protein structure prediction computer-aided pharmaceutical design: modeling receptor flexibility
DESCRIPTION
a). 1. 2. 3. 4. b). Analysis of Biomolecular Interactions Using a Robotics-Inspired Approach with Applications to Tissue Engineering. David Schwarz 1 [email protected]. Allison Heath 1 [email protected]. Cecilia Clementi 2 [email protected]. Lydia E. Kavraki 3 [email protected]. - PowerPoint PPT PresentationTRANSCRIPT
• Protein structure prediction
• Computer-aided pharmaceutical design: Modeling receptor flexibility
• Applications to molecular simulation
Work on this paper by the authors has been supported in part by NSF 0205671, EIA-0216467, a Texas ATP grant, a Whitaker Biomedical Engineering Grant and a Sloan Fellowship to Lydia Kavraki. David Schwarz has been partially supported by a National Defense Science and Engineering Graduate Fellowship from the Office of Naval Research and a President’s Graduate Fellowship from Rice University.
1) Generation of molecular dynamics simulation trajectorya) Start with known protein
structure (from RCSB Protein Data Bank)
b) Run 2 nanosecond simulation (1,000,000 steps)
1 Dept. of Computer Science, Rice University, 2 Dept. of Chemistry, Rice University, 3 Dept. of Computer Science and Dept. of Bioengineering, Rice University
David Schwarz1
[email protected] Moll1
[email protected] E. [email protected]
Allison Heath1
Analysis of Biomolecular Interactions Using a Robotics-Inspired Approach with Applications to Tissue Engineering
Two known structures of HIV-1 protease, a protein vital to the life cycle of the human immunodeficiency virus, bound to inhibitors.
A pharmaceutical company screening the bulky inhibitor on the right, but only testing it on the closed protein structure on the left,
would fail to identify it as a potential inhibitor, and therefore a potential drug.
HIV-1 protease structures generated
by molecular dynamics
2) Determination of collective coordinates by principal component analysis (PCA) of trajectory
First principal
component of HIV-1 protease
from simulation of structure
4HVP
a) Singular value decomposition on representative conformations from trajectory
b) Output: Set of vectors representing
coordinated motions of receptor, in order of decreasing contribution to overall variation of structure
Geometric Space Search: Molecular Expansive Spaces
• Loosely based on Expansive Spaces Tree (EST) path planning algorithm from robotics
• Designed for rapid coverage of space• Here we adapt an EST-like
method for coverage molecular conformation spaces
• Algorithm:
• Existing point chosen randomly for expansion based on:
• Energy of explored points• Average distance to nearest
neighbors• Number of times point has
already been used for expansion
• New point generated within set radius of chosen point
• Two candidate methods to get new point:• Simple (Gaussian neighbor
generation)• More complex (Random
bounce walk)
1 2
3 4
a)
b)
Illustration of space-covering properties of expansive spaces search. Each point represents a conformation of the receptor.
a) Expansive searchb) Random walk
Results
Acknowledgements
• Experiments to determine effectiveness of search algorithm independent of physical model • Molecular docking experiments on results of search to determine usefulness as drug-design target structures
• Experiments with alternative parameterizations (such as dihedral coordinates)
Work in Progress and Future Work
• Results are for conformational searches of HIV-1 protease starting from PDB structures 1AID and 4HVP and FK506-binding protein (FKBP) starting from PDB structures 1A7X-A and 1FKR-17.• RMSD = Root Mean Squared Distance
HIV-1 protease Inhibitors (drug candidates)
• Explicitly modeling receptor flexibility is computationally impossible
• Collective coordinates = reduced basis for motion of the receptor (dimensionality reduction)
• Example: HIV-1 protease
• 3120 atoms, each with three Cartesian degrees of freedom (x,y,z), for a total of 9360 dimensions—computationally intractable
• use first five principal components as a reduced basis—five dimensional space likely to be tractable
Cecilia [email protected]
• Dimensional reduction: Collective coordinates• Powerful search algorithm: Expansive spaces search
Dimensional reduction: Collective Coordinates
Why model protein flexibility?
Our approach
FKBP
• Distinct structures: At least 1 Å RMSD apart• Monte Carlo Simulation is a standard but slow conformational search method• Expansive search generates more distinct structures than Monte Carlo, and complex neighbor generation scheme works best
• Set diameter: Maximum distance between any two structures in result set• Expansive search consistently generates broader search sets than random walk or Monte Carlo simulation• Indicates better coverage of conformation space