Mn<sup>2+</sup>-induced structural flexibility enhances the entire catalytic cycle and the cleavage of mismatches in prokaryotic argonaute proteins

Zheng, Lirong; Zhou, Bingxin; Yang, Yu; Zan, Bing; Zhong, Bozitao; Wu, Banghao; Feng, Yan; Liu, Qian; Hong, Liang

doi:10.1039/d3sc06221j

Cited by 2 publications

(1 citation statement)

References 73 publications

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“…The results were analyzed by QuantStudioTM Design & Analysis Software v1.5.1. The activities are determined as the slope of the time dependence of fluorescence intensity resulting from the cleavage function before it reaches the plateau. , The guide DNA and target DNA used for cleavage are listed in Table S8. Three independent experiments are conducted to determine the cleavage activity.…”

Section: Methodsmentioning

confidence: 99%

Protein Engineering with Lightweight Graph Denoising Neural Networks

Zhou,

Zheng,

et al. 2024

J. Chem. Inf. Model.

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Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establishes a lightweight graph neural network scheme for protein structures, which efficiently analyzes the microenvironment of amino acids in wild-type proteins and reconstructs the distribution of the amino acid sequences that are more likely to pass natural selection. This distribution serves as a general guidance for scoring proteins toward arbitrary properties on any order of mutations. Our proposed solution undergoes extensive wet-lab experimental validation spanning diverse physicochemical properties of various proteins, including fluorescence intensity, antigen−antibody affinity, thermostability, and DNA cleavage activity. More than 40% of PROTLGN-designed single-site mutants outperform their wild-type counterparts across all studied proteins and targeted properties. More importantly, our model can bypass the negative epistatic effect to combine single mutation sites and form deep mutants with up to seven mutation sites in a single round, whose physicochemical properties are significantly improved. This observation provides compelling evidence of the structure-based model's potential to guide deep mutations in protein engineering. Overall, our approach emerges as a versatile tool for protein engineering, benefiting both the computational and bioengineering communities.

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Section: Methodsmentioning

confidence: 99%

Protein Engineering with Lightweight Graph Denoising Neural Networks

Zhou,

Zheng,

et al. 2024

J. Chem. Inf. Model.

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A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity

Zhou,

Zheng,

et al. 2023

Preprint

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Computation or deep learning-based functional protein generation methods address the urgent demand for novel biocatalysts, allowing for precise tailoring of functionalities to meet specific requirements. This emergence leads to the creation of highly efficient and specialized proteins with wide-ranging applications in scientific, technological, and biomedical domains. This study establishes a conditional protein diffusion model, namely CPDiffusion, to deliver diverse protein sequences with desired functions. While the model is free from extensive training data and the sampling process involves little guidance on the type of generated amino acids, CPDiffusion effectively secures essential highly conserved residues that are crucial for protein functionalities. We employed CPDiffusion and generated 27 artificially designed Argonaute proteins, programmable endonucleases applied for easy-to-implement and high-throughput screenings in gene editing and molecular diagnostics, that mutated approximately 200-400 amino acids with 40% sequence identities to those from nature. Experimental tests demonstrate the solubility of all 27 artificially-designed proteins (AP), with 24 of them displaying DNA cleavage activity. Remarkably, 74% of active APs exhibited superior activity compared to the template protein, and the most effective one showcased a remarkable nearly nine-fold enhancement of enzymatic activity. Moreover, 37% of APs exhibited enhanced thermostability. These findings emphasize CPDiffusion's remarkable capability to generate long-sequence proteins in a single step while retaining or enhancing intricate functionality. This approach facilitates the design of intricate enzymes featuring multi-domain molecular structures through in silico generation and throughput, all accomplished without the need for supervision from labeled data.

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Mn²⁺-induced structural flexibility enhances the entire catalytic cycle and the cleavage of mismatches in prokaryotic argonaute proteins

Abstract: Prokaryotic Argonaute (pAgo) proteins, a class of DNA/RNA-guided programmable endonucleases, have been extensively utilized in nucleic acid-based biosensors. The specific binding and cleavage of nucleic acids by pAgo proteins, which...

Cited by 2 publications

References 73 publications

Protein Engineering with Lightweight Graph Denoising Neural Networks

Protein Engineering with Lightweight Graph Denoising Neural Networks

A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity

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Mn2+-induced structural flexibility enhances the entire catalytic cycle and the cleavage of mismatches in prokaryotic argonaute proteins

Abstract: Prokaryotic Argonaute (pAgo) proteins, a class of DNA/RNA-guided programmable endonucleases, have been extensively utilized in nucleic acid-based biosensors. The specific binding and cleavage of nucleic acids by pAgo proteins, which...

Cited by 2 publications

References 73 publications

Protein Engineering with Lightweight Graph Denoising Neural Networks

Protein Engineering with Lightweight Graph Denoising Neural Networks

A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity

Contact Info

Product

Resources

About

Mn²⁺-induced structural flexibility enhances the entire catalytic cycle and the cleavage of mismatches in prokaryotic argonaute proteins