Skip to main content

It's a package for evaluation of predicted poses, right?

Project description

I have a structure prediction model and now I want to know how well it performs in reproducing the reference structures. But there are so many possible metrics, some for monomers, some for complexes! Is there a package that handles this for me?


Try

pepp'r

It’s a Package for Evaluation of Predicted Poses, Right?


Yes, indeed! It allows you to compute a variety of metrics on your structure predictions for assessing their quality. It supports

  • all CASP/CAPRI metrics and more

  • small molecules to huge protein complexes

  • easy extension with custom metrics

  • a command line interface and a Python API

Installation

peppr is available via PyPI:

$ pip install peppr

Usage example

Using the CLI, you can either compute a single metric for a system…

$ peppr run dockq reference.cif poses.cif

… or run an entire prediction model evaluation on many systems.

# Select the metrics you want to compute (here: RMSD and lDDT)
$ peppr create peppr.pkl monomer-rmsd monomer-lddt

# Run the evaluation on predicted poses and their corresponding references
$ peppr evaluate-batch peppr.pkl "systems/*/reference.cif" "systems/*/poses"

# Select the aggregation method over poses (here: Top-3 and Oracle) and report the results
$ peppr tabulate peppr.pkl table.csv top3 oracle

Available metrics

  • RMSD

  • TM-score

  • lDDT

  • lDDT-PLI

  • fnat

  • iRMSD

  • LRMSD

  • DockQ

… and more!

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

peppr-0.9.0.tar.gz (70.4 kB view details)

Uploaded Source

Built Distribution

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

peppr-0.9.0-py3-none-any.whl (76.3 kB view details)

Uploaded Python 3

File details

Details for the file peppr-0.9.0.tar.gz.

File metadata

  • Download URL: peppr-0.9.0.tar.gz
  • Upload date:
  • Size: 70.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for peppr-0.9.0.tar.gz
Algorithm Hash digest
SHA256 5f696edcba8f3e9457aad8761ab9c560d6ecf219fb8a1f3b9937c0108c742e8e
MD5 098e8cb8cb2ce3116a6887a6b644f60f
BLAKE2b-256 7ef1e25c28b575df0469639ec2c9e43fd0d2a4afebb1f85f2e9dc6c71ffed92a

See more details on using hashes here.

Provenance

The following attestation bundles were made for peppr-0.9.0.tar.gz:

Publisher: main.yml on aivant/peppr

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file peppr-0.9.0-py3-none-any.whl.

File metadata

  • Download URL: peppr-0.9.0-py3-none-any.whl
  • Upload date:
  • Size: 76.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for peppr-0.9.0-py3-none-any.whl
Algorithm Hash digest
SHA256 957fa15635d70cf9378fc40a14f00ec85ac0a8f7489c8d883c81a5cf8fa02724
MD5 c008209ba6d844f871749f360b002482
BLAKE2b-256 135791c0c5251d8282243c11a3fec5680107bbbe92d9c2b21952fd2e1ff23fa8

See more details on using hashes here.

Provenance

The following attestation bundles were made for peppr-0.9.0-py3-none-any.whl:

Publisher: main.yml on aivant/peppr

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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