丝氨酸蛋白酶抑制剂家族neuroserpin的比较序列和结构分析

Q2 Medicine In Silico Biology Pub Date : 2010-02-15 DOI:10.1145/1722024.1722031
Kuchi Srikeerthana, P. De Causmaecker
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引用次数: 0

摘要

神经丝氨酸蛋白酶抑制剂(serpins)是组织型纤溶酶原激活物(tPA)的选择性抑制剂,通常有220多个残基。天然人类神经丝氨酸蛋白的晶体结构已由Sayaka等人以2.1 Å分辨率报道。神经丝氨酸蛋白的天然折叠由五链β- a片和一个可移动的螺旋反应中心环(RCL)组成。该结构还包含一个有助于抑制tPA的ω环(Ω-loop)和一个在折叠、复合物形成和聚合中起重要作用的螺旋'F'。在本研究中,我们通过比较序列分析确定了neuroserpin家族的新成员,并分析了新发现的亲戚中反应性中心环、omega环、螺旋'F'和其他共识残基的保守性,这些残基与其他蛇蛋白分支的共识序列不同。通过对neuroserpin及其结构相似蛋白的结构分析比较,揭示了neuroserpin家族成员之间独特的结构模式和稳定相互作用。
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Comparative sequence and structural analyses of neuroserpin: the serine protease inhibitor family
Neuroserpin, a clade of serine proteinase inhibitors (serpins) is a selective inhibitor of tissue-type plasminogen activator (tPA) and usually has more than 220 residues. The crystal structure of native human neuroserpin has been reported by Sayaka et al., [17] at 2.1 Å resolution. The native fold of neuroserpin is composed of a five stranded β-sheet A and a mobile helical reactive center loop (RCL). The structure also contains an omega loop (Ω-loop), which contributes to the inhibition of tPA and a helix 'F' that plays an important role in folding, complex formation and polymerization. In this study, we identify new members of the neuroserpin family by comparative sequence analyses, and we analyze the conservation of the reactive center loop, the omega loop, the helix 'F' and other consensus residues, in the newly found relatives, which differ from the consensus sequences of other clades of serpins. By comparative structural analyses of neuroserpin with its structurally similar proteins, we reveal the structural patterns and the stabilizing interactions, that are unique among the members of neuroserpin family.
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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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