Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.

Summary: We have created the GOR V web server for protein secondary structure prediction. The GOR V algorithm combines information theory, Bayesian statistics and evolutionary information. In its fifth version, the GOR method reached (with the full jack-knife procedure) an accuracy of prediction Q₃ of 73.5%. Although GOR V has been among the most successful methods, its online unavailability has been a deterrent to its popularity. Here, we remedy this situation by creating the GOR V server. Availability: The GOR V server is freely accessible to public users and private institutions at http://gor.bb.iastate.edu/ Contact:kloczkow@iastate.edu

Tyrosinase is a key enzyme target to design new chemical ligands against melanogenesis. In the current review, different chemical derivatives are explored which have been used as anti-melanogenic compounds. These are different chemical compounds naturally present in plants and semi-synthetic and synthetic compounds inspired by these natural products, such as kojic acid produced by several species of fungi; arbutin—a glycosylated hydroquinone extracted from the bearberry plant; vanillin—a phenolic aldehyde extracted from the vanilla bean, etc. After enzyme inhibition screening, various chemical compounds showed different therapeutic effects as tyrosinase inhibitors with different values of the inhibition constant and IC₅₀. We show how appropriately designed scaffolds inspired by the structures of natural compounds are used to develop novel synthetic inhibitors. We review the results of numerous studies, which could lead to the development of effective anti-tyrosinase agents with increased efficiency and safety in the near future, with many applications in the food, pharmaceutical and cosmetics industries.

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.