In silico protein engineering is transforming drug discovery, and the Docking Run Interpretation and Annotation Tool (DoRIAT) emerges as a standout innovation in this field. Developed by Etcembly, DoRIAT refines protein docking analyses using a Bayesian framework to accurately evaluate TCR-pHLA binding, which is crucial for T-cell-based therapies.
Traditional docking engines often produce inconsistent results, mixing biologically relevant structures with ineffective ones. DoRIAT addresses this by scoring docking outputs based on geometric binding mode parameters, outperforming conventional methods like DeepRank and GNN-DOVE in identifying native-like protein conformations.
The framework enables efficient ranking of models close to experimental structures and supports ensemble-based analyses for deeper biological insights. By integrating Gaussian Process regression with molecular docking, DoRIAT enhances scalability and precision, paving the way for breakthroughs in cancer immunotherapy and other applications.
For scientists and investors, DoRIAT exemplifies the growing synergy between AI-driven modelling and biological experimentation.
For more detail download our publication: Doriat: a Bayesian framework for interpreting and annotating docking runs.
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