Exploration of Biochemical Reactivity with a QM/MM Growing String Method

In this work, we have implemented the single-ended growing string method within our in-house QM/MM package, QoMMMa. The goal of the implementation was to facilitate generation of QM/MM reaction pathways with minimal user input, and also to improve the quality of the pathways generated as compared to the widely used adiabatic mapping approach. We have validated the algorithm against a reaction which has been studied extensively in previous computational investigations – the Claisen rearrangement catalysed by chorismate mutase. The nature of the transition state and the height of the barrier was predicted well using our algorithm, and more than 88% of the pathways generated were deemed to be of production quality. Compared to using adiabatic mapping for similar problems, we found that our technique is slightly less efficient, but readily produces better pathways and requires less user intervention. Given that we generated a relatively large number of pathways to fully validate our technique, we have also investigated the underlying distribution of QM/MM barrier heights. We show that this distribution is not strictly Gaussian and discuss the implications for studying reactivity with static QM/MM approaches.