bioRxiv2020
Many biophysical systems and proteins undergo mesoscopic conformational changes in order to perform their biological function. However, these conformational changes often result from a cascade of atomistic interactions within a much larger overall object. For simulations of such systems, the computational cost of retaining high-resolution structural and dynamical information whilst at the same time observing large scale motions over long times is high. Furthermore, this cost is only going to increase as ever larger biological objects are observed experimentally at high resolution. We derive a generalised mechano-kinetic simulation framework which aims to compensate for these disparate time scales, capable of dynamically exploring a defined mechanical energy landscape whilst at the same time performing kinetic transitions between discretely defined states. With insight from the theory of Markov state models and transition state theory, the framework models continuous dynamical motion at coarse-grained length scales whilst simultaneously including fine-detail, chemical reactions or conformational changes implicitly using a kinetic model. The kinetic model is coupled to the dynamic model in a highly generalised manner, such that kinetic transition rates are continuously modulated by the dynamic simulation. Further, it can be applied to any defined continuous energy landscape, and hence, any existing dynamic simulation framework. We present a series of analytical examples to validate the framework, and showcase its capabilities for studying more complex systems by simulating protein unfolding via single-molecule force spectroscopy on an atomic force microscope. Author summaryOur intention with this work is to provide a generalised, highly coarse-grained model to allow kinetic processes (conformational changes, protein unfolding, chemical reactions etc) to occur within the context of a dynamic simulation. Performing computationally intensive dynamic simulations to obtain kinetic information can be unjustifiably costly and scientifically inefficient, and so we instead want to emphasise that experimentally available kinetic information can be used to infer the underlying dynamics they result from. We hope our work can begin a discussion on the topic of computationally efficient science, and continue the drive towards collaborative science between theory and experimentation.