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model-family passport · Review date not recorded

RoseTTAFold / All-Atom

The three-track network that followed folding into all-atom space.

4/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using RoseTTAFold / All-Atom?

SupportedEvidence supports the stated context with explicit boundaries
Best suited forPrediction
Evidence supportsCASP14 / complex modelling: Peer-reviewed
Evidence does not establishUniversal superiority, therapeutic success, clinical utility or regulatory acceptance.
Major limitationPerformance depends on the evaluation dataset and operating conditions.
Current registry recordRoseTTAFold All-Atom2 recorded releases · Review date not recorded. A newer version is not assumed to be universally better.

What it is

RoseTTAFold introduced a 'three-track' architecture reasoning jointly over sequence, distances and coordinates. RoseTTAFold All-Atom (2024) models proteins together with small molecules, nucleic acids and covalent modifications. David Baker's lab is the epicenter of open protein AI and shared the 2024 Nobel Prize in Chemistry.

Evidence trail

BioAtlas keeps the path from source to decision visible. A connection records provenance; it does not imply that evidence is sufficient for every context.

Sources4 connectedPrimary resources and normalized claims
Claims1 normalizedProtein structure prediction
EntityRoseTTAFold / All-Atommodel-family · RoseTTAFold All-Atom
ReviewReview date not recordedReview date not claimed
ConclusionContext requiredAdd to an evaluation before operational use

Model passport

Entity typemodel-family
OrganizationInstitute for Protein Design, UW
Model family introduced2024
AccessOpen source
Commercial useRestricted / verify terms
DeploymentSelf-hosted
ComputeGPU recommended
Domainsstructure
Biology → representation → computation → evidence

How RoseTTAFold / All-Atom represents biology

model-familystructure

Category is navigation. These fields describe the model-specific computational transformation and deliberately override broad category defaults.

1 · Biological inputs
Biomolecular sequence or structure context
2 · Input representation
Biomolecular sequence
3 · Internal representation
Geometric representation
4 · Architecture
Structure-prediction model
5 · Learning objective
Structure prediction
6 · Output representation
3D coordinates

Biological scale

Modalities & tasks

ProteinPrediction

Registry, claims and frontier intelligence

Versioned registry

RoseTTAFold All-Atom

2 version records · latest curated year 2024. Model-family identity remains separate from capability and access changes.

Explore version lineage →

Inputs and outputs

Inputs

Biomolecular sequence or structure context

Outputs

3D structuresConfidence estimates

Scientific and technical profile

Scientific principles

Multi-track message passingEvolutionary couplingSE(3)-equivariance

Technology

Three-track networkSE(3)-TransformerAll-atom representation
Ideas before algorithms

Scientific lineage

Explore all foundations

These are transparent concept matches—not claims that one scientist alone caused this model. Each connection is based on the model’s recorded domain, scientific principles, technical terms or an explicit lineage link.

Structural biology

Anfinsen’s dogma—the thermodynamic hypothesis

Christian B. Anfinsen

Protein structure prediction, inverse folding and generative protein design all assume that sequence strongly constrains structure and function.

Explicit model lineage
Genomics & cell systems

X-ray evidence for the helical structure of DNA

Rosalind Franklin & Raymond Gosling

Structural genomics and sequence-to-structure reasoning began with experimentally grounded molecular geometry.

Matched concepts: nucleic, structure, sequence
Structural biology

First atomic structures of globular proteins

John Kendrew & Max Perutz

Protein structure prediction and structure-based design became meaningful because experimental crystallography established the target reality to predict against.

Matched concepts: fold, 3d structure, coordinates
Computational intelligence

Information, entropy and communication

Claude E. Shannon

Sequence modelling, cross-entropy training, language models, mutual information and representation learning all use Shannon’s framework.

Matched concepts: sequence, representation

Evaluation evidence

Dataset or evaluationCASP14 / complex modelling
Task or metricStructure prediction
Evidence statusPeer-reviewed
Open source ↗

Task-specific evidence only; not comparable as a universal leaderboard score.

Protein structure prediction

CASP14 / complex modelling

RoseTTAFold All-Atom · CASP14 and held-out structure targets
peer-reviewed

Competitive three-track structure prediction reported in the primary publication.

Claim caveats
  • Results across RoseTTAFold generations are not interchangeable.
  • All-atom complex evaluation requires a task-specific benchmark.

Known limitations

  • Performance depends on the evaluation dataset and operating conditions.
  • Task-specific benchmark results should not be compared across unlike domains.
  • Outputs require task-specific scientific and experimental validation.

Milestones

2024

David Baker won the 2024 Nobel Prize in Chemistry.

Evidence

Foundation for RFdiffusion & ProteinMPNN.