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Explore the prediction-based 'biophysical variation' of proteins along its sequence. The predictions reflect 'emerging' properties, so what the sequence is capable of, not necessarily what it will do in a particular context, for example when it adopts a specific fold.
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The predicted biophysical features are the following:
-[⏯️ Open Jupyter Notebook on Google Colab](https://colab.research.google.com/github/Bio2Byte/public_notebooks/blob/main/Bio2ByteTools_v3.0.5_SingleSequence_demo.ipynb)
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#### Title: `Bio2ByteTools_v3_singleseq_pypi_with_files.ipynb` (latest version supporting FASTA files as input)
-[⏯️ Open Jupyter Notebook on Google Colab](https://colab.research.google.com/github/Bio2Byte/public_notebooks/blob/main/Bio2ByteTools_v3_singleseq_pypi_with_files.ipynb)
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##### 📓 Title: `Bio2ByteTools_v3_singleseq_pypi.ipynb` (version including PSP predictions)
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Example of usage of our Bio2Byte tools package for single sequence file inputs (in FASTA format) using v3.0.5
-[⏯️ Open Jupyter Notebook on Google Colab](https://colab.research.google.com/github/Bio2Byte/public_notebooks/blob/main/Bio2ByteTools_v3_singleseq_pypi.ipynb)
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##### 📓 Title: `AlphaFold_+_B2B_Tools.ipynb`
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Run a set of biophysical feature predictions on top of an Alphafold thee-dimensional predicted structure.
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Run the biophysical feature predictions on top of an Alphafold thee-dimensional predicted structure.
-[⏯️ Open Jupyter Notebook on Google Colab](https://colab.research.google.com/github/Bio2Byte/public_notebooks/blob/main/AlphaFold_%2B_B2B_Tools.ipynb)
-[⏯️ Open Jupyter Notebook on Google Colab](https://colab.research.google.com/github/Bio2Byte/public_notebooks/blob/main/Bio2Byte_AlphaFold_Analysis.ipynb)
-[⏯️ Open Jupyter Notebook on Google Colab](https://colab.research.google.com/github/Bio2Byte/public_notebooks/blob/main/Bio2ByteTools_v3.0.5_SingleSequence_demo_bioconda.ipynb)
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