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Sampling testing, timing and license #6

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Sampling testing, timing and license #6

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8f89123
fixed hardcoded training dataset path to sidechainnet pkl
fatimadavila Sep 27, 2024
0c954a5
tested and cleaned fine_tune_ppi-abag.sh zenodo dataset might be broken
fatimadavila Sep 28, 2024
11ef7e7
changed to dummy wandb entity
fatimadavila Sep 28, 2024
442f23c
minor change to tar line in fine tuning train script. tested and clea…
fatimadavila Sep 30, 2024
847c16b
tested and cleaned sample_antibody_sequences.sh
fatimadavila Sep 30, 2024
4725014
tested and cleaned
fatimadavila Sep 30, 2024
352d762
tested and cleaned
fatimadavila Sep 30, 2024
7dd5c7a
changed utils.plotting import in AntibodySequenceSampler.py
fatimadavila Sep 30, 2024
d72bd0a
added timing table and modified readme
fatimadavila Oct 15, 2024
0fce812
modified readme file
fatimadavila Oct 15, 2024
92622cd
more timing changes to readme
fatimadavila Oct 15, 2024
3044598
MIT license
fatimadavila Oct 15, 2024
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6 changes: 3 additions & 3 deletions AntibodySequenceSampler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,9 +15,9 @@

from tqdm import tqdm

from utils.utils_plotting \
import plot_seq_logo, plot_histogram_for_array,\
sequences_to_probabilities
from utils.plotting \
import plot_seq_logo,\
sequences_to_probabilities #plot_histogram_for_array

torch.set_default_dtype(torch.float64)
torch.set_grad_enabled(False)
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21 changes: 21 additions & 0 deletions LICENSE
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@@ -0,0 +1,21 @@
MIT License

Copyright (c) 2024 Gray Lab

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
183 changes: 179 additions & 4 deletions README.md
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Expand Up @@ -33,7 +33,9 @@ Download and extract trained models from [Zenodo](https://zenodo.org/records/831
tar -xvzf model.tar.gz
```

## Sampling protein sequences
## Sampling sequences

### Sampling protein sequences
To design/generate all positions on the protein, run:
```bash
MODEL=trained_models/ProtEnT_backup.ckpt
Expand All @@ -52,7 +54,7 @@ The above command samples all sequences at 100% masking (i.e. only coord informa
python3 ProteinSequenceSampler.py --help
```

## Sampling antibody sequences without partner context
### Sampling antibody sequences without partner context
To design/generate all positions on the protein, run:
```bash
MODEL=trained_models/ProtEnT_backup.ckpt
Expand All @@ -76,7 +78,7 @@ The above command samples all sequences at 100% masking (i.e. only coord informa
python3 ProteinSequenceSampler.py --help
```

## Sampling interface residues with partner context
### Sampling interface residues with partner context
To generate/design the interface residues for the first partner (order determined by partners.json), run:

```bash
Expand All @@ -99,7 +101,7 @@ python3 PPIAbAgSequenceSampler.py \
# --partner_name both
```

## Sampling antibody interface residues with antigen context
### Sampling antibody interface residues with antigen context
```
MODEL=trained_models/ProtAbAgEnT_backup.ckpt
OUTDIR=./sampled_abag_sequences
Expand All @@ -120,6 +122,179 @@ python3 PPIAbAgSequenceSampler.py \
# --mask_ab_indices 10,11,12
```

### Performance: Timing for Protein Design Tasks (CPU vs GPU)
- Timing values are displayed in the format `mm:ss.000` (minutes:seconds.milliseconds).
- Each GPU run was conducted on 1 node, utilizing 6 processes per task on an NVIDIA A100 GPU.
- Each CPU run was conducted on 1 node, with 8 processes per CPU

<table>
<thead>
<tr>
<th style="text-align:center;">Sequence Design Task</th>
<th style="text-align:center;">CPU/GPU</th>
<th style="text-align:left;">No. of Designs</th>
<th style="text-align:left;">Real Time</th>
<th style="text-align:left;">User Time</th>
<th style="text-align:left;">System Time</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="8" style="text-align:center;">Protein Monomer Sequence Design (126 amino acids)</td>
<td rowspan="4" style="text-align:center;">CPU</td>
<td>100</td>
<td>01:18.639</td>
<td>00:28.506</td>
<td>00:03.628</td>
</tr>
<tr>
<td>1,000</td>
<td>00:46.927</td>
<td>00:33.980</td>
<td>00:04.286</td>
</tr>
<tr>
<td>10,000</td>
<td>01:10.349</td>
<td>01:03.870</td>
<td>00:04.842</td>
</tr>
<tr>
<td>100,000</td>
<td>02:16.108</td>
<td>06:14.454</td>
<td>00:12.911</td>
</tr>
<tr>
<td rowspan="4" style="text-align:center;">GPU</td>
<td>100</td>
<td>00:49.538</td>
<td>00:06.923</td>
<td>00:01.888</td>
</tr>
<tr>
<td>1,000</td>
<td>00:56.923</td>
<td>00:12.329</td>
<td>00:02.101</td>
</tr>
<tr>
<td>10,000</td>
<td>00:37.218</td>
<td>00:57.562</td>
<td>00:02.771</td>
</tr>
<tr>
<td>100,000</td>
<td>01:59.589</td>
<td>08:33.594</td>
<td>00:09.799</td>
</tr>
<tr>
<td rowspan="8" style="text-align:center;">Protein-Protein Interface</td>
<td rowspan="4" style="text-align:center;">CPU</td>
<td>100</td>
<td>01:13.022</td>
<td>01:16.282</td>
<td>00:08.224</td>
</tr>
<tr>
<td>1,000</td>
<td>00:43.972</td>
<td>01:22.581</td>
<td>00:08.596</td>
</tr>
<tr>
<td>10,000</td>
<td>01:19.130</td>
<td>02:22.664</td>
<td>00:09.561</td>
</tr>
<tr>
<td>100,000</td>
<td>03:28.817</td>
<td>12:41.153</td>
<td>00:17.398</td>
</tr>
<tr>
<td rowspan="4" style="text-align:center;">GPU</td>
<td>100</td>
<td>00:11.688</td>
<td>00:09.020</td>
<td>00:03.329</td>
</tr>
<tr>
<td>1,000</td>
<td>00:39.591</td>
<td>00:18.655</td>
<td>00:03.423</td>
</tr>
<tr>
<td>10,000</td>
<td>00:49.310</td>
<td>01:46.022</td>
<td>00:04.493</td>
</tr>
<tr>
<td>100,000</td>
<td>03:01.718</td>
<td>16:08.428</td>
<td>00:14.877</td>
</tr>
<tr>
<td rowspan="8" style="text-align:center;">Antibody-Antigen Interface</td>
<td rowspan="4" style="text-align:center;">CPU</td>
<td>100</td>
<td>01:18.330</td>
<td>02:45.636</td>
<td>00:16.683</td>
</tr>
<tr>
<td>1,000</td>
<td>00:48.824</td>
<td>03:00.106</td>
<td>00:16.751</td>
</tr>
<tr>
<td>10,000</td>
<td>01:37.904</td>
<td>05:21.302</td>
<td>00:18.257</td>
</tr>
<tr>
<td>100,000</td>
<td>05:27.519</td>
<td>27:10.781</td>
<td>00:27.179</td>
</tr>
<tr>
<td rowspan="4" style="text-align:center;">GPU</td>
<td>100</td>
<td>01:35.224</td>
<td>00:13.541</td>
<td>00:04.228</td>
</tr>
<tr>
<td>1,000</td>
<td>00:47.984</td>
<td>00:29.034</td>
<td>00:03.739</td>
</tr>
<tr>
<td>10,000</td>
<td>01:11.780</td>
<td>03:00.415</td>
<td>00:04.555</td>
</tr>
<tr>
<td>100,000</td>
<td>04:24.885</td>
<td>28:10.995</td>
<td>00:14.905</td>
</tr>
</tbody>
</table>

## Training
### Installation
Model was trained with older versions of torch and pytorch_lightning. Newer versions are not backward compatible. The following instructions work for python 3.9 and cuda 11.1.
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