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Implementation of Conformational Space Annealing into CHARMM and its Applications. Jinhyuk Lee 1 , Jinwoo Lee 1,2 , Milan Hodoscek 3 , Bernard R. Brooks 3 , and Jooyoung Lee 1. 1 School of Computational Sciences and Center for In Silico Protein Science,

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slide1

Implementation of Conformational Space Annealing into CHARMM and its Applications

Jinhyuk Lee1, Jinwoo Lee1,2, Milan Hodoscek3, Bernard R. Brooks3, and Jooyoung Lee1

1 School of Computational Sciences and Center for In Silico Protein Science,

Korea Institute for Advanced Study, South Korea.

2 Department of Mathematics, Kwangwoon University, South Korea.

3 Laboratory of Biophysical Chemistry, National Institutes of Health, Building 50,

South Drive, Bethesda, Maryland 20892 USA.

slide2

Contents

Methodology

  • General global optimization methods
  • Conformational Space Annealing (CSA)
  • Implementation into CHARMM
  • Example input

Issues

  • D-forms happen to exist

Applications

  • Small peptide folding – comparison with MCM
  • NMR structure determination
  • RNA folding
  • Chain building
why better global optimizer
Why Better Global Optimizer?
  • Physics based energy functions for macromolecules are not at all satisfactory.
  • Thus using better optimizer does not at all guarantee better output.
  • Then, do we still want better global optimizer ?
  • How about choosing a more simpler optimizer with an 'appropriate' energy function to it ?
  • Actually, many recent researches are following that line.
interesting some pieces of recent works
Interesting some pieces of recent works
  • MODELLER-WITH-BETTER-OPTIMIZER are proven to be better than the usual MODELLER
  • MSA-WITH-BETTER-OPTIMIZER are proven to outperforms the usual progressive alignments
  • MODIFIED-SCWRL-WITH-BETTER-OPTIMIZER have compiled the most accurate side-chains
  • What distinguished points have made these successes ?
a special type of energy functions
A special type of energy functions
  • CONFIDENT distant restraints from pdb templates constituted the MODELLER energy.
  • CONFIDENT structurally aligned pairs of AAs constituted the MSA energy.
  • FIXED-CONFIDENT-REGIONS and SCWRL for the rest constituted the side-chain energy.
  • In summary, if CONFIDENT informations constitute an energy, we could maximally combinatorially combine the informations using BETTER-GLOBAL-OPTIMIZER.
implementation of csa to charmm
Implementation of CSA to CHARMM
  • We wanted to open CSA to public.
  • We wanted to utilize versatile CHARMM combined with CSA to seek more success.
slide7

Conformational Space Annealing (CSA)‏

J. Comput. Chem. (1997)‏

  • Key idea: GA + “annealing in conformational space”.
    • Pool evolves to have lower energies whilealways maintaining “diverse” conformations.
    • Tendencies to “the diverse” and “the lower” competes with each other.
    • CSA focuses on the “diversity” in its early stages, and gradually shifts its focus to the “lowering” by utilizing single parameter Dcut
slide14

Main-node (Master)

Sub-nodes (Slave)

Local minimization

Removing D-forms routines

Distribute jobs into the assigned nodes

Energies

Structures

Define Flexible Dihedrals

Run several independent but connected calculations using subsystems

Define various CSA options

slide15

Contents

Issues

  • D-forms happen to exist
slide16

D-Forms

D-form

L-form

From wikipedia.org

Two enantiomers of a generic amino acid

Two mirror images of GB1

The naturally occurring amino acids are L-forms. However, during the optimization process, D-forms may occur. To remove this artifact, we introduced additional transient penalty against D-form amino acids. Final models are all in the L-form without the penalty.

slide17

Amino Acid

Nucleic Acid (RNA)

Chiral centers

Blues * : Linkers

Arrows : Flexible Dihedrals

slide19

First Bank Generation

Bank Generation

Initial Bank

Initial Bank

Mini. with increased force constants of bond, angle, improper angles related to chiral carbon

Mini. with increased force constants of bond, angle, improper angles related to chiral carbon

Randomize dihedrals

D-form exist ?

D-form exist ?

Yes

Yes

Mini. with normal force constants

Mini. with normal force constants

No

No

Yes

Yes

D-form exist ?

D-form exist ?

Use the structure generated with increased force constants

Use the structure generated with increased force constants

No

No

L-form Bank

L-form Bank

slide20

Applications

  • Exploring peptide energy landscapes – comparison with Monte Carlo with Minimization (MCM)
  • NMR structure determination
  • RNA structure
  • Chain building (?) – free modeling targets
slide21

Exploring peptide energy landscapes

  • The prediction of protein/peptide structure at the atomic level is a challenging task of theoretical interest and practical use.
  • The accurate modeling of biomolecules poses two principal problems. First, an accurate description of potential energy is necessary to find the native structure from a large number of local structures. The energy landscape must be modeled accurately to predict the native structure (“Force Field Accuracy”).
  • Second, structure prediction is hindered from the presence of the multiple minima and energetic barriers. Since the large conformational space increased in the number of residues (“Sampling Issue”).
  • The primary goal of the section is to assess the feasibility of protein structure prediction using energy functions.
  • Can the conformational spaces of three peptide be sampled sufficiently to find low energy states with five different implicit models?
  • The lowest energy conformations found in multiple simulations starting from an extended conformation were compared with those studied by the previous work (Steinbach).
slide22

Models – De novo Folding

  • Three models studied by Steinbach (2004)
  • Five solvation models
  • EEF1 (Effective Energy Function)
  • SASA (Solvent Accessible Surface Area)
  • ACE (Analytical Continuum Electrostatics) 19
  • ACE 22
slide23

* : Re-minimization

in native structure

EEF1

● : The lowest energy structure in MCM by Steinbach

SASA

ACE19

ACE22

ACE22/

CMAP

BS1

UB

Trpcage

slide24

Conclusions I

  • For each peptide, we have studied five different energy landscapes, each defined by a particular protein force field and implicit solvent model.
  • Native-like structures were found at the lowest EEF1 energy, supporting the reliability and transferability of the EEF1 model.
  • CSA can find the lowest energy structures than MCM/MCMA does.
  • The improvements of the force fields will provide more reliable tertiary structures of proteins.
slide25

Applications

  • Exploring peptide energy landscapes – comparison with Monte Carlo with Minimization (MCM)
  • NMR structure determination
  • RNA structure
  • Chain building (?) – free modeling targets
slide26

NMR Restraints

1. Distance (NOE)

- general distance (H-H)

- hydrogen bond (O-N, HN-O etc)

2. Dihedral Angle

- backbone φ and ψ

3. Dipolar Coupling

- NH interdistance vector vs Magnetic field

4. Chemical Shift

- Peptide plane orientation (NHC plane)

slide27

Schematic Illustration

Global optimization on potential energy (U)

Randomized-dihedrals structures

Final structures (Ensemble)

Extended structure

Conformational Space Annealing (CSA)

Structure quality measures

- Structure convergence

- NOE + DIH violations

- Clash score

- Ramachandran preference

slide29

a Local RMSD/Global RMSD

b Pairwise RMSD over ensemble

c Average percentage of Ramachandran preference

d Clashscore : number of serious steric overlap (> 0.4 Angs) per 1000 atoms

e RMSD of NOE restraints

U = Ucharmm + w Unoe

= Ubond + w1 Uvdw + w2 Unoe

NOE violations (red: NMR, green: ours)

slide30

1998년 Science 지에 ion channel 의 X-선구조

여러 연구진이 다양한 종류와 형태의 이온 통로구조를 밝혀내고 있다.

선구적으로 이온 통로를 밝힌 공로로 2003년 노벨 화학상 수상

MacKinnon, Science (1998)

slide31

리간드에 의해 활성이 조절되는 pentameric ligan-gated ion channel (pLGIC)

Open  Closed

N. Bocquet, Science (2009)

slide32

Viral Protein “U” (VPU)

  • A small membrane protein whose sequence is encoded in the genome of HIV-1 (Human immunodeficiency virus 1)
  • A hydrophobic TM helix in the N-terminal domain and amphipathic helices in the C-terminal domain
  • Vpu affects the budding of new virus
  • particles, and the hydrophobic
  • TM helix (residues 7-25) may play
  • an important role as an ion channel
  • that is selective for monovalent
  • cations such as Na+ and K+
  • 4. The ion channel is formed by the homo-oligomers of four to seven helices.
  • Computational studies have suggested that the most probable oligomeric state is a pentamer. However, a recent study suggested that there might be more than one possible oligomeric state.
  • 6. The atomic structure of Vpu is an essential and crucial starting point for rational drug design.
  • 7. Vpu has also served as a model system (VpuTM) for method development in structure determination of membrane proteins.
slide33

Based on rigid-body assembly calculations with the AMBER potential, the Opella group proposed that both oligomers (tetramer and pentamer) have the strong preference of being the right-handed conformation due to energetically unfavorable clashes of the large Trp22 sidechains inside the pore in the left-handed conformation.

They also argued that the resulting pore lining of Ile17 in the right-handed conformation can be supported by the fact that the highly conserved Ile17 may have an essential role in its ion channel activity.

Modeling based on the experimental data from site-directed Fourier transform infrared dichroism supports the left-handed pentamer conformation and reveals a pore occluded by Trp residues at the end of the TM domain.

S. Park JMB (2003)

A. Kukol, Biophys. J. (1999)

slide34

Tetramer

Pentamer

Definition of

crossing angle

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