@@ -26,7 +26,6 @@ OpenFold also supports inference using AlphaFold's official parameters.
OpenFold has the following advantages over the reference implementation:
-**Faster inference** on GPU for chains with < 1500 residues.
-**Inference on extremely long chains**, made possible by our implementation of low-memory attention
([Rabe & Staats 2021](https://arxiv.org/pdf/2112.05682.pdf)). OpenFold can predict the structures of
sequences with more than 4000 residues on a single A100, and even longer ones with CPU offloading.
...
...
@@ -35,17 +34,19 @@ kernels support in-place attention during inference and training. They use
4x and 5x less GPU memory than equivalent FastFold and stock PyTorch
implementations, respectively.
-**Efficient alignment scripts** using the original AlphaFold HHblits/JackHMMER pipeline or [ColabFold](https://github.com/sokrypton/ColabFold)'s, which uses the faster MMseqs2 instead. We've used them to generate millions of alignments.
-**Faster inference** on GPU for short chains.
## Installation (Linux)
All Python dependencies are specified in `environment.yml`. For producing sequence
alignments, you'll also need `kalign`, the [HH-suite](https://github.com/soedinglab/hh-suite),
and one of {`jackhmmer`, [MMseqs2](https://github.com/soedinglab/mmseqs2)(nightly build)}
installed on on your system. Finally, some download scripts require `aria2c`.
installed on on your system. You'll need `git-lfs` to download OpenFold parameters.
Finally, some download scripts require `aria2c`.
For convenience, we provide a script that installs Miniconda locally, creates a
`conda` virtual environment, installs all Python dependencies, and downloads
"OpenFold is a trainable PyTorch reimplementation of AlphaFold 2. For the purposes of inference, it is practically identical to the original (\"practically\" because ensembling is excluded from OpenFold (recycling is enabled, however)).\n",
"\n",
"In this notebook, OpenFold is run with DeepMind's publicly released parameters for AlphaFold 2.\n",
"In this notebook, OpenFold is run with your choice of our original OpenFold parameters or DeepMind's publicly released parameters for AlphaFold 2.\n",
"\n",
"**Note**\n",
"\n",
...
...
@@ -43,7 +43,7 @@
"\n",
"**Licenses**\n",
"\n",
"This Colab uses the [AlphaFold model parameters](https://github.com/deepmind/alphafold/#model-parameters-license), made available under the Creative Commons Attribution 4.0 International ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)) license. The Colab itself is provided under the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0). See the full license statement below.\n",
"This Colab supports inference with the [AlphaFold model parameters](https://github.com/deepmind/alphafold/#model-parameters-license), made available under the Creative Commons Attribution 4.0 International ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)) license. The Colab itself is provided under the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0). See the full license statement below.\n",
