De novo protein design by inversion of the AlphaFold structure prediction network

By Casper A. Goverde, Benedict Wolf, Hamed Khakzad, S. Rosset, Bruno E. Correia. 2022

83 citations recorded by the source corpus.

De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in computational design strategies, de novo design of protein structures remains challenging, given the vast size of the sequence-structure space. AlphaFold2 (AF2), a state-of-the-art neural network architecture, achieved remarkable accuracy in predicting protein structures from amino acid sequences. This raises the question whether AF2 has learned the principles of protein folding sufficiently for de novo design. Here, we sought to answer this question by inverting the AF2 network, using the prediction weight set and a loss function to bias the generated sequences to adopt a target fold. Initial design trials resulted in de novo designs with an overrepresentation of hydrophobic residues on the protein surface compared to their natural protein family, requiring additional surface optimization. In silico validation of the designs showed protein structures with the correct fold, a hydrophilic surface and a densely packed hydrophobic core. In vitro validation showed that several designs were folded and stable in solution with high melting temperatures. In summary, our design workflow solely based on AF2 does not seem to fully capture basic principles of de novo protein design, as observed in the protein surface’s hydrophobic vs. hydrophilic patterning. However, with minimal post-design intervention, these pipelines generated viable sequences as assessed experimental characterization. Thus such pipelines show the potential to contribute to solving outstanding challenges in de novo protein design.

Subfields: Materials Chemistry, Molecular Biology

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In Materials Chemistry

1. David Baker
2. Frank DiMaio
3. Ivan V. Anishchenko
4. Minkyung Baek
5. Qian Cong
6. Yi Zhao
7. Zhenyu Yang
8. Jingi Yeo

In Molecular Biology

1. David Baker
2. Frank DiMaio
3. Ivan V. Anishchenko
4. Minkyung Baek
5. Sergey Ovchinnikov
6. Qian Cong
7. Milot Mirdita
8. Yang Zhang

Citations in the corpus

• AlphaFold2 and its applications in the fields of biology and medicine (2023, methodology): “Goverde et al. 113 designed a pipeline for de novo protein design based on AF2.”
• AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences (2023, background): “Accessing accurate protein structures paves the way for D 369 an enhanced understanding of protein function, providing researchers with the tools necessary for modulating these proteins or engineering new ones ( 8–10 ).”
• Efficient and accurate prediction of protein structure using RoseTTAFold2 (2023, methodology): “Many recent efforts have made use of AF2’s evoformer and IPA architecture in carrying out various protein structure prediction and design tasks 11–14 ; RF2 provides an orthogonal structure prediction framework to AF that enables in silico screening of designs.”