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

社会性昆虫能够执行复杂的任务，如觅食、合作狩猎和筑巢，这些都超出了孤立个体的能力。在探索环境寻找食物的同时，蚂蚁会将信息素存款在地面上。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巢穴。信息素信号慢慢衰减，但那些偶然发现从巢穴到食物并返回的短路线的蚂蚁将花费更大比例的时间沿着该路线的任何一段，因此加强了信息素的踪迹。这些较强的痕迹通过stigmergy吸引其他蚂蚁-一种对信息素气味的天生吸引力-导致最短路线的自我优化，从而发展出理想的觅食路径。这种“群体智能”-在简单个体群体中出现的集体智能-已被用于数学算法，以解决通信网络和机器人技术中遇到的复杂优化问题，但从未被应用于结构生物学。我们希望将这种算法应用于从核磁共振（NMR）光谱数据确定蛋白质结构的问题，这通常需要训练有素的光谱学家数周甚至数月才能完成。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探索它们的构象空间，类似于一群真实的蚂蚁探索它们自己的物理领土。蚂蚁通过信息素线索进行通信：理想的原子间距离的全球列表，这些距离是由蚂蚁在模拟NMR数据后确定的，并在分子动力学模拟中用作几何约束。如果任何一个生物分子蚂蚁遇到了满足实验数据的结构元素（如α-螺旋转弯或β-发夹环），这个知识就会通过几何距离约束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，蚂蚁合作寻找原子间距离的最佳集合，从而确定自己的解决方案结构。我们最初的试验是如此成功，以至于我们对群集智能NMR（SI-NMR）概念进行了专利保护。我们现在希望将该技术开发到普遍适用于所有蛋白质的程度，并且也适用于蛋白质-蛋白质和蛋白质-抑制剂复合物的结构测定。人们希望这种全新的SI-NMR技术将成为学术界和工业NMR界公认的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.