Identifying the nature of the interface in protein-protein complexes

Q2 Medicine In Silico Biology Pub Date : 2010-02-15 DOI:10.1145/1722024.1722040
Pralay Mitra
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引用次数: 2

Abstract

The role of molecular recognition is critical to the proper self-assembly of biological macromolecules and their function. Shape complementarity of the mutual recognition interfaces is one of the important factors that guide this interaction. The lock-and-key mechanism involving enzyme-substrate is a classical hallmark of shape complementarities at work in biochemical reaction. Recognition principles between macromolecular entities, however, has been difficult formulate. Sensitive surface complementarity recognition algorithms are computationally prohibitive, while accuracy of the heuristic methods is limited by the choice of proper biochemical information. This is a major drawback in understanding macromolecular recognition which entails critical assessment of biochemical information involving large interacting interfaces. Here we data mine on a number of biochemical parameters to highlight their individual merits and demerits and propose specific properties suitable for designing heuristic algorithms. The work is expected to find utility within bioinformatics algorithms seeking docking macromolecules and designing of protein complex interfaces.
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鉴定蛋白质-蛋白质复合物界面的性质
分子识别的作用对生物大分子的正确自组装及其功能至关重要。互认界面的形状互补性是指导这种交互的重要因素之一。涉及酶-底物的锁-钥匙机制是生物化学反应中形状互补的经典标志。然而,大分子实体之间的识别原理一直难以表述。敏感的表面互补识别算法在计算上是禁止的,而启发式方法的准确性受到选择适当的生化信息的限制。这是理解大分子识别的一个主要缺点,大分子识别需要对涉及大型相互作用界面的生化信息进行关键评估。在这里,我们对一些生化参数进行数据挖掘,以突出它们各自的优点和缺点,并提出适合设计启发式算法的特定属性。这项工作有望在寻求对接大分子和设计蛋白质复合物界面的生物信息学算法中找到实用价值。
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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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