| 纯度 | >90%SDS-PAGE. |
| 种属 | Human |
| 靶点 | CAD |
| Uniprot No | P27708 |
| 内毒素 | < 0.01EU/μg |
| 表达宿主 | E.coli |
| 表达区间 | 1456-1846aa |
| 氨基酸序列 | MTSQKLVRLPGLIDVHVHLREPGGTHKEDFASGTAAALAGGITMVCAMPNTRPPIIDAPALALAQKLAEAGARCDFALFLGASSENAGTLGTVAGSAAGLKLYLNETFSELRLDSVVQWMEHFETWPSHLPIVAHAEQQTVAAVLMVAQLTQRSVHICHVARKEEILLIKAAKARGLPVTCEVAPHHLFLSHDDLERLGPGKGEVRPELGSRQDVEALWENMAVIDCFASDHAPHTLEEKCGSRPPPGFPGLETMLPLLLTAVSEGRLSLDDLLQRLHHNPRRIFHLPPQEDTYVEVDLEHEWTIPSHMPFSKAHWTPFEGQKVKGTVRRVVLRGEVAYIDGQVLVPPGYGQDVRKWPQGAVPQLPPSAPATSEMTTTPERPRRGIPGLPD |
| 预测分子量 | 50.1 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3-4条关于CAD(计算机辅助设计)在重组蛋白工程中应用的参考文献示例，涵盖优化表达、稳定性及功能设计等方向：
---
1. **文献名称**：*"Computational Design of Recombinant Protein Expression Platforms in E. coli"*
**作者**：Baker, D. et al.
**摘要**：提出了一种基于计算工具(Rosetta软件)的蛋白质设计方法，用于优化重组蛋白在大肠杆菌中的表达。通过密码子偏好性分析和结构稳定性模拟，成功提高多种目标蛋白的可溶性表达水平，实验验证显示表达量提升最高达3倍。
---
2. **文献名称**：*"Enhancing Thermostability of Recombinant Enzymes via Machine Learning-Guided Mutagenesis"*
**作者**：Yang, K. & Zhang, Y.
**摘要**：结合分子动力学模拟与机器学习模型，预测并筛选出提高重组酶热稳定性的关键突变位点。实验证实，改造后的酶在60℃下活性保留率提高至90%，为工业应用提供新策略。
---
3. **文献名称**：*"De Novo Design of Functional Recombinant Proteins Using Deep Learning"*
**作者**：Jumper, J. et al.
**摘要**：利用AlphaFold2和深度生成模型，从头设计具有特定结合位点的重组蛋白。通过计算生成的新型蛋白在体外实验中成功识别目标抗原，验证了计算机辅助设计在功能蛋白开发中的潜力。
---
4. **文献名称**：*"Rational Engineering of Recombinant Antibody Fragments via CAD: A Case Study on Affinity Maturation"*
**作者**：Löffler, P. et al.
**摘要**：采用计算机辅助的理性设计策略，对重组抗体可变区进行亲和力优化。通过表面电荷调节和互补位重塑，获得亲和力提升10倍的抗体变体，并保持低免疫原性。
---
**备注**：以上文献为示例性质，实际引用时需核实具体论文标题、作者及摘要内容。若需真实文献，建议通过PubMed或Web of Science以关键词“computational design recombinant protein”或“CAD protein engineering”检索近年高被引研究。
CAD (Cathopsin-ASM Direct) recombinant proteins are engineered fusion proteins combining functional domains from distinct biomolecules to achieve tailored therapeutic or diagnostic applications. Emerging from advancements in genetic engineering and structural biology, these chimeric proteins leverage modular design principles to integrate complementary properties—such as targeting specificity, enzymatic activity, or immune modulation—into a single molecule. For example, CAD-based constructs often fuse cell-penetrating peptides with enzyme domains (e.g., acid sphingomyelinase, ASM) to enhance intracellular delivery for lysosomal storage disorder therapies.
The development of CAD recombinant proteins is driven by the need to overcome limitations of conventional biologics, including poor bioavailability, immunogenicity, and off-target effects. By optimizing linkers, folding efficiency, and post-translational modifications, researchers improve stability and functionality. Platforms like Escherichia coli, yeast, or mammalian cell systems are employed for scalable production, followed by chromatographic purification to ensure clinical-grade quality.
Current applications span enzyme replacement therapies, cancer immunotherapy (e.g., antibody-enzyme conjugates), and precision diagnostics. Challenges persist in balancing molecular complexity with manufacturability and predicting in vivo behavior. Innovations in computational protein design, AI-driven domain pairing, and high-throughput screening are accelerating the rational engineering of CAD proteins. As personalized medicine grows, these multifunctional biologics represent a promising frontier for addressing unmet clinical needs through customizable, multi-mechanistic interventions.
×
