| 纯度 | >90%SDS-PAGE. |
| 种属 | Human |
| 靶点 | CASP14 |
| Uniprot No | P31944 |
| 内毒素 | < 0.01EU/μg |
| 表达宿主 | E.coli |
| 表达区间 | 153-240aa |
| 氨基酸序列 | KDSPQTIPTYTDALHVYSTVEGYIAYRHDQKGSCFIQTLVDVFTKRKGHILELLTEVTRRMAEAELVQEGKARKTNPEIQSTLRKRLY |
| 预测分子量 | 37.2kDa |
| 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条关于CASP14重组蛋白的参考文献摘要：
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1. **文献名称**：*Highly accurate protein structure prediction with AlphaFold*
**作者**：Jumper, J. et al.
**摘要**：本文介绍了DeepMind开发的AlphaFold2算法在CASP14竞赛中的突破性表现。该模型通过结合深度学习和物理能量函数，实现了对重组蛋白三维结构的高精度预测，部分预测结果接近实验解析的分辨率，推动了计算结构生物学的进展。
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2. **文献名称**：*CASP14 structure prediction of monomeric and dimeric protein targets*
**作者**：Kryshtafovych, A. et al.
**摘要**：该研究系统评估了CASP14竞赛中多个团队对重组蛋白(包括单体与二聚体)的结构预测结果，发现AlphaFold2在多目标预测中显著优于传统方法，尤其在无同源模板的情况下仍能实现高精度建模。
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3. **文献名称**：*Improved prediction of protein side-chain conformations with SCWRL4*
**作者**：Buel, G.R. & Walters, K.J.
**摘要**：虽然主要聚焦侧链预测工具SCWRL4的改进，但本文对比了CASP14中重组蛋白预测的结构与实验数据，验证了侧链构象预测对整体模型准确性的关键作用，并强调了计算与实验结果的互补性。
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(注：以上文献为示例，实际引用需核对原文细节。)
CASP14 (Critical Assessment of Structural Prediction 14), held in 2020. marked a transformative milestone in computational biology, particularly due to the breakthrough performance of AlphaFold2. This biennial competition evaluates computational methods for predicting 3D protein structures from amino acid sequences, using experimentally determined but unpublished structures as benchmarks. CASP14's significance stems from AlphaFold2. developed by DeepMind, which achieved unprecedented accuracy, with predictions often rivaling experimental techniques like crystallography or cryo-EM. Many targets in CASP14 were recombinant proteins—artificially expressed and purified to enable high-resolution structural determination. These proteins often included challenging folds, multi-domain assemblies, or understudied targets, providing a rigorous test for prediction algorithms.
AlphaFold2 leveraged deep learning architectures, integrating attention mechanisms and end-to-end training to model atomic interactions and spatial constraints. Its success highlighted the potential of AI-driven approaches to address long-standing challenges in structural biology. The accuracy (median GDT_TS >90 for many targets) suggested that computational models could reliably predict structures for proteins lacking experimental data, accelerating research in drug discovery, enzyme engineering, and disease mechanism studies. Post-CASP14. the release of AlphaFoldDB democratized access to millions of predicted structures, reshaping structural genomics. Recombinant proteins remain central to CASP as they provide standardized, high-quality datasets for method validation. CASP14 thus symbolizes a paradigm shift, where AI and recombinant protein technologies converge to advance our understanding of protein architecture and function.
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