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[前沿资讯 ] 学者构建安全高效微生物控制策略 进入全文
科学网
华南理工大学生物科学与工程学院教授熊伟团队与美国国家可再生能源实验室合作，开发出一种基于集群建模与CRISPR干扰（CRISPRi）的新型微生物控制策略。相关成果近日发表于《细胞系统》（Cell Systems）。 研究提出了一种全新的代谢稳健性调控方法。利用计算建模工具，预测并锁定了微生物核心代谢网络中的关键靶点，这些靶点对代谢网络的稳健性具有显著影响。通过CRISPR干扰技术，研究团队能够在实验中精准调控这些基因的表达，定量控制微生物适应度，抑制微生物的生长。 实验过程中，研究团队首先开发了一个基于“稳健性预测”的计算框架，通过模拟酶活性波动对代谢网络的影响，筛选出对稳健性最敏感的基因靶点，如大肠杆菌核心代谢网络中的磷酸果糖激酶、丙酮酸激酶等。得益于CRISPRi技术对调控效率的提升，实验中使用了精简版的Cas12m蛋白和经过优化的遗传绝缘体RiboJ，这种组合不仅大幅减少了基因回路的泄漏，还显著增强了多重基因调控的效率与稳定性。 为了确保安全性，研究还关注了多靶点调控，设计了单基因、多基因联合调控策略，证明通过同时靶向多个代谢稳健性关键节点，可以显著降低微生物逃逸的概率。例如，四重基因靶点（ppc、metE、ptsH和cysH）的CRISPRi设计在实验中表现出极强的生长抑制效果，其逃逸频率远低于当前公认的设定标准。 在多种实验室和自然环境模拟条件下，该系统均能保持较高的稳定性和安全性，在葡萄糖、甘油、乙酸等多种碳源条件的验证中始终有效。在经过长达数周的遗传稳定性测试后，仅观察到少量引导RNA突变，没有出现目标基因功能丧失的情况。研究团队还通过LuxR-AHL诱导系统开发了一种“关闭式”CRISPRi回路，在无诱导剂条件下，能够有效终止微生物的增殖，进一步提升了逃逸控制能力。
[前沿资讯 ] 天津工业生物技术研究所在通过阻断几丁质合成酶表达提高菌丝蛋白转化率方面取得新进展 进入全文
中科院天津工业生物技术研究所
威尼斯镰刀菌在发酵生产真菌蛋白方面具有诸多显著优势，如营养丰富、安全性良好、能够可持续大规模生产等，因此被广泛应用于真菌肉类替代品及其他相关产品中。然而，利用天然菌株生产菌丝体蛋白时，存在转化率低、蛋白含量低等问题，这也导致了较高的生产成本。经研究团队前期研究发现，威尼斯镰刀菌菌丝中高膳食纤维含量是导致大量碳损失的关键因素之一，基于此，降低真菌细胞壁中膳食纤维的含量成为提高菌株转化效率的关键要点。 中国科学院天津工业生物技术研究所李德茂研究员带领的工业生物系统工程研究团队，以降低威尼斯镰刀菌菌丝体蛋白发酵生产中其细胞壁膳食纤维合成为突破口，通过生信分析与评估，对威尼斯镰刀菌中全部共12个几丁质合成酶基因进行了深入研究，精准地锁定了最有希望降低几丁质含量的基因并将其敲除，成功获得几丁质含量下降26%，菌体和蛋白转化率分别提高16%、36%的转化子。然后通过转录组分析，靶定以阻断副产物乙醇合成为主的丙酮酸代谢途径来进一步减少碳代谢流流失。最终使菌体和蛋白的转化率得到了进一步提升，菌体转化率提高了29%，蛋白转化率提高了40%。
[学术文献 ] Model-assisted CRISPRi/a library screening reveals central carbon metabolic targets for enhanced recombinant protein production in yeast 进入全文
Metabolic Engineering
Production of recombinant proteins is regarded as an important breakthrough in the field of biomedicine and industrial biotechnology. Due to the complexity of the protein secretory pathway and its tight interaction with cellular metabolism, the application of traditional metabolic engineering tools to improve recombinant protein production faces major challenges. A systematic approach is required to generate novel design principles for superior protein secretion cell factories. Here, we applied a proteome-constrained genome-scale protein secretory model of the yeast Saccharomyces cerevisiae (pcSecYeast) to simulate α-amylase production under limited secretory capacity and predict gene targets for downregulation and upregulation to improve α-amylase production. The predicted targets were evaluated using high-throughput screening of specifically designed CRISPR interference/activation (CRISPRi/a) libraries and droplet microfluidics screening. From each library, 200 and 190 sorted clones, respectively, were manually verified. Out of them, 50% of predicted downregulation targets and 34.6% predicted upregulation targets were confirmed to improve α-amylase production. By simultaneously fine-tuning the expression of three genes in central carbon metabolism, i.e. LPD1, MDH1, and ACS1, we were able to increase the carbon flux in the fermentative pathway and α-amylase production. This study exemplifies how model-based predictions can be rapidly validated via a high-throughput screening approach. Our findings highlight novel engineering targets for cell factories and furthermore shed light on the connectivity between recombinant protein production and central carbon metabolism.
[学术文献 ] Deep learning for NADNADP cofactor prediction and engineering using transformer attention analysis in enzymes 进入全文
Metabolic Engineering
Understanding and manipulating the cofactor preferences of NAD(P)-dependent oxidoreductases, the most widely distributed enzyme group in nature, is increasingly crucial in bioengineering. However, large-scale identification of the cofactor preferences and the design of mutants to switch cofactor specificity remain as complex tasks. Here, we introduce DISCODE (Deep learning-based Iterative pipeline to analyze Specificity of COfactors and to Design Enzyme), a novel transformer-based deep learning model to predict NAD(P) cofactor preferences. For model training, a total of 7,132 NAD(P)-dependent enzyme sequences were collected. Leveraging whole-length sequence information, DISCODE classifies the cofactor preferences of NAD(P)-dependent oxidoreductase protein sequences without structural or taxonomic limitation. The model showed 97.4% and 97.3% of accuracy and F1 score, respectively. A notable feature of DISCODE is the interpretability of its transformer layers. Analysis of attention layers in the model enables identification of several residues that showed significantly higher attention weights. They were well aligned with structurally important residues that closely interact with NAD(P), facilitating the identification of key residues for determining cofactor specificities. These key residues showed high consistency with verified cofactor switching mutants. Integrated into an enzyme design pipeline, DISCODE coupled with attention analysis, enables a fully automated approach to redesign cofactor specificity.
[前沿资讯 ] Argonne team breaks new ground in AI-driven protein design 进入全文
Eurekalert
Harnessing the power of artificial intelligence (AI) and the world’s fastest supercomputers, a research team led by the U.S. Department of Energy’s (DOE) Argonne National Laboratory has developed an innovative computing framework to speed up the design of new proteins. One of the key innovations of the team’s MProt-DPO framework is its ability to integrate different types of data streams, or “multimodal data.” It combines traditional protein sequence data with experimental results, molecular simulations and even text-based narratives that provide detailed insights into each protein’s properties. This approach has the potential to accelerate protein discovery for a wide range of applications.
[学术文献 ] S-PLM: Structure-Aware Protein Language Model via Contrastive Learning Between Sequence and Structure 进入全文
Advanced Science
Proteins play an essential role in various biological and engineering processes. Large protein language models (PLMs) present excellent potential to reshape protein research by accelerating the determination of protein functions and the design of proteins with the desired functions. The prediction and design capacity of PLMs relies on the representation gained from the protein sequences. However, the lack of crucial 3D structure information in most PLMs restricts the prediction capacity of PLMs in various applications, especially those heavily dependent on 3D structures. To address this issue, S-PLM is introduced as a 3D structure-aware PLM that utilizes multi-view contrastive learning to align the sequence and 3D structure of a protein in a coordinated latent space. S-PLM applies Swin-Transformer on AlphaFold-predicted protein structures to embed the structural information and fuses it into sequence-based embedding from ESM2. Additionally, a library of lightweight tuning tools is provided to adapt S-PLM for diverse downstream protein prediction tasks. The results demonstrate S-PLM's superior performance over sequence-only PLMs on all protein clustering and classification tasks, achieving competitiveness comparable to state-of-the-art methods requiring both sequence and structure inputs. S-PLM and its lightweight tuning tools are available at https://github.com/duolinwang/S-PLM/.
