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Environment for ProteinKG25 Generation

Main requirements

  • python 3.7
  • biopython 1.37
  • goatools

For extracting the definition of the GO term, you need to modify the code in goatools library. The changes in goatools.obo_parser are as follows:

# line 132
elif line[:5] == "def: ":
    rec_curr.definition = line[5:]

# line 169
self.definition = ""

Pre-training Data Preparation

You can download ProteinKG25 from Google Drive.

The whole compressed package includes following files:

  • go_def.txt: GO term definition described in text. The GO term name and corresponding definition were concatenated by colon.
  • go_type.txt: The ontology type which the specific GO term belong to. The index is correponding to GO ID in go2id.txt file.
  • go2id.txt: The ID mapping of GO terms.
  • go_go_triplet.txt: GO-GO triplet data (Not used). The triplet data constitutes the interior structure of Gene Ontology. The format for each triplet is < h r t>, where h and t are respectively head entity and tail entity, both GO term nodes. r is relation between two GO terms, e.g. is_a and part_of.
  • protein_seq.txt: Protein sequence data. The whole protein sequence data are used as inputs.
  • protein2id.txt: The ID mapping of proteins.
  • protein_go_train_triplet.txt: Protein-GO triplet data. The triplet data constitutes the exterior structure of Gene Ontology, i.e. Gene annotation. The format for each triplet is <h r t>, where h and t are respectively head entity and tail entity. Each Protein-GO triplet represent a specific gene annotation, where the head entity is a specific protein and tail entity is the corresponding annotated GO term (Attribute), e.g. protein binding function. r is relation between the protein and GO term.
  • ❗protein_go_train_triplet_v2.txt (Default): Protein-GO triplet data filtered to remove triplets that contain protein with 100% sequence similarity with proteins in downstream tasks. We used blastp to identify similar proteins.
  • relation2id.txt: The ID mapping of relations.

For generating your own pre-training data, you need to download the following raw data:

  • go.obo: the structure data of Gene Ontology. The download link and detailed format can be found in Gene Ontology`
  • uniprot_sprot.dat: protein Swiss-Prot database. [link]
  • goa_uniprot_all.gpa: Gene Annotation data. [link]

After downloading these raw data and configuring associated paths in gen_onto_protein_data.py, you can execute the following script to generate pre-training data:

python gen_onto_protein_data.py