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[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。
[前沿资讯 ] 学者发现迄今为止人类微生物组中最有效的溶菌酶 进入全文
科学网
8月6日，《细胞报告》（Cell Reports）发表了广东工业大学生物医药学院教授林章凛团队与华南理工大学生物学院副教授杨晓锋团队合作的最新研究成果。他们在人工智能的生物学应用方面取得重要进展，成功构建一种新型的人工智能框架——DeepMineLys，并发现迄今为止在人类微生物组中最有效的溶菌酶。 “DeepMineLys不仅能够挖掘溶菌酶，它具备蛋白质挖掘的广泛应用潜力，为未来的生物学研究提供了一个有力的工具。”论文共同通讯作者林章凛表示，DeepMineLys的成功得益于构建了涵盖广泛噬菌体溶菌酶的全面训练数据集，集成了TAPE等先进算法和编码技术，采用了三层卷积神经网络和双轨架构等几个关键因素，极大地提升了模型的预测性能。
[学术文献 ] Sex-Specific Effects of Environmental Exposure to the Antimicrobial Agents Benzalkonium Chloride and Triclosan on the Gut Microbiota and Health of Zebrafish (Danio rerio) 进入全文
Environmental Science & Technology
The use of disinfectants containing benzalkonium chloride (BAC) has become increasingly widespread in response to triclosan (TCS) restrictions and the COVID-19 pandemic, leading to the increasing presence of BAC in aquatic ecosystems. However, the potential environmental health impacts of BAC on fish remain poorly explored. In this study, we show that BAC and TCS can induce the gut dysbiosis in zebrafish (Danio rerio), with substantial effects on health. Breeding pairs of adult zebrafish were exposed to environmentally relevant concentrations of BAC and TCS (0.4–40 μg/L) for 42 days. Both BAC and TCS exposure perturbed the gut microbiota, triggering the classical NF-κB signaling pathway and resulting in downstream pathological toxicity associated with inflammatory responses, histological damage, inhibited ingestion, and decreased survival. These effects were dose-dependent and sex-specific, as female zebrafish were more susceptible than male zebrafish. Furthermore, we found that BAC induced toxicity to a greater extent than the restricted TCS at environmentally relevant concentrations, which is particularly concerning. Our results suggest that environmental exposure to antimicrobial chemicals can have ecological consequences by perturbing the gut microbiota, a previously underappreciated target of such chemicals. Rigorous ecological analysis should be conducted before widely introducing replacement antimicrobial compounds into disinfecting products.
[学术文献 ] Geometric deep learning of protein–DNA binding specificity 进入全文
Nature Methods
Predicting protein–DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein–DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding specificity, to a wide range of DNA sequences. This information is not directly accessible in a single structure. Here, to access this information, we present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep-learning model designed to predict binding specificity from protein–DNA structure. DeepPBS can be applied to experimental or predicted structures. Interpretable protein heavy atom importance scores for interface residues can be extracted. When aggregated at the protein residue level, these scores are validated through mutagenesis experiments. Applied to designed proteins targeting specific DNA sequences, DeepPBS was demonstrated to predict experimentally measured binding specificity. DeepPBS offers a foundation for machine-aided studies that advance our understanding of molecular interactions and guide experimental designs and synthetic biology.
