Awesome AI based Protein Design

This is a collection of research papers for AI-based Protein Design. And the repository will be continuously updated to track the frontier of AI based protein design.

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Table of Contents

Overview of Protein Design

AI tools have solved the protein structure prediction problem. This problem derives the spatial structure from the amino acid sequence and achieves atomic-level prediction accuracy, such as Alphafold 2. It combines previous protein structure prediction models to automatically learn protein design methods, thus truly serving human pharmaceutical needs.

The specific practices of protein design vary widely, and the problem definitions applicable to different design processes are also very different. Here are some examples:

  1. A problem of predicting amino acid sequences from spatial structure (the inverse of Alphafold), which assumes that the spatial structure of the desired protein can be derived through molecular dynamics simulations, etc.
  2. A problem of protein structure completion problem for a given partial structure, such as the recent Science [1] by the famous David Baker group. This assumes that only partial structural matches can be found.
  3. Combination of the fitted energy function with MD simulation for protein design, such as the recent Nature [2] of Liu Haiyan's team in China.

In addition, many methods can be used for protein design, and the corresponding AI problem definitions are also very different. This article lists some high-level articles in AI -based protein design, which will be continuously updated in the future.

Papers

format:
- [title](paper link) [links]
  - author1, author2, and author3...
  - publisher
  - keyword
  - code
  - experiment environment

Nature

  • A backbone-centred energy function of neural networks for protein design

    • B Huang, Y Xu, X Hu, Y Liu, S Liao, J Zhang, C Huang
    • Keyword: energy function, MD simulation, backbone-centred
  • De novo protein design by deep network hallucination

    • Ivan Anishchenko, Samuel J. Pellock, Tamuka M. Chidyausiku, Theresa A. Ramelot, Sergey Ovchinnikov, Jingzhou Hao, Khushboo Bafna, Christoffer Norn, Alex Kang, Asim K. Bera, Frank DiMaio, Lauren Carter, Cameron M. Chow, Gaetano T. Montelione & David Baker
    • Keywords: hallucination, inpainting, protein design
  • Design of protein-binding proteins from the target structure alone

    • Longxing Cao, Brian Coventry, Inna Goreshnik, Buwei Huang, William Sheffler, Joon Sung Park, Kevin M. Jude, Iva Marković, Rameshwar U. Kadam, Koen H. G. Verschueren, Kenneth Verstraete, Scott Thomas Russell Walsh, Nathaniel Bennett, Ashish Phal, Aerin Yang, Lisa Kozodoy, Michelle DeWitt, Lora Picton, Lauren Miller, Eva-Maria Strauch, Nicholas D. DeBouver, Allison Pires, Asim K. Bera, Samer Halabiya, Bradley Hammerson, Wei Yang, Steffen Bernard, Lance Stewart, Ian A. Wilson, Hannele Ruohola-Baker, Joseph Schlessinger, Sangwon Lee, Savvas N. Savvides, K. Christopher Garcia & David Baker
    • Keywords: binding cite

Nature Biomedical Engineering

Nature Communications

Science

  • Robust deep learning based protein sequence design using ProteinMPNN

    • J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, D. Baker
    • Keywords: language model, structure prediction
  • Scaffolding protein functional sites using deep learning

    • Jue Wang, Sidney Lisanza, David Juergens, Doug Tischer, Joseph L. Watson, Karla M. Castro, Robert Ragotte, Amijai Saragovi, Lukas F. Milles, Minkyung Baek, Ivan Anishchenko, Wei Yang, Derrick R. Hicks, Marc Expòsit, Thomas Schlichthaerle, Jung-Ho Chun, Justas Dauparas, Nathaniel Bennett, Basile I. M. Wicky, Andrew Muenks, Frank DiMaio, Bruno Correia, Sergey Ovchinnikov, David Baker
    • Keywords: functional site, deep learning, hallucination, inpainting

ICML, ICLR or NeurIPS

arxiv or bioarxiv

Others

Reference

[1] Wang, Jue, et al. "Scaffolding protein functional sites using deep learning." Science 377.6604 (2022): 387-394.

[2] Huang, Bin, et al. "A backbone-centred energy function of neural networks for protein design." Nature 602.7897 (2022): 523-528.

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