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# EvoDiff

### Description
In this work, we introduce a general-purpose diffusion framework, EvoDiff, that combines evolutionary-scale data with the distinct conditioning capabilities of diffusion models for controllable protein generation in sequence space. EvoDiff generates high-fidelity, diverse, and structurally-plausible proteins that cover natural sequence and functional space. Critically, EvoDiff can generate proteins inaccessible to structure-based models, such as those with disordered regions, while maintaining the ability to design scaffolds for functional structural motifs, demonstrating the universality of our sequence-based formulation. We envision that EvoDiff will expand capabilities in protein engineering beyond the structure-function paradigm toward programmable, sequence-first design.
In this work, we introduce a general-purpose diffusion framework, EvoDiff, that combines evolutionary-scale data with
the distinct conditioning capabilities of diffusion models for controllable protein generation in sequence space.
EvoDiff generates high-fidelity, diverse, and structurally-plausible proteins that cover natural sequence and functional
space. Critically, EvoDiff can generate proteins inaccessible to structure-based models, such as those with disordered
regions, while maintaining the ability to design scaffolds for functional structural motifs, demonstrating the
universality of our sequence-based formulation. We envision that EvoDiff will expand capabilities in protein engineering
beyond the structure-function paradigm toward programmable, sequence-first design.

We evaluate our sequence and MSA models – EvoDiff-Seq and EvoDiff-MSA, respectively – across a range of generation tasks to demonstrate their power for controllable protein design. Below, we provide documentation for running our models.
We evaluate our sequence and MSA models – EvoDiff-Seq and EvoDiff-MSA, respectively – across a range of generation tasks
to demonstrate their power for controllable protein design. Below, we provide documentation for running our models.

----

Expand All @@ -28,21 +35,19 @@ We evaluate our sequence and MSA models – EvoDiff-Seq and EvoDiff-MSA, respect
----

# Installation
To download our code, we recommend creating a clean conda environment with python ```v3.8.5```. After installing Anaconda, you can do so by running

To download our code, we recommend creating a clean conda environment with python ```v3.8.5```.
```
conda create --name evodiff python=3.8.5
```

In that new environment, to download our code, run:
In that new environment, install EvoDiff:
```
pip install evodiff
pip install git+https://github.com/microsoft/evodiff.git # bleeding edge, current repo main branch
```

You will also need to install PyTorch (we tested our models on ` v2.0.1 `), PyTorch Geometric, and PyTorch Scatter.

Our downstream analysis scripts make use of a variety of tools we do not include in our package. To run the scripts, please download the following packages first:
Our downstream analysis scripts make use of a variety of tools we do not include in our package installation. To run the
scripts, please download the following packages in addition to EvoDiff:
* [TM score](https://zhanggroup.org/TM-score/)
* [Omegafold](https://github.com/HeliXonProtein/OmegaFold)
* [ProteinMPNN](https://github.com/dauparas/ProteinMPNN)
Expand All @@ -51,78 +56,75 @@ Our downstream analysis scripts make use of a variety of tools we do not include
* [DISOPRED3](https://github.com/psipred/disopred)
* [DR-BERT](https://github.com/maslov-group/DR-BERT)

Please follow the setup instructions outlined by the authors of those tools.
We refer to the setup instructions outlined by the authors of those tools.

## Data
We obtain sequences from the [Uniref50 dataset](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4375400/), which contains approximately 45 million protein sequences. The Multiple Sequence Alignments (MSAs) are from the [OpenFold dataset](https://www.biorxiv.org/content/10.1101/2022.11.20.517210v2), which contains 401,381 MSAs for 140,000 unique Protein Data Bank (PDB) chains and 16,000,000 UniClust30 clusters. The intrinsically disordered regions (IDR) data was obtained from the [Reverse Homology GitHub](https://github.com/alexxijielu/reverse_homology/).
We obtain sequences from the [Uniref50 dataset](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4375400/), which contains
approximately 42 million protein sequences.
The Multiple Sequence Alignments (MSAs) are from the [OpenFold dataset](https://www.biorxiv.org/content/10.1101/2022.11.20.517210v2),
which contains 401,381 MSAs for 140,000 unique Protein Data Bank (PDB) chains and 16,000,000 UniClust30 clusters.
The intrinsically disordered regions (IDR) data was obtained from the [Reverse Homology GitHub](https://github.com/alexxijielu/reverse_homology/).

For the scaffolding structural motifs task, we provide pdb files used for conditionally generating sequences in the [examples/scaffolding-pdbs](https://github.com/microsoft/evodiff/tree/main/examples/scaffolding-pdbs) folder. We also provide
For the scaffolding structural motifs task, we provide pdb and fasta files used for conditionally generating sequences in the [examples/scaffolding-pdbs](https://github.com/microsoft/evodiff/tree/main/examples/scaffolding-pdbs) folder. We also provide
We provide pdb files used for conditionally generating MSAs in the [examples/scaffolding-msas](https://github.com/microsoft/evodiff/tree/main/examples/scaffolding-msas) folder.

## Generated sequences

To access the UniRef50 test sequences, use the following code:

```
test_data = UniRefDataset('data/uniref50/', 'rtest', structure=False) # To access the test sequences
```

We provide all generated sequences and MSAs on the [EvoDiff Zenodo](https://zenodo.org/record/8329165). We provide 6 files with the following columns:
* ` esmif_predictions_unconditional_structure_generations.csv`
* sequence: predicted protein sequence from protein structure (using ESM-IF1 model)
* seq len: length of generated sequence
* model: 'foldingdiff' or 'rfdiffusion'
* ` idr_conditional_generations.csv`
* sequence: subsampled sequence that contains IDR
* seq len: length of generated sequence
* gen_idrs: the generated IDR sequence
* original_idrs: the original IDR sequence
* start_idxs: indices corresponding to start of motif
* end_idxs: indices corresponding to end of motif
* model: model type used for generations
* ` msa_evolution_conditional_generations.csv`
* sequence: generated query sequences
* seq len: length of generated sequence
* model: model type used for generations
* ` msa_scaffold.csv` (generations made using EvoDiff-msa model)
* pdb: pdb code used for task
* seqs: generated motif
* start_idxs: indices corresponding to start of motif
* end_idxs: indices corresponding to end of motif
* seq len: length of generated sequence
* scores: average predicted local distance difference test (pLDDT) of sequence
* rmsd: RMSD between predicted motif coordinates and desired motif coordinates
* model: model type used for generations
* ` seq_scaffold.csv` (generations made using EvoDiff-seq model)
* pdb: pdb code used for task
* seqs: generated motif
* start_idxs: indices corresponding to start of motif
* end_idxs: indices corresponding to end of motif
* seq len: length of generated sequence
* scores: average predicted local distance difference test (pLDDT) of sequence
* rmsd: RMSD between predicted motif coordinates and desired motif coordinates
* model: model type used for generations
* ` unconditional_generations.csv`
* sequence: generated sequence
* min hamming dist: minimum Hamming distance between generated sequence and all training sequences
* seq len: length of generated sequence
* model: model type used for generations
'sequence', 'min hamming dist', 'seq len', 'model'

Here is an example of downloading the `unconditional_generations.csv` file:
We provide all generated sequences and MSAs on the [EvoDiff Zenodo](https://zenodo.org/record/8329165).

Here is an example of downloading our unconditional generated sequences from `unconditional_generations.csv` file:

```
curl -O https://zenodo.org/record/8329165/files/unconditional_generations.csv?download=1
```

To extract all unconditionally generated sequences created using the EvoDiff-seq `oadm-38M` model, run the following code:
* Unconditional generations from sequence based models: ` unconditional_generations.csv`
* `sequence`: generated sequence
* `min hamming dist`: minimum Hamming distance between generated sequence and all training sequences
* `seq len`: length of generated sequence
* `model`: model type used for generations, models: `evodiff_oadm_38M`, `evodiff_oadm_640M`, `evodiff_d3pm_uniform_38M`, \
`evodiff_d3pm_uniform_640M`, `evodiff_d3pm_blosum_38M`, `evodiff_d3pm_blosum_640M`, `carp_38M`, `carp_640M`, `lr_ar_38M` \
`lr_ar_38M`, `lr_ar_640M`, `esm_1b`, or `esm_2`

To extract all unconditionally generated sequences created using the EvoDiff-seq `oadm-640M` model, run the following code:
```
import pandas as pd
df = pd.read_csv('unconditional_generations.csv', index_col = 0)
subset = df.loc[df['model'] == 'evodiff_oadm_38M']
subset = df.loc[df['model'] == 'evodiff_oadm_640M']
```

Other data that can be downloaded
* Sequence predictions for unconditional structure generation baselines ` esmif_predictions_unconditional_structure_generations.csv`
* `sequence`: predicted protein sequence from protein structure (using ESM-IF1 model)
* `seq len`: length of generated sequence
* `model`: 'foldingdiff' or 'rfdiffusion'
* Sequence generation via evolutionary alignments: ` msa_evolution_conditional_generations.csv`
* `sequence`: generated query sequences
* `seq len`: length of generated sequence
* `model`: model type used for generations: `evodiff_msa_oadm_maxsub`, `evodiff_msa_oadm_randsub`, `esm_msa_1b`, or `potts`
* Generated IDRs: ` idr_conditional_generations.csv`
* `sequence`: subsampled sequence that contains IDR
* `seq len`: length of generated sequence
* `gen_idrs`: the generated IDR sequence
* `original_idrs`: the original IDR sequence
* `start_idxs`: indices corresponding to start of IDR in sequence
* `end_idxs`: indices corresponding to end of IDR in sequence (inclusive)
* `model`: model type used for generations `evodiff_seq_oadm_640M` or `evodiff_msa_oadm_maxsub`
* Successfully generated scaffolds ` msa_scaffold.csv` (EvoDiff-MSA generations) or `seq_scaffold.csv` (Evodiff-Seq generations)
* `pdb`: pdb code corresponding to scaffold task
* `seqs`: generated scaffold and motif
* `start_idxs`: indices corresponding to start of motif
* `end_idxs`: indices corresponding to end of motif
* `seq len`: length of generated sequence
* `scores`: average predicted local distance difference test (pLDDT) of sequence
* `rmsd`: motifRMSD between predicted motif coordinates and crystal motif coordinates
* `model`: model type used for generations

## Loading pretrained models
To load a model:
```
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