Proteins are active, flexible machines that perform a range of different functions. Innovative experimental approaches may now provide limited partial information about conformational changes along motion pathways of proteins. There is therefore a need for computational approaches that can efficiently incorporate prior information into motion prediction schemes. In this paper, we present PathRover, a general setup designed for the integration of prior information into the motion planning algorithm of rapidly exploring random trees (RRT). Each suggested motion pathway comprises a sequence of low-energy clash-free conformations that satisfy an arbitrary number of prior information constraints. These constraints can be derived from experimental data or from expert intuition about the motion. The incorporation of prior information is very straightforward and significantly narrows down the vast search in the typically high-dimensional conformational space, leading to dramatic reduction in running time. To allow the use of state-of-the-art energy functions and conformational sampling, we have integrated this framework into Rosetta, an accurate protocol for diverse types of structural modeling. The suggested framework can serve as an effective complementary tool for molecular dynamics, Normal Mode Analysis, and other prevalent techniques for predicting motion in proteins. We applied our framework to three different model systems. We show that a limited set of experimentally motivated constraints may effectively bias the simulations toward diverse predicates in an outright fashion, from distance constraints to enforcement of loop closure. In particular, our analysis sheds light on mechanisms of protein domain swapping and on the role of different residues in the motion.
Incorporating external knowledge into computational frameworks is a challenge of prime importance in many fields of biological research. In this study, we show how computational power can be harnessed to make use of limited external information and to more effectively simulate the molecular motion of proteins. While experimentally solved protein structures restrict our knowledge to static molecular “snapshots”, a vast number of proteins are flexible entities that constantly change shape. Protein motion is therefore intrinsically related to protein function. State-of-the-art experimental approaches are still limited in the information that they provide about protein motion. Therefore, we suggest here a very general computational framework that can take into account diverse external constraints and include experimental information or expert intuition. We explore in detail several biological systems of prime interest, including domain swapping and substrate binding, and show how limited partial information enhances the accuracy of predictions. Suggested motion pathways form detailed lab-testable hypotheses and can be of great interest to both experimentalists and theoreticians.