# Read and analyse an MTZ file

## Introduction

Main aim of this tutorial is to load the full content of an MTZ file in the workspace, to analyse and modify data, and to write the modified content into a new MTZ file.

## Sample MTZ file

Some sample files are stored as external data in this package. These are the MTZ files available with the current release. To access the file, first load the cry package.

library(cry)

Next, have a look at what is included in the external-data directory of cry.

datadir <- system.file("extdata",package="cry")
print(all_files)
#>  [1] "1dei-sf.cif"               "1dei_phases.mtz"
#>  [3] "2ol9_phases.mtz"           "6vww_xds_ascii_merged.hkl"
#>  [5] "AMS_DATA.cif"              "e-65-00i60-Isup2.rtv"
#>  [7] "shelxc.log"                "shelxd.log"
#>  [9] "shelxe_i.log"              "shelxe_o.log"
#> [11] "xds00_ascii.hkl"

We are interested in the MTZ file “1dei_phases.mtz”. This file can be loaded using function readMTZ.

## The structure of an MTZ file in R

Let’s load “1dei_phases.mtz” in memory. The created object is a named list.

filename <- file.path(datadir,"1dei_phases.mtz")
#> Warning in readChar(f, 4): truncating string with embedded nuls
class(objMTZ)
#> [1] "list"
names(objMTZ)
#> [1] "reflections"  "header"       "batch_header"

Each component of is either a named list, or a data frame. More specifically:

1. reflections

It’s a data frame. It contains the actual x-ray diffraction data from a given crystal.

2. header

It’s a named list. It contains information on the crystal used to obtain the x-ray diffraction data and on the diffraction experiment, in general.

3. batch_header It’s a named list. It gets filled only when the MTZ file
includes so-called “unmerged” data.

For the merged MTZ file we are exploring in this tutorial, the batch_header is NULL.

## The MTZ header

This is a named list. The names hint at the content of the specific components.

hdr <- objMTZ$header class(hdr) #> [1] "list" names(hdr) #> [1] "TITLE" "NCOL" "CELL" "SORT" "SYMINF" "RESO" "NDIF" #> [8] "SYMM" "PROJECT" "CRYSTAL" "DATASET" "DCELL" "DWAVEL" "COLUMN" #> [15] "COLSRC" For example, NCOL is the number of columns of the reflections data frame, CELL contains the crystal unit cell parameters and SYMM includes information on the crystal symmetry. A merged MTZ file stores reflections x-ray data in a hierarchical way. The main purpose of collecting x-ray data is to determine the 3D structure of the molecule arranged in an ordered crystallographic lattice. Reflections related to a same molecule can come from different crystals. Different datasets, corresponding to different rotation sweeps from the same crystal, can be related to a crystal. Each MTZ file can thus include data from several crystals, each crystal giving origin to several datasets. Normally, a single MTZ file will be part of a same project, but this does not necessarily have to be the case. In summary, the hierarchy defining each dataset is: Project $$\quad\Rightarrow\quad$$ Crystal $$\quad\Rightarrow\quad$$ Dataset For the file loaded we find: print(objMTZ$header$PROJECT) #> id pname #> 1 0 HKL_base #> 2 1 sf_convert print(objMTZ$header$CRYSTAL) #> id cname #> 1 0 HKL_base #> 2 1 cryst_1 print(objMTZ$header$DATASET) #> id dname #> 1 0 HKL_base #> 2 1 data_1 The only dataset in this MTZ file is called data_1. It was obtained from x-ray diffraction of a crystal called cryst_1. This crystal is one of the samples belonging to a project called sf_convert. All MTZ files contain, by default, a dataset HKL_base which comes from the crystal HKL_base, which is part of the project HKL_base; this peculiar project is always present to make sure that data observations made only of the Miller indices, are always included. More information on the header’s content can be read in the documentation for readMTZ or readMTZHeader. ## The MTZ reflections Data are actually contained in this component. It is a data frame whose columns have the names found in the header component COLUMN[,1]: objMTZ$header$COLUMN[,1] #> [1] "H" "K" "L" "FP" "SIGFP" #> [6] "FC" "PHIC" "FC_ALL" "PHIC_ALL" "FWT" #> [11] "PHWT" "DELFWT" "PHDELWT" "FOM" "FC_ALL_LS" #> [16] "PHIC_ALL_LS" objMTZ$reflections[1:5,]
#>   H K  L        FP     SIGFP        FC PHIC    FC_ALL PHIC_ALL       FWT PHWT
#> 1 0 0  4 420.23508 48.025589 411.46274    0 441.54556        0 398.92459    0
#> 2 0 0  6 784.55963 53.490089 719.30823  180 780.32288      180 788.79639  180
#> 3 0 0  8 266.88480 18.889168 156.35133    0 145.58990      360 388.17963  360
#> 4 0 0 10  60.62275 21.459837  79.47448    0  82.45022        0  30.81676    0
#> 5 0 0 14  94.31614  5.330165  42.39147    0  46.47320      360 142.01843  360
#>       DELFWT PHDELWT       FOM FC_ALL_LS PHIC_ALL_LS
#> 1  42.620972     180 1.0000000 460.96564           0
#> 2   8.473511     180 1.0000000 743.42645         180
#> 3 242.589722     360 0.9999999 145.39508         360
#> 4  51.633461     180 0.9341952  81.84018           0
#> 5  95.545235     360 0.9992544  42.30125         360

A data frame is the appropriate class for observations like the crystallographic x-ray data. One reason for this is, for instance, that grouping or selection according to a specific criterion can be carried out very easily with a data frame. Let us, as an example, select part of the data using a condition on the Miller indices. We can be interested to select reflections for which $$h$$ has a specific value; this is shown in the following chunk of code.

# List the different values of H
unique(objMTZ$reflections$H)
#>  [1]  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
#> [26] 25 26 27 28 29 30 31 32 33 34 35

# Select all reflections with H=1
idx <- which(objMTZ$reflections$H == 1)
length(idx)  # 373 reflections have H=0
#> [1] 373

# Save these reflections in a different object
refs <- objMTZ$reflections[idx,] # Show the first 10 selected reflections refs[1:10,] #> H K L FP SIGFP FC PHIC FC_ALL PHIC_ALL #> 338 1 0 4 69.46197 7.137606 67.48492 1.800000e+02 38.35452 1.800000e+02 #> 339 1 0 5 55.82940 1.932214 137.18262 9.000000e+01 120.74961 8.999999e+01 #> 340 1 0 6 296.25644 22.226982 283.05109 3.617508e-06 302.89392 2.897944e-06 #> 341 1 0 7 294.59216 23.033159 276.94324 2.700000e+02 294.35089 2.700000e+02 #> 342 1 0 8 77.35909 3.981911 69.41461 2.083899e-06 64.56831 2.083780e-06 #> 343 1 0 9 46.07975 4.606244 32.10196 2.700001e+02 30.71046 2.700001e+02 #> 344 1 0 12 118.85085 2.718317 90.03316 0.000000e+00 94.01305 0.000000e+00 #> 345 1 0 13 56.22640 4.300251 56.44312 8.999997e+01 53.37141 8.999997e+01 #> 346 1 0 14 52.02279 4.389729 70.51323 1.800000e+02 77.82328 1.800000e+02 #> 347 1 1 3 292.26788 26.617142 374.87027 1.705684e+02 279.07269 1.674537e+02 #> FWT PHWT DELFWT PHDELWT FOM FC_ALL_LS #> 338 39.99242 1.800000e+02 1.6379013 1.800000e+02 0.5639557 50.18671 #> 339 12.13563 2.700000e+02 132.8852386 2.700000e+02 0.9727311 127.82761 #> 340 289.61896 2.897945e-06 13.2749634 1.800000e+02 1.0000000 290.93329 #> 341 294.83344 2.700000e+02 0.4825439 2.700000e+02 1.0000000 268.81738 #> 342 87.53786 2.083780e-06 22.9695587 2.083780e-06 0.9831176 73.09277 #> 343 32.66603 2.700001e+02 1.9555779 2.700001e+02 0.6876827 31.74592 #> 344 143.68866 0.000000e+00 49.6756134 0.000000e+00 1.0000000 90.22993 #> 345 59.07624 8.999997e+01 5.7048264 8.999997e+01 0.9999542 56.48866 #> 346 26.22218 1.800000e+02 51.6010971 8.662659e-06 0.9999989 70.44583 #> 347 294.21536 1.674537e+02 15.1426640 1.674536e+02 0.9807579 297.09183 #> PHIC_ALL_LS #> 338 1.800000e+02 #> 339 8.999999e+01 #> 340 3.346423e-06 #> 341 2.700000e+02 #> 342 1.979033e-06 #> 343 2.700001e+02 #> 344 0.000000e+00 #> 345 8.999997e+01 #> 346 1.800000e+02 #> 347 1.674240e+02 # Find out the range of FP/sigFP for the selected reflections range(refs$FP/refs$SIGFP) #> [1] 2.374856 56.249997 Clearly, a lot of different operations and investigations can be carried out on the selected data. A second example involves data for which the signal-to-noise ratio (FP/sigFP) is greater than 1, as shown in the following snippet. idx <- which(objMTZ$reflections$FP/objMTZ$reflections$SIGFP >= 1) length(idx) # 8377 reflections have FP/sigFP >= 1 #> [1] 8377 # The reflections can be extracted refs <- objMTZ$reflections[idx,]

# Histogram of I/sigI
hist(refs$FP/refs$SIGFP,breaks=30,
main="FP/sigFP",xlab="FP/sigFP")

A third example is related to the reflections’ resolution. Once it has been calculated (cell parameters are needed to do that), data can be selected within a given resolution range (a so-called resolution shell). There is a function in cry, called hkl_to_reso, which calculates the resolution in angstroms for a reflection of Miller indices $$h, k, l$$.

# Extract cell parameters from header
cpars <- objMTZ$header$CELL
print(cpars)
#> [1] 57.4 56.3 23.0 90.0 90.0 90.0
a <- cpars[1]; b <- cpars[2]; c <- cpars[3]
aa <- cpars[4]; bb <- cpars[5]; cc <- cpars[6]

# Resolution of reflection (1,0,0) (0,1,0) (0,0,1)
hkl_to_reso(1,0,0,a,b,c,aa,bb,cc)
#> [1] 57.4
hkl_to_reso(0,1,0,a,b,c,aa,bb,cc)
#> [1] 56.3
hkl_to_reso(0,0,1,a,b,c,aa,bb,cc)
#> [1] 23

# Reflections with higher Miller indices have higher resolutions
hkl_to_reso(10,0,0,a,b,c,aa,bb,cc)
#> [1] 5.74
hkl_to_reso(10,0,-20,a,b,c,aa,bb,cc)
#> [1] 1.127592

Once resolutions are calculated (and the calculation is done on data having the same order as the original reflections), data within a given resolution shell can be selected.

resos <- hkl_to_reso(objMTZ$reflections$H,
objMTZ$reflections$K,
objMTZ$reflections$L,
a,b,c,aa,bb,cc)

# Resolution range for all data
range(resos)
#> [1] 1.600050 9.981333

# Select data with resolution between 5 and 9 angstroms
idx <- which(resos >= 5 & resos <= 9)
length(idx)  # Only 314 reflections
#> [1] 314

## Output to a new MTZ file

In order to investigate specific ideas it might be worth to modify the observations in a specific way and to write them out to a new MTZ file to be later handled by the tools proper of the CCP4 family of programs for crystallography. The cry function to write reflections content to an MTZ file is called writeMTZ. In order to make this a flexible function, it has been deemed appropriate to take as input the three named lists returned by readMTZ. These will have been changed by the user, prior to use with writeMTZ. By default, the MTZ file title is left unchanged and the batch_header list is set to NULL; thus by default the MTZ file is assumed to be a merged-reflections file. Special attention will have to be devoted to parts of the header list, which store information on the observations. These are, for instance, NCOL, SORT, RESO, COLUMN, etc. An example can help to clarify this concept.

A typical modifications of the observed data is when high-resolution reflections are eliminated. This means that the number of reflections and other quantities in the header will have to be changed.

# Copy original data to 2 separate lists
refs <- objMTZ$reflections hdr <- objMTZ$header
length(refs[,1])  # Number of reflections before cut
#> [1] 8377
print(hdr$NCOL[2]) #> [1] 8377 # Cut data to 5 angstroms resolution # (see previous chunk of code for resos) idx <- which(resos >= 5) refs <- refs[idx,] length(refs[,1]) # Now there are only 333 observations #> [1] 333 # Modify specific parts of header hdr$NCOL[2] <- length(refs[,1])            # Number of reflections
hdr$RESO <- sort(1/range(resos[idx])^2) # Resolution range hdr$DATASET[2,2] <- "Data cut to 5A"  # Dataset
obsmin <- apply(refs,2,min,na.rm=TRUE)
obsmax <- apply(refs,2,max,na.rm=TRUE)
hdr$COLUMN[,3] <- obsmin # COLUMN hdr$COLUMN[,4] <- obsmax

The data frame COLSRC contains the date and time at which the specific data columns have been generated. This can be changed with the cry function change_COLSRC; the new date and time is the current one.

# Original COLSRC
print(hdr$COLSRC) #> labels created id #> 1 H CREATED_11/10/2017_01:55:14 0 #> 2 K CREATED_11/10/2017_01:55:14 0 #> 3 L CREATED_11/10/2017_01:55:14 0 #> 4 FP CREATED_11/10/2017_01:55:14 1 #> 5 SIGFP CREATED_11/10/2017_01:55:14 1 #> 6 FC CREATED_11/10/2017_01:55:14 1 #> 7 PHIC CREATED_11/10/2017_01:55:14 1 #> 8 FC_ALL CREATED_11/10/2017_01:55:14 1 #> 9 PHIC_ALL CREATED_11/10/2017_01:55:14 1 #> 10 FWT CREATED_11/10/2017_01:55:14 1 #> 11 PHWT CREATED_11/10/2017_01:55:14 1 #> 12 DELFWT CREATED_11/10/2017_01:55:14 1 #> 13 PHDELWT CREATED_11/10/2017_01:55:14 1 #> 14 FOM CREATED_11/10/2017_01:55:14 1 #> 15 FC_ALL_LS CREATED_11/10/2017_01:55:14 1 #> 16 PHIC_ALL_LS CREATED_11/10/2017_01:55:14 1 # Change date and time hdr <- change_COLSRC(hdr) # New COLSRC print(hdr$COLSRC)
#>         labels                     created id
#> 1            H CREATED_02/05/2021_10:26:46  0
#> 2            K CREATED_02/05/2021_10:26:46  0
#> 3            L CREATED_02/05/2021_10:26:46  0
#> 4           FP CREATED_02/05/2021_10:26:46  1
#> 5        SIGFP CREATED_02/05/2021_10:26:46  1
#> 6           FC CREATED_02/05/2021_10:26:46  1
#> 7         PHIC CREATED_02/05/2021_10:26:46  1
#> 8       FC_ALL CREATED_02/05/2021_10:26:46  1
#> 9     PHIC_ALL CREATED_02/05/2021_10:26:46  1
#> 10         FWT CREATED_02/05/2021_10:26:46  1
#> 11        PHWT CREATED_02/05/2021_10:26:46  1
#> 12      DELFWT CREATED_02/05/2021_10:26:46  1
#> 13     PHDELWT CREATED_02/05/2021_10:26:46  1
#> 14         FOM CREATED_02/05/2021_10:26:46  1
#> 15   FC_ALL_LS CREATED_02/05/2021_10:26:46  1
#> 16 PHIC_ALL_LS CREATED_02/05/2021_10:26:46  1

Once all changes have been done, the list components are ready to be saved out to a new MTZ file called, in this specific instance, “new.mtz”.

# Temporary directory for output
tdir <- tempdir()
fname <- file.path(tdir,"new.mtz")

# Write changed data to the new MTZ file
writeMTZ(refs,hdr,fname,title="New truncated dataset")

Data from the new MTZ file can either be explored using CCP4 programs, or read back into R using again readMTZ.

# Read data from "new.mtz"
#> Warning in readChar(f, 4): truncating string with embedded nuls
#> [1] "COLUMN  H                             H                 0                11    0"
#> [1] "COLUMN  K                             H                 0                11    0"
#> [1] "COLUMN  L                             H                 0                 4    0"
#> [1] "COLUMN  FP                            F            12.105          1095.345    1"
#> [1] "COLUMN  SIGFP                         Q             1.365            96.585    1"
#> [1] "COLUMN  FC                            F             5.035          1055.738    1"
#> [1] "COLUMN  PHIC                          P                 0               360    1"
#> [1] "COLUMN  FC_ALL                        F             1.210          1136.601    1"
#> [1] "COLUMN  PHIC_ALL                      P                 0               360    1"
#> [1] "COLUMN  FWT                           F             0.133          1054.090    1"
#> [1] "COLUMN  PHWT                          P                 0               360    1"
#> [1] "COLUMN  DELFWT                        F             0.070           393.415    1"
#> [1] "COLUMN  PHDELWT                       P                 0               360    1"
#> [1] "COLUMN  FOM                           W             0.023             1.000    1"
#> [1] "COLUMN  FC_ALL_LS                     F             1.537          1171.841    1"
#> [1] "COLUMN  PHIC_ALL_LS                   P                 0               360    1"