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-0.1.0.tar.gz (32.4 kB view details)

Uploaded Source

Built Distribution

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

peppr-0.1.0-py3-none-any.whl (37.3 kB view details)

Uploaded Python 3

File details

Details for the file peppr-0.1.0.tar.gz.

File metadata

  • Download URL: peppr-0.1.0.tar.gz
  • Upload date:
  • Size: 32.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for peppr-0.1.0.tar.gz
Algorithm Hash digest
SHA256 6d3c026a1e9a8598ca1bc28c432463ebbaf47217f8dbc9e084afd52a4a339745
MD5 82ef30328c76417b227a15cbd646e867
BLAKE2b-256 8ece0689e566f16efd9cafbb093de42fcc49d73bee2feaee52b3ffbf3d60bc25

See more details on using hashes here.

Provenance

The following attestation bundles were made for peppr-0.1.0.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-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: peppr-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 37.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for peppr-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 3fec5d80641fd8bf6ba1581e717a43ba0a447a9d88b8f9bd3ea3246c2ca83b30
MD5 39203e68546b6c7c0644a60575f1e15b
BLAKE2b-256 7e066affd86ef4ae1aa18233ed80ff1451c3d48eaa8724c7f7d73bc477a32f66

See more details on using hashes here.

Provenance

The following attestation bundles were made for peppr-0.1.0-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