breast and prostate cancer diseases were obtained from the TCGA database. We mapped the somatic mutations to the individual protein domains and the local false discovery rate was used to identify significantly mutated domains in each cancer type. Significantly mutated domains were found to be enriched in cancer disease pathways. However, we found that the inner cores of the DCN did not contain any of the significantly mutated domains. We observed that the inner core protein domains are highly conserved and these domains co-exist in large numbers with other protein domains. CONCLUSION: Mutations and domain co-occurrence networks provide a framework for understanding hierarchal designs in protein function from a network perspective. This study provides evidence that a majority of protein domains in the inner core of the DCN have a lower mutation frequency and that protein domains present in the peripheral regions of the k-core contribute more heavily to the disease. These findings may contribute further to drug development.