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

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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.