Rapid Sampling of Molecular Motions with Prior Information Constraints

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

Algorithmic Tools for Assisting Prediction of Structure and Motion in Transmembrane Proteins (Research Proposal)

Transmembrane (TM) proteins are crucial mediators of cell-to-cell signaling and transport processes. This makes them attractive targets for drug discovery, as well as for improving our understanding of cellular processes. Yet, it is difficult to determine their high-resolution (< 4˚A) structures and even more difficult to derive the mechanism behind transport processes by computational means. The objective of this research is to provide algorithmic tools towards the structure and motion prediction of TM proteins. Given an amino-acid sequence, prediction of the 3D structure of a protein is one of the most difficult and challenging tasks of molecular biology. However, we intend to exploit the restrictive constraints imposed on the 3D structures of TM proteins to provide efficient tools to support this task. We have recently developed a novel method for assigning TM segments into helices seen in cryo-EM structures, which is a crucial step for 3D-structure prediction of TM proteins. Transporter proteins (e.g., LacY and GlpT) are integral membrane proteins that selectively mediate the passage of molecules across the membrane. We propose to study and provide computational tools towards conformational gating in transporter proteins. Conformational gating is a mechanism for protein-substrate binding specificity. Understanding this mechanism in terms of a substrate’s motion and protein conformational changes during the transportation may improve our understanding of cell processes and allow us to manipulate this mechanism, i.e., preventing or exploiting it for substrate (drug) delivery. The task of conformational-gating prediction can be rephrased as a motion-planing problem and is strongly related to the movable-object problem. It is known to be NP-hard and its complexity scales significantly with the number of degrees-of-freedom (dofs). Even small proteins may comprise a large number of dofs, implying that we are facing a real computational challenge

Generation, Comparison, and Merging of Pathways between Protein Conformations: Gating in K-Channels

We present a general framework for the generation, alignment, comparison, and hybridization of motion pathways between two known protein conformations. The framework, which is rooted in probabilistic motion-planning techniques in robotics, allows for the efficient generation of collision-free motion pathways, while considering a wide range of degrees of freedom involved in the motion. Within the framework, we provide the means to hybridize pathways, thus producing, the motion pathway of the lowest energy barrier out of the many pathways proposed by our algorithm. This method for comparing and hybridizing pathways is modular, and may be used within the context of molecular dynamics and Monte Carlo simulations. The framework was implemented within the Rosetta software suite, where the protein is represented in atomic detail. The K-channels switch between open and closed conformations, and we used the overall framework to investigate this transition. Our analysis suggests that channel-opening may follow a three-phase pathway. First, the channel unlocks itself from the closed state; second, it opens; and third, it locks itself in the open conformation. A movie that depicts the proposed pathway is available in the Supplementary Material (Movie S1) and at http://www.cs.tau.ac.il/∼angela/SuppKcsA.html