26 Ab Initio - Threading (1)

8/8/2019 26 Ab Initio - Threading (1)

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Construyendo modelos 3D de proteinas

‘fold recognition / threading’



Why make a structural model for your protein ?

The structure can provide clues to the function

through structural similarity with other proteins

With a structure it is easier to guess the location of active sites

We can apply docking algorithms to the structures

(both with other proteins and with small molecules)

With a structure we can plan more precise experiments in the lab



Protein Modeling Methods

• Ab initio methods:solution of a protein folding problemsearch in conformational space

• Energy-based methods:energy minimizationmolecular simulation

• Knowledge-based methods:homology modelingfold recognition / threading



Why do we need Ab Initio Methods?

data taken from PDB

http://www.rcsb.org/pdb/holdings.html

New folds and those sequences with very little sequence

homology <15%



Protein Modeling Methods

• Ab initio methods:solution of a protein folding problemsearch in conformational space

• Energy-based methods:energy minimizationmolecular simulation

• Knowledge-based methods:homology modelingfold recogniion



Predicting Protein Structure:Predicting Protein Structure:

Threading / Fold RecognitionThreading / Fold Recognition

BasisBasis

It is estimated there are only around 1000 to

10 000 stable folds in nature**

Fold recognition is essentially finding the best

fit of a sequence to a set of candidate folds**

Select the best sequence-fold alignment using afitness scoring function**



The Threading Problem

• Find the best way to “mount” the residue

sequence of one protein on a known

structure taken from another protein



Why is it called threading ?

• threading a specific sequence through all

known folds

• for each fold estimate the probability that

the sequence can have that fold



Threading: Basic StrategyThreading: Basic Strategy

Sequence

Template

SpatialInteractions

dhgakdflsdfjaslfkjsdlfjsdfjasd

Library

of folds

Query

Scoring & selection



Two structurally

similar proteins

Spatial adjacencies

(interactions)

Possible threadingwith a sequence



Input/Output of Protein Threading

Pairwiseamino acid

scoringfunction

Amino acidsequence

a[1..n]

g(…)

Core segments

C[1..m]

T

HREADI

NG



Fold recognition (Threading)

The sequence:

+

Known protein folds

SLVAYGAAM

structural model



Input:

sequence

H bond donor

H bond acceptor

GlycinHydrophobic

Library of folds of known proteins



S=20S=5S=-2

Z=5Z=1.5Z= -1

H bond donor

H bond acceptor Glycin

Hydrophobic



10100 N

::::::::

10100167-9987-242

10100-80101-50101

GextGopY…DCA

Amino acid type

Positio

n

on

sequence



Fold recognition/ Threading

Disadvantages:

• threading methods seldom lead to the alignmentquality that is needed for homology modeling.

• less than 30% of the predicted first hits are trueremote homologues (PredictProtein).



Threading resources

• TOPITS

Heuristic Threader, part of larger structure

prediction system• 3DPSSM

Integrated system, does its own MSA and

secondary structure predictions and then threading

• GenThreader

Similar to 3DPSSM



In homology modelling, construction of the side chains isdone using the template structures when there is high

similarity between the built protein and the templates

In spite of the huge size of the problem (because each side

chain influences its neighbours) there are quite succesful

algorithms to this problem.

Side chain construction

Without such similarity the construction can be done using

rotamer libraries

A compromise between the probability of the rotamer and its

fitness in specific position determines the score. Comparing

the scores of all the rotamer for a given amino acid determines

the preferred rotamer.



P h e

A s nC o n f o r m a t i o n - a g i v e n s e t

o f d i h e d r a l a n g l e w h i c h

d e f i n e s a s t r u c t u r e .

R o t a m e r - e n e r g e t i c a l l yf a v o u r a b l e c o n f o r m a t i o n .



Ab initio

The sequence

SLVAYGAAM

structural model



Ab initio methods for modelling

This field is of great theoretical interest but, so far, of verylittle practical applications. Here there is no use of sequencealignments and no direct use of known structures

The basic idea is to build empirical function that simulatesreal physical forces and potentials of chemical contacts

If we will have perfect function and we will be able to scan

all the possible conformations, then we will be able to detectthe correct fold

di i i SP di i P i S



Predicting Protein Structure:Predicting Protein Structure:

Ab Initio Ab Initio MethodsMethods

Sequence

Secondarystructure

Prediction

Tertiarystructure

Low energystructures

PredictedstructureEnergy

Minimization

Validation

Mean field

potentials



Ab initio Methods

Simplified modelssimplified alphabet (HP)

simplified representation (lattice)

Build-up techniques

Deterministic methods

quantum mechanics

diffusion equations

Stochastic searchesMonte Carlo

genetic algorithms



Rosetta approach

• Rosetta (David Baker) consistently outstanding performer inlast two CASPs

• Integrated method

– I-Sites: much finer grained substructures than secondarystructures. A library of all structures each AA 9mer isfound in (taken from PDB)

– Heuristic global energy function to estimate quality of folds

– Monte Carlo search through assignments of I-Sites to

minimize energy function.• Also, HMMSTR, HMM-driven method for assigning I-

Sites.



Rosetta is way ahead

• CASP 4 results.

• CASP 5 similar, but not as dramatic.



Fully automated predictions

• CAFASP-2

• Meta-servers work best

– Integrate predictions from several other servers

– Significantly better predictions than any

individual approach

• Several public metaservers available: – http://bioinfo.pl/Meta/ is best all-around

26 Ab Initio - Threading (1)

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