State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, PR China.
BMC Bioinformatics. 2009 Aug 21;10:257. doi: 10.1186/1471-2105-10-257.
Nowadays, more and more novel enzymes can be easily found in the whole enzyme pool with the rapid development of genetic operation. However, experimental work for substrate screening of a new enzyme is laborious, time consuming and costly. On the other hand, many computational methods have been widely used in lead screening of drug design. Seeing that the ligand-target protein system in drug design and the substrate-enzyme system in enzyme applications share the similar molecular recognition mechanism, we aim to fulfill the goal of substrate screening by in silico means in the present study.
A computer-aided substrate screening (CASS) system which was based on the enzyme structure was designed and employed successfully to help screen substrates of Candida antarctica lipase B (CALB). In this system, restricted molecular docking which was derived from the mechanism of the enzyme was applied to predict the energetically favorable poses of substrate-enzyme complexes. Thereafter, substrate conformation, distance between the oxygen atom of the alcohol part of the ester (in some compounds, this oxygen atom was replaced by nitrogen atom of the amine part of acid amine or sulfur atom of the thioester) and the hydrogen atom of imidazole of His224, distance between the carbon atom of the carbonyl group of the compound and the oxygen atom of hydroxyl group of Ser105 were used sequentially as the criteria to screen the binding poses. 223 out of 233 compounds were identified correctly for the enzyme by this screening system. Such high accuracy guaranteed the feasibility and reliability of the CASS system.
The idea of computer-aided substrate screening is a creative combination of computational skills and enzymology. Although the case studied in this paper is tentative, high accuracy of the CASS system sheds light on the field of computer-aided substrate screening.
如今,随着基因操作的飞速发展,越来越多的新型酶可以在整个酶库中轻易找到。然而,新型酶的底物筛选的实验工作既费力、耗时又昂贵。另一方面,许多计算方法已广泛应用于药物设计中的先导筛选。鉴于药物设计中的配体-靶蛋白体系和酶应用中的底物-酶体系具有相似的分子识别机制,我们旨在通过计算手段来实现酶的底物筛选目标。
我们设计并成功应用了一种基于酶结构的计算机辅助底物筛选(CASS)系统,以帮助筛选南极假丝酵母脂肪酶 B(CALB)的底物。在该系统中,应用源于酶作用机制的受限分子对接来预测底物-酶复合物的能量有利构象。此后,依次使用底物构象、酯的醇部分的氧原子(在某些化合物中,该氧原子被酸胺的氨基中的氮原子或硫酯的硫原子取代)与 His224 的咪唑上的氢原子之间的距离、化合物的羰基碳原子与 Ser105 的羟基氧原子之间的距离作为筛选结合构象的标准。该筛选系统正确识别出了 233 种酶中的 223 种化合物。如此高的准确性保证了 CASS 系统的可行性和可靠性。
计算机辅助底物筛选的思想是计算技能和酶学的创造性结合。尽管本文研究的案例是试探性的,但 CASS 系统的高精度为计算机辅助底物筛选领域带来了曙光。