Robust deep learning–based protein sequence design using ProteinMPNN | Zendy

Justas Dauparas | Zendy; Ivan Anishchenko | Zendy; Nathaniel R. Bennett | Zendy; Hua Bai | Zendy; Robert J. Ragotte | Zendy; Lukas F. Milles | Zendy; Basile I. M. Wicky | Zendy; Alexis Courbet | Zendy; Robbert J. de Haas | Zendy; Neville P. Bethel | Zendy; Philip J. Y. Leung | Zendy; Timothy F. Huddy | Zendy; Samuel J. Pellock | Zendy; Doug Tischer | Zendy; F. Chan | Zendy; Brian Koepnick | Zendy; Hannah Nguyen | Zendy; Alex Kang | Zendy; Banumathi Sankaran | Zendy; Asim K. Bera | Zendy; Neil P. King | Zendy; David Baker | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Robust deep learning–based protein sequence design using ProteinMPNN

Author(s) -

Justas Dauparas,

Ivan Anishchenko,

Nathaniel R. Bennett,

Hua Bai,

Robert J. Ragotte,

Lukas F. Milles,

Basile I. M. Wicky,

Alexis Courbet,

Robbert J. de Haas,

Neville P. Bethel,

Philip J. Y. Leung,

Timothy F. Huddy,

Samuel J. Pellock,

Doug Tischer,

F. Chan,

Brian Koepnick,

Hannah Nguyen,

Alex Kang,

Banumathi Sankaran,

Asim K. Bera,

Neil P. King,

David Baker

Publication year - 2022

Publication title -

science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 12.556

H-Index - 1186

eISSN - 1095-9203

pISSN - 0036-8075

DOI - 10.1126/science.add2187

Subject(s) - protein design , in silico , deep learning , sequence (biology) , protein sequencing , computational biology , peptide sequence , computer science , protein structure prediction , protein structure , artificial intelligence , chemistry , biophysics , biochemistry , biology , gene

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research