AI agents are learning to read @biohub protein models @GaoShanghua @AdaFang_ @_yepeng
https://aiscientist.tools/posts/ai-agents-learn-to-read-protein-models
We explored how AI agents powered by ToolUniverse @ScientistTools can interact with new ESM models
🧬 Mutation and loss-of-function analysis
Agents compare reference and mutant proteins, identify SAE features most affected by a mutation, and connect those perturbations to structural and functional consequences. The agents then relate these changes to experimental evidence, including deep mutational scanning measurements, to explain potential loss-of-function mechanisms
🧪 Functional mechanism exploration
Agents analyze protein representations to identify functional tracks associated with specific molecular activities. By linking SAE features to protein regions, structures, and annotations, the agents can generate hypotheses about how proteins carry out their functions
Check out new SAE-enabled ToolUniverse skills for variant interpretation, loss-of-function analysis, structural annotation, functional mechanism interpretation, and evaluation against experimental datasets
@HarvardDBMI @harvardmed @Harvard @broadinstitute @KempnerInst
