# RosettaCommons/RoseTTAFold

This package contains deep learning models and related scripts for RoseTTAFold

Repository: https://github.com/RosettaCommons/RoseTTAFold
Canonical: https://ross.abutalabs.com/products/rosettafold
Language: Python
License: MIT
License Family: permissive
Last push: 2024-02-15T15:28:32+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1891, "days_push": 930, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2258, forks 459 (observed 2026-08-28T04:06:31.540498+00:00)

## What it is
RoseTTAFold is the official implementation of a deep learning system for predicting protein structures and interactions using a three-track neural network. It provides Python models, scripts, and pretrained weights for monomer structure prediction, complex modeling, and protein-protein interaction screening.

## Use cases
- predict the 3D structure of a protein from its amino acid sequence
- model protein-protein complexes and interactions
- screen for protein-protein interactions in yeast using a fast 2-track model
- run structure prediction pipelines comparable to AlphaFold
- fold proteins using MSA and template information
- estimate model confidence and accuracy with DeepAccNet

## When to choose
- you need to predict protein structures from sequence with a published, well-cited method
- you want to model protein complexes or screen PPIs computationally
- you have Linux with NVIDIA GPUs and can download large sequence/structure databases
- you want an open-source (MIT code) alternative to AlphaFold with Rosetta integration

## When to avoid
- you need a lightweight tool without large database downloads (BFD is 272G, templates 100G+)
- you need commercial use of the trained weights (they are non-commercial under the Rosetta-DL license)
- you work on Windows or macOS without GPU support
- you need a maintained turnkey web service rather than a research codebase

## Facets
- artifact type: library
- maturity: stable
- function: deep-learning, machine-learning, sdk
- domain: bioinformatics, deep-learning, machine-learning
- platform: python
- tags: protein-structure-prediction, protein-folding, alphafold-alternative, computational-biology, pytorch, complex-modeling, ppi-screening, linux, gpu

## Member repositories
- RosettaCommons/RoseTTAFold (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:31.540498+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:43:38.475759+00:00, confidence not recorded.
  - readme: https://github.com/RosettaCommons/RoseTTAFold (fetched 2026-08-28T04:06:31.540498+00:00, sha b6ac192125f7)
- Data as of 2026-08-30T08:39:29.467469+00:00.