TEACHER
Date:29 July (Friday)
Time: 09:00 – 10:30 (GMT+8)
Chief Executive Officer
NE47 Bio Inc
Over the past few years, machine learning has enabled huge advances in our ability to understand and predict protein properties. Large protein language models are able to learn from evolution to generate functional proteins de novo and accurately predict variant effects. AlphaFold2 and RoseTTAFold have revolutionized protein structure prediction. New versions, extensions, and applications of these machine learning models are being explored every day. In this talk, I will discuss recent advances in protein machine learning with a focus on their applications to protein engineering. I will show that machine learning can be used to design protein variants with significantly improved efficacy, as demonstrated by a case study in designing potent SARS-CoV-2 binding antibodies. Despite their promise, many challenges remain in incorporating these very large models into routine protein engineering methods. Our OpenProtein.AI platform aims to address this by providing access to state-of-the-art algorithms through intuitive and user-friendly workflows in a web-based system.