Improving Protein Function Prediction with Molecular Dynamics Simulations. Dariya Glazer Russ Altman
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1 Improving Protein Function Prediction with Molecular Dynamics Simulations Dariya Glazer Russ Altman
2 Motivation Sometimes the 3D structure doesn t score well for a known function. The experimental structure may have an altered configuration of atoms. Possible Solution Treat molecules as dynamic entities, sample other conformations and test for function
3 Potential Energy function: " bonds U = K b ( b i! b o ) 2 + K # (# i! # o ) 2 " angles + " K [ $ 1! cos ( n$ i + % )] dihedrals - / + " & / pairs /. ' ) ( r o r ij 12 * ',! 2) + ( r o r ij *, " ch arg es q i q j r ij
4 Molecular Dynamics Given the equations for energy, and the time scale, perform a large numerical simulation to see how the molecule moves. Time step = ps Starting point = experimentally defined structure (already in good local minima) Include water molecules around protein Goal: study dynamic motion of protein
5 Numerical Solution for M.D. x(t +!t) = x(t) + v(t)!t + [ 4a(t ) " a(t "!t)]!t2 6 v(t +!t) = v(t)+ [ 2a(t +!t) + 5a(t) " a(t "!t)]!t 6 U kinetic = 1 2 # m i v i (t) 2 U =... potential U = U + U total kinetic potential F = du / dx a = F / m
6 Static Structure: n = 1 Molecules Come Alive Simulation Trajectory Structural Diversity: n >>>> 1
7 Starting Static Structures: PDB 5 x HOLO 5 x APO with bound ligand without the ligand Simulations: GROMACS, duration 1ns, explicit solvation Scoring Function: FEATURE, resolution 1Ǻ
8 FEATURE & Ca 2+ Model Threshold score of this model is 50 MODEL FEATURES ATOM-NAME-IS-ANY ATOM-NAME-IS-C ATOM-NAME-IS-N ATOM-NAME-IS-O ATOM-NAME-IS-S ATOM-NAME-IS-OTHER HYDROXYL AMIDE AMINE CARBONYL RING-SYSTEM PEPTIDE VDW-VOLUME CHARGE NEG-CHARGE POS-CHARGE CHARGE-WITH-HIS HYDROPHOBICITY MOBILITY SOLVENT-ACCESSIBILITY RESIDUE_NAME_IS_GLU RESIDUE_NAME_IS_GLY RESIDUE_NAME_IS_LEU RESIDUE_NAME_IS_LYS RESIDUE_NAME_IS_PRO RESIDUE_NAME_IS_VAL RESIDUE_NAME_IS_HOH RESIDUE_CLASS1_IS_HYDROPHOBIC RESIDUE_CLASS1_IS_CHARGED RESIDUE_CLASS1_IS_POLAR RESIDUE_CLASS1_IS_UNKNOWN RESIDUE_CLASS2_IS_NONPOLAR RESIDUE_CLASS2_IS_POLAR RESIDUE_CLASS2_IS_BASIC RESIDUE_CLASS2_IS_ACIDIC RESIDUE_CLASS2_IS_UNKNOWN SECONDARY_STRUCTURE1_IS_3HELIX SECONDARY_STRUCTURE1_IS_4HELIX SECONDARY_STRUCTURE1_IS_BRIDGE SECONDARY_STRUCTURE1_IS_STRAND SECONDARY_STRUCTURE1_IS_TURN SECONDARY_STRUCTURE1_IS_BEND SECONDARY_STRUCTURE1_IS_COIL SECONDARY_STRUCTURE1_IS_HET SECONDARY_STRUCTURE2_IS_HELIX SECONDARY_STRUCTURE2_IS_BETA SECONDARY_STRUCTURE2_IS_COIL SHELL
9 Simulation Analysis Local Grid: to observe dynamics of a known binding site Keep only the topmost score for each frame Global Grid: to find all possible binding sites in a given structure Keep all scores over 50 for each frame
10 Local Grid: Non-Sites Score Low 1B9A LEU15N Ca Model 1K94 THR78CG2 Ca Model Simulation Original Score Model Threshold
11 Results Agree With Experiments HOLO APO 1B9A CA109 Ca Model 1B8C CA109 Ca Model Simulation Original Score Model Threshold
12 FEATURE Scores vs. Local Structures Ori:
13 Results: 1DNK 80 1DNK Active Site Ca Model FEATURE Scores Frames x2.5ps Simulation Original Score Model Threshold Active Site of the molecule, Ca 2+ binds with the ligand Exists in both HOLO and APO MD helps FEATURE identify this site in both HOLO and APO
14 Results: 1MJW 60 1MJW MUT Ca Model FEATURE Scores Frames x2.5ps Simulation Original Score Model Threshold Site opened up by mutation which breaks stable salt bridge Exists only in APO MD helps FEATURE identify this site in APO
15 Summary TOTAL = 12 SITES Static FEATURE 7 HOLO and 3 APO MD + FEATURE 10 HOLO and 9 APO Fold Improvement
16 Protein adopts functional conformations during simulation.
17 Simbios: National Center for Physics-Based Simulation of Biological Structure NIH-funded resource to promote biomedical computing (one of seven) Devoted to biological structural simulations at all scales Two-fold mission: Perform high quality research Disseminate software and models to others Also publish quarterly magazine Biomedical Computation Review
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20 Biomedicalcomputationreview.stanford.edu
21 Conclusions We can build statistical models of functional sites in proteins based on 3D structures We can use these models to recognize function in new structures We can extend the range of our models by adding physics-based simulation Challenges: Discovering and labeling new functions Applying in high-throughput to all structures Using for drug design/discovery
22 Thanks! Mike Liang, Shirley Wu, Dariya Glazer, Inbal Halperin, Jessica Ebert
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