Not all proteins form well defined three-dimensional structures in their native states. Some amino-acid sequences appear to strongly favour the disordered state, whereas some can apparently transition between disordered and ordered states under the influence of changes in the biological environment, thereby playing an important role in processes such as signalling. Although important biologically, for the structural biologist disordered regions of proteins can be disastrous even preventing successful structure determination. The accurate prediction of disorder is therefore important, not least for directing the design of expression constructs so as to maximize the chances of successful structure determination. Such design criteria have become integral to the construct-design strategies of laboratories within the Structural Proteomics In Europe (SPINE) consortium. This paper assesses the current state of the art in disorder prediction in terms of prediction reliability and considers how best to use these methods to guide construct design. Finally, it presents a brief discussion as to how methods of prediction might be improved in the future.
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http://dx.doi.org/10.1107/S0907444906033580 | DOI Listing |
J Imaging Inform Med
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Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
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Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
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January 2025
Department of Engineering Technology, University of Houston, Houston, TX, USA.
Functional near-infrared spectroscopy (fNIRS) is an increasingly popular neuroimaging technique that measures cortical hemodynamic activity in a non-invasive and portable fashion. Although the fNIRS community has been successful in disseminating open-source processing tools and a standard file format (SNIRF), reproducible research and sharing of fNIRS data amongst researchers has been hindered by a lack of standards and clarity over how study data should be organized and stored. This problem is not new in neuroimaging, and it became evident years ago with the proliferation of publicly available neuroimaging datasets.
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