SENSE-PPI can be used to predict pairwise interactions between proteins. The input is a FASTA file with protein sequences.
The output is a .tsv file with predictions as well as a secondary .tsv file with only positive interactions. By default, the predictions are made in "all vs all" manner: all possible protein pairs are considered.
.. code-block:: console
In order to copmute the predictions for all possible pairs from FASTA file, the following command can be used:
(.venv) $ pip install senseppi
.. code-block:: bash
Commands
senseppi predict proteins.fasta
By default, if no model is provided, the pre-trained model on human PPIs is used.
List of commands
------------
There are 5 commands available in the package:
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@@ -24,3 +29,242 @@ There are 5 commands available in the package:
- `create_dataset`: creates a dataset from the STRING database based on the taxonomic ID of the organism.
fasta_file FASTA file on which to extract the ESM2 representations and then test.
options:
-h, --help show this help message and exit
-v, --version show program's version number and exit
--min_len MIN_LEN Minimum length of the protein sequence. The sequences with smaller length will not be considered and will be deleted from the fasta file. (Default: 50)
--max_len MAX_LEN Maximum length of the protein sequence. The sequences with larger length will not be considered and will be deleted from the fasta file. (Default: 800)
--device {cpu,gpu,mps,auto}
Device to use for computations. Options include: cpu, gpu, mps (for MacOS), and auto.If not selected the device is set by torch automatically. WARNING: mps
is temporarily disabled, if it is chosen, cpu will be used instead. (Default: auto)
Predict args:
--model_path MODEL_PATH
A path to .ckpt file that contains weights to a pretrained model. If None, the preinstalled senseppi.ckpt trained version is used. (Trained on human PPIs)
(Default: None)
--pairs_file PAIRS_FILE
A path to a .tsv file with pairs of proteins to test (Optional). If not provided, all-to-all pairs will be generated. (Default: None)
-o OUTPUT, --output OUTPUT
A path to a file where the predictions will be saved. (.tsv format will be added automatically) (Default: predictions)
--with_self Include self-interactions in the predictions.By default they are not included since they were not part of training but they can be included by setting this
Prediction threshold to determine interacting pairs that will be written to a separate file. Range: (0, 1). (Default: 0.5)
Args_model:
--batch_size BATCH_SIZE
Batch size for training/testing. (Default: 32)
ESM2 model args:
ESM2: Extract per-token representations and model outputs for sequences in a FASTA file. The representations are saved in --output_dir_esm folder so they can be reused in
multiple runs. In order to reuse the embeddings, make sure that --output_dir_esm is set to the correct folder.
--output_dir_esm OUTPUT_DIR_ESM
output directory for extracted representations (Default: esm2_embs_3B)
pairs_file A path to a .tsv file with pairs of proteins to test.
fasta_file FASTA file on which to extract the ESM2 representations and then evaluate.
options:
-h, --help show this help message and exit
-v, --version show program's version number and exit
--min_len MIN_LEN Minimum length of the protein sequence. The sequences with smaller length will not be considered and will be deleted from the fasta file. (Default: 50)
--max_len MAX_LEN Maximum length of the protein sequence. The sequences with larger length will not be considered and will be deleted from the fasta file. (Default: 800)
--device {cpu,gpu,mps,auto}
Device to use for computations. Options include: cpu, gpu, mps (for MacOS), and auto.If not selected the device is set by torch automatically. WARNING: mps
is temporarily disabled, if it is chosen, cpu will be used instead. (Default: auto)
Predict args:
--model_path MODEL_PATH
A path to .ckpt file that contains weights to a pretrained model. If None, the preinstalled senseppi.ckpt trained version is used. (Trained on human PPIs)
(Default: None)
-o OUTPUT, --output OUTPUT
A path to a file where the test metrics will be saved. (.tsv format will be added automatically) (Default: test_metrics)
--crop_data_to_model_lims
If set, the data will be cropped to the limits of the model: evaluations will be done only for proteins >50aa and <800aa. WARNING: this will modify the
original input files.
Args_model:
--batch_size BATCH_SIZE
Batch size for training/testing. (Default: 32)
ESM2 model args:
ESM2: Extract per-token representations and model outputs for sequences in a FASTA file. The representations are saved in --output_dir_esm folder so they can be reused in
multiple runs. In order to reuse the embeddings, make sure that --output_dir_esm is set to the correct folder.
--output_dir_esm OUTPUT_DIR_ESM
output directory for extracted representations (Default: esm2_embs_3B)
pairs_file A path to a .tsv file containing training pairs. Required format: 3 tab separated columns: first protein, second protein (protein names have to be present
in fasta_file), label (0 or 1).
fasta_file FASTA file on which to extract the ESM2 representations and then train.
options:
-h, --help show this help message and exit
-v, --version show program's version number and exit
--min_len MIN_LEN Minimum length of the protein sequence. The sequences with smaller length will not be considered and will be deleted from the fasta file. (Default: 50)
--max_len MAX_LEN Maximum length of the protein sequence. The sequences with larger length will not be considered and will be deleted from the fasta file. (Default: 800)
--device {cpu,gpu,mps,auto}
Device to use for computations. Options include: cpu, gpu, mps (for MacOS), and auto.If not selected the device is set by torch automatically. WARNING: mps
is temporarily disabled, if it is chosen, cpu will be used instead. (Default: auto)
Training args:
Arguments for training the model.
--valid_size VALID_SIZE
Fraction of the training data to use for validation. (Default: 0.1)
--seed SEED Global training seed. (Default: None)
--num_epochs NUM_EPOCHS
Number of training epochs. (Default: 100)
--num_devices NUM_DEVICES
Number of devices to use for multi GPU training. (Default: 1)
--num_nodes NUM_NODES
Number of nodes to use for training on a cluster. (Default: 1)
--early_stop EARLY_STOP
Number of epochs to wait before stopping the training (tracking is done with validation loss). By default, the is no early stopping. (Default: None)
Args_model:
--lr LR Learning rate for training. Cosine warmup will be applied. (Default: 0.0001)
--batch_size BATCH_SIZE
Batch size for training/testing. (Default: 32)
ESM2 model args:
ESM2: Extract per-token representations and model outputs for sequences in a FASTA file. The representations are saved in --output_dir_esm folder so they can be reused in
multiple runs. In order to reuse the embeddings, make sure that --output_dir_esm is set to the correct folder.
--output_dir_esm OUTPUT_DIR_ESM
output directory for extracted representations (Default: esm2_embs_3B)
genes Name of gene to fetch from STRING database. Several names can be typed (separated by whitespaces).
options:
-h, --help show this help message and exit
-v, --version show program's version number and exit
--min_len MIN_LEN Minimum length of the protein sequence. The sequences with smaller length will not be considered and will be deleted from the fasta file. (Default: 50)
--max_len MAX_LEN Maximum length of the protein sequence. The sequences with larger length will not be considered and will be deleted from the fasta file. (Default: 800)
--device {cpu,gpu,mps,auto}
Device to use for computations. Options include: cpu, gpu, mps (for MacOS), and auto.If not selected the device is set by torch automatically. WARNING: mps
is temporarily disabled, if it is chosen, cpu will be used instead. (Default: auto)
General options:
--model_path MODEL_PATH
A path to .ckpt file that contains weights to a pretrained model. If None, the preinstalled senseppi.ckpt trained version is used. (Trained on human PPIs)
(Default: None)
-s SPECIES, --species SPECIES
Species from STRING database. Default: H. Sapiens (Default: 9606)
-n NODES, --nodes NODES
Number of nodes to fetch from STRING database. (Default: 10)
-r SCORE, --score SCORE
Score threshold for STRING connections. Range: (0, 1000). (Default: 0)
List of proteins to delete from the graph. Several names can be specified separated by whitespaces. (Default: None)
Args_model:
--batch_size BATCH_SIZE
Batch size for training/testing. (Default: 32)
ESM2 model args:
ESM2: Extract per-token representations and model outputs for sequences in a FASTA file. The representations are saved in --output_dir_esm folder so they can be reused in
multiple runs. In order to reuse the embeddings, make sure that --output_dir_esm is set to the correct folder.
--output_dir_esm OUTPUT_DIR_ESM
output directory for extracted representations (Default: esm2_embs_3B)
species The Taxon identifier of the organism of interest.
options:
-h, --help show this help message and exit
--interactions INTERACTIONS
The physical links (full) file from STRING for the organism of interest. (Default: None)
--sequences SEQUENCES
The sequences file downloaded from the same page of STRING. For both files see https://string-db.org/cgi/download (Default: None)
--not_remove_long_short_proteins
If specified, does not remove proteins shorter than --min_length and longer than --max_length. By default, long and short proteins are removed.
--min_length MIN_LENGTH
The minimum length of a protein to be included in the dataset. (Default: 50)
--max_length MAX_LENGTH
The maximum length of a protein to be included in the dataset. (Default: 800)
--max_positive_pairs MAX_POSITIVE_PAIRS
The maximum number of positive pairs to be included in the dataset. If None, all pairs are included. If specified, the pairs are selected based on the
combined score in STRING. (Default: None)
--combined_score COMBINED_SCORE
The combined score threshold for the pairs extracted from STRING. Ranges from 0 to 1000. (Default: 500)
--experimental_score EXPERIMENTAL_SCORE
The experimental score threshold for the pairs extracted from STRING. Ranges from 0 to 1000. Default is None, which means that the experimental score is
