imspy-predictors 0.5.1

ML-based predictors for CCS, retention time, and fragment intensity in mass spectrometry.

imspy-predictors

ML-based predictors for CCS, retention time, and fragment intensity in mass spectrometry.

Installation

pip install imspy-predictors

For remote model access via Koina servers:

pip install imspy-predictors[koina]

Features

• CCS Prediction: Deep learning models for collision cross section / ion mobility prediction
• Retention Time Prediction: GRU-based retention time predictors
• Fragment Intensity Prediction: Prosit 2023 timsTOF intensity predictor
• Charge State Prediction: Binomial and deep learning charge state distribution models
• Koina Integration: Access remote prediction models via Koina servers (optional)

Available Koina Remote Models

When using pip install imspy-predictors[koina], remote models can be configured in TimSim's [models] TOML section:

Task	Model Name
RT	Deeplc_hela_hf, Chronologer_RT, AlphaPeptDeep_rt_generic, Prosit_2019_irt
CCS	AlphaPeptDeep_ccs_generic, IM2Deep
Intensity	prosit, alphapeptdeep, ms2pip

[models]
rt_model = "AlphaPeptDeep_rt_generic"
ccs_model = ""                          # "" = local PyTorch model
intensity_model = "prosit"

Quick Start

from imspy_predictors import (
    load_deep_ccs_predictor,
    load_deep_retention_time_predictor,
    Prosit2023TimsTofWrapper,
)

# Load CCS predictor
ccs_model = load_deep_ccs_predictor()

# Load RT predictor
rt_model = load_deep_retention_time_predictor()

# Load intensity predictor
intensity_model = Prosit2023TimsTofWrapper()

Submodules

• ccs/: CCS / ion mobility prediction
• rt/: Retention time prediction
• intensity/: Fragment intensity prediction (Prosit)
• ionization/: Charge state distribution prediction
• koina_models/: Koina remote model access (requires koinapy)
• utilities/: Tokenizers for ML models

Dependencies

• imspy-core: Core data structures (required)
• PyTorch: Deep learning framework (required)
• koinapy: Koina API client (optional, for remote models)

Optional Dependencies

Some functionality requires additional packages:

• imspy-search: For PSM-based predictions using sagepy
• imspy-simulation: For simulation utilities (e.g., flatten_prosit_array)

Related Packages

• imspy-core: Core data structures and timsTOF readers
• imspy-search: Database search functionality
• imspy-simulation: Simulation tools for timsTOF data
• imspy-vis: Visualization tools

License

MIT License - see LICENSE file for details.