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-0.3.3.tar.gz (39.5 kB view details)

Uploaded Source

Built Distribution

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

peppr-0.3.3-py3-none-any.whl (44.5 kB view details)

Uploaded Python 3

File details

Details for the file peppr-0.3.3.tar.gz.

File metadata

  • Download URL: peppr-0.3.3.tar.gz
  • Upload date:
  • Size: 39.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for peppr-0.3.3.tar.gz
Algorithm Hash digest
SHA256 8807bfedc0d8a0bb05e4423bcf71015c92525fbb579c3b62c9ae87cd97dceba1
MD5 3323b8d1473384f7c314ccf25858d040
BLAKE2b-256 55642fd5fec037fcab8e5cabe7d91438824829cab0e1f5b5ec478041492fe72e

See more details on using hashes here.

Provenance

The following attestation bundles were made for peppr-0.3.3.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-0.3.3-py3-none-any.whl.

File metadata

  • Download URL: peppr-0.3.3-py3-none-any.whl
  • Upload date:
  • Size: 44.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for peppr-0.3.3-py3-none-any.whl
Algorithm Hash digest
SHA256 5f1c42dd753fe8d73fe2d3f2b0acff346ec0953ec0ee0ab17b8deb4fc8c20cac
MD5 18762b2098ddd8c877f76c12cea221c7
BLAKE2b-256 2e7b714cbf5c2c14cfa1b04cd87a0e6f47526b1dc564d677533649d080810292

See more details on using hashes here.

Provenance

The following attestation bundles were made for peppr-0.3.3-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