KIDDS 월간 세미나 시리즈 - 2023년 1월

일시: 2023/1/27 (금) 오전 10시

Title: Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models

Abstract: There has been considerable recent progress in designing new proteins using deep learning methods. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher order symmetric architectures, has yet to be described. Diffusion models have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. In this talk, we will describe how by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We will describe the power and generality of the method, called RoseTTAFold Diffusion, showing data experimentally characterizing hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RoseTTAFold Diffusion enables the design of diverse, complex, functional proteins from simple molecular specifications.

연사: Joseph Watson & David Juergens (University of Washington)

링크: https://snu-ac-kr.zoom.us/j/3907204837

주최: KIDDS, 차세대 인실리코 단백질 디자인 센터