Hasil untuk "Biology"

Christof Angermueller, Tanel Pärnamaa, Leopold Parts et al.

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.

D. Artis, H. Spits

S. Piccolo, S. Dupont, M. Cordenonsi

D. Christianson

The year 2017 marks the twentieth anniversary of terpenoid cyclase structural biology: a trio of terpenoid cyclase structures reported together in 1997 were the first to set the foundation for understanding the enzymes largely responsible for the exquisite chemodiversity of more than 80000 terpenoid natural products. Terpenoid cyclases catalyze the most complex chemical reactions in biology, in that more than half of the substrate carbon atoms undergo changes in bonding and hybridization during a single enzyme-catalyzed cyclization reaction. The past two decades have witnessed structural, functional, and computational studies illuminating the modes of substrate activation that initiate the cyclization cascade, the management and manipulation of high-energy carbocation intermediates that propagate the cyclization cascade, and the chemical strategies that terminate the cyclization cascade. The role of the terpenoid cyclase as a template for catalysis is paramount to its function, and protein engineering can be used to reprogram the cyclization cascade to generate alternative and commercially important products. Here, I review key advances in terpenoid cyclase structural and chemical biology, focusing mainly on terpenoid cyclases and related prenyltransferases for which X-ray crystal structures have informed and advanced our understanding of enzyme structure and function.

S. Sahl, S. Hell, S. Jakobs

D. Chicco

Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experience to run a data mining project effectively, and therefore can follow incorrect practices, that may lead to common mistakes or over-optimistic results. With this review, we present ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that we observed hundreds of times in multiple bioinformatics projects. We believe our ten suggestions can strongly help any machine learning practitioner to carry on a successful project in computational biology and related sciences.

J. Cavender-Bares, Kenneth H Kozak, P. Fine et al.

C. Winterbourn

C. Urbich, S. Dimmeler

R. Foelix

B. Honig, A. Nicholls

G. Caughley

J. Wiens, C. Graham

B. Halkier, J. Gershenzon

Toren Finkel, Nikki J. Holbrook

A. Lusis

G. Whitesides, E. Ostuni, S. Takayama et al.

L. Wolpert

Tiffany Tsang, C. I. Davis, Donita C. Brady

Metals are vital for life as they are necessary for essential biological processes. Traditionally, metals are categorized as either dynamic signals or static cofactors. Redox-inactive metals such as calcium (Ca), potassium (K), sodium (Na), and zinc (Zn) signal through large fluctuations in their metal-ion pools. In contrast, redox-active transition metals such as copper (Cu) and iron (Fe) drive catalysis and are largely characterized as static cofactors that must be buried and protected within the active sites of proteins, due to their ability to generate damaging reactive-oxygen species through Fenton chemistry. Cu has largely been studied as a static cofactor in fundamental processes from cellular respiration to pigmentation, working through cytochrome c oxidase and tyrosinase, respectively. However, within the last decade, a new paradigm in nutrient sensing and protein regulation - termed 'metalloallostery' - has emerged, expanding the repertoire of Cu beyond the catalytic proteins to dynamic signaling molecules essential for cellular processes that impact normal physiology and disease states. In this Primer we introduce both the 'traditional' and emerging roles for Cu in biology and the many ways in which Cu intersects with human health.

M. Greene, Barbara Schmidt, B. McClure et al.

Integrative Biology is the study of living organisms at different levels of organization, from molecular biology to biosphere ecology. Our undergraduate curriculum is designed to offer a firm foundation for understanding life processes