Protein structure determination from nuclear magnetic resonance (NMR) spectroscopy using swarm intelligence

总体而言，群居昆虫能够执行复杂的任务，如觅食、合作狩猎和筑巢，这些都是孤立的个体无法完成的。蚂蚁在探索环境寻找食物时，会将信息素储存在地面上。一旦找到食物，一只满载的蚂蚁就会沿着信息素的踪迹返回到巢穴。信息素信号会慢慢衰减，但那些偶然发现从巢穴到食物再回来的短路径的蚂蚁，会花更多的时间沿着这条路径的任何一段，从而加强信息素的踪迹。这些更强的路径通过污名吸引其他蚂蚁--一种天生对信息素气味的吸引--导致最短路径的自我优化，从而开发出理想的觅食路径。这种“群体智能”--一群简单的个体之间出现的集体智能--已经被用于数学算法，以解决通信网络和机器人中遇到的复杂优化问题，但它以前从未被应用于结构生物学。我们希望将这样的算法应用于根据核磁共振光谱数据确定蛋白质结构的问题，这是一项通常需要训练有素的光谱学家几周甚至几个月才能完成的任务。我们已经构建了一群生物分子蚂蚁，它们能够通过分子动力学模拟来探索它们的构象空间，类似于一群真正的蚂蚁探索自己的物理领域。蚂蚁通过信息素轨迹进行交流：理想原子间距离的全球列表，由蚂蚁在模拟核磁共振数据后制定，并在分子动力学模拟中作为几何约束应用。如果任何一只生物分子蚂蚁遇到满足实验数据的结构元件(如α-螺旋转动或β-发夹环)，这一知识就会通过几何距离限制传递给群中的其他蚂蚁，从而鼓励它们采用相同的局部构象。因此，随着时间的推移，蚂蚁合作寻找最优的原子间距离集，从而确定它们自己的解结构。我们最初的试验非常成功，因此我们为群体智能核磁共振(SI-核磁共振)概念提供了专利保护。我们现在希望将这项技术发展到对所有蛋白质都适用的程度，并同样适用于蛋白质-蛋白质和蛋白质-抑制物复合体的结构确定。人们希望，这种全新的SI-核磁共振技术将成为学术界和工业界核磁共振领域中最先进的蛋白质结构测定方法。

Collectively, social insects are capable of performing complex tasks such as foraging for food, cooperative hunting and nest building that are beyond the capabilities of the isolated individuals. Whilst exploring their environment searching for food, ants deposit pheromones on the ground. Once food is found, a laden ant retraces its steps following the pheromone trail back to the nest. The pheromone signals slowly decay, but those ants which, by chance, find short routes from nest to food and back again will spend a greater proportion of their time along any one section of that route, and thus intensify the pheromone trail. These stronger trails draw in other ants through stigmergy - an innate attraction to the pheromone scent - resulting in a self-optimisation of the shortest routes and thus the development of ideal foraging pathways. Such 'swarm intelligence' - the emergence of collective intelligence amongst groups of simple individuals - has been used in mathematical algorithms to solve complex optimisation problems encountered in communication networks and robotics, but never before has it been applied to structural biology. We want to apply such algorithms to the problem of protein structure determination from nuclear magnetic resonance (NMR) spectroscopy data, a task which typically takes a trained spectroscopist weeks or even months to perform. We have constructed a swarm of biomolecular ants which are capable of exploring their conformational space by molecular dynamics simulations, analogous to a colony of real ants exploring their own physical territory. The ants communicate via a pheromone trail: a global list of ideal interatomic distances that are laid down by the ants following simulation of the NMR data, and that are applied as geometric restraints in the molecular dynamics simulations. If any one of the biomolecular ants encounters a structural element that satisfies the experimental data (such as an alpha-helical turn or a beta-hairpin loop), this knowledge is communicated to the other ants in the swarm via the geometric distance restraints thus encouraging them to adopt the same local conformation. Therefore, over time, the ants cooperate in finding the optimal set of inter-atomic distances and therefore determine their own solution structure. Our initial trials were so successful that we patent-protected the swarm-intelligence NMR (SI-NMR) concept. We now want to develop the technique to the point where it is universally applicable to all proteins, and also to the structure determination of protein-protein and protein-inhibitor complexes. It is hoped that this brand new SI-NMR technology would then become accepted as the state-of-the-art method for protein structure determination in both the academic and industrial NMR communities.