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Update README.md
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README.md
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This model is trained to predict general binding sites of proteins using on the sequence. This is a finetuned version of
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`esm2_t6_8M_UR50D`, trained on [this dataset](https://huggingface.co/datasets/AmelieSchreiber/general_binding_sites). The data is
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not filtered by family, and thus the model may be overfit to some degree. In the Hugging Face Inference API widget to the right
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there are three protein sequence examples. The first is a DNA binding protein
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The second and third were obtained using [EvoProtGrad](https://github.com/Amelie-Schreiber/sampling_protein_language_models/blob/main/EvoProtGrad_copy.ipynb)
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a Markov Chain Monte Carlo method of (in silico) directed evolution of proteins based on a form of Gibbs sampling. The mutatant-type
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This model is trained to predict general binding sites of proteins using on the sequence. This is a finetuned version of
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`esm2_t6_8M_UR50D`, trained on [this dataset](https://huggingface.co/datasets/AmelieSchreiber/general_binding_sites). The data is
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not filtered by family, and thus the model may be overfit to some degree. In the Hugging Face Inference API widget to the right
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there are three protein sequence examples. The first is a DNA binding protein truncated to the first 1022 amino acid residues
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([see UniProt entry here](https://www.uniprot.org/uniprotkb/D3ZG52/entry)).
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The second and third were obtained using [EvoProtGrad](https://github.com/Amelie-Schreiber/sampling_protein_language_models/blob/main/EvoProtGrad_copy.ipynb)
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a Markov Chain Monte Carlo method of (in silico) directed evolution of proteins based on a form of Gibbs sampling. The mutatant-type
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