Conformation-Driven Molecular Computing

康拉德，迈克尔CCR-9610054韦恩州立大学蛋白质和其他生物大分子的高识别能力主要来自基于形状的相互作用。 锁键或拼图般的配合的图片经常被用来描述这种类型的分子水平模式识别。 这个研究项目的目标是利用这种强大的内在形式的模式识别来解决计算问题。 其基本思想体现在计算的自组装模型中。 撞击在器件上的信号模式被转换成分子构象，然后分子构象自组织形成更高水平的复合物（或马赛克）。 不同镶嵌图共有的形状特征表示不同的输入模式族。 识别这些形状特征的酶控制装置的输出。 潜在困难的符号模式识别问题的解决方案以这种方式转换为自由能最小化驱动的大分子自组织过程。 由于难以预测构象相互作用的结构和功能后果，这种类型的装置不能以常规意义编程。 然而，它们非常适合于通过定向进化（由于生物大分子的高结构-功能可塑性）进行成型，并通过作用于装置内部结构的错误反馈进行适应。 基于形状的酶识别用于将输入信号模式（以化学形式编码）转换为输出活性（通过光谱检测）。 用于这一目的的特定蛋白质是线粒体苹果酸脱氢酶，这是一种容易获得的碳水化合物代谢酶，将来可以很容易地与其他酶偶联，以建立一个特殊用途的模式处理器的库，这些处理器可以用作连接到能够执行复杂的代谢-动作任务的计算网络中的常规机器的协处理器。

CONRAD, MichaelCCR-9610054Wayne State UniversityThe high recognition capacity of proteins and other biological macromolecules derives largely from shape-based interactions. The pictures of a lock-key or jigsaw-like fit is frequently used to describe this type of molecular level pattern recognition. The goal of this research project is to harness this powerful intrinsic form of pattern recognition to solve computational problems. The basic idea is captured in the self-assembly model of computing. Signal patterns impinging on a device are transduced to molecular conformations which then self-organize to form a higher level complex (or mosaic). Shape features common to different mosaics represent different families of input patterns. Enzymes that recognize these shape features control the output of the device. The solution of potentially difficult symbolic pattern recognition problems is converted in this way to a macromolecular self-organization process driven by free energy minimization. Devices of this type cannot be programmed in a conventional sense, because of the difficulty of predicting the structural and functional consequences of conformational interactions. However, they are well suited to molding through directed evolution (due to the high structure-function plasticity of biological macromolecules) and to adaptation through error feedback acting on the internal structure of the device.Chemical implementation of this scheme can operate on the basis of the conformational responses of single proteins to different patterns of milieu features. Shape-based enzyme recognition serves to transform input signal patterns (coded in chemical form) to output activity (detected spectroscopically). The particular protein being used for this purpose is mitochrondrial malate dehydrogenase, a readily available enzyme of carbohydrate metabolism, that could in the future easily be coupled to other enzymes to build up a repertoire of special purpose pattern processors that can be utilized as co-processors for a conventional machine linked into computational networks capable of performing complex recognition-action tasks.