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Expanding the sequence spaces of synthetic binding protein using deep learning-based framework ProteinMPNN

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Abstract

Synthetic binding proteins (SBPs) with small size, marked solubility and stability, and high affinity are important for protein-based research, treatment, and diagnostics. Over the last several decades, site-directed mutagenesis and directed evolution of privileged protein scaffold make up the great majority of SBPs. The groundbreaking advancement of deep learning (DL) in recent years has revolutionized the problem of protein structure prediction and design. Here, for the first time, the cutting-edge DL framework ProteinMPNN was applied to fulfill the de novo design of 7,245 new synthetic proteins covering 55 different scaffolds based on the original SBPs collected in our SYNBIP database. Comprehensive bioinformatics analysis indicated that, in addition to the excellent performance of sequence recovery, the designed synthetic proteins have a significant improvement in solubility and thermal stability compared to the currently known SBPs. Meanwhile, 8 incredibly suitable protein scaffolds for ProteinMPNN have been identified, from which the designed synthetic proteins calculate displayed good performance on binding ability to their corresponding protein targets. Therefore, the DL-based framework shown great potential in target-directed de novo generation of synthetic protein library with high quality, which could assist experimental biologists to rational protein engineering to discover novel functional protein binders.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 21505009), the Entrepreneurship and Innovation Support Plan for Chinese Overseas Students of Chongqing (cx2020127).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China

    Yanlin Li, Wantong Jiao, Ruihan Liu, Xuejin Deng & Weiwei Xue

  2. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China

    Feng Zhu

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  1. Yanlin Li
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Correspondence to Feng Zhu or Weiwei Xue.

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Electronic supplementary material Supplementary material is available in the online version of this article at journal.hep.com.cn and link.springer.com

Yanlin Li received her bachelor’s degree in 2023 from the School of Pharmacy, Chongqing University, China. She is currently working toward a Master’s degree at Chongqing University, China. Her research interests mainly include protein design and database construction.

Wantong Jiao is a class of 2021 undergraduate at the School of Pharmacy, Chongqing University, China. She is interested in the molecular mechanism of drug and target recognition and interaction as well as the research of new drug dosage forms.

Ruihan Liu is currently studying for a master’s degree at Chongqing University in China. Her research interests mainly include database construction and structure-based drug design and screening.

Xuejin Deng received a bachelor’s degree from Guizhou University, China. She is currently pursuing a master’s degree at the School of Pharmacy, Chongqing University, China. Her research interest is protein design.

Feng Zhu is the Deputy Director of B&R International School of Medicine and a Distinguished Professor of Pharmaceutical Sciences at Zhejiang University, China. He obtained a bachelor’s and master’s degrees in Physics from Beijing Normal University, and a PhD in Pharmacy from the National University of Singapore. Based on artificial intelligence and OMIC (proteomics and metabolomics) technologies, their team conducts systematical exploration on the druggability and system profile of therapeutic targets, develops novel methods and online tools for target discovery, and further studies the mechanism underlying the interaction between drugs and their targets.

Weiwei Xue is an associate professor of Pharmaceutical Sciences at Chongqing University, China. He received a bachelor’s degree in Chemistry (2009) and a PhD in Cheminformatics (2014) from Lanzhou University, China. He worked as a visiting scholar in the Institute for Protein Design at the University of Washington (2018-2019), USA. The research in Dr. Xue’s Lab is focused on developing disease- and therapeutic-related bioinformatics databases and tools, and combing artificial intelligence and molecular modeling approaches to design innovative small molecules or protein binders against molecular targets of complex diseases, including psychiatric disorders, viral infection, and cancer. He has published more than 90 peer-reviewed papers in the area of bioinformatics and computational drug design.

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Li, Y., Jiao, W., Liu, R. et al. Expanding the sequence spaces of synthetic binding protein using deep learning-based framework ProteinMPNN. Front. Comput. Sci. 19, 195903 (2025). https://doi.org/10.1007/s11704-024-31060-3

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