Sample Protein Functional - Stanford Universityforum.stanford.edu/events/posterslides/SamplingProtein... · 2009-04-22 · 3 Motivation (I) Proteins The major molecules that carry

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Sample Protein Functional

Peggy Yao, Jean-Claude LatombeBiomedical Informatics, Compute ScienceStanford University

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Conformations

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Motivation (I)

ProteinsThe major molecules that carry out our metabolic activities.Function by interact with other molecules.

The interaction is largely determined by structures.

DrugsSmall molecules that inhibit or facilitate the interactions between proteins and some specific molecules.

Computer-aided drug designDesign drugs based on protein structural modeling.

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Motivation (II)

Protein structures are dynamicConformation selection theory

Experimental techniques can only provide very few conformations,which are not necessarily functional conformations.

Need computational methods

ProteinFunctional

ConformationSampling

+

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Problem Definition

Sampling protein functional conformations

Entire conformation space

Folded states

Functional (ligand/ion-binding) states

Input: one folded state conformation

Output: one or more functional conformations

General approach: sample protein conformations, and use available function-prediction or ligand-docking methods to check the conformations.

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Challenges and Observations

Lots of variable elements and constraintsVariable elements

Hundreds of atom positions, bond lengths, bond angles, dihedral angles, etc.

ConstraintsHundreds of bondsNo steric clash

ObservationsNot all variable elements are truly variable

For example, helices and sheets.

…

…

…D1

Di

Dj

Dk

Dn

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Research Framework

Protein Structure Model

SamplingMethod

Applications:Protein-ligand

InteractionStudy

Bond Constraints

Model

RigidityAnalysis

Aim 1:

To develop a good way to model bonds as constraints to facilitate efficient exploration of the conformation space.

Aim 2:

To develop a sampling approach to reach the functional states efficiently.

Aim 3:

To apply the sampling method to function-prediction or ligand docking.

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Linkage Model

Variable elements: Dihedral angles

Assumptions:Fixed bond lengthsFixed bond angles

N

Cα

C

N

O

C

HCβ

H

H

HH Alanine

φ ψ

χ

… …

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Rigidity Analysis

Model bonds as Distance Constraint Graph.3D Pebble Game

An algorithm to identify rigid regions, over-constrained regions, and collective motion regions.

C

N

O

… Cα

C

NH …

C

N

Cα

C

N

O

φ

ψ

…

OO

H

H

H

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Bond Constraint Model

Question: what are the bonds shall we model as distance constraints?

Bond typesCovalent bonds

Strong and stableNon-covalent bonds

Hydrogen bondsHydrophobic interactionsMany, weak, and dynamic

BondSet

Essential bonds to allow the conformational change

model

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Hydrogen Bond Selection

Learn H-bond stability from MD (Molecular Dynamics) simulation

Stability measurement: P(presence)P(presence) vs. Energy Decision-tree

P(presence) vs. H-bond Energy (1EIA, Amber03, MD1)

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

<=-7.8 >-7.6 >-7.2 >-6.8 >-6.4 >-6 >-5.6 >-5.2 >-4.8 >-4.4 >-4 >-3.6 >-3.2 >-2.8 >-2.4 >-2 >-1.6 >-1.2 >-0.8 >-0.4 >0

H-bond Energy interval (kcal/mol)

P(p

rese

nce)

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

Per

cent

age

P(presnce) percentage

Angle_D_A_AA

SSE_typeChain_type>129

Range0.46

SS

0.320.59

MA,DA,DL

0.86 0.17

>39

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Conformation Sampling

Goal: Start from a folded state conformation, efficiently sample the valid conformation space until obtain a functional conformation.Assume there exists a software which can recognize the conformation at the functional state.

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Functional

Randomly-Guided Conformation Sampling Tree

R4

R9

R2

R1

S0

S1

S4S3

S2

While (not reaching the goal):

1. Generate a random structure.

2. Find the node closest to the random structure, say node i.

3. Identify all H-bonds in node i.

4. Select a subset of H-bonds to be constraints, together with all covalent bonds.

5. Linear-interpolate i to the random structure for 100 steps while maintaining all rigid bodies.

6. Insert the new node into the tree as node i+1.

R8

R7R6

R5

S5

S6 S7

S8

S9

R3

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Preliminary Results

Catabolite Gene Activator Protein (1G6N)200 amino acids => more than 800 total DOFsGenerated a tree with 100 nodes.

Green: initial conformation

Cyan: goal conformation

Magenta: best achieved