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LIGYSIS

This repository contains the original version of our original ligand site analysis LIGYSIS pipeline, which was employed to analyse all biologically relevant protien-ligand interactions on the PDBe [1], which results are served in the LIGYSIS web server. The code for the web server can be found here.

DOI

LIGYSIS makes extensive use of the PDBe-KB [2] and PDBe APIs [3] to retrieve transformation matrices, all PDB IDs mapping to a given UniProt [4] ID and more. LIGYSIS analyses protein-ligand contacts from BioLiP defined biologically relevant ligands from PISA-defined preferred biological assemblies [5]. These protein-ligand interactions are used to cluster ligands together into binding sites, which are then characterised by using evolutionary divergence, human genetic variation and structural features such as Relative Solvent Accessibility (RSA) [6] or secondary structure.

Pipeline methodology

The pipeline can be summarised in the following steps:

  1. Transformation matrices are retrieved for each chain mapping to a UniProt ID from the PDBe-KB FTP site.

  2. Segment superposition data, i.e., segment-clustered protein chains and representative chains, are retrieved from the PDBe GRAPH API superposition endpoint [7].

    Note: The following steps are repeated in a loop now for each Segment within the protein.

  3. Retrieve experimental data for each structure from the PDBe REST API experiment endpoint

  4. Filter out structures according to resolution (Current dataset considers ALL structures).

  5. Download of biological assemblies from PDBe via ProIntVar [8].

  6. Protein-ligand interactions calculation with pdbe-rpeggio [9].

  7. Mapping of PDB residues to UniProt through SIFTS [10], encoded in the mmCIF format.

  8. Ligand clustering into binding sites using SciPy [11].

  9. Transformation of all chains employing PDBe-KB matrices with BioPython [12](atom.transform(rotation, translation)).

  10. Simplification of superposed chains. This consists in keeping protein atoms only for one of the superposed structures, and heteroatoms for the rest. This is done to generate a lower-weight superposition (all ligands to a single protein scaffold).

  11. Generation of ChimeraX [13] superposition visualisation script.

  12. Relative solvent accessibility (RSA) and secondary structure elements calculation with DSSP [14] via ProIntVar.

  13. Multiple sequence alignment with jackhmmer [15].

  14. Shenkin amino acid divergence score calculation [16, 17].

  15. Missense enrichment score calculation with VarAlign [18, 19].

  16. RSA-based clustering label and functional score calculation [20].

Dependencies

Third party dependencies for these notebooks include:

Other standard python libraries:

For more information on the dependencies, refer to the .yml files in the ENVS directory. To install all the dependencies, refer to the installation manual.

Environments

The ENVS folder contains three .yml files describing the necessary packages and dependencies for the different parts of the pipeline and analysis.

Note that there are no .yml files for the ARPEGGIO, DSSP, HMMER environments, as these are created from the command line without the need of a .yml file.

Installation

For complete installation instructions refer here.

Downloading SwissProt

This is the database used for our analysis, but can be changed according to the user purposes, e.g. TrEMBL. What is important is to add the correct path in the configuration file. To download SwissProt, follow the next steps.

# download SwissProt in fasta format (88MB)
wget https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz

# decrompress the file
gzip -d uniprot_sprot.fasta.gz

Downloading gnomAD v2.1

This is the database used for our analysis, but can be changed according to the user purposes, e.g. v > 2.1. What is important is to add the correct path in the configuration file. To download gnomAD v2.1 [26], follow the next steps.

# download gnomAD Exomves vcf (large file 58GB)
wget https://storage.googleapis.com/gcp-public-data--gnomad/release/2.1.1/vcf/exomes/gnomad.exomes.r2.1.1.sites.vcf.bgz

For more information, refer to gnomAD.

After downloading gnomAD, it is required to run VEP [27] on it, as VarAlign uses its annotations. Information on how to do so here.

Execution

LIGYSIS can be run like this:

python ligysis.py P00517

This needs to be done within the LIGYSIS environment.

The programme has a single mandatory argument. up_acc is the corresponding UniProt accession identifier of the protein of interest. In this example: P00517, which corresponds to bovine cAMP-dependent protein kinase catalytic subunit alpha.

To carry out the last step and add the RSA-derived Cluster labels and functional scores, the predict_rsa_labels.py needs to be executed on the DEEP_LEARNING environment. This is how to run it:

python predict_rsa_labels.py output/ P00517_1

This script requires two mandatory argument: output_dir the name of the main output directory. This is where the ouput for all submissions will be saved and seg_name, which is the name of the segment of interest. Segment names are formed by UniProt ID + "_" + Segment ID, for this example it would be: P00517_1, corresponding to Segment 1 of P00517. This script will use a multilayer perceptron model [6] to predict RSA-based Cluster labels and functional scores for each defined binding site.

Help and manual

To get help or information about the LIGYSIS pipeline, run:

python ligysis.py -h

which will print the manual of the programme:

usage: ligysis.py [-h] [--resolution RESOLUTION]
                  [--experimental_methods EXPERIMENTAL_METHODS]
                  [--clust_method CLUST_METHOD] [--clust_dist CLUST_DIST]
                  [--hmm_iters HMM_ITERS]
                  [--cons_thresh_high CONS_THRESH_HIGH]
                  [--cons_thresh_low CONS_THRESH_LOW]
                  [--mes_thresh MES_THRESH] [--override] [--override_variants]
                  [--override_arpeggio] [--override_trans] [--override_simple]
                  [--override_dssp] [--override_pdb]
                  up_acc

Clusters ligands and defines binding sites.

positional arguments:
  up_acc                UniProt accession number of the protein of interest.

optional arguments:
  -h, --help            show this help message and exit
  --resolution RESOLUTION
                        Resolution threshold to consider a structure high-
                        resolution. Default is inf.
  --experimental_methods EXPERIMENTAL_METHODS
                        Experimental method used to determine structures.
                        Default is 'ALL' for all methods.
  --clust_method CLUST_METHOD
                        Ligand clustering method (default: average)
  --clust_dist CLUST_DIST
                        Ligand clustering distance threshold (default: 0.50)
  --hmm_iters HMM_ITERS
                        Number of iterations for JACKHMMER (default: 3)
  --cons_thresh_high CONS_THRESH_HIGH
                        Conservation high threshold (default: 75)
  --cons_thresh_low CONS_THRESH_LOW
                        Conservation low threshold (default: 25)
  --mes_thresh MES_THRESH
                        MES threshold (default: 1.0)
  --override            Override any previously generated files.
  --override_variants   Override any previously generated files (ONLY VARIANTS
                        SECTION).
  --override_arpeggio   Override any previously generated files (ONLY ARPEGGIO
                        RAW SECTION).
  --override_trans      Override any previously generated files (ONLY
                        TRANSFORMATION).
  --override_simple     Override any previously generated files (ONLY CIF
                        SIMPLIFICATION).
  --override_dssp       Override any previously generated files (ONLY DSSP
                        SECTION).
  --override_pdb        Override MMCIF ASYM and BIO downloads.

To get help or information about the RSA-label and scores prediction script, run:

python predict_rsa_labels.py -h

which will print the manual of the programme:

usage: predict_rsa_labels.py [-h] output_dir seg_name

This script predicts RSA cluster labels and calculates RSA-based functional score (FS)

positional arguments:
  output_dir  This is the main output directory, i.e., name of the directory where the different protein outputs are stored.
  seg_name    This is the Segment name, which is UniProt ID + "_" + Segment ID

options:
  -h, --help  show this help message and exit

Citation

If you use LIGYSIS pipeline, please cite:

Utgés JS, MacGowan SA, Ives CM, Barton GJ. Classification of likely functional class for ligand binding sites identified from fragment screening. Commun Biol. 2024 Mar 13;7(1):320. doi: 10.1038/s42003-024-05970-8. PMID: 38480979; PMCID: PMC10937669.

Utgés JS, Barton GJ. Comparative evaluation of methods for the prediction of protein-ligand binding sites. J Cheminform. 2024 Nov 11;16(1):126. doi: 10.1186/s13321-024-00923-z. PMID: 39529176; PMCID: PMC11552181.

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