Skip to main content

Predict per-residue Fdewet using SASA + MPNN

Project description

FastHydroMap

FastHydroMap predicts per-residue dewetting free energies (Fdewet) from protein structures and trajectories.

FastHydroMap overview

Quick Start

Use a fresh Python environment. Python 3.11 to 3.14 are supported.

pip install fasthydromap
fasthydromap install-torch
fasthydromap predict your_structure.pdb -o outputs/your_structure_fdewet

fasthydromap install-torch defaults to the CPU build, which is usually the right choice for current FastHydroMap workloads because SASA preprocessing dominates runtime.

Advanced installation options, Docker usage, GPU Torch variants, and release workflows are documented in INSTALL.md and PYPI_RELEASE.md.

Inputs

FastHydroMap supports:

  • Single protein structures in PDB format
  • Protein trajectories in DCD or XTC format together with a matching topology PDB

Typical usage:

# Single structure
fasthydromap predict examples/1A1U.pdb -o outputs/1A1U_fdewet

# Trajectory
fasthydromap predict-trajectory examples/proteinG.pdb examples/proteinG_short.dcd -o outputs/proteinG_fdewet

Outputs

For a single structure, FastHydroMap writes:

  • *.csv: one row per residue with Fdewet; with --parts, intrinsic and context columns are included
  • *.pdb: a copy of the input structure with predicted Fdewet written to B-factors

For a trajectory, FastHydroMap writes wide CSV files containing one row per frame and one column per residue. Use --parts to also write intrinsic, context, and per-frame summary CSVs.

Model Scope

FastHydroMap was trained on structured single-chain proteins and the 20 canonical amino-acid chemistries. Predictions for PTMs and other non-canonical chemistries should be treated cautiously.

Visualization

FastHydroMap writes Fdewet values to the B-factor column of output PDBs, so you can color structures directly in molecular viewers.

ChimeraX:

color bfactor range 4,6.5 palette ^lipophilicity

PyMOL:

spectrum b, red_white_blue, minimum=4, maximum=6.5

For dynamic hydrophobicity visualization in a MD trajectory, see the teaching-oriented example script scripts/chimerax_fdewet_trajectory_example.py with a ChimeraX implementation you can adjust.

Acknowledgements

Shell Lab and Shea Group

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fasthydromap-0.1.2.tar.gz (206.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

fasthydromap-0.1.2-py3-none-any.whl (202.4 kB view details)

Uploaded Python 3

File details

Details for the file fasthydromap-0.1.2.tar.gz.

File metadata

  • Download URL: fasthydromap-0.1.2.tar.gz
  • Upload date:
  • Size: 206.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.15

File hashes

Hashes for fasthydromap-0.1.2.tar.gz
Algorithm Hash digest
SHA256 cb2e69d12eeb00ea40a4f3212af252a5897b29bc8b718b21a559eecb93244c42
MD5 1c60d65fd897271f73bb90c3000c2971
BLAKE2b-256 310f2886ec4be713d5deec929bd93eb8be6fdee2e4a4f4f78ffa28bac73f947e

See more details on using hashes here.

File details

Details for the file fasthydromap-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: fasthydromap-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 202.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.15

File hashes

Hashes for fasthydromap-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 a51036a7982982f0cd5fc46de849c80d945a53f51f4194880563937e78b5f6e0
MD5 458e8858040bed6128a8e22ab7d9c382
BLAKE2b-256 9a23a4d06441d1ab9ff542ba9f5347efdc43bf9e3ecf13bf0e68c584a20cf429

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page