> ## Documentation Index
> Fetch the complete documentation index at: https://proto.evodesign.org/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Tools
> Bioinformatics tools that Proto orchestrates for sequence analysis, structure prediction, and more
# Tools
For full tool documentation, API references, and standalone usage guides, see the **[Proto Tools docs](/docs/tools/introduction)**.
Tools are standardized Python implementations of 120+ bioinformatics tools: structure predictors, sequence scorers, gene annotators, and alignment engines. When a [constraint](/docs/language/concepts/constraints) needs to predict a protein's 3D structure or search for sequence motifs, it calls a tool.
Tools are rarely called directly; constraints call them behind the scenes, and the [optimizer](/docs/language/concepts/optimizers) manages caching and execution. Understanding tools nonetheless helps in configuring constraints effectively.
## The Input / Config / Output Pattern
Every tool follows the same three-part pattern using Pydantic models:
* **Input**: *What* to analyze. The primary data: sequences, structures, files.
* **Config**: *How* to analyze it. Parameters and settings. Always optional; sensible defaults are built in.
* **Output**: Results plus standardized metadata (execution time, success status, tool ID, warnings).
## Tool Categories
Predict 3D structures from sequences.
**AlphaFold2**, **AlphaFold3**, **Boltz2**, **Chai1**, **ESMFold**, **Protenix**, **ViennaRNA**
Generate novel protein backbone structures.
**RFDiffusion3**
Sample conformational ensembles.
**BioEmu**
Design sequences for target structures.
**ProteinMPNN**, **LigandMPNN**, **FAMPNN**
Protein language models for scoring and sampling.
**ESM2**, **ESM3**
Autoregressive models for generation and scoring.
**Evo1**, **Evo2**, **ProGen2**
Predict functional effects from genomic sequences.
**Enformer**, **Borzoi**, **AlphaGenome**, **Segmasker**
Annotate sequences with genes, domains, and motifs.
**PyHMMER**, **CRISPR-tracr**, **MinCED**
Search databases and align sequences.
**BLAST**, **MMseqs2**, **MAFFT**, **ColabFold Search**
Find open reading frames in DNA.
**Orfipy**, **Prodigal**
Predict splice sites and specificity.
**SpliceTransformer**
Fetch sequences and structures from public databases.
**UniProt**, **PDB**, **NCBI**, **SequenceFetch**
Align and compare 3D protein structures.
**TMAlign**, **USAlign**
## How Tools Connect to Constraints
When a [constraint](/docs/language/concepts/constraints) such as `structure_plddt_constraint` is created, the constraint function internally calls the appropriate tool. Here is the flow:
**The tool is configured through the constraint's `function_config`.** For example, the `structure_plddt_constraint` config selects which structure predictor to use:
```python python icon="python" theme={null}
from proto_language.constraint import structure_plddt_constraint
# The constraint internally calls the specified tool
Constraint(
inputs=[protein_segment],
function=structure_plddt_constraint,
function_config={
"structure_tool": "esmfold", # Which tool to use: "esmfold", "boltz2", "alphafold3", ...
},
weight=2.0,
)
```
### Tool Caching
When the same sequence is evaluated by multiple constraints that use the same tool, the tool cache prevents redundant computation:
```python python icon="python" theme={null}
# Both constraints use ESMFold internally.
# The second call hits the cache -- no redundant GPU work.
plddt_constraint = Constraint(
inputs=[segment],
function=structure_plddt_constraint,
function_config={"structure_tool": "esmfold"},
)
ptm_constraint = Constraint(
inputs=[segment],
function=structure_ptm_constraint,
function_config={"structure_tool": "esmfold"},
)
```
The optimizer manages cache lifecycle. See [Optimizers: Tool Cache Management](/docs/language/concepts/optimizers#tool-cache-management) for configuration options.
## GPU vs CPU Tools
### GPU Tools
Deep learning models that require NVIDIA GPUs.
* **Structure Prediction:** AlphaFold3, Boltz2, Chai1, ESMFold, Protenix
* **Inverse Folding:** ProteinMPNN, LigandMPNN
* **Language Models:** ESM2, ESM3, Evo2, ProGen2
* **Sequence Scoring:** Enformer, Borzoi, AlphaGenome
* **Structure Design:** RFDiffusion3
* **Structure Dynamics:** BioEmu
* **RNA Splicing:** SpliceTransformer
### CPU Tools
Classical bioinformatics algorithms. Run anywhere.
* **Gene Annotation:** PyHMMER, MinCED
* **Sequence Alignment:** BLAST, MMseqs2, MAFFT, ColabFold Search
* **ORF Prediction:** Orfipy, Prodigal
* **Structure Prediction:** ViennaRNA (RNA only)
When designing optimization pipelines, put CPU-based filter constraints early to screen out bad proposals before GPU-based scoring constraints run. This minimizes expensive GPU time.
## Next Steps
Full tool API references, standalone usage guides, and detailed documentation
See how tools power constraint evaluation
Generators that implement language model tools
How optimizers manage tool caching