AstraPTM: Context-Aware PTM Prediction Model for Large-Scale Proteins
Orbion’s transformer-powered AstraPTM delivers robust, context-aware PTM detection—engineered to excel at proteins up to 3,000 residues. By leveraging ESM2 embeddings and advanced self-attention, it uncovers both common and rare modifications that are critical for deeper functional and structural insights.
Why AstraPTM?
Harnessing a two-step approach, AstraPTM first identifies which residues are likely to carry PTMs, then pinpoints which specific modifications may be present. Its context-driven design enables the discovery of novel or under-characterized PTMs.
Binary and Multi-label
AstraPTM addresses both tasks—spotting the presence of PTMs at the residue level and classifying their likely types—all within a single framework.
Context-Aware
Proteins are complex, and context is crucial. AstraPTM handles sequences up to 3,000 residues, integrating ESM2 embeddings to capture both local motifs and long-range dependencies.
Novel PTM Discovery
By leveraging robust context-awareness and a large training dataset, AstraPTM flags potential PTMs that might not be fully classified yet, opening doors to new scientific findings.
Proven Accuracy on Major PTMs
Benchmark results confirm AstraPTM’s reliability across diverse PTMs. The model exhibits high accuracy (≈89%) with a strong balance of precision (≈86%) and recall (≈90%).
The Architecture
AstraPTM employs a Transformer-based design augmented with positional encoding. Trained on over 95,000 proteins enriched with ESM2 and Orbion’s residue- and protein-level embeddings, this framework captures both short- and long-range interactions for robust PTM prediction.
See AstraPTM in action.
Curious how AstraPTM streamlines PTM identification from a simple sequence input? Book a demo, and let us showcase you how to harness AstraPTM's capabilities for your research.
