RCA S1(receptor-b ind ing cancer an tigen expressed on S iSo ce lls)是一种新近发现的肿瘤相关抗原,为Ⅱ型跨膜蛋白,表达在多种不同类型的肿瘤细胞表面,而且RCA S1的表达与肿瘤的分化、侵袭性和恶性程度呈正相关,并提示不良预后。...RCA S1(receptor-b ind ing cancer an tigen expressed on S iSo ce lls)是一种新近发现的肿瘤相关抗原,为Ⅱ型跨膜蛋白,表达在多种不同类型的肿瘤细胞表面,而且RCA S1的表达与肿瘤的分化、侵袭性和恶性程度呈正相关,并提示不良预后。最近研究表明,RCA S1能诱导活化后的免疫细胞发生凋亡,这可能是RCA S1表达在肿瘤细胞表面进而逃避机体免疫监视的机制之一。展开更多
Structure-based protein classification can be based on the similarities in primary, second or tertiary structures of proteins. A method using virtual-bond-angles series that transformed the protein space configuration...Structure-based protein classification can be based on the similarities in primary, second or tertiary structures of proteins. A method using virtual-bond-angles series that transformed the protein space configuration into a sequence was used for the classification of three-dimensional structures oi proteins. By transforming the main chains formed by C^a atoms of proteins into sequences, the series of virtual-bond-angles corresponding to the tertiary structure of the proteins were constructed. Then a distance-based hierarchical clustering method similar to Ward method was introduced to classify these virtual-bond-angles series of proteins. 200 files of protein structures were selected from Brookheaven protein data bank, and 11 clusters were classified.展开更多
文摘RCA S1(receptor-b ind ing cancer an tigen expressed on S iSo ce lls)是一种新近发现的肿瘤相关抗原,为Ⅱ型跨膜蛋白,表达在多种不同类型的肿瘤细胞表面,而且RCA S1的表达与肿瘤的分化、侵袭性和恶性程度呈正相关,并提示不良预后。最近研究表明,RCA S1能诱导活化后的免疫细胞发生凋亡,这可能是RCA S1表达在肿瘤细胞表面进而逃避机体免疫监视的机制之一。
文摘Structure-based protein classification can be based on the similarities in primary, second or tertiary structures of proteins. A method using virtual-bond-angles series that transformed the protein space configuration into a sequence was used for the classification of three-dimensional structures oi proteins. By transforming the main chains formed by C^a atoms of proteins into sequences, the series of virtual-bond-angles corresponding to the tertiary structure of the proteins were constructed. Then a distance-based hierarchical clustering method similar to Ward method was introduced to classify these virtual-bond-angles series of proteins. 200 files of protein structures were selected from Brookheaven protein data bank, and 11 clusters were classified.
