easypqp-rs

---

easypqp-rs is a Rust library for in-silico peptide library generation, with Python bindings for integration with the python EasyPQP library.

Features

• Fast in-silico library generation using Rust

• Includes a command-line tool for batch library generation

• Python bindings for integration within the easypqp Python package

• Configurable via JSON for fine-tuning predictions, fragmentation settings, and NCE/instrument profiles

Rust Binary CLI Example

easypqp-rs has an optional standalone command-line interface (CLI) binary for generating in-silico libraries. This can be used independently of the EasyPQP Python package if you prefer.

easypqp-insilico ./config.json

Configuration Reference

The tool is configured via a JSON file. Below is a comprehensive guide to all available parameters.

Complete Example Configuration

Click to expand full example config.json

{
  "database": {
    "fasta": "path/to/proteins.fasta",
    "enzyme": {
      "name": "Trypsin/P",
      "cleave_at": "KR",
      "restrict": "P",
      "c_terminal": null,
      "min_len": 7,
      "max_len": 50,
      "missed_cleavages": 2
    },
    "peptide_min_mass": 500.0,
    "peptide_max_mass": 5000.0,
    "generate_decoys": true,
    "decoy_tag": "rev_",
    "static_mods": {
      "C": 57.0215
    },
    "variable_mods": {
      "M": [15.9949],
      "[": [42.0106]
    },
    "max_variable_mods": 2
  },
  "insilico_settings": {
    "precursor_charge": [2, 3, 4],
    "max_fragment_charge": 2,
    "min_transitions": 6,
    "max_transitions": 6,
    "fragmentation_model": "HCD",
    "allowed_fragment_types": ["b", "y"],
    "rt_scale": 100.0
  },
  "dl_feature_generators": {
    "retention_time": {
      "model_path": "path/to/rt_model.safetensors",
      "constants_path": "path/to/rt_model_const.yaml",
      "architecture": "rt_cnn_tf"
    },
    "ion_mobility": {
      "model_path": "path/to/ccs_model.safetensors",
      "constants_path": "path/to/ccs_model_const.yaml",
      "architecture": "ccs_cnn_tf"
    },
    "ms2_intensity": {
      "model_path": "path/to/ms2_model.pth",
      "constants_path": "path/to/ms2_model_const.yaml",
      "architecture": "ms2_bert"
    },
    "device": "cpu",
    "instrument": "timsTOF",
    "nce": 20.0,
    "batch_size": 64,
    "fine_tune_config": {
      "fine_tune": false,
      "train_data_path": "",
      "batch_size": 256,
      "epochs": 3,
      "learning_rate": 0.001,
      "save_model": false
    }
  },
  "peptide_chunking": 0,
  "output_file": "insilico_library.tsv",
  "write_report": true,
  "parquet_output": false
}

Configuration Sections

1. Database Settings (REQUIRED)

database - FASTA file, enzyme, modifications, and decoy generation

Parameter	Type	Default	Description
fasta	string	REQUIRED	Path to FASTA protein database file
generate_decoys	boolean	true	Auto-generate decoy sequences by reversing protein sequences
decoy_tag	string	"rev_"	Prefix added to decoy protein names
peptide_min_mass	number	500.0	Minimum peptide mass in Daltons
peptide_max_mass	number	5000.0	Maximum peptide mass in Daltons
max_variable_mods	integer	2	Maximum number of variable modifications per peptide

Enzyme Configuration:

"enzyme": {
  "name": "Trypsin/P",          // Enzyme name (for reference)
  "cleave_at": "KR",             // Amino acids where enzyme cleaves
  "restrict": "P",               // Amino acid that prevents cleavage if following cleavage site
  "c_terminal": true,            // Cleavage occurs C-terminal to the cleavage site
  "min_len": 7,                  // Minimum peptide length
  "max_len": 50,                 // Maximum peptide length
  "missed_cleavages": 2          // Number of allowed missed cleavages
}

Static Modifications:

"static_mods": {
  "C": 57.0215    // Carbamidomethylation of Cysteine
}

Variable Modifications:

"variable_mods": {
  "M": [15.9949],    // Oxidation of Methionine
  "[": [42.0106]     // N-terminal Acetylation
}

Common modification masses:

• Carbamidomethyl (C): 57.0215
• Oxidation (M): 15.9949
• Phosphorylation (STY): 79.9663
• N-terminal Acetylation: 42.0106
• Deamidation (NQ): 0.9840

2. In-Silico Library Settings (REQUIRED)

insilico_settings - Precursor/fragment charges, transitions, fragmentation model

Parameter	Type	Default	Description
precursor_charge	array[int]	[2, 3, 4]	Precursor charge states to generate
max_fragment_charge	integer	2	Maximum fragment ion charge
min_transitions	integer	6	Minimum number of transitions per precursor
max_transitions	integer	6	Maximum number of transitions per precursor
fragmentation_model	string	"HCD"	Fragmentation type: "HCD", "CID", or "ETD"
allowed_fragment_types	array[string]	["b", "y"]	Allowed fragment ion types: "b", "y"
rt_scale	number	100.0	Retention time scaling factor (multiplies predicted RT)

[!NOTE] The current MS2 intensity prediction models only support "b" and "y" fragment ions.

Example:

"insilico_settings": {
  "precursor_charge": [2, 3],
  "max_fragment_charge": 1,
  "min_transitions": 6,
  "max_transitions": 12,
  "fragmentation_model": "HCD",
  "allowed_fragment_types": ["b", "y"],
  "rt_scale": 1.0
}

3. Deep Learning Models (OPTIONAL)

Note

If no retention_time, ion_mobility, or ms2_intensity fields are provided under dl_feature_generators, pretrained models will be automatically downloaded and used. The current default pretrained models used are:

• RT: rt_cnn_tf - A CNN-Transformer model trained on the ProteomicsML repository RT dataset. This model is based on AlphaPeptDeep's CNN-LSTM implementation, with the LSTM replaced by a Transformer encoder.
• CCS: ccs_cnn_tf - A CNN-Transformer model trained on the ProteomicsML repository CCS dataset. This model is also based on AlphaPeptDeep's CNN-LSTM implementation, with the LSTM replaced by a Transformer encoder.
• MS2: ms2_bert - A BERT-based model retreived from AlphaPeptDeep's pretrained models.

dl_feature_generators - Custom or pretrained RT/IM/MS2 prediction models

If this section is omitted or empty, pretrained AlphaPeptDeep models will be automatically downloaded and used.

Model Configuration:

Each model (RT, IM, MS2) requires three files:

{
  "model_path": "path/to/model.safetensors",     // Model weights (.pth or .safetensors)
  "constants_path": "path/to/model_const.yaml",  // Model configuration constants
  "architecture": "model_architecture_name"       // Architecture identifier
}

Parameter	Type	Default	Description
retention_time	object	pretrained	Custom RT prediction model
ion_mobility	object	pretrained	Custom IM/CCS prediction model (timsTOF only)
ms2_intensity	object	pretrained	Custom MS2 intensity prediction model
device	string	"cpu"	Compute device: "cpu", "cuda", or "mps" (Apple Silicon)
instrument	string	"timsTOF"	Instrument type: "QE" or "timsTOF"
nce	number	20.0	Normalized collision energy for fragmentation
batch_size	integer	64	Batch size for model inference
fine_tune_config	object	see below	Optional fine-tuning configuration

Supported Architectures:

• RT: "rt_cnn_tf", "rt_lstm", "rt_transformer"
• IM/CCS: "ccs_cnn_tf", "ccs_lstm"
• MS2: "ms2_bert", "ms2_transformer"

4. Fine-Tuning (OPTIONAL)

fine_tune_config - Transfer learning on experimental data

Fine-tune pretrained models on your own experimental data for improved accuracy.

Parameter	Type	Default	Description
fine_tune	boolean	false	Enable fine-tuning
train_data_path	string	""	Path to training data TSV file
batch_size	integer	256	Training batch size
epochs	integer	3	Number of training epochs
learning_rate	number	0.001	Learning rate for optimizer
save_model	boolean	false	Save fine-tuned model weights to disk

Training Data Format (TSV):

Required columns:

• sequence: Modified sequence with square bracket notation (e.g., MGC[+57.0215]AAR)
• precursor_charge: Precursor charge state
• retention_time: Experimental retention time
• ion_mobility: CCS value (only if using timsTOF)
• fragment_type: Fragment ion type (b, y, etc.)
• fragment_series_number: Fragment position
• product_charge: Fragment charge
• intensity: Normalized fragment intensity

Example:

"fine_tune_config": {
  "fine_tune": true,
  "train_data_path": "experimental_data.tsv",
  "batch_size": 256,
  "epochs": 5,
  "learning_rate": 0.0001,
  "save_model": true
}

5. Output Settings (OPTIONAL)

Output file format, reporting, and memory management

Parameter	Type	Default	Description
output_file	string	"insilico_library.tsv"	Path for output library file
write_report	boolean	true	Generate HTML quality control report
parquet_output	boolean	false	Output in Parquet format instead of TSV
peptide_chunking	integer	0	Peptides per chunk (0 = auto-calculate based on memory)

Peptide Chunking:

• 0 (default): Automatically calculate chunk size based on available memory (recommended)
• > 0: Manual chunk size for processing large FASTA files with limited RAM
• Larger chunks = faster processing but more memory usage

Minimal Configuration

The minimum required configuration only needs a FASTA file:

{
  "database": {
    "fasta": "proteins.fasta"
  }
}

All other parameters will use sensible defaults and pretrained models will be auto-downloaded.

Command-Line Overrides

You can override JSON configuration values via command-line arguments:

easypqp-insilico config.json \
  --fasta my_proteins.fasta \
  --output_file my_library.tsv \
  --no-write-report \
  --parquet

Available flags:

• --fasta <PATH>: Override database FASTA file
• --output_file <PATH>: Override output file path
• --no-write-report: Disable HTML report generation
• --parquet: Output in Parquet format instead of TSV