D. Diaz

发表

Discovery of Novel Gain-of-Function Mutations Guided by Structure-Based Deep Learning.

Isaac Donnell, Andrew D Ellington, Raghav Shroff, 2020, ACS synthetic biology.

Machine learning-aided engineering of hydrolases for PET depolymerization

Daniel J. Diaz, H. Alper, A. Ellington, 2022, Nature.

HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing

Adam R. Klivans, Daniel J. Diaz, Zhangyang Wang, 2023, ICLR.

Stability Oracle: A Structure-Based Graph-Transformer for Identifying Stabilizing Mutations

Adam R. Klivans, James M. Loy, Daniel J. Diaz, 2023, bioRxiv.

Two sequence- and two structure-based ML models have learned different aspects of protein biochemistry

C. Wilke, A. Ellington, Tianlong Chen, 2023, bioRxiv.

Using machine learning to predict the effects and consequences of mutations in proteins.

Daniel J. Diaz, C. Wilke, A. Ellington, 2023, Current opinion in structural biology.

Improved Bst DNA Polymerase Variants Derived via a Machine Learning Approach.

Daniel J. Diaz, A. Ellington, R. Shroff, 2021, Biochemistry.

Deep learning redesign of PETase for practical PET degrading applications

Daniel J. Diaz, H. Alper, A. Ellington, 2021, bioRxiv.

Learning the local landscape of protein structures with convolutional neural networks

James M. Loy, Daniel J. Diaz, C. Wilke, 2021, Journal of Biological Physics.

Synthetic microbial sensing and biosynthesis of amaryllidaceae alkaloids

Daniel J. Diaz, H. Alper, A. Ellington, 2023, bioRxiv.

A structure-based deep learning framework for protein engineering

Isaac Donnell, Andrew D. Ellington, Raghav Shroff, 2019, bioRxiv.

H OT P ROTEIN : A N OVEL F RAMEWORK FOR P ROTEIN T HERMOSTABILITY P REDICTION AND E DITING

Adam R. Klivans, Zhangyang Wang, A. Ellington, 2023 .