Multimodal Protein Language Models for Deciphering Protein Function

Explore a cutting-edge lecture on multimodal protein language models for deciphering protein function. Delve into the innovative approach developed by the Zitnik Lab at Harvard Medical School, which integrates textual protein descriptions with sequence-structure protein language models. Learn how this integration bridges the gap between understanding structural aspects of proteins and gaining a functional view of vast protein space. Discover the model's capabilities in text-based prediction of protein targets, multimodal protein captioning, and facilitating Q&A sessions with scientists of varying expertise levels. Examine the model's ability to generalize to new phenotypes in a zero-shot manner, making it versatile for diverse tasks even with scarce functional annotations. Gain insights into the future potential of "AI scientists" as generative agents capable of skeptical learning and reasoning to empower biomedical research. This lecture, presented by Marinka Zitnik, Owen Queen, and Yepeng Huang, offers a comprehensive look at the intersection of artificial intelligence and protein function analysis.