Protein motions, ranging from molecular flexibility to large-scale conformational change, play an essential role in many biochemical processes. Despite the explosion in our knowledge of structural and functional data, our understanding of protein movement is very limited because it is difficult to measure experimentally and computationally expensive to simulate. In previous work, we have developed and validated a method based on motion planning for mapping protein folding pathways from unstructured conformations to the native state. In this paper, we propose a novel method based on rigidity theory to sample conformation space more effectively and we describe extensions of our framework to automate the mapping process and to map transitions between specified conformations. Our experimental results show that these additions both improve the accuracy of our maps and enable us to study a broader range of motions for larger proteins. For example, we show that rigidity-based sampling results in maps that capture subtle folding differences between protein G and its mutations, NuG1 and NuG2, and we illustrate how our technique can be used to study large-scale conformational changes in calmodulin, a 148 residue signaling protein known to undergo conformational changes when it binds to Ca2+. Finally, we announce our web-based protein folding server which includes a publically available archive of protein motions:

This un-edited manuscript has been accepted for publication in Biophysical Journal and is freely available on BioFAST at www.biophysj.org. The final copyedited version of the paper may be found at www.biophysj.org.

Abstract not found

Protein structure and motion plays an essential role in nearly all forms of life. Understanding both protein folding and protein conformational change can bring deeper insight to many biochemical processes and even into some devastating diseases thought to be the result of protein misfolding. Experimental methods are currently unable to capture detailed, large-scale motions. Traditional computational approaches (e.g., molecular dynamics and Monte Carlo simulations) are too expensive to simulate time periods long enough for anything but small peptide fragments. This research aims to model such molecular movement using a motion framework originally developed for robotic applications called the Probabilistic Roadmap Method. The Probabilistic Roadmap Method builds a graph, or roadmap, to model the connectivity of the movable object’s valid motion space. We previously applied this methodology to study protein folding and obtained promising results for several small proteins. Here, we extend our existing protein folding framework to handle larger proteins