| 纯度 | >85%SDS-PAGE. |
| 种属 | Human |
| 靶点 | CRNN |
| Uniprot No | Q9UBG3 |
| 内毒素 | < 0.01EU/μg |
| 表达宿主 | E.coli |
| 表达区间 | 1-495aa |
| 氨基酸序列 | MPQLLQNING IIEAFRRYAR TEGNCTALTR GELKRLLEQE FADVIVKPHD PATVDEVLRL LDEDHTGTVE FKEFLVLVFK VAQACFKTLS ESAEGACGSQ ESGSLHSGAS QELGEGQRSG TEVGRAGKGQ HYEGSSHRQS QQGSRGQNRP GVQTQGQATG SAWVSSYDRQ AESQSQERIS PQIQLSGQTE QTQKAGEGKR NQTTEMRPER QPQTREQDRA HQTGETVTGS GTQTQAGATQ TVEQDSSHQT GRTSKQTQEA TNDQNRGTET HGQGRSQTSQ AVTGGHAQIQ AGTHTQTPTQ TVEQDSSHQT GSTSTQTQES TNGQNRGTEI HGQGRSQTSQ AVTGGHTQIQ AGSHTETVEQ DRSQTVSHGG AREQGQTQTQ PGSGQRWMQV SNPEAGETVP GGQAQTGAST ESGRQEWSST HPRRCVTEGQ GDRQPTVVGE EWVDDHSRET VILRLDQGNL HTSVSSAQGQ DAAQSEEKRG ITARELYSYL RSTKP |
| 预测分子量 | kDa |
| 蛋白标签 | His tag N-Terminus |
| 缓冲液 | PBS, pH7.4, containing 0.01% SKL, 1mM DTT, 5% Trehalose and Proclin300. |
| 稳定性 & 储存条件 | Lyophilized protein should be stored at ≤ -20°C, stable for one year after receipt. Reconstituted protein solution can be stored at 2-8°C for 2-7 days. Aliquots of reconstituted samples are stable at ≤ -20°C for 3 months. |
| 复溶 | Always centrifuge tubes before opening.Do not mix by vortex or pipetting. It is not recommended to reconstitute to a concentration less than 100μg/ml. Dissolve the lyophilized protein in distilled water. Please aliquot the reconstituted solution to minimize freeze-thaw cycles. |
以下是关于CRNN(Cornulin)重组蛋白研究的示例参考文献(注：内容为示例性概括，实际文献请查询学术数据库)：
1. **《CRNN在食管鳞状细胞癌中的表达及功能研究》**
作者：Zhang Y, et al.
摘要：研究分析了CRNN在食管鳞癌组织中的表达下调现象，通过重组CRNN蛋白体外实验证实其可抑制癌细胞增殖并促进分化，提示其作为肿瘤抑制因子的潜在作用。
2. **《重组Cornulin蛋白在表皮屏障形成中的机制》**
作者：Lee SH, et al.
摘要：利用大肠杆菌系统表达重组CRNN蛋白，发现其通过调控角质形成细胞钙离子信号通路，促进表皮终末分化，为皮肤屏障功能研究提供分子依据。
3. **《CRNN重组表达优化及其抗菌活性分析》**
作者：Wang X, et al.
摘要：开发了基于昆虫细胞的重组CRNN高效表达体系，证实纯化后的蛋白对金黄色葡萄球菌具有显著抑制作用，拓展了其在感染性疾病中的应用潜力。
4. **《CRNN基因多态性与慢性炎症疾病关联研究》**
作者：Chen L, et al.
摘要：通过构建CRNN重组蛋白与免疫细胞共培养模型，发现特定基因变异导致蛋白功能缺失，与慢性胃炎等炎症性疾病风险升高相关。
提示：以上为模拟文献，实际研究需参考PubMed、Web of Science等平台的具体论文数据。
CRNN (Convolutional Recurrent Neural Network) recombinant proteins represent an innovative integration of deep learning and bioengineering to optimize protein design and production. Recombinant proteins, engineered through genetic modification of host organisms, are widely used in therapeutics (e.g., insulin, monoclonal antibodies), industrial enzymes, and research tools. Traditional protein engineering relies on iterative experimental screening or structure-based rational design, which can be time-consuming and limited in exploring vast sequence spaces.
CRNN-based approaches address these challenges by leveraging hybrid neural networks. Convolutional layers extract local spatial patterns from protein sequences or structural data (e.g., amino acid motifs, secondary structures), while recurrent layers model long-range dependencies and temporal relationships within sequences. This architecture enables predictive modeling of protein properties—such as stability, solubility, or binding affinity—directly from sequence or structural inputs. For recombinant protein development, CRNN models can guide the design of optimized variants with enhanced expression yields or functional characteristics, reducing reliance on high-throughput experimental screening.
Recent applications include predicting codon optimization schemes for heterologous expression, identifying mutation hotspots for functional improvement, and designing fusion proteins with tailored pharmacokinetic profiles. By integrating multi-modal data (sequence, structural, or expression datasets), CRNNs accelerate the design-test-learn cycle, offering a cost-effective pathway for customizing recombinant proteins. While still evolving, this synergy between deep learning and synthetic biology holds promise for advancing precision biomanufacturing and expanding the functional diversity of engineered proteins.
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