Generative models for graph-based protein design

John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaKonferenssiesitysScientificvertaisarvioitu

11 Sitaatiot (Scopus)


Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice. A significant aspect of this challenge is the complex coupling between protein sequence and 3D structure, and the task of finding a viable design is often referred to as the inverse protein folding problem. We develop generative models for protein sequences conditioned on a graph-structured specification of the design target. Our approach efficiently captures the complex dependencies in proteins by focusing on those that are long-range in sequence but local in 3D space. Our framework significantly improves upon prior parametric models of protein sequences given structure, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models.

TilaJulkaistu - 2019
OKM-julkaisutyyppiEi oikeutettu
TapahtumaDeep Generative Models for Highly Structured Data - New Orleans, Yhdysvallat
Kesto: 6 toukok. 20196 toukok. 2019


WorkshopDeep Generative Models for Highly Structured Data
LyhennettäDGS@ICLR Workshop
KaupunkiNew Orleans
MuuDGS@ICLR Workshop


Sukella tutkimusaiheisiin 'Generative models for graph-based protein design'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä