Insights into All-Atom Protein Structure Prediction via in silico Simulations2013 Sigma Xi Student Research Showcase Daniel Wang

RESEARCH GOALS

To utilize in silico methods to perform de novo simulations of protein folding pathways and

predict the functional structures of proteins.

Proteins – Biological Workhorses

A map of 3200 protein interactions between 1700 proteins (Image from http://www.mdc-berlin.de/en/news/archive/2008/20080910-erwin_schr_dinger_prize_2008_goes_to_resea/index.html)

The central dogma of biology(Image from http://www.nyu.edu/

projects/vogel/Pics/centraldogma_2

BACKGROUND

Proteins serve a plethora of vital functions: growth and repair, cell-to-cell signaling, defense against pathogens, movement, catalyzing reactions

~130,000 binary protein-protein interactions in a human cell at any given time

Protein function is determined by specific 3-dimensional structure

Proteins gain specific functions through folding, a poorly understood process in which a chain of amino acids assembles into a specific three-dimensional structure

No current method exists to predict the functional structure of a protein from its amino acid sequence

The protein folding process has remained a mystery to biochemists for several decades. Understanding this process would allow for:

Greater insight into protein function Clues into how proteins may misfold and aggregate to

cause a range of diseases, such as Alzheimer’s and Parkinson’s

Random Coil Structure

3-dimensional native structure

Protein Folding Problem

(Image from http://www.ks.uiuc.edu/villin-folding-process)

Folding Funnel Model Modern folding model =

energy of a protein with respect to systemic changes in geometry and is represented by funnel-shaped energy landscapes

Protein chain must negotiate multiple folding pathways with valley traps and mountain barriers

Conformational entropy that is lost during the folding process is compensated by an increase in free energy as the global minimum is approached

Thermodynamic protein folding funnel(Image from http://www.learner.org/courses/physics/visual/visual.html?shortname=funnel)

MD simulations calculate the physical movements of atoms in a system over a period of time, known as a trajectory.

Timesteps in the femtosecond (10-15 of a second) scale, MD simulations offer insight into intra- and inter-molecular interactions at an atomistic level

Molecular Dynamics Simulations

Implicit molecular dynamics environment (Image from http://www.yasara.org/benchmarks.htm)

Schematic of molecular dynamics steps

Replica Exchange MD

Allows for larger conformational searches by utilizing independent realizations of a system, known as replicas.

Each replica is coupled to a different thermostat temperature. Replicas are exchanged at regular time intervals, effectively allowing conformations to escape low temperature kinetic traps by “jumping” to alternate minima being sampled at higher temperatures

Schematic of replica exchange molecular dynamics

METHODOLOGY

Rationale: Model protein systems were selected to represent a variety of structural motifs ubiquitous to all proteins, including

• α-helices, • β-pleated sheets• globular shape

Selection of Prototypical Peptides

Tc5b (trp-cage) globular, hydrophobic core, 20 residues

Chignolin (CLN025) beta hairpin, 10 residues

K19 alanine-rich alpha helix, 12 residues

Construction of Peptide Sequences• Atomic coordinates for peptides

obtained from Protein Data Bank as nuclear magnetic resonance or X-ray crystallography data

• Structures evaluated by MolProbity structure evaluation program to perform steric adjustments

Equilibration Protocol

1 | Energy minimization

 System is restrained but

atoms added by Molprobity and

LEaP are allowed to move.

 

2 | Langevin Dynamics

 System is slowly heated to

appropriate temperature

 

3 | Energy MinimizationBackbone is

restrained but side chains are free to

move 

4 | Langevin Dynamics

 System is slowly heated to

appropriate temperature

 

5 | Langevin Dynamics

 Lower restraints 

6 | Langevin Dynamics

 Lower restraints 

7 | Production MD RunUnrestrained all-atoms molecular

dynamics to assess system stability in simulation environment

 

Successive rounds of energy minimization and molecular dynamics with decreasing restraints

allows the system to transition from the experimental environment to the simulation

environment

All-Atom Molecular DynamicsSimulations and analyses were carried out using the Assisted Model Building with Energy Refinement (AMBER)

1. tleapPreparation module

o prmtop: input molecular topology, force field parameters

o inpcrd: input coordinate file and velocities

o mdin: input control data for the minimization/simulation run

o GB8 implicit solvent model

2. AMBERSimulation Engine

o Determine the number of replicas and temperatures for each replica using tslop3 REM temperature program

o Submit job script to NICS Kraken

o Energy and coordinate trajectory file is written over simulation time (minimum 100ns for all systems)

3. ptrajPost-trajectory analysis

oExtract temperature replica trajectories

oBackbone root mean square deviation (RMSD) analysis: a quantitative comparison between a representative structure of a folded cluster and the native structure

Backbone RMSD generally increases via a “plateau” profile, alternating between periods of stabilization and spikes from imposed positional restraints

RMSD <1.5Å during unrestrained molecular dynamics indicates that all three structures are stable in the simulation environment

Peptides are stable in the simulation environment

ff12SB force field and solvent parameters are ideal for

RESULTS

Equilibration produces stable structures

trp-cage K19

chignolin