Lebeda F J, Olson M A
Department of Cell Biology and Biochemistry, U.S. Army Medical Research Institute of Infectious Diseases, Frederick, Maryland 21702-5011, USA.
J Nat Toxins. 1998 Oct;7(3):227-38.
Predictions were made of the secondary, two-dimensional (2-D) structures and side-chain solvent accessibilities of the light (L) chains of the clostridial neurotoxins (botulinum neurotoxin serotypes A-G and tetanus neurotoxin). An artificial neural network was used to make these predictions from a multiple alignment of their primary structures and was the approach used in making successful predictions for the C-fragments of these neurotoxins (Lebeda et al., J. Prot. Chem., 17:311, 1998). We also exploited the fact that the L-chains are Zn-dependent proteases. Although no other metalloproteases were found to be sequentially homologous to these neurotoxin L-chains, a sequence clustering algorithm showed that several bacterially derived Zn-dependent proteases, including thermolysin, were the most similar. A 2-D structure topology map for the type A L-chain was constructed by using thermolysin as a design template. As in thermolysin, the region containing the Zn-binding sequence motif, which is part of the active site in these neurotoxins, was predicted to be minimally solvent accessible. On the other hand, the locations of residues with highly exposed side chains were predicted to occur in non-periodic structure elements. Together, these 2-D structure and solvent accessibility predictions can be used to identify important solvent-exposed regions of the L-chain. These regions may include sites that interact with residues of the neurotoxin heavy chain, sites that bind to vesicle-docking substrates or sites that form antibody epitopes.
对肉毒杆菌神经毒素（肉毒杆菌神经毒素血清型A - G和破伤风神经毒素）轻链（L链）的二级二维（2 - D）结构和侧链溶剂可及性进行了预测。利用人工神经网络根据其一级结构的多序列比对进行这些预测，这也是之前成功预测这些神经毒素C片段时所采用的方法（Lebeda等人，《蛋白质化学杂志》，17:311，1998）。我们还利用了L链是锌依赖性蛋白酶这一事实。尽管未发现其他金属蛋白酶与这些神经毒素L链具有序列同源性，但序列聚类算法表明，包括嗜热菌蛋白酶在内的几种细菌来源的锌依赖性蛋白酶最为相似。以嗜热菌蛋白酶为设计模板构建了A型L链的二维结构拓扑图。与嗜热菌蛋白酶一样，包含锌结合序列基序（这些神经毒素活性位点的一部分）的区域预计溶剂可及性最小。另一方面，预测侧链高度暴露的残基位置会出现在非周期性结构元件中。总之，这些二维结构和溶剂可及性预测可用于识别L链重要的溶剂暴露区域。这些区域可能包括与神经毒素重链残基相互作用的位点、与囊泡对接底物结合的位点或形成抗体表位的位点。
