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Current methods in structural proteomics and its applications in biological sciences

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ABSTRACT

A broad working definition of structural proteomics (SP) is that it is the process of the high-throughput characterization of the three-dimensional structures of biological macromolecules. Recently, the process for protein structure determination has become highly automated and SP platforms have been established around the globe, utilizing X-ray crystallography as a tool. Although protein structures often provide clues about the biological function of a target, once the three-dimensional structures have been determined, bioinformatics and proteomics-driven strategies can be employed to derive their biological activities and physiological roles. This article reviews the current status of SP methods for the structure determination pipeline, including target selection, isolation, expression, purification, crystallization, diffraction data collection, structure solution, refinement and functional annotation.

No MeSH data available.


Accuracy and details. a Representative X-ray diffraction pattern collected on a Marresearch GmbH imaging plate system. The diffraction extends to a maximum of 1.9 Å resolution at the edge of the image. b Representative portion of an electron density map at 0.96 Å resolution. The sticks represent the individual atoms for the amino acids that constitute the protein (carbon, gray; nitrogen, blue; oxygen, red; sulfur, yellow) and the chicken wire represents the corresponding experimental electron density for these atoms. c Histogram depicting the distribution of resolutions for protein structures in the PDB as of August 2011
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Fig6: Accuracy and details. a Representative X-ray diffraction pattern collected on a Marresearch GmbH imaging plate system. The diffraction extends to a maximum of 1.9 Å resolution at the edge of the image. b Representative portion of an electron density map at 0.96 Å resolution. The sticks represent the individual atoms for the amino acids that constitute the protein (carbon, gray; nitrogen, blue; oxygen, red; sulfur, yellow) and the chicken wire represents the corresponding experimental electron density for these atoms. c Histogram depicting the distribution of resolutions for protein structures in the PDB as of August 2011

Mentions: Typically, data extending to 2.5 Å resolution or higher are desirable for novel proteins and protein–ligand complexes, so that the model can be fitted unambiguously into the electron density map. However, in more challenging cases, data at 3 Å resolution or lower may be sufficient to fit the overall fold of a protein or the constituents of a multi-protein complex. A typical X-ray diffraction image, the electron density map to atomic resolution and the distribution of resolutions for protein structures in the PDB are depicted in Fig. 6. However, in many cases, diffraction properties of crystals are not known in advance, especially when crystals are small (in the micrometer range) and cannot be prescreened using in-house instrumentation prior to a synchrotron trip. It often takes a significant amount of time at the synchrotron to screen these sub-micron crystals to identify a well-diffracting crystal suitable for data collection. Whilst collecting data at the synchrotron beamline, the user must make decisions about the parameters of the experiment—exposure time, rotation range, oscillation angle, detector distance, beam size and wavelength—based on their experience, visual inspection of the diffraction images and information output by data-processing packages. Most of the instrumentation in the experimental station is computationally controlled using software packages such as Blu-Ice (McPhillips et al. 2002), CBASS (Skinner et al. 2006), MxCube (Gabadinho et al. 2010) and JBlue-Ice (Stepanov et al. 2011). However, very often an intuitive decision is made by the user on the exposure time to use. In cases where this has been overestimated, it can lead to significant radiation damage before the completion of data collection. In addition, an inappropriate data collection strategy can lead to the failure of an experiment. Computationally efficient modeling of the data statistics for any combination of data collection parameters provides a foundation for making a rational choice. The modeling of data statistics using a few test images allows one to quantitatively select which screened crystal gives the highest resolution using an appropriate rotation range and X-ray radiation dose prior to data collection (Bourenkov and Popov 2006, 2010).Fig. 6


Current methods in structural proteomics and its applications in biological sciences
Accuracy and details. a Representative X-ray diffraction pattern collected on a Marresearch GmbH imaging plate system. The diffraction extends to a maximum of 1.9 Å resolution at the edge of the image. b Representative portion of an electron density map at 0.96 Å resolution. The sticks represent the individual atoms for the amino acids that constitute the protein (carbon, gray; nitrogen, blue; oxygen, red; sulfur, yellow) and the chicken wire represents the corresponding experimental electron density for these atoms. c Histogram depicting the distribution of resolutions for protein structures in the PDB as of August 2011
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3376864&req=5

Fig6: Accuracy and details. a Representative X-ray diffraction pattern collected on a Marresearch GmbH imaging plate system. The diffraction extends to a maximum of 1.9 Å resolution at the edge of the image. b Representative portion of an electron density map at 0.96 Å resolution. The sticks represent the individual atoms for the amino acids that constitute the protein (carbon, gray; nitrogen, blue; oxygen, red; sulfur, yellow) and the chicken wire represents the corresponding experimental electron density for these atoms. c Histogram depicting the distribution of resolutions for protein structures in the PDB as of August 2011
Mentions: Typically, data extending to 2.5 Å resolution or higher are desirable for novel proteins and protein–ligand complexes, so that the model can be fitted unambiguously into the electron density map. However, in more challenging cases, data at 3 Å resolution or lower may be sufficient to fit the overall fold of a protein or the constituents of a multi-protein complex. A typical X-ray diffraction image, the electron density map to atomic resolution and the distribution of resolutions for protein structures in the PDB are depicted in Fig. 6. However, in many cases, diffraction properties of crystals are not known in advance, especially when crystals are small (in the micrometer range) and cannot be prescreened using in-house instrumentation prior to a synchrotron trip. It often takes a significant amount of time at the synchrotron to screen these sub-micron crystals to identify a well-diffracting crystal suitable for data collection. Whilst collecting data at the synchrotron beamline, the user must make decisions about the parameters of the experiment—exposure time, rotation range, oscillation angle, detector distance, beam size and wavelength—based on their experience, visual inspection of the diffraction images and information output by data-processing packages. Most of the instrumentation in the experimental station is computationally controlled using software packages such as Blu-Ice (McPhillips et al. 2002), CBASS (Skinner et al. 2006), MxCube (Gabadinho et al. 2010) and JBlue-Ice (Stepanov et al. 2011). However, very often an intuitive decision is made by the user on the exposure time to use. In cases where this has been overestimated, it can lead to significant radiation damage before the completion of data collection. In addition, an inappropriate data collection strategy can lead to the failure of an experiment. Computationally efficient modeling of the data statistics for any combination of data collection parameters provides a foundation for making a rational choice. The modeling of data statistics using a few test images allows one to quantitatively select which screened crystal gives the highest resolution using an appropriate rotation range and X-ray radiation dose prior to data collection (Bourenkov and Popov 2006, 2010).Fig. 6

View Article: PubMed Central

ABSTRACT

A broad working definition of structural proteomics (SP) is that it is the process of the high-throughput characterization of the three-dimensional structures of biological macromolecules. Recently, the process for protein structure determination has become highly automated and SP platforms have been established around the globe, utilizing X-ray crystallography as a tool. Although protein structures often provide clues about the biological function of a target, once the three-dimensional structures have been determined, bioinformatics and proteomics-driven strategies can be employed to derive their biological activities and physiological roles. This article reviews the current status of SP methods for the structure determination pipeline, including target selection, isolation, expression, purification, crystallization, diffraction data collection, structure solution, refinement and functional annotation.

No MeSH data available.