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[学术文献 ] Metabolic engineering of Corynebacterium glutamicum for the production of pyrone and pyridine dicarboxylic acids 进入全文
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Environmental concerns from plastic waste are driving interest in alternative monomers from bio-based sources. Pseudoaromatic dicarboxylic acids are promising alternatives with chemical structures similar to widely used petroleum-based aromatic dicarboxylic acids. However, their use in polyester synthesis has been limited due to production challenges. Here, we report the fermentative production of five pseudoaromatic dicarboxylic acids, including 2-pyrone-4,6-dicarboxylic acid (PDC) and pyridine dicarboxylic acids (PDCAs: 2,3-, 2,4-, 2,5-, and 2,6-PDCA), from glucose using five engineered Corynebacterium glutamicum strains. A platform C. glutamicum chassis strain was constructed by modulating the expression of nine genes involved in the synthesis and degradation pathways of precursor protocatechuate (PCA) and the glucose-uptake system. Comparative transcriptome analysis of the engineered strain against wild-type C. glutamicum identified iolE (NCgl0160) as a target for PDC production. Optimized fed-batch fermentation conditions enabled the final engineered strain to produce 76.17 ± 1.24 g/L of PDC. Using this platform strain, we constructed 2,3-, 2,4-, and 2,5-PDCA-producing strains by modulating the expression of key enzymes. Additionally, we demonstrated a previously uncharacterized pathway for 2,3-PDCA biosynthesis. The engineered strains produced 2.79 ± 0.005 g/L of 2,3-PDCA, 494.26 ± 2.61 mg/L of 2,4-PDCA, and 1.42 ± 0.02 g/L of 2,5-PDCA through fed-batch fermentation. To complete the portfolio, we introduced the 2,6-PDCA biosynthetic pathway to an L-aspartate pathway–enhanced C. glutamicum strain, producing 15.01 ± 0.03 g/L of 2,6-PDCA in fed-batch fermentation. The metabolic engineering strategies developed here will be useful for the production of pseudoaromatic chemicals.
[学术文献 ] Adaptive laboratory evolution recruits the promiscuity of succinate semialdehyde dehydrogenase to repair different metabolic deficiencies 进入全文
NATURE COMMUNICATIONS
Promiscuous enzymes often serve as the starting point for the evolution of novel functions. Yet, the extent to which the promiscuity of an individual enzyme can be harnessed several times independently for different purposes during evolution is poorly reported. Here, we present a case study illustrating how NAD(P)+-dependent succinate semialdehyde dehydrogenase of Escherichia coli (Sad) is independently recruited through various evolutionary mechanisms for distinct metabolic demands, in particular vitamin biosynthesis and central carbon metabolism. Using adaptive laboratory evolution (ALE), we show that Sad can substitute for the roles of erythrose 4-phosphate dehydrogenase in pyridoxal 5’-phosphate (PLP) biosynthesis and glyceraldehyde 3-phosphate dehydrogenase in glycolysis. To recruit Sad for PLP biosynthesis and glycolysis, ALE employs various mechanisms, including active site mutation, copy number amplification, and (de)regulation of gene expression. Our study traces down these different evolutionary trajectories, reports on the surprising active site plasticity of Sad, identifies regulatory links in amino acid metabolism, and highlights the potential of an ordinary enzyme as innovation reservoir for evolution.
[学术文献 ] Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity 进入全文
Nature Communications
Biocatalysis is an attractive approach for the synthesis of chiral pharmaceuticals and fine chemicals, but assessing and/or improving the enantioselectivity of biocatalyst towards target substrates is often time and resource intensive. Although machine learning has been used to reveal the underlying relationship between protein sequences and biocatalytic enantioselectivity, the establishment of substrate fitness space is usually disregarded by chemists and is still a challenge. Using 240 datasets collected in our previous works, we adopt chemistry and geometry descriptors and build random forest classification models for predicting the enantioselectivity of amidase towards new substrates. We further propose a heuristic strategy based on these models, by which the rational protein engineering can be efficiently performed to synthesize chiral compounds with higher ee values, and the optimized variant results in a 53-fold higher E-value comparing to the wild-type amidase. This data-driven methodology is expected to broaden the application of machine learning in biocatalysis research.
[前沿资讯 ] 科学家研发新型去饱和化酶，解锁烯还原酶的逆反应性实现不对称去饱和化 进入全文
科学网
西湖大学叶宇轩课题组在Nature Chemistry期刊上发表了一篇题为“Unmasking the Reverse Catalytic Activity of ‘Ene’-Reductases for Asymmetric Carbonyl Desaturation”的研究成果。该论文解锁了烯还原酶的全新非天然去饱和化反应性，把它们从还原酶改造成为了去饱和化酶。合成了一系列含有远端四级手性中心的高价值环己烯酮产物；此酶催化反应体系条件温和、操作简单、易于放大；系统的机理研究加深了人们对于烯还原酶催化去饱和化过程中重要基元反应的理解。
[前沿资讯 ] AI模型设计六种性能更优蛋白质 进入全文
科学网
美国麻省总医院布莱根分院和贝斯以色列女执事医疗中心团队开发了一款名为EVOLVEpro的AI工具，被认为是蛋白质工程领域的一项重大突破。团队在最新一期《科学》杂志上展示了通过该工具设计的6种具有不同用途的蛋白质，证明了EVOLVEpro能够提高蛋白质的稳定性、精确度及效率。 团队使用EVOLVEpro对6种蛋白质进行了设计。结果显示，经过EVOLVEpro优化的两种单克隆抗体对目标的黏附力增强了30倍；微型CRISPR核酸酶执行基因编辑的效率提升了5倍；用于基因编辑的蛋白质在向基因组不同位置插入序列的能力提高了两倍；Bxb1整合酶在将DNA片段植入细胞以实现可编程基因整合的效率增加了4倍；而用于RNA合成的T7 RNA聚合酶，在准确复制RNA方面的能力更是提升了100倍。 团队指出，这款工具的最大优势在于它不受自然进化限制。借助AI，他们可以根据特定需求优化蛋白质，创造出性能更佳、速度更快、强度更高的蛋白质，使其更有效地与目标结合，进而改善治疗方法或增强其功能性。
[前沿资讯 ] 科学家设计出一种高效蛋白质口袋生成算法 进入全文
科学网
近日，中国科学技术大学认知智能全国重点实验室教授刘淇和哈佛大学医学院教授Marinka Zitnik 课题组合作，设计了深度生成算法PocketGen，用于生成与小分子结合的蛋白质口袋序列和空间结构。11月15日，相关研究成果发表于《自然-机器智能》。 该算法由双层图Transformer编码器和蛋白质预训练语言模型两部分组成。两者分别对应蛋白质的结构信息和序列信息。通过两个部分同时进行信息处理和不断迭代，最终生成所需要的蛋白质口袋。 PocketGen在计算效率和蛋白质口袋设计的成功率方面表现亮眼，是目前全球最高效、最高成功率的蛋白质口袋设计算法之一。在实验中，PocketGen模型不仅在亲和力和结构合理性等指标上超过传统方法，在计算效率方面也有大幅提高，相比传统方法提高10倍以上。审稿人也对该工作给予高度评价，认为“与最先进的方法相比，该方法显著提高了结合亲和力和有效性，表现出更快的性能和更高的成功率。” PocketGen推进了深度生成模型用于功能蛋白质设计，为进一步理解蛋白质设计规律并开展生物实验验证奠定了基础，未来在药物开发、生物传感器、酶催化等领域具有广泛的应用前景。
