This project aims to fine-tune a protein language model (pLM) to predict the probabilities of various post-translational modifications (PTMs) occurring at specific positions within a protein sequence. By leveraging deep learning, we seek to enhance our ability to identify PTM sites, ultimately aiding in protein function annotation, drug discovery, and disease research.
Post-translational modifications (PTMs) are essential biochemical alterations that regulate protein function, stability, and interactions. These modifications influence nearly every cellular process and play a crucial role in diseases like cancer, neurodegeneration, and immune disorders. Despite their importance, accurately predicting PTM sites remains a major challenge due to the complexity and variability of modifications.
- Advance scientific discovery: Help build an open-source tool that improves our ability to identify and understand PTMs.
- Improve protein function prediction: Many functional annotations rely on accurate PTM site predictions.
- Enhance drug discovery: PTM dysregulation is implicated in various diseases, and better prediction methods can assist in drug target identification.
- Collaborate with experts: Work with bioinformatics and machine learning researchers on cutting-edge problems.
- Fine-tune a pLM model: Adapt a foundation protein language model for PTM site prediction.
- Create a benchmark dataset: Curate high-quality training data from existing PTM databases.
- Improve accuracy & generalizability: Develop robust prediction methods across different protein families and organisms.
- Open collaboration: Build an accessible and community-driven tool for researchers.
To participate in this project, contribute to model training, or get access to training data, join our community on the OBML Discord server.
Contributions are welcome from bioinformaticians, AI researchers, software engineers, and domain experts! Whether you have experience in deep learning, sequence analysis, or data curation, your input can help shape this project.
We look forward to your contributions! 🚀