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12 changes: 12 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -7,6 +7,18 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

---

## [1.11.0] - 2023-07-06

### 1.11.0 Added

- Support for running AlphaFold jobs on non-accelerated instance types by setting `gpu=0` and submitting job to the CPUOnDemandJobQueue.
- Support for passing a dictionary of environment variables to AlphaFold jobs.
- Support for c/m/r6i instance types in the CPU on-demand compute environment.
- Support for c/m/r7g instance types on the Graviton on-demand and spot compute environments.
- Added model version information to README.

---

## [1.10.4] - 2023-06-12

### 1.10.4 Changed
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16 changes: 16 additions & 0 deletions README.md
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Expand Up @@ -104,6 +104,8 @@ Please visit [https://github.com/EddyRivasLab/hmmer](https://github.com/EddyRiva

### 4.2. AlphaFold

[Version 2.3.1](https://github.com/deepmind/alphafold/releases/tag/v2.3.1) from 1/12/2023.

Please visit [https://github.com/deepmind/alphafold](https://github.com/deepmind/alphafold) for more information about the AlphaFold2 algorithm.

The original AlphaFold 2 citation is
Expand Down Expand Up @@ -138,6 +140,8 @@ The AlphaFold-Multimer citation is

### 4.3. OpenFold

Commit [109442b14e6184fbee45e2696f21b052eb3fb1e5](https://github.com/aqlaboratory/openfold/commit/109442b14e6184fbee45e2696f21b052eb3fb1e5) from November 23, 2022.

Please visit [https://github.com/aqlaboratory/openfold](https://github.com/aqlaboratory/openfold) for more information about the OpenFold algorithm.

The OpenFold citation is
Expand All @@ -155,6 +159,8 @@ The OpenFold citation is

### 4.4. OmegaFold

Commit [313c873ad190b64506a497c926649e15fcd88fcd](https://github.com/HeliXonProtein/OmegaFold/commit/313c873ad190b64506a497c926649e15fcd88fcd) from December 12, 2022.

Please visit [https://github.com/HeliXonProtein/OmegaFold](https://github.com/HeliXonProtein/OmegaFold) for more information about the OmegaFold algorithm.

The OmegaFold citation is
Expand All @@ -175,6 +181,8 @@ The OmegaFold citation is

### 4.5. RFDesign

Commit [bba6992283de63faba6ff727bb4bc68327a5356c](https://github.com/RosettaCommons/RFDesign/commit/bba6992283de63faba6ff727bb4bc68327a5356c) from November 21, 2022.

Please visit [https://github.com/RosettaCommons/RFDesign](https://github.com/RosettaCommons/RFDesign) for more information about the RFDesign hallucinate and inpainting algorithms.

The RFDesign citation is
Expand All @@ -195,6 +203,8 @@ The RFDesign citation is

### 4.6. ESMFold

Commit [74d25cba46a7fd9a9f557ff41ed1d8e9f131aac3](https://github.com/facebookresearch/esm/commit/74d25cba46a7fd9a9f557ff41ed1d8e9f131aac3) from November 26, 2023.

Please visit [https://github.com/facebookresearch/esm](https://github.com/facebookresearch/esm) for more information about the ESMFold algorithm.

The ESMFold citation is
Expand All @@ -211,6 +221,8 @@ The ESMFold citation is

### 4.7. ProteinMPNN

Commit [be1d37b6699dcd2283ab5b6fc8cc88774e2c80e9](https://github.com/dauparas/ProteinMPNN/commit/be1d37b6699dcd2283ab5b6fc8cc88774e2c80e9) from March 24, 2023.

Please visit [https://github.com/dauparas/ProteinMPNN](https://github.com/dauparas/ProteinMPNN) for more information about the ProteinMPNN algorithm.

The ProteinMPNN citation is
Expand All @@ -230,6 +242,8 @@ The ProteinMPNN citation is

### 4.8. DiffDock

Commit [3c3c728cf2e444cf8df45b58067604d982159471](https://github.com/gcorso/DiffDock/commit/3c3c728cf2e444cf8df45b58067604d982159471) from March 27, 2023.

Please visit [https://github.com/gcorso/DiffDock](https://github.com/gcorso/DiffDock) for more information about the DiffDock algorithm.

The DiffDock citation is
Expand All @@ -245,6 +259,8 @@ The DiffDock citation is

### 4.9. RFDiffusion

Commit [5606075d45bd23aa60785024b203ed6b0f6d2cd0](https://github.com/RosettaCommons/RFdiffusion/commit/5606075d45bd23aa60785024b203ed6b0f6d2cd0) from June 28, 2023.

Please visit [https://github.com/RosettaCommons/RFdiffusion](https://github.com/RosettaCommons/RFdiffusion) for more information about the RFDiffusion algorithm.

The RFDiffusion citation is
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Expand Up @@ -211,9 +211,9 @@ Resources:
AllocationStrategy: BEST_FIT_PROGRESSIVE
InstanceRole: !Ref InstanceProfile
InstanceTypes:
- m5
- r5
- c5
- m6i
- r6i
- c6i
LaunchTemplate:
LaunchTemplateId: !Ref InstanceLaunchTemplate
Version: $Latest
Expand Down Expand Up @@ -243,6 +243,9 @@ Resources:
- m6g
- r6g
- c6g
- m7g
- r7g
- c7g
LaunchTemplate:
LaunchTemplateId:
Ref: InstanceLaunchTemplate
Expand Down Expand Up @@ -276,6 +279,9 @@ Resources:
- m6g
- r6g
- c6g
- m7g
- r7g
- c7g
LaunchTemplate:
LaunchTemplateId: !Ref InstanceLaunchTemplate
Version: $Latest
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Expand Up @@ -112,8 +112,8 @@ Resources:
Value: 8
- Type: MEMORY
Value: 16000
- Type: GPU
Value: 1
# - Type: GPU
# Value: 1
Volumes:
- Name: database
Host:
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5 changes: 4 additions & 1 deletion infrastructure/docker/rfdiffusion/Dockerfile
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Expand Up @@ -13,7 +13,10 @@ RUN apt-get -q update \
&& rm -rf /var/lib/apt/lists/* \
&& apt-get autoremove -y \
&& apt-get clean \
&& git clone --depth=1 https://github.com/RosettaCommons/RFdiffusion.git /app/RFdiffusion \
&& git clone https://github.com/RosettaCommons/RFdiffusion.git /app/RFdiffusion \
&& pushd /app/RFdiffusion \
&& git checkout 5606075d45bd23aa60785024b203ed6b0f6d2cd0 \
&& popd \
&& pip install -q --no-cache-dir \
dgl==1.0.2+cu116 -f https://data.dgl.ai/wheels/cu116/repo.html \
torch==1.12.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116 \
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54 changes: 53 additions & 1 deletion notebooks/quick-start-protein-folding.ipynb
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@@ -1,6 +1,7 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "pc5-mbsX9PZC"
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "W4JpOs6oA-QS"
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Submit Sequence Alignment and Folding Jobs"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"AlphaFold can also be run in CPU-only mode for long sequences"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"job_name = target.target_id + \"_AlphaFold2Job_CPU_\" + datetime.now().strftime(\"%Y%m%d%s\")\n",
"alphafold2_cpu_job = AlphaFold2Job(\n",
" job_name=job_name,\n",
" target_id=target.target_id,\n",
" fasta_s3_uri=target.get_fasta_s3_uri(),\n",
" msa_s3_uri=target.get_msas_s3_uri(),\n",
" output_s3_uri=target.get_predictions_s3_uri() + \"/\" + job_name,\n",
" boto_session=boto_session,\n",
" cpu=4,\n",
" memory=15, # Why not 16? ECS needs about 1 GB for container services\n",
" gpu=0,\n",
")\n",
"alphafold2_cpu_submission = batch_environment.submit_job(\n",
" alphafold2_cpu_job, job_queue_name=\"CPUOnDemandJobQueue\", depends_on=[jackhmmer_submission]\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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"metadata": {},
"outputs": [],
"source": [
"for job in [jackhmmer_job, openfold_job, alphafold2_job, omegafold_job, esmfold_job]:\n",
"for job in [jackhmmer_job, openfold_job, alphafold2_job, alphafold2_cpu_job, omegafold_job, esmfold_job]:\n",
" print(\n",
" f\"Job {job.describe_job()[0]['jobName']} is in status {job.describe_job()[0]['status']}\"\n",
" )"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Download and Visualize Results"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Once the jobs are finished, download and view the results"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot Predicted Structures"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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2 changes: 1 addition & 1 deletion setup.py
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setup(
name='batchfold',
version='1.10.4',
version='1.11.0',
description='A modular architecture for running protein structure analysis on AWS Batch.',
author='Brian Loyal',
author_email='bloyal@amazon.com',
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