jbenbudd/ADPrLlama
jbenbudd/ADPrLlama is a 7 billion parameter LoRA-fine-tuned model based on GreatCaptainNemo/ProLLaMA_Stage_1, designed for predicting ADP-ribosylation (ADPr) post-translational modification sites. It processes 21-residue peptide windows and outputs predicted sites in a structured format. This model specializes in biochemical sequence analysis, specifically for identifying ADPr sites.
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ADPrLlama: Predicting ADP-ribosylation Sites
ADPrLlama is a 7 billion parameter language model, specifically a LoRA-fine-tuned version of GreatCaptainNemo/ProLLaMA_Stage_1. Its primary function is to predict ADP-ribosylation (ADPr) post-translational modification (PTM) sites within protein sequences.
Key Capabilities
- Specialized Prediction: Focuses exclusively on identifying ADPr PTM sites.
- Input Format: Processes 21-residue peptide windows as input.
- Output Format: Provides predictions in a structured format, e.g.,
Sites=<R5,D12,...>. - Biochemical Application: Tailored for specific tasks in bioinformatics and proteomics.
Good For
- Biomedical Researchers: Ideal for scientists studying protein modifications and their biological implications.
- Bioinformatics Pipelines: Can be integrated into workflows requiring automated ADPr site prediction.
- Targeted Analysis: Useful for focused analysis of ADPr events in specific proteins or pathways.
This model is currently a training-only stub, with final performance metrics (ROC, accuracy, precision, recall, F1, confusion matrix) intended to be generated by a companion evaluation notebook on a held-out test set.