of phase diagrams of proteins. Thus, it adds to the general understanding of protein behavior in liquid formulation and presents a convenient methodology applicable to any
protein solution.
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the pharmacological interest in these transporters for drug delivery. In humans two transporters, PepT1 and PepT2, occur. So far only structures of five bacterial homologues are available. Eukaryotic POTs exist of 12 transmembrane helices whereas prokaryotic POTs have two additional transmembrane helices. It was possible to clone different prokaryotic transporter constructs into pET expression vectors as well as the PepT-like transporter (CtPOT) of Chaetomium thermophilum, a thermophilic eukaryote, and the human PepT2. Two transporters of Shewanella oneidensis (PepTSo2) modified with the thermostabilized apocytochrome b562RIL as well as the CtPOT and PepT2 were expressed in Escherichia coli cells. These transporters except PepT2 were solubilized and purified. Crystals were obtained of all proteins but it was not possible to solve any structure so far due to limited crystal diffraction. Less
Due to an avalanche of structural biology manuscripts submitted to scientific journals, in many recent cases descriptions of experimental methodology (and sometimes even experimental results) are pushed to supplementary materials that are only published online and sometimes may not be reviewed as thoroughly as the main body of a manuscript. Trouble may arise when experimental results are contradicting the results obtained by other scientists, which requires (in the best case) the reexamination of the original raw data or independent repetition of the experiment according to the published description of the experiment. There are reports that a significant fraction of experiments obtained in academic laboratories cannot be repeated in an industrial environment (Begley CG & Ellis LM, Nature 483(7391):531�3, 2012). This is not an indication of scientific fraud but rather reflects the inadequate description of experiments performed on different equipment and on biological samples that were produced with disparate methods. For that reason the goal of a modern data management system is not only the simple replacement of the laboratory notebook by an electronic one but also the creation of a sophisticated, internally consistent, scalable data management system that will combine data obtained by a variety of experiments performed by various individuals on diverse equipment. All data should be stored in a core database that can be used by custom applications to prepare internal reports, statistics, and perform other functions that are specific to the research that is pursued in a particular laboratory.
This chapter presents a general overview of the methods of data management and analysis used by structural genomics (SG) programs. In addition to a review of the existing literature on the subject, also presented is experience in the development of two SG data management systems, UniTrack and LabDB. The description is targeted to a general audience, as some technical details have been (or will be) published elsewhere. The focus is on �data management,� meaning the process of gathering, organizing, and storing data, but also briefly discussed is �data mining,� the process of analysis ideally leading to an understanding of the data. In other words, data mining is the conversion of data into information. Clearly, effective data management is a precondition for any useful data mining. If done properly, gathering details on millions of experiments on thousands of proteins and making them publicly available for analysis�even after the projects themselves have ended�may turn out to be one of the most important benefits of SG programs. Less