非键相互作用对于生物体系中的分子识别和结合过程起着关键作用。然而,传统的方法并不能在残基水平自动批量计算非键相互作用。近年来,已经发展了一些方法和工具进行非键相互作用的计算分析。该文研究发展了一种可以自动计算残基间非键相互作用的方法,即用Perl脚本调用Discovery Studio 2.0(DS 2.0,Accelrys Inc.)底层模块中的非键相互作用协议,实现了直接利用命令行批量计算非键相互作用能量,而无需通过DS2.0的图形界面。该方法扩展了DS2.0的计算模块,并于近期运用到了复合结构的研究分析中。
Learned association between context and drug abuse is essential for the drug conditioned place preference （CPP）, which is an animal model widely used to measure drug reward. Synaptic plasticity, in the form of long-term potentiation （LTP） and depression （LTD）, is regarded as a proposed cellular substrate of learning and memory. However, the exact role of LTP/LTD in addiction is not known yet. Therefore, by bioinformatics we designed peptides aiming to interfere with LTP and LTD respectively, to study their individual role in the expression of morphine CPP. We found that the interfering peptide Pep-A2 can specifically block hippocampal LTP in CA1 region, whereas Pep-A3 can block LTD in this region. Treatment of either of their cell penetrating forms （Tat-A2 or Tat-A3） before test can block the expression of Morphine CPP in mice. These results suggested that both LTP and LTD are required in the drug-associated learning and memory.
