SLAI Seminar 25th Session will be discussing the topic on "Structure Prediction from Proteins to RNAs: Going beyond AlphaFold", from 10:00am to 11:30am, January 26th (Monday) at B311 Lecture Hall, online participation is welcome (Tencent Meeting ID: 245-394-752)

About the Speaker:

Professor Yaoqi Zhou is a Senior Investigator at Shenzhen Bay Laboratory and Associate Director of the Institute of Systems and Physical Biology. He is also the Scientific Founder of Ribopeutic, Inc. and the author of the bestselling book “Departure: A Journey of Scientific Life Beyond Comfort Zones” 《出发，不断走出舒适区的科研生活之旅》. Prior to this, he served as a Full Professor at Indiana University and a Chair Professor at Griffith University in Australia.

With long-term dedication to structural bioinformatics, Professor Zhou pioneered the use of shallow neural network learning in 2009 to predict continuous protein backbone dihedral angles, paving the way for end-to-end protein structure prediction and foreshadowing the Nobel Prize–winning work of AlphaFold 2. In 2014, he led the development of the AI-driven protein sequence design methods SPIN/SPIN2, regarded as "the starting point of artificial intelligence in protein design". His research group has consistently ranked among the top performers in international competitions for protein/RNA structure prediction and functionprediction. His Google Scholar citations exceed 20,000 with an H-index of 79. Since returning to China, he has secured major research grants from the Ministry of Science and Technology, the National Natural Science Foundation of China, and the Department of Science and Technology of Guangdong Province. He currently focuses on AI- and high-throughput experiment–based fundamental and applied research on proteins/RNA, as well as drug development and delivery.

In 2022, he co-founded Ribopeutic, Inc., which developed an internationally leading AI-enabled, dry-wet integrated closed-loop platform for RNA target and small-molecule drug discovery, providing innovative RNA-targeting therapeutics to address unmet clinical needs. The company recently completed nearly 100 million RMB in pre-A funding.

Abstract：

AlphaFold 2 revolutionized the accuracy of protein structure prediction. However, its performance heavily relies on uncontrollable quality and quantity of natural homologs that can be detected in protein sequence libraries for a given sequence. The lack of quality homologs is one key reason why only 36% of human proteome residues were predicted with high confidence. This problem was not solved with the arrival of AlphaFold 3 or other updated techniques. The situation for RNAs is even worse: there is simply no reliable way of searching RNA homologs if its secondary structure is unknown because RNAs are poorly conserved in sequence space. Here we will show how lab-generated homologous sequences and language models can help. The advancement paves the way for a promising future of rapid, cost-effective structure prediction for proteins and RNAs by integrating AI with high-throughput sequencing.