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[学术文献 ] Mutagenesis techniques for evolutionary engineering of microbes – exploiting CRISPR-Cas, oligonucleotides, recombinases, and polymerases 进入全文
Trends in Microbiology
The natural process of evolutionary adaptation is often exploited as a powerful tool to obtain microbes with desirable traits. For industrial microbes, evolutionary engineering is often used to generate variants that show increased yields or resistance to stressful industrial environments, thus obtaining superior microbial cell factories. However, even in large populations, the natural supply of beneficial mutations is typically low, which implies that obtaining improved microbes is often time-consuming and inefficient. To overcome this limitation, different techniques have been developed that boost mutation rates. While some of these methods simply increase the overall mutation rate across a genome, others use recent developments in DNA synthesis, synthetic biology, and CRISPR-Cas techniques to control the type and location of mutations. This review summarizes the most important recent developments and methods in the field of evolutionary engineering in model microorganisms. It discusses how both in vitro and in vivo approaches can increase the genetic diversity of the host, with a special emphasis on in vivo techniques for the optimization of metabolic pathways for precision fermentation.
[学术文献 ] Approaching Optimal pH Enzyme Prediction with Large Language Models 进入全文
ACS Synthetic Biology
Enzymes are widely used in biotechnology due to their ability to catalyze chemical reactions: food making, laundry, pharmaceutics, textile, brewing─all these areas benefit from utilizing various enzymes. Proton concentration (pH) is one of the key factors that define the enzyme functioning and efficiency. Usually there is only a narrow range of pH values where the enzyme is active. This is a common problem in biotechnology to design an enzyme with optimal activity in a given pH range. A large part of this task can be completed in silico, by predicting the optimal pH of designed candidates. The success of such computational methods critically depends on the available data. In this study, we developed a language-model-based approach to predict the optimal pH range from the enzyme sequence. We used different splitting strategies based on sequence similarity, protein family annotation, and enzyme classification to validate the robustness of the proposed approach. The derived machine-learning models demonstrated high accuracy across proteins from different protein families and proteins with lower sequence similarities compared with the training set. The proposed method is fast enough for the high-throughput virtual exploration of protein space for the search for sequences with desired optimal pH levels.
[学术文献 ] Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites 进入全文
Nature Communications
Annotating active sites in enzymes is crucial for advancing multiple fields including drug discovery, disease research, enzyme engineering, and synthetic biology. Despite the development of numerous automated annotation algorithms, a significant trade-off between speed and accuracy limits their large-scale practical applications. We introduce EasIFA, an enzyme active site annotation algorithm that fuses latent enzyme representations from the Protein Language Model and 3D structural encoder, and then aligns protein-level information with the knowledge of enzymatic reactions using a multi-modal cross-attention framework. EasIFA outperforms BLASTp with a 10-fold speed increase and improved recall, precision, f1 score, and MCC by 7.57%, 13.08%, 9.68%, and 0.1012, respectively. It also surpasses empirical-rule-based algorithm and other state-of-the-art deep learning annotation method based on PSSM features, achieving a speed increase ranging from 650 to 1400 times while enhancing annotation quality. This makes EasIFA a suitable replacement for conventional tools in both industrial and academic settings. EasIFA can also effectively transfer knowledge gained from coarsely annotated enzyme databases to smaller, high-precision datasets, highlighting its ability to model sparse and high-quality databases. Additionally, EasIFA shows potential as a catalytic site monitoring tool for designing enzymes with desired functions beyond their natural distribution.
[学术文献 ] DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures 进入全文
Briefings in Bioinformatics
Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes' kcat is always time-consuming. Recently, the prediction of kcat using deep learning models has mitigated this problem. However, the accuracy and robustness in kcat prediction still needs to be improved significantly, particularly when dealing with enzymes with low sequence similarity compared to those within the training dataset. Herein, we present DeepEnzyme, a cutting-edge deep learning model that combines the most recent Transformer and Graph Convolutional Network (GCN) to capture the information of both the sequence and 3D-structure of a protein. To improve the prediction accuracy, DeepEnzyme was trained by leveraging the integrated features from both sequences and 3D-structures. Consequently, DeepEnzyme exhibits remarkable robustness when processing enzymes with low sequence similarity compared to those in the training dataset by utilizing additional features from high-quality protein 3D-structures. DeepEnzyme also makes it possible to evaluate how point mutations affect the catalytic activity of the enzyme, which helps identify residue sites that are crucial for the catalytic function. In summary, DeepEnzyme represents a pioneering effort in predicting enzymes' kcat values with improved accuracy and robustness compared to previous algorithms. This advancement will significantly contribute to our comprehension of enzyme function and its evolutionary patterns across species.
[学术文献 ] Machine learning-guided multi-site combinatorial mutagenesis enhances the thermostability of pectin lyase 进入全文
International Journal of Biological Macromolecules
Enhancing the thermostability of enzymes is crucial for industrial applications. Methods such as directed evolution are often limited by the huge sequence space and combinatorial explosion, making it difficult to obtain optimal mutants. In recent years, machine learning (ML)-guided protein engineering has become an attractive tool because of its ability to comprehensively explore the sequence space of enzymes and discover superior mutants. This study employed ML to perform combinatorial mutation design on the pectin lyase PMGL-Ba from Bacillus licheniformis, aiming to improve its thermostability. First, 18 single-point mutants with enhanced thermostability were identified through semi-rational design. Subsequently, the initial library containing a small number of low-order mutants was utilized to construct an ML model to explore the combinatorial sequence space (theoretically 196,608 mutants) of single-point mutants. The results showed that the ML-predicted second library was successfully enriched with highly thermostable combinatorial mutants. After one iteration of learning, the best-performing combinatorial mutant in the third library, P36, showed a 67-fold and 39-fold increase in half-life at 75 °C and 80 °C, respectively, as well as a 2.1-fold increase in activity. Structural analysis and molecular dynamics simulations provided insights into the improved performance of the engineered enzyme.
[前沿资讯 ] 天津工业生物所等在尿苷二磷酸葡萄糖基转移酶RrUGT3催化机制研究方面取得进展 进入全文
中科院天津工业生物所
近日，中国科学院天津工业生物技术研究所盛翔/苏浩团队与山东大学杜磊团队通过分子动力学模拟和量子化学计算方法等多尺度计算模拟方法，结合定点突变实验和晶体结构解析，阐明了RrUGT3催化反应机理以及区域选择性调控机制，为该酶的工程改造奠定了重要的理论基础。该工作通过将RrUGT3与其同源酶晶体结构进行比对，发现远离活性中心的loop区（命名为Ω-1 loop）展现出了极大的柔性，并且此结构域存在于多种UGT酶中，可能是该家族酶的普遍特征。RrUGT3定点突变实验结果表明，Ω-1 loop区上的残基Y314对酶的活性至关重要，由此推测该loop区在反应时会发生构象变化至活性中心影响催化反应，但该变化对底物的结合以及催化活性的影响机制尚未阐明。本研究利用分子动力学（MD）模拟方法探究了Ω-1 loop区构象变化的微观机制。计算结果表明，在酶与底物的结合过程中，Ω-1 loop区会发生从“open”到“closed”的构象变化。这一构象变化是形成稳定的酶-底物三元复合物的关键，该三元复合物只有在Ω-1 loop区“closed”状态下才能稳定存在。另外，该状态下残基Y314可通过水桥与糖受体HBA形成氢键并将糖受体稳定在活性位点，有利于糖基化反应的发生。 基于“closed”状态的酶-底物三元复合物，本研究进一步采用量子力学/分子力学（QM/MM）组合方法和量子化学（QC）团簇方法解析了其化学反应机理及区域选择性调控机制。计算结果表明，HBA酚羟基和醇羟基位点糖基化的反应能垒分别为15.5 kcal/mol和19.8 kcal/mol，因此反应更倾向于发生酚羟基糖基化反应，与实验结果趋向一致。基于计算结果，本研究提出了酚羟基糖基化较稳定的底物结合模式和较低的反应活化能是影响RrUGT3具有区域选择性的原因。此外，RrUGT3的催化能力主要源自于其底物结合口袋的极性微环境，它有利于UDPG的焦磷酸根处于单质子化状态从而促进反应发生。该项研究获得了RrUGT3酶催化过程中构象变化和化学反应机理等诸多重要信息，拓宽了对UGT酶催化机制的认识，为开展UGT酶的理性设计和改造工作提供了重要的理论指导，并为非天然O-糖苷的制备提供了新的思路。
