1. Using Motion Planning to Study Ligand Binding and Protein Folding Nancy Amato,Guang Song and Burchan Bayazit Department of Computer Science Texas A&M University {amato,gsong,burchanb}@cs.tamu.eduamatogsongburchanb http://www.cs.tamu.edu/faculty/amato

2. Given: an environment (descriptions of moveable object A and obstacles B), and start and goal positions of A Find: a valid path (continuous sequence of valid configurations of A) from start to goal Motion Planning start goal obstacles

3. Motivation: Paper Folding Box: 12 => 5 dofPeriscope: 11 dof A Motion planning approach to ligand binding. Singh, Latombe, Brutlag, ISMB’99

4. Outline PRMs for Computational Biology (PRM: Probabilistic Roadmap Method) —Conformation Space —Modeling —Potential Functions —Roadmap Construction Protein Folding Pathways Ligand Binding

5. 1. Connect start and goal to roadmap Query processing startgoal Probabilistic Roadmap Methods (PRMs) [Kavraki, Svestka, Latombe,Overmars 1995] C-obst Roadmap Construction (Pre-processing) 2. Connect pairs of nodes to form roadmap - simple, deterministic local planner (e.g., straightline) - discard paths that are in collision (collision check) 1. Randomly generate configurations (nodes) - discard nodes that are in collision (collision check) C-obst 2. Find path in roadmap between start and goal - regenerate plans for edges in roadmap Configuration Space

6. Node Generation Node Connection (build edges) Query System Structure of PRM: object oriented approach Node Validation PRM motion planning framework Dynamics Local Connection Potential Functions Collision Detector Collision-free edge

7. Motion Planning in robotics and computational biology Configuration Space A robot Collision Detector Roadmap Construction (A connectivity graph) Query (find collision-free path) Conformation Space Model ‘ligand’ or protein as articulated robots Potential Calculation Roadmap Construction (A connectivity graph) Query (find energetically feasible path) Although the problems appear different, they can both use the same motion planning framework, which works in the abstract C-space.

8. PRM : Node Generation 1.Randomly generate a conformation, determine all atoms’ coordinates. 2.compute potential energy E of conformation and retain node with probability P(E): ( note, this is just one way to do it.)

9. PRM: Roadmap Connection 1.Find k closest nodes for each roadmap node 2.Calculate weight of straightline path [Singh, Latombe, Brutlag, 1999] where Pi is probability of moving between intermediate configurations i and i+1 Low weight more energetically feasible.

10. PRM: Querying the Roadmap ( A 10-ALA folding case) start goal Add start and goal to roadmap 1. Extract smallest weight path (energetically most feasible) between them

11. I. Protein Folding Pathways

12. Key Issues Goal: Study folding pathways to the known native fold. Validation —PRM roadmaps give us folding pathways, but how can we test if they are (close to) the natural folding path? Potential Functions — The paths in the PRM roadmaps are selected based on the potential function used to create the map. — How accurate does the potential function need to be to produce roadmaps with good paths? More accurate => more time and fewer nodes (less coverage)

13. Validation of Folding Pathways Compare the order that secondary structures form in our paths with experimental results — We used pulse labeling & N-state exchange results from Woodward et al. 1999 as suggested by Dr. Marty Scholtz at Texas A&M. Protein GB1 — 56 residues — 1 alpha helix — 4 beta-strands Pulse-labeling result alpha helix (yellow) and beta-4 (red, and beta-3) form first

14. Potential Functions: Goal: simple, yet accurate enough for PRMs Strategy: study importance of various terms and design a potential for PRM usage — may also yield insight into which terms are most influential in governing the folding process Potentials we have used (derivations of the potential function in Levitt 1983) —van der Waals only —van der Waals + hydrogen bonds — van der Waals + hydrogen bonds + disulphide bonds — … + hydrophobic effect

15. Protein G results: PHI vs. PSI distribution. Nodes sampled around native fold using a set of normal distributions: {5,10,20,40, 80,160}. Protein G’s native fold has one alpha-helix & four beta strands, which is reflected here by the dense samples around alpha & beta regions.

16. RMSD is the distance from protein G’s native fold. The sample (10000 nodes) suggests a funnel structure around the native fold. Distribution of roadmap nodes :

17. Peaks show where some atoms are close and Van der Waals term dominates (which is approximated by a constant). Bigger roadmaps have smoother paths. Peaks right before goal indicate the tight packing of the native fold. Potential Profiles for different size of roadmaps:

18. While some peaks result from local sparse distribution of nodes, to optimize the final folding path, more samples around the peaks can improve the path. More samples around the peaks

19. Folding pathways for different start conformations d ef abc Letter labels Numerical Labels : Folding from the extended conformation (reverse order). : Folding path from a random node in the roadmap.

20. Protein GB1 Movie

21. Results of Protein A Nodes are sampled around the native fold using a set of normal distributions. Protein A is an all-alpha protein, which is shown by dense sampling at the alpha region.

22. The overall ‘funnel’ shape is different from that of protein G, which possibly reflects different folding behaviors. Note also the the narrow ‘funnel’ for RMSD< 10 A. This may suggest that region contains only the packing of secondary structures to native fold (therefore potential changes little) Protein A: Potential vs. RMSD distribution

23. the packing of secondary structure. Both paths share some segments near the goal, suggesting the folding path near the native fold may be quite independent of start conformations. Folding from different start conformations a bcdef

24. Protein Folding pathways: Summary of Preliminary Results Our results seem to be in accordance with the pulse- labeling experiments for proteins GB1and A — more proteins must be studied Potential Energy Function… —van der Waals terms + Hydrogen & disulphide bonds can capture some (high level) characteristics of the folding process — but they are probably not enough - what next? How can we analyze the paths contained in the PRM roadmaps? http://www.cs.tamu.edu/faculty/amato/dsmft/

25. II. Ligand Binding

26. Ligand Binding Automated docking algorithms — AutoDock,Dock,FlexX,FLOG,FTDock,Gold, etc. — Often simplified by rigid ligand assumption PRM Approach (Singh, et.al., 1999) —rapidly explores high dimensional space —We use PRM variant better suited for ligand binding User-Computer interaction — haptics (sense of touch) helps the user to understand molecular interaction — User may improve the PRM by suggesting candidate site regions.

27. Our Approach Generate Binding Site Canditates —Generate sample nodes (automated or user collected) —Push nodes to local minima —Connect nodes Recognize Binding Sites —Choose largest connected component ( accessibility) —Discard nodes with potential larger than E max —Score remaining nodes

28. Generate Candidates: Automatic Node Generation Generate a collision free base Generate random values for other joint angles Keep this configuration if the potential is less than E max Protein Ligand base

29. Grid Potential/Force Create a grid in space Calculate the contribution of protein atoms to each grid point Precalculate Real-Time Find the grid points where ligand atoms are located Calculate the potential/force

30. PHANToM User attaches haptic device to ligand, and moves it around user feels the forces on ligand ligand is rigid force calculation is too slow, so use extrapolation techniques (grid potential) Ligand configurations (candidate sites) passed to planner automatically sampled at regular intervals Generate Canditates: Haptic Interaction

31. Approximate Gradient Descent Push nodes to local minima For each node sample n close nodes Choose the node with lowest potential among them Repeat until local minima or iteration limit is reached

32. Recognizing Binding Sites Pick low energy configurations in the largest connected component For each node in the selected configuration, uniformly sample n configurations within a distance r (i.e., construct a local roadmap) Find the score of the selected configuration as the average potential of the sampled configurations (local roadmap)

33. Potential v.s. Scoring Function 1STP (protein=streptavidin, ligand (11 dof) =biotin) PotentialScore

34. Experiments Questions: What is the effect of rigid vs. flexible ligands? Can OBPRM identify binding sites? Can user provide helpful information?

35. Experiments First Experiment treat ligand as rigid body Second Experiment user collects and automated planner fine tunes Third Experiment treat ligand as articulated (i.e., flexible) Complexes(protein:ligand:degree of freedom) 1A5Z:L-Lactate Dehyrogenase:Oxamate:7 1LDM: M 4 -Lactate Dehyrogenase:Oxamate:7 1STP:Streptavidin:Biotin:11 } In Singh,et.al., 1999

36. Results (1A5Z and 1STP) Timing. Rigid. User. Articulated. Binding RMSD vs Score (1A5Z, 7dof) Timing. Rigid. User. Articulated. Binding RMSD vs Score (1STP, 11dof)

37. Results (1LDM) Timing RMSD vs Score (7dof). User. Articulated. Binding

38. Conclusion In our examples, we could generate and identify configurations in the true binding site Scoring function may be improved Our results may used as input for other automated docking programs User input improves efficiency, and haptic feedback helps the user better understand the problem We need a better user interface

39. More at http://www.cs.tamu.edu/faculty/amato For contact: amato@cs.tamu.edu