Preparation of diffraction data

We will assume that the diffraction data has been converted to CNS format using to_cns or CCP4. There will then be separate reflection files containing information for the native and each derivative.

The information for the native and the derivatives is then merged into a single reflection file, at the same time giving each dataset a unique name (eg. f_nat, f_kuof, f_phga). The merge and renaming are performed with the CNS task file merge.inp.

      cns_solve < merge.inp > merge.out  [10 seconds]

The output of the merge.inp task file is a CNS formatted reflection file. This will contain a header which describes the data items in the file, followed by the data items themselves. The file is plain ASCII and can be viewed with any text viewer/editor. Example header from a CNS reflection file contained multiple data sets for MIR phasing:

 NREFlection=      6735
 ANOMalous=FALSe { equiv. to HERMitian=TRUE}
 DECLare NAME=S_NAT                  DOMAin=RECIprocal   TYPE=REAL END
 DECLare NAME=F_KUOF                 DOMAin=RECIprocal   TYPE=REAL END
 DECLare NAME=S_KUOF                 DOMAin=RECIprocal   TYPE=REAL END
 DECLare NAME=F_PHGA                 DOMAin=RECIprocal   TYPE=REAL END
 DECLare NAME=S_PHGA                 DOMAin=RECIprocal   TYPE=REAL END
 DECLare NAME=F_NAT                  DOMAin=RECIprocal   TYPE=REAL END
 INDE     4    0    0 S_NAT=   632.700 F_KUOF=     0.000 S_KUOF=   572.276
                     F_PHGA=   226.494 S_PHGA=     3.728 F_NAT=     0.000
 INDE     6    0    0 S_NAT=     4.184 F_KUOF=   315.062 S_KUOF=     4.109
                     F_PHGA=   419.044 S_PHGA=     4.877 F_NAT=   304.297
 INDE     8    0    0 S_NAT=     3.414 F_KUOF=    73.576 S_KUOF=     2.804
                     F_PHGA=   166.853 S_PHGA=     2.307 F_NAT=    60.085

After merging of the native/derivative information the datasets must be scaled together (assuming that this has not been done in some other way prior to entering CNS). The scaling is performed with the CNS task file scale.inp.

      cns_solve < scale.inp > scale.out  [15 seconds]

In this case the native data extends to a resolution (2.8Å) which is high enough to perform Wilson scaling. The derivative datasets will be then scaled to the native. The Wilson scaling will place the data on an approximate absolute scale.