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@@ -27,7 +27,7 @@ This model strikes a balance between reconstruction fidelity and structural loca
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  - Encoding Structures into Tokens (See [genbio-ai/AIDO.StructureEncoder](https://huggingface.co/genbio-ai/AIDO.StructureEncoder))
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  - Decoding Tokens into Structures (See [genbio-ai/AIDO.StructureDecoder](https://huggingface.co/genbio-ai/AIDO.StructureDecoder))
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- - Reconstructing Structures (See [below](#reconstructing-structures))
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  - Structure Prediction (See [this section](https://huggingface.co/genbio-ai/AIDO.Protein2StructureToken-16B/blob/main/README.md#structure-prediction) in genbio-ai/AIDO.Protein2StructureToken-16B)
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  ## Results
@@ -42,12 +42,10 @@ This model strikes a balance between reconstruction fidelity and structural loca
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  ## How to Use
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  Please see `experiments/AIDO.StructureTokenizer` in [Model Generator](https://github.com/genbio-ai/modelgenerator) for more details.
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- ### Reconstructing Structures
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-
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- #### Setup
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  Install [Model Generator](https://github.com/genbio-ai/modelgenerator)
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- #### Data preparation
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  To reproduce the reconstruction results in the paper, we provide a preprocessed CASP15 dataset at [genbio-ai/sample-structure-dataset](https://huggingface.co/datasets/genbio-ai/sample-structure-dataset). It could be downloaded via
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  ```bash
@@ -75,7 +73,7 @@ python experiments/AIDO.StructureTokenizer/register_dataset.py \
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  You need to replace the `folder_path` and the `registry_path` in the following steps accordingly.
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- #### Running Encoding and Decoding Task
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  If you use the provided CASP15 dataset, you can run the combined encoding and decoding task using the following command:
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  ```bash
@@ -107,7 +105,7 @@ The input and the output can be summarized as follows:
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  - Currently, this function only supports single GPU inference due to the file saving mechanism. We plan to support multi-GPU inference in the future.
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  - The reconstructed structures are aligned to the original structures using the Kabsch algorithm. This makes it easier to visualize and compare the structures.
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- #### Visualizing the Reconstructed Structures
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  We use VS Code + [Protein Viewer Extension](https://marketplace.visualstudio.com/items?itemName=ArianJamasb.protein-viewer) to visualize the protein structures. It's a beginner-friendly tool for VS Code users. You could also use your preferred protein structure viewer to visualize the structures (e.g., PyMOL, ChimeraX, etc.), but here we focus on this extension.
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  - Encoding Structures into Tokens (See [genbio-ai/AIDO.StructureEncoder](https://huggingface.co/genbio-ai/AIDO.StructureEncoder))
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  - Decoding Tokens into Structures (See [genbio-ai/AIDO.StructureDecoder](https://huggingface.co/genbio-ai/AIDO.StructureDecoder))
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+ - Reconstructing Structures (See [below](#how-to-use))
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  - Structure Prediction (See [this section](https://huggingface.co/genbio-ai/AIDO.Protein2StructureToken-16B/blob/main/README.md#structure-prediction) in genbio-ai/AIDO.Protein2StructureToken-16B)
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  ## Results
 
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  ## How to Use
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  Please see `experiments/AIDO.StructureTokenizer` in [Model Generator](https://github.com/genbio-ai/modelgenerator) for more details.
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+ ### Setup
 
 
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  Install [Model Generator](https://github.com/genbio-ai/modelgenerator)
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+ ### Data preparation
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  To reproduce the reconstruction results in the paper, we provide a preprocessed CASP15 dataset at [genbio-ai/sample-structure-dataset](https://huggingface.co/datasets/genbio-ai/sample-structure-dataset). It could be downloaded via
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  ```bash
 
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  You need to replace the `folder_path` and the `registry_path` in the following steps accordingly.
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+ ### Running Encoding and Decoding Task
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  If you use the provided CASP15 dataset, you can run the combined encoding and decoding task using the following command:
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  ```bash
 
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  - Currently, this function only supports single GPU inference due to the file saving mechanism. We plan to support multi-GPU inference in the future.
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  - The reconstructed structures are aligned to the original structures using the Kabsch algorithm. This makes it easier to visualize and compare the structures.
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+ ### Visualizing the Reconstructed Structures
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  We use VS Code + [Protein Viewer Extension](https://marketplace.visualstudio.com/items?itemName=ArianJamasb.protein-viewer) to visualize the protein structures. It's a beginner-friendly tool for VS Code users. You could also use your preferred protein structure viewer to visualize the structures (e.g., PyMOL, ChimeraX, etc.), but here we focus on this extension.
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