no code implementations • 26 May 2023 • Kadina E. Johnston, Clara Fannjiang, Bruce J. Wittmann, Brian L. Hie, Kevin K. Yang, Zachary Wu
Directed evolution of proteins has been the most effective method for protein engineering.
1 code implementation • 30 Sep 2022 • Kevin E. Wu, Kevin K. Yang, Rianne van den Berg, James Y. Zou, Alex X. Lu, Ava P. Amini
The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases.
1 code implementation • 14 Jun 2022 • Mingyang Hu, Fajie Yuan, Kevin K. Yang, Fusong Ju, Jin Su, Hui Wang, Fei Yang, Qiuyang Ding
Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions.
no code implementations • bioRxiv 2022 • Christian Dallago, Jody Mou, Kadina E. Johnston, Bruce J. Wittmann, Nicholas Bhattacharya, Samuel Goldman, Ali Madani, Kevin K. Yang
Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications.
1 code implementation • 8 Sep 2021 • Samuel Goldman, Ria Das, Kevin K. Yang, Connor W. Coley
However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates.
no code implementations • 10 Jun 2021 • Brian L. Hie, Kevin K. Yang
Machine-learning models that learn from data to predict how protein sequence encodes function are emerging as a useful protein engineering tool.
no code implementations • 9 Apr 2021 • Zachary Wu, Kadina E. Johnston, Frances H. Arnold, Kevin K. Yang
Protein engineering seeks to identify protein sequences with optimized properties.
no code implementations • 17 Apr 2019 • Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue
Importantly, we show that our objective function can be efficiently decomposed as a difference of submodular functions (DS), which allows us to employ DS optimization tools to greedily identify sets of constraints that increase the likelihood of finding items with high utility.
no code implementations • 27 Nov 2018 • Kevin K. Yang, Zachary Wu, Frances H. Arnold
Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions.