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n Prospective Science Teachers Conceptual Understanding About Proteins and Protein Synthesis  [PDF]
Olcay Sinan,Sacit K?SE,Halil Aydin,Kutret Gezer
Journal of Applied Sciences , 2007,
Abstract: In present study, it was aimed to determine the effects of traditional teaching on levels of conceptual understanding of prospective science teachers on protein and protein synthesis before, after and six months after of instruction. Firstly, according to the views of the expert in the area, concept analysis was carried out about protein and protein synthesis. Considering the concept analysis, a six-item conceptual understanding test was prepared and administered as the pre-test, post-test and delayed post-test. As a result of the study, it was determined that the prospective science teachers had some difficulties in understanding concepts about protein and protein synthesis and traditional instruction was insufficient to overcome these problems. Especially, it was revealed that the candidates had severe misconceptions about process of protein synthesis and structure of protein. Finally, some suggestions were presented with the support of the findings obtained from this study.
Some No Longer Unknowns in Science  [PDF]
Arieh Ben-Naim
Open Journal of Biophysics (OJBIPHY) , 2012, DOI: 10.4236/ojbiphy.2012.21001
Abstract: Three out of 125 “big questions of science”, can now be claimed to have been answered. All of these questions involve water; its structure, its role in protein folding and its role in protein-protein association.
An improved method of potential of mean force for protein-protein interactions
Yu Su,WenFei Li,Jian Zhang,Jun Wang,Wei Wang
Chinese Science Bulletin , 2008, DOI: 10.1007/s11434-008-0036-8
Abstract: In this work, the traditional method of potential of mean force (PMF) is improved for describing the protein-protein interactions. This method is developed at atomic level and is distance-dependent. Compared with the traditional method, our model can reasonably consider the effects of the environmental factors. With this modification, we can obtain more reasonable and accurate pair potentials, which are the pre-requisite for precisely describing the protein-protein interactions and can help us to recognize the interaction rules of residues in protein systems. Our method can also be applied to other fields of protein science, e.g., protein fold recognition, structure prediction and prediction of thermostability.
An improved method of potential of mean force for protein-protein interactions
SU Yu,LI WenFei,ZHANG Jian,WANG Jun,WANG Wei,

科学通报(英文版) , 2008,
Abstract: In this work, the traditional method of potential of mean force (PMF) is improved for describing the protein-protein interactions. This method is developed at atomic level and is distance-dependent. Compared with the traditional method, our model can reasonably consider the effects of the environ- mental factors. With this modification, we can obtain more reasonable and accurate pair potentials, which are the pre-requisite for precisely describing the protein-protein interactions and can help us to recognize the interaction rules of residues in protein systems. Our method can also be applied to other fields of protein science, e.g., protein fold recognition, structure prediction and prediction of thermo- stability.
Perspective in the History of Science and Technology  [PDF]
Eberhard Knobloch
Advances in Historical Studies (AHS) , 2013, DOI: 10.4236/ahs.2013.23014
Abstract: Perspective in the History of Science and Technology
Why and how protein aggregation has to be studied in vivo  [cached]
Ami Diletta,Natalello Antonino,Lotti Marina,Doglia Silvia Maria
Microbial Cell Factories , 2013, DOI: 10.1186/1475-2859-12-17
Abstract: The understanding of protein aggregation is a central issue in different fields of protein science, from the heterologous protein production in biotechnology to amyloid aggregation in several neurodegenerative and systemic diseases. To this goal, it became more and more evident the crucial relevance of studying protein aggregation in the complex cellular environment, since it allows to take into account the cellular components affecting protein aggregation, such as chaperones, proteases, and molecular crowding. Here, we discuss the use of several biochemical and biophysical approaches that can be employed to monitor protein aggregation within intact cells, focusing in particular on bacteria that are widely employed as microbial cell factories.
In vivo measurement of protein functional changes
Aili Wang, Zhicheng Zhang, Qinyi Zhao
International Journal of Biological Sciences , 2009,
Abstract: Conformational changes in proteins are fundamental to all biological functions. In protein science, the concept of protein flexibility is widely used to describe protein dynamics and thermodynamic properties that control protein conformational changes. In this study, we show that urea, which has strong sedative potency, can be administered to fish at high concentrations, and that protein functional changes related to anesthesia induction can be measured in vivo. Ctenopharyngodon idellus (the grass carp) has two different types of N-methyl d-aspartate (NMDA) receptors, urea-insensitive and urea-sensitive, which are responsible for the heat endurance of fish. The urea-sensitive NMDA receptor showed high protein flexibility, the gamma aminobutyric acid (GABA) receptor showed less flexibility, and the protein that is responsible for ethanol anesthesia showed the lowest flexibility. The results suggest that an increase in protein flexibility underlies the fundamental biophysical mechanisms of volatile general anesthetics.
Materials Science and Protein Crystallography Using the MX Beamline Control Toolkit  [PDF]
William M. Lavender
Physics , 2002,
Abstract: MX is a portable beamline control system that has been described at previous NOBUGS meetings. This talk will briefly review MX and then discuss important changes and improvements made since the last meeting in 2000. For materials science, work has focused on extending the support for multichannel analyzers and for fast data acquisition using quick scans. MX MCA support has focused on the development of interfaces to the X-Ray Instrumentation Associates DXP-2X and X10P (Saturn) MCAs. The MX DXP-2X support has been used by MR-CAT at the Advanced Photon Source to readout a 13-element Ge detector at input count rates of up to 1.5*10^6 counts per second per detector channel. The other major addition is support for quick scans using multichannel scalers. Quick scanning is now routinely used for XAFS and diffraction measurements at MR-CAT and will soon be implemented on some MX crystallography beamlines as well. We have also begun work to allow XIA MCAs to be read out during quick scans. For protein crystallography, we have primarily focused on implementing MX for new beamlines, namely, SER-CAT at the APS and GCPCC at CAMD, with others pending at the APS. Progress has also been made on the integration of MX with vendor CCD and robotics software.
Living With Radical Uncertainty. The Exemplary case of Folding Protein  [PDF]
Ignazio Licata
Physics , 2010,
Abstract: Laplace's demon still makes strong impact on contemporary science, in spite of the fact that Logical Mathematics outcomes, Quantum Physics advent and more recently Complexity Science have pointed out the crucial role of uncertainty in the World's descriptions. We focus here on the typical problem of folding protein as an example of uncertainty, radical emergence and a guide to the "simple" principles for studying complex systems.
Comment on Yu et al., "High Quality Binary Protein Interaction Map of the Yeast Interactome Network." Science 322, 104 (2008)  [PDF]
Aaron Clauset
Physics , 2009,
Abstract: We test the claim by Yu et al. -- presented in Science 322, 104 (2008) -- that the degree distribution of the yeast (Saccharomyces cerevisiae) protein-interaction network is best approximated by a power law. Yu et al. consider three versions of this network. In all three cases, however, we find the most likely power-law model of the data is distinct from and incompatible with the one given by Yu et al. Only one network admits good statistical support for any power law, and in that case, the power law explains only the distribution of the upper 10% of node degrees. These results imply that there is considerably more structure present in the yeast interactome than suggested by Yu et al., and that these networks should probably not be called "scale free."
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