ProteinLigandBenchmarks

Protein-Ligand Benchmark Dataset for testing Parameters and Methods of Free Energy Calculations.

Documentation

Documentation for the protein-ligand-benchmark package is hosted at readthedocs.

Related Publication

The LiveCoMS article on "Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks" provides accompanying information to this benchmark dataset and how to use it for alchemical free energy calculations. For any suggestions of improvements please raise an issue in its GitHub repository protein-ligand-benchmark-livecoms.

Installation

The repository uses git-lfs (large file storage) for the storage of all the data file. Ideally git-lfs is installed first before cloning the repository.

conda create -n plbenchmark python=3.7 git-lfs conda activate plbenchmark git lfs clone https://github.com/openforcefield/protein-ligand-benchmark.git cd protein-ligand-benchmark conda env update --file environment.yml pip install -e .

Getting Started

Example notebooks can be found in the Documentation and in examples. Paper repository here.

Data file tree and file description

The data is organized as followed:

data ├── targets.yml # list of all targets and their directories ├── <date>_<target_name_1> # directory for target 1 │ ├── 00_data # metadata for target 1 │ │ ├── edges.yml # edges/perturbations │ │ ├── ligands.yml # ligands and activities │ │ └── target.yml # target │ ├── 01_protein # protein data │ │ ├── crd # coordinates │ │ │ ├── cofactors_crystalwater.pdb # cofactors and cyrstal waters (might be empty if there are none) │ │ │ └── protein.pdb # aminoacid residues │ │ └── top # topology(s) │ │ │ └── amber99sb-star-ildn-mut.ff # force field spec. │ │ │ ├── cofactors_crystalwater.top# Gromacs TOP file of cofactors and crystal water (might be empty if there are none) │ │ │ ├── protein.top # Gromacs TOP file of amino acid residues │ │ │ └── *.itp # Gromacs ITP file(s) to be included in TOP files │ └── 02_ligands # ligands │ ├── lig_<name_1> # ligand 1 │ │ ├── crd # coordinates │ │ │ └── lig_<name_1>.sdf # SDF file │ │ └── top # topology(s) │ │ └── openff-1.0.0.offxml # force field spec. │ │ ├── fflig_<name_1>.itp # Gromacs ITP file : atom types │ │ ├── lig_<name_1>.itp # Gromacs ITP file │ │ ├── lig_<name_1>.top # Gromacs TOP file │ │ └── posre_lig_<name_1>.itp # Gromacs ITP file : position restraint file │ ├── lig_<name_2> # ligand 2 │ … │ └── 03_hybrid # edges (perturbations) │ ├── edge_<name_1>_<name_2> # edge between ligand 1 and ligand 2 │ │ └── water # edge in water │ │ ├── crd # coordinates │ │ │ ├── mergedA.pdb # merged conf based on coords of ligand 1 │ │ │ ├── mergedB.pdb # merged conf based on coords of ligand 2 │ │ │ ├── pairs.dat # atom mapping │ │ │ └── score.dat # similarity score │ │ └── top # topology(s) │ │ └── openff-1.0.0.offxml # force field spec. │ │ ├── ffmerged.itp # Gromacs ITP file │ │ ├── ffMOL.itp # Gromacs ITP file │ │ └── merged.itp # Gromacs ITP file │ … ├── <date>_<target_name_2> # directory for target 2 …

Description of meta data YAML files

targets.yml

This file lists all the registered targets in the benchmark set. Each entry denotes one target and contains the following information:

mcl1_sample: name: mcl1_sample date: 2020-08-26 dir: 2020-08-26_mcl1_sample

mcl1_sample is the entry name and each entry has three sub-entries: - name is the target name, which is usually the same as the entry name of the target. - date is the date when the target was initially added to the benchmark set. - dir is the directory name where all the data for the target is found. Usually it is the date and the name field, connected by a underscore _.

target.yml

This file is found in the meta data directory of each target: <date>_<target_name>/00_data/target.yml. It contains additionally information about the target:

alternate: iridium_classifier: HT iridium_score: 0.3 pdb: 6O6F associated_sets: - Schrodinger JACS comments: hydrophobic interactions contributing to binding date: 2019-12-13 dpi: 0.26 id: 9 iridium_classifier: HT iridium_score: 0.41 name: mcl1 netcharge: 4 e pdb: 4HW3 references: calculation: - 10.1021/ja512751q - 10.1021/acs.jcim.9b00105 - 10.1039/C9SC03754C measurement: - 10.1021/jm301448p

Explanation of the entries:

• alternate: Alternate X-ray structure which could be used
  • iridium_classifier: Iridium classifier of the alternate structure
  • iridium_score: Iridium score of the alternate structure
  • pdb: PDB ID of the alternate structure
• associated_sets: list of benchmark set tags, where this target is in (e.g. "Schrodinger JACS")
• comments: hydrophobic interactions contributing to binding
• date: date when the target was initially added to the benchmark set.
• dpi: diffraction precision index of the used structure (quality metric for the structure)
• id: a given ID
• iridium_classifier: Iridium classifier of the used structure
• iridium_score: Iridium score of the used structure
• name: name/identifier of the target
• netcharge: total charge of the prepared protein (this should be equalized with counter ions during preparation of the simulation system)
• pdb: PDB ID of the used structure
• references: doi to references
  • calculation: list of references where this target was used in calculations
  • measurement: list of references of affinity measurements

ligands.yml

This file is found in the meta data directory of each target: <date>_<target_name>/00_data/ligands.yml. It contains information of the ligands of one target. One entry looks like this:

lig_23: measurement: comment: Table 2, entry 23 doi: 10.1021/jm301448p error: 0.03 type: ki unit: uM value: 0.37 name: lig_23 smiles: '[H]c1c(c(c2c(c1[H])c(c(c(c2OC([H])([H])C([H])([H])C([H])([H])C3=C(Sc4c3c(c(c(c4[H])[H])[H])[H])C(=O)[O-])[H])[H])[H])[H])[H]'

Explanation of the entries:

• measurement: affinity measurement entry
  • comment: comment about the measurement
  • doi: DOI (digital object identifier) pointing to the reference for this measurement
  • error: Error of measurement, null if not reported
  • type: type of measurement observable, ki (binding equilibrium constant), ic50 (IC50 value), pic50 (pIC50 value), or dg (free energy of binding) are accepted entries.
  • unit: Unit of value and error entries.
  • value: Value of the measurement.
• name: name of ligand, which always starts with lig_, followed by a unique identifier.
• smiles: SMILES string of the ligand, with charge state information and chirality information.

edges.yml

This file is found in the meta data directory of each target: <date>_<target_name>/00_data/edges.yml. It contains information of the edges of one target. One entry looks like this:

edge_50_60: ligand_a: lig_50 ligand_b: lig_60

Each entry is just a list of two ligand identifiers.

Summary

Summary of the contents of the Protein-Ligand Benchmark Dataset. It contains the available protein targets with corresponding PDB ID and number of ligands.

| Target | PDB | N. Lig. | | --------- |:----:|--------:| | bace | 4DJW | 36 | | bacehunt | 4JPC | 32 | | bacep2 | 3IN4 | 12 | | cdk2 | 1H1Q | 16 | | cdk8 | 5HNB | 33 | | cmet | 4R1Y | 12 | | eg5 | 3L9H | 28 | | galectin | 5E89 | 8 | | hif2a | 5TBM | 42 | | jnk1 | 2GMX | 21 | | mcl1 | 4HW3 | 42 | | p38 | 3FLY | 34 | | pde10 | 4BBX | 35 | | pde2 | 6EZF | 21 | | pfkfb3 | 6HVI | 40 | | ptp1b | 2QBS | 23 | | shp2 | 5EHR | 26 | | syk | 4PV0 | 44 | | thrombin | 2ZFF | 11 | | tnks2 | 4UI5 | 27 | | tyk2 | 4GIH | 16 |

Release History

Releases follow the major.minor.micro scheme recommended by PEP440, where - major increments denote a change that may break API compatibility with previous major releases - minor increments denote addition of new targets or addition and larger changes to the API - micro increments denote bugfixes, addition of API features, changes of coordinates or topologies, and changes of metadata

Contributions

• Authors David Hahn
• Data Contributors The authors of the following publications, especially Vytautas Gapsys and Christina E. M. Schindler.
  • V. Gapsys et al., Large scale relative protein ligand binding affinities using non-equilibrium alchemy, Chem. Sci., 2020,11, 1140-1152
  • Christina E. M. Schindler et al., Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects, J. Chem. Inf. Model. 2020, 60, 11, 5457–5474
  • Laura Perez Benito et al., Predicting Activity Cliffs with Free-Energy Perturbation, J. Chem. Theory Comput. 2019, 15, 3, 1884–1895
• Discussions and Suggestions Christopher I. Bayly, Marko Breznik, Hannah E. Bruce Macdonald, John D.Chodera, Katharina Meier, Antonia S. J. S. Mey, David L. Mobley, Laura Perez Benito, Gary Tresadern, Gregory L. Warren and all members of the Open Force Field Initiative
• Code review and discussions Matt Thompson, Jeffrey Wagner

License

MIT. See the License File for more information.

CC-BY-4.0 for data (content of directory data). See the License File for more information.

Copyright

Copyright (c) 2021, Open Force Field Consortium, David F. Hahn

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.1.