TY - JOUR
T1 - Processing of X-ray diffraction data collected in oscillation mode
AU - Otwinowski, Zbyszek
AU - Minor, Wladek
PY - 1997
Y1 - 1997
N2 - Macromolecular crystallography is an iterative process. Rarely do the first crystals provide all the necessary data to solve the biological problem being studied. Each step benefits from experience learned in previous steps. To monitor the progress, the HKL package provides two tools: (i) Statistics, both weighted (χ2) and unweighted (R-merge), are provided. The Bayesian reasoning and multicomponent error model facilitates the obtaining of proper error estimates; and (ii) visualization of the process plays a double role: it helps the operator to confirm that the process of data reduction, including the resulting statistics, is correct, and it allows one to evaluate problems for which there are no good statistical criteria. Visualization also provides confidence that the point of diminishing returns in data collection and reduction has been reached. At that point the effort should be directed to solving the structure. The methods presented here have been applied to solve a large variety of problems, from inorganic molecules with 5 Å unit cell to rotavirus of 700 Å diameter crystallized in 700 x 1000 x 1400 Å cell. Overall quality of the method has been tested by many researchers by successful application of the programs to MAD structure determinations.
AB - Macromolecular crystallography is an iterative process. Rarely do the first crystals provide all the necessary data to solve the biological problem being studied. Each step benefits from experience learned in previous steps. To monitor the progress, the HKL package provides two tools: (i) Statistics, both weighted (χ2) and unweighted (R-merge), are provided. The Bayesian reasoning and multicomponent error model facilitates the obtaining of proper error estimates; and (ii) visualization of the process plays a double role: it helps the operator to confirm that the process of data reduction, including the resulting statistics, is correct, and it allows one to evaluate problems for which there are no good statistical criteria. Visualization also provides confidence that the point of diminishing returns in data collection and reduction has been reached. At that point the effort should be directed to solving the structure. The methods presented here have been applied to solve a large variety of problems, from inorganic molecules with 5 Å unit cell to rotavirus of 700 Å diameter crystallized in 700 x 1000 x 1400 Å cell. Overall quality of the method has been tested by many researchers by successful application of the programs to MAD structure determinations.
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U2 - 10.1016/S0076-6879(97)76066-X
DO - 10.1016/S0076-6879(97)76066-X
M3 - Article
C2 - 27754618
AN - SCOPUS:0031059866
SN - 0076-6879
VL - 276
SP - 307
EP - 326
JO - Methods in Enzymology
JF - Methods in Enzymology
ER -