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  ---
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  widget:
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- - text: "MNSVTVSHAPYYIVYHDDWEPVMSQLVEFYNEVASWLLRDETSPIPPKFFIQLKQMLRNKRVCVCGILPYPIDGTGVPFESPNFTKKSIKEIASSISRLTGVIDYKGYNLNIIDGVIPWNYYLSCKLGETKSHAIYWDKISKLLLQHITKHVSVLYCLGKTDFSNIRAKLESPVTTIVGYHPAARDRQFEKDRSFEIINELLELDNKVPINWAQGFIY"
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  example_title: "Protein Sequence 1"
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- - text: "MNSVTVSHAPYTIAYHDDWEPVMSQLVEFYNEAASWLLRDETSPIPSKFNIQLKQPLRNKRVCVFGIDPYPKDGTGVPFESPNFTKKSIKEIASSISRLMGVIDYEGYNLNIIDGVIPWNYYLSCKLGETKSHAIYWDKISKLLLQHITKHVSVLYCLGKTDFSNIRAKLESPVTTIVGYHPSARDRQFEKDRSFEIINVLLELDNKVPLNWAQGFIY"
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  example_title: "Protein Sequence 2"
 
 
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  license: mit
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  datasets:
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  - AmelieSchreiber/general_binding_sites
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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.
 
 
 
 
 
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  ## Training
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  ---
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  widget:
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+ - text: "MEPLDDLDLLLLEEDSGAEAVPRMEILQKKADAFFAETVLSRGVDNRYLVLAVETKLNERGAEEKHLLITVSQEGEQEVLCILRNGWSSVPVEPGDIIHIEGDCTSEPWIVDDDFGYFILSPDMLISGTSVASSIRCLRRAVLSETFRVSDTATRQMLIGTILHEVFQKAISESFAPEKLQELALQTLREVRHLKEMYRLNLSQDEVRCEVEEYLPSFSKWADEFMHKGTKAEFPQMHLSLPSDSSDRSSPCNIEVVKSLDIEESIWSPRFGLKGKIDVTVGVKIHRDCKTKYKIMPLELKTGKESNSIEHRGQVILYTLLSQERREDPEAGWLLYLKTGQMYPVPANHLDKRELLKLRNQLAFSLLHRVSRAAAGEEARLLALPQIIEEEKTCKYCSQMGNCALYSRAVEQVHDTSIPEGMRSKIQEGTQHLTRAHLKYFSLWCLMLTLESQSKDTKKSHQSIWLTPASKLEESGNCIGSLVRTEPVKRVCDGHYLHNFQRKNGPMPATNLMAGDRIILSGEERKLFALSKGYVKRIDTAAVTCLLDRNLSTLPETTLFRLDREEKHGDINTPLGNLSKLMENTDSSKRLRELIIDFKEPQFIAYLSSVLPHDAKDTVANILKGLNKPQRQAMKKVLLSKDYTLIVGMPGTGKTTTICALVRILSACGFSVLLTSYTHSAVDNILLKLAKFKIGFLRLGQSHKVHPDIQKFTEEEMCRLRSIASLAHLEELYNSHPVVATTCMGISHPMFSRKTFDFCIVDEASQISQPICLGPLFFSRRFVLVGDHKQLPPLVLNREARALGMSESLFKRLERNESAVVQLTIQYRMNRKIMSLSNKLTYEGKLECGSDRVANAVITLPNLKDVRLEFYADYSDNPWLAGVFEPDNPVCFLNTDKVPAPEQIENGGVSNVTEARLIVFLTSTFIKAGCSPSDIGIIAPYRQQLRTITDLLARSSVGMVEVNTVDKYQGRDKSLILVSFVRSNEDGTLGELLKDWRRLNVAITRAKHKLILLGSVSSLKRFPPLEKLFDHLNAEQLISNLPSREHESLYHILGDCQRD"
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  example_title: "Protein Sequence 1"
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+ - text: "MNSVTVSHAPYYIVYHDDWEPVMSQLVEFYNEVASWLLRDETSPIPPKFFIQLKQMLRNKRVCVCGILPYPIDGTGVPFESPNFTKKSIKEIASSISRLTGVIDYKGYNLNIIDGVIPWNYYLSCKLGETKSHAIYWDKISKLLLQHITKHVSVLYCLGKTDFSNIRAKLESPVTTIVGYHPAARDRQFEKDRSFEIINELLELDNKVPINWAQGFIY"
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  example_title: "Protein Sequence 2"
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+ - text: "MNSVTVSHAPYTIAYHDDWEPVMSQLVEFYNEAASWLLRDETSPIPSKFNIQLKQPLRNKRVCVFGIDPYPKDGTGVPFESPNFTKKSIKEIASSISRLMGVIDYEGYNLNIIDGVIPWNYYLSCKLGETKSHAIYWDKISKLLLQHITKHVSVLYCLGKTDFSNIRAKLESPVTTIVGYHPSARDRQFEKDRSFEIINVLLELDNKVPLNWAQGFIY"
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+ example_title: "Protein Sequence 3"
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  license: mit
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  datasets:
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  - AmelieSchreiber/general_binding_sites
 
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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, 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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+ protein sequences in theory should have similar binding sites to the wild-type protein sequence, but perhaps with higher binding affinity.
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+ Testing this out on the model, we see the two proteins indeed have the same binding sites, which validates to some degree that the model
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+ has learned to predict binding sites well (and that EvoProtGrad works as intended).
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  ## Training
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