Accurate and efficient protein sequence design through learning concise local environment of residues
Bin HuangiD, Tingwen Fan, Kaiyue WangiD, Haicang ZhangiD, Chungong YuiD, Shuyu Nie, Yangshuo Qi, Wei‐Mou ZhengiD, Jian Zhong HaniD, Zheng FaniD, Shiwei SuniD, Sheng YeiD, Huaiyi YangiD, Dongbo BuiD
Abstract
Protein sequence design has been widely applied in rational protein engineering and increasing the design accuracy and efficiency is highly desired. Here we present ProDESIGN-LE, an accurate and efficient design approach, which adopts a concise but informative representation of residues local environment and trains a transformer to select an appropriate residue at a position from its local environment. ProDESIGN-LE iteratively applies the transformer on the positions in the target structure, eventually acquiring a designed sequence with all residues fitting well with their local environments. ProDESIGN-LE designed sequences for 68 naturally occurring and 129 hallucinated proteins within 20 seconds per protein on average, and the predicted structures from the designed sequences perfectly resemble the target structures with state-of-the-art average TM-score exceeding 0.80. We further experimentally validated ProDESIGN-LE by designing five sequences for an enzyme, chloramphenicol O-acetyltransferase type III (CAT III), and recombinantly expressing the proteins in E. coli. Of these proteins, three exhibited excellent solubility, and one yielded monomeric species with circular dichroism spectra consistent with the natural CAT III protein.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 34
- FWCI
- 2.93
- Field percentile
- top 7%
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Highly accurate and robust protein sequence design with CarbonDesign
Milong Ren, Chungong Yu, … Haicang Zhang·bioRxiv·Aug 7, 2023
- Context-aware geometric deep learning for protein sequence design
L. Krapp, F. Meireles, … M. Dal Peraro·Nature CommunicationsPeer-reviewed·Jul 25, 2024
- Protein sequence design by explicit energy landscape optimization
C. Norn, B. I. M. Wicky, … S. Ovchinnikov·bioRxiv·Jul 23, 2020
- Protein sequence design with a learned potential
Namrata Anand, Raphael R. Eguchi, … Po‐Ssu Huang·bioRxiv·Jan 6, 2020
- OPUS-Design: Designing Protein Sequence from Backbone Structure with 3DCNN and Protein Language Model
Gang Xu, Yulu Yang, … Jianpeng Ma·bioRxiv·Aug 20, 2024
- Proteus software for physics-based protein design
D. Mignon, K. Druart, … T. Simonson·bioRxiv·Jun 30, 2020