Protein Folding and Motions

In this research, we study protein folding pathways and protein motions. It is critical that we better understand protein motion and the folding process for several reasons: understanding the folding process can give insight into how to develop better structure prediction algorithms, treatments for diseases such as Alzheimer's and Mad Cow disease can be found by studying protein misfolding, and many biochemical processes are regulated by protein motion.

We propose a technique for computing protein folding pathways and motions based on the successful probabilistic roadmap (PRM) method for robotics motion planning. Our technique can compute thousands of pathways in just a few hours on a single desktop PC. We validated our approach against known experimental results for several small proteins. We have also been able to study protein folding rates and population kinetics extracted from our protein folding landscapes.

Please submit your proteins to our Protein Folding Server for analysis by our motion planning technique. Visit our Protein Folding and Motions Homepage for more information.

Protein Folding and Motions Publications

Ligand Binding

In this project, we present a framework for studying ligand binding which is based on techniques recently developed in the robotics motion planning community. We are especially interested in locating binding sites on the protein for a ligand molecule. Our work investigates the performance of a fully automated motion planner, as well improvements obtained when supplementary user input is collected using a haptic device.

Our results applying an obstacle-based probabilistic roadmap motion planning algorithm (OBPRM) to some known protein-ligand pairs are very encouraging. In particular, we were able to automatically generate configurations close to, and correctly identify, the true binding site in the three protein-ligand complexes we tested. We find that user input helps the planner, and a haptic device helps the user to understand the protein structure by enabling them to feel the forces which are hard to visualize.

Visit our Ligand Binding Homepage for more information.

Ligand Binding Publications

RNA Folding

In this research, we explore a novel motion planning based approach to approximately map the energy landscape of an RNA molecule. Our method is based on the probabilistic roadmap (PRM) motion planners we have successfully applied to study protein folding. The key advantage of our method is that it provides a sparse map that captures the main features of the landscape.

In this work, we also present new computational tools that can be used to study kinetics-based functions for RNA such as population kinetics, folding rates, and the folding of particular subsequences. We provide several different simulation results to validate our method against known experimental data. We also show how our method can study kinetics-based functions for two different case studies.

Visit our RNA Folding Homepage for more information.

RNA Folding Publications

Neuron PRM
The brain has extraordinary computational power to represent and interpret complex natural environments. These natural computations are essentially determined by the topology and geometry of the brain's architectures.

We present a framework to construct a 3D model of a cortical network using probabilistic roadmap methods. Although not the usual motion planning problem, our objective of building a network that encodes the pathways of the cortical network is analogous to the PRM objective of constructing roadmaps that contain a representative sample of feasible paths. We represent the network as a large-scale directed graph, and use L-systems and statistics data to `grow' neurons that are morphologically indistinguishable from real neurons.

Visit our Neuron PRM Homepage for more information.

Neuron PRM Publications