| 纯度 | >85%SDS-PAGE. |
| 种属 | Human |
| 靶点 | GOR |
| Uniprot No | Q8IX06 |
| 内毒素 | < 0.01EU/μg |
| 表达宿主 | E.coli |
| 表达区间 | 1-675aa |
| 氨基酸序列 | MLRATAPCWFPPGYPEAKKVAEEAALEASRHLGGEQSQAGAPEGSKMLRATAPCWFRPGYPEAKKVAKEAAPEASRHLGAEQSPAGAPEGSKMLRATAPCWFPPGYPEAKKVAEEAALEAPEFPLPSHQPAQSFGLWVPQMHKQASAFVDIQAEPQNRGPAVPPAWPKMVTESCYFPAQRGSACRLPAAPRLTERPSGVRISAPRKRKTIAHSSSPCLVTGYTDAKRTRVASSSQRSRGSKVGRQPGKTRNRSGMACKTTATTSSKRIVRRASLPSLSLKKPIILRSSGCQVPTVLRRGYLQLFTEECLKFCASKQEAEEKALNEEKVAYDCSPNKNRYLNVVLNTLKRLKGLTPSSMPGLSRAALYSRLQEFLLTQDQLKENGYPFPHPERPGGAVLFTGQGKGPGDSSCRVCCRCGTEYLVSSSGRCVRDQLCYYHWGRVRSSQVAGGRVSQYTCCAAAPGSVGCQVAKQHVRDGRKESLDGFVETFKKELSRDAYPGIYALDCEMCYTTHGLELTRVTVVDADMRVVYDTFVKPDNEIVDYNTRFSGVTEADVAKTSITLPQVQAILLSFFSAQTILIGHSLESDLLALKLIHSTVVDTAVLFPHYLGFPYKRSLRNLAADYLAQIIQDSQDGHNSSEDASACLQLVMWKVRQRAQIQPRHRSASPAALACP |
| 预测分子量 | 79.8 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GOR方法在蛋白质二级结构预测领域的3篇经典文献及其摘要概述：
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1. **文献名称**：*Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins*
**作者**：Garnier, J., Osguthorpe, D. J., & Robson, B.
**摘要**：该研究提出了GOR方法(GOR-I)，基于信息论和统计模型预测蛋白质的α螺旋、β折叠和卷曲结构。通过分析已知结构的蛋白质，验证了该方法在二级结构预测中的基础框架和准确性，为后续改进奠定了基础。
2. **文献名称**：*GOR secondary structure prediction method version IV*
**作者**：Garnier, J., Gibrat, J. F., & Robson, B.
**摘要**：本文介绍了GOR-IV版本，通过整合更大的数据集和优化算法参数，显著提升了预测精度(尤其对β折叠)。研究还探讨了其在重组蛋白设计中的应用潜力，如通过结构预测优化表达和功能分析。
3. **文献名称**：*Protein secondary structure prediction based on position-specific scoring matrices*
**作者**：Jones, D. T.
**摘要**：虽然主要介绍PSIPRED方法，但该研究对比了GOR等传统方法的局限性，指出其在处理同源序列数据时的不足，为理解GOR在当代结构预测中的角色提供了参考。
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**备注**：GOR方法本身是理论工具，若需针对“重组蛋白”应用的具体案例文献，建议补充蛋白质名称或研究背景以精确检索。
The GOR (Garnier-Osguthorpe-Robson) method, introduced in the late 1970s, revolutionized protein secondary structure prediction by leveraging statistical information derived from amino acid sequences. Unlike early approaches, GOR incorporated directional dependencies between residues, enabling improved accuracy in identifying α-helices, β-sheets, and coils. This innovation laid groundwork for computational biology tools critical in recombinant protein research.
Recombinant proteins, engineered via DNA cloning and expression in heterologous systems (e.g., E. coli, yeast), require precise structural characterization to ensure functionality. The GOR algorithm aids in predicting folding patterns early in design, guiding codon optimization, solubility enhancement, and mutagenesis strategies. For instance, anticipating aggregation-prone regions (e.g., exposed β-strands) allows researchers to modify sequences for improved stability during expression.
Historically, recombinant protein production faced challenges like low yields, misfolding, and inclusion body formation. Integrating bioinformatics tools like GOR mitigated these issues by enabling rational design. Modern iterations combine GOR principles with machine learning, enhancing predictions for complex or disordered regions. Today, this synergy supports diverse applications—from therapeutic antibodies to industrial enzymes—underscoring GOR's enduring relevance in bridging sequence-structure-function relationships for tailored protein engineering.
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