Conformation-Driven Molecular Computing

蛋白质和其他生物大分子的高识别能力主要来自于基于形状的相互作用。锁键或拼图匹配的图片经常被用来描述这种分子水平的模式识别。这个研究项目的目标是利用这种强大的模式识别的内在形式来解决计算问题。其基本思想体现在计算的自组装模型中。撞击设备的信号模式被转导成分子构象，然后自组织形成更高水平的复合体（或马赛克）。不同马赛克共有的形状特征代表不同的输入图案族。识别这些形状特征的酶控制着设备的输出。以这种方式将潜在困难的符号模式识别问题的解决方案转换为由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，这种类型的设备无法按照传统意义进行编程。然而，它们非常适合通过定向进化（由于生物大分子的高结构-功能可塑性）和通过作用于设备内部结构的误差反馈来适应。该方案的化学实现可以基于单个蛋白质对不同模式环境特征的构象响应进行操作。基于形状的酶识别用于将输入信号模式（以化学形式编码）转换为输出活动（通过光谱检测）。用于此目的的特殊蛋白质是线粒体苹果酸脱氢酶，这是一种易于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模式处理器的库，可以用作连接到能够执行复杂识别动作任务的计算网络的传统机器的辅助处理器。

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.