README.md

mmCIF files

wwPDB provides three kinds of PDBx/mmCIF files:

• coordinate files (models, "normal" mmCIF)
• structure factors (experimental data, SF-mmCIF)
• Chemical Component Dictionary with the main file named components.cif.

How quickly these files can be parsed?

For big files, we test components.cif from Aug 2021 - 327 MB, coordinate file 3j3q.cif - 230 MB, and reflection file 4v9p - 248 MB.

These three files are pretty big (although the PDB has 10x bigger SF-mmCIF files). To add more representative files I used two entries from MMTF benchmark: 4fxz (428KB) and 1cjb (836KB) that represent structures around the 50 and 75 percentile of the PDB size distribution (no. 50 from the corresponding sample_NN.csv files there).

The data files can be downloaded from wwPDB:

DATA="ftp://ftp.wwpdb.org/pub/pdb/data"
wget $DATA/monomers/components.cif.gz
wget $DATA/structures/divided/mmCIF/j3/3j3q.cif.gz
wget $DATA/structures/divided/structure_factors/v9/r4v9psf.ent.gz
wget $DATA/structures/divided/mmCIF/fx/4fxz.cif.gz
wget $DATA/structures/divided/mmCIF/cj/1cjb.cif.gz
gunzip *.gz

Note: benchmarks made by an author of one of the benchmarked programs are always suspicious. This benchmark was prepared by the author of Gemmi.

CIF parsing

In this section I benchmarked function calls and helper programs that work with CIF files on the syntactic level. The programs do different things, so I compare apples to oranges, but it gives you some idea what to expect.

The scripts used here are in the read-cif subdirectory. They either call commands:

• gemmi-validate - parses and copies all the data into a DOM structure
• gemmi-validate-fast - parses a file w/o copying any data
• cif_api - runs cif2_syncheck from cif_api, as in a benchmark in this paper.
• cif_api_fast - the same, but with the -f option (does not create DOM)

or are Python scripts:

• gemmi-read - reads the CIF file from Python
• pycodcif - runs parse from pycodcif
• iotbx-read - calls iotbx.cif.reader() from cctbx/iotbx (Python)
• py-mmcif - reads using mmcif.io.PdbxReader from py-mmcif (developed at RCSB)
• py-mmcif-iab - reads using mmcif.io.IoAdapterCore from py-mmcif
• pdbecif - uses Python parser from pdbecif (developed at PDBe).

The benchmark was run using the run.sh script from this repository:

for prog in read-cif/*; do ./run.sh $prog; done

Each combination of script/program and cif file was timed 10 times and the fastest time was reported. The benchmark was run on a Linux system, CPU i7-4790, 16GB RAM with 12GB limit on the program memory (ulimit -v 12582912). The libraries were installed from binaries (mostly from PyPI) or compiled with GCC 7 with default options (-O2 or -O3).

Run time (seconds) and peak memory (MB)

program	4fxz	mem	1cjb	mem	3j3q	mem	r4v9psf	mem	CCD	mem
gemmi-validate	0.01	8.5	0.01	13.0	5.0	2278	3.6	2312	5.1	2331
gemmi-validate-fast	0.00	4.3	0.00	4.4	1.7	237	1.5	254	2.0	334
cif_api	2.40	9.8	5.20	9.8	> 1h	10	635.6	9	587.0	9
cif_api_fast	0.01	6.2	0.02	6.1	7.4	8	5.2	6	9.4	6
gemmi-read	0.17	42.0	0.18	47.0	7.8	3369	6.0	2493	8.3	3207
pycodcif	0.27	34.0	0.52	58.0	OOM		111.3	9851	OOM
iotbx-read	0.34	98.0	0.40	120.0	OOM		28.5	8771	OOM
py-mmcif	0.16	20.9	0.26	25.6	65.5	3217	55.3	1939	72.8	3291
py-mmcif-iab	0.11	26.7	0.16	35.8	38.7	6008	26.1	3897	53.6	6535
pdbecif	0.05	14.9	0.07	18.9	15.6	3025	11.1	1942	41.6	2671

OOM = out-of-memory error

For comparison (scripts in the alt subdirectory):

• decoding 1cjb MMTF file with mmtf-python takes 0.11s (32MB of memory),
• parsing 1cjb mmJSON with Python built-in parser takes 0.03s (17MB of memory).

Creating a structural model

Here we benchmark reading coordinate mmCIF files and interpreting their content as a structural model, which usually involves building a model-chain-residue-atom hierarchy.

The scripts that are run are in the read-model subdirectory. They all happen to be Python scripts:

• gemmi-structure - calls gemmi.read_structure() from Python. Internally, it first copies all the data into DOM structure, and then creates a hierarchy copying the data again.
• biopython - calls MMCIFParser().get_structure() from BioPython
• biopython-fast - calls FastMMCIFParser().get_structure() (BioPython has two parsers - only the slower one aims to parse mmCIF files correctly)
• clipper-python - calls clipper.MMDBfile().read_file() from clipper-python
• iotbx-pdb - calls iotbx.pdb.input() from cctbx/iotbx
• atomium - calls atomium.open() from atomium

These benchmarks can be run only with coordinate files. Here I use only two of them: 1cjb.cif and 3j3q.cif:

for prog in read-model/*; do ./run-one.sh $prog 1cjb.cif; done
for prog in read-model/*; do ./run-one.sh $prog 3j3q.cif; done

program	1cjb	mem	3j3q	mem
gemmi-structure	0.19	48	11.0	4085
biopython	0.55	53	138.8	6197
biopython-fast	0.35	55	67.0	6986
clipper-python	ERR		55.6	2854
iotbx-pdb	0.43	119	ERR
atomium	0.85	96	108.5	10017

The table reports elapsed time in seconds and peak memory in MB.

For comparison:

• parsing 1cjb MMTF file with BioPython parser takes 0.26s and 47MB mem.

Other benchmarks

Parsing small CIF files: https://github.com/cod-developers/CIF-parsers

Parsing PDB files: https://github.com/jgreener64/pdb-benchmarks

Thoughts

mmJSON would be the format of choice in computing if it was one of the formats supported by the wwPDB. Currently wwPDB provides coordinates in 3 formats: PDBx/mmCIF (master format), PDB (legacy format) and PDBML (XML-based one).