Paulino Gómez Puertas Bioinformática.

Predicción de estructura de proteínas.Modelado por homología.

Paulino Gómez Puertas Bioinformática.

Predicción de estructura de proteínas.

¿Por qué predecir la estructura de una proteína?.

¿Cómo predecir la estructura de una proteína?

Paulino Gómez Puertas Bioinformática.

Kinesin Motility

Model of kinesin dimerwalking along a microtubuleprotofilament.(Hoenger et al.2000).

Mandelkow lab http://www.mpasmb-hamburg.mpg.de/ktdock/

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Virtual docking:

carnitine

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Protein structure determination

Xray crystallography NMR

By D. Devos

Paulino Gómez Puertas Bioinformática.

EMBL

PDB

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Predicción de estructura de proteínas.

¿Por qué predecir la estructura de una proteína?.

¿Cómo predecir la estructura de una proteína?

Paulino Gómez Puertas Bioinformática.

MAKEFGIPAA VAGTVLNVVE AGGWVTTIVS ILTAVGSGGL SLLAAAGRES IKAYLKKEIKKKGKRAVIAW

Physical principles: not in the next few years? Protein structure prediction

Paulino Gómez Puertas Bioinformática.

“AB INITIO ”

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Molecular dynamics simulation.

ab initio methods

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Alain Leppinette. CAB.

Paulino Gómez Puertas Bioinformática.

Paulino Gómez Puertas Bioinformática.

Epot = Σi Ebonding + Σi Enon-bonding

Σi Ebonding = Σi Ebond + Σi Eangle + Σi Edihedral + Σi Eimproper

Σi Enon-bonding = Σi Eelectrostatic + Σi ELennard-Jones

Molecular Mechanics

Molecular DynamicsF = m · a ; F = m ·v/t ; Epot = F · f(i)

Epot = m · v/t · f(i)

Epot = F · f(i) (Epot grav. = m · g · h)

Paulino Gómez Puertas Bioinformática.

bonding terms

( )20bbonds

bonds bbk21E −= ∑

( )∑ −=angles

20angle k

21E θθθ

( )[ ]∑ −+=dihedrals

0ddihedral cos1k21E θθ

non-bonding terms

∑=ij ij

ji

0elect r

qq4

1Eεπε

repulsive

atractive

∑ −=−ij

6ij

ij12ij

ijJonesLennard r

BrA

E

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Paulino Gómez Puertas Bioinformática.

PROTEIN UNFOLDING

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http://folding.stanford.edu/

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Paulino Gómez Puertas Bioinformática.

CA(2+)-REGULATED ACTIN-BINDING PROTEIN.

VILLIN CONSISTS OF A LARGE CORE FRAGMENT, THE AMINO-TERMINAL PORTION, AND A SMALL HEADPIECE, THE CARBOXYL-TERMINAL PORTION. THE HEADPIECE BINDS F-ACTIN STRONGLY IN BOTH THE PRESENCE AND ABSENCE OF CALCIUM.

MAJOR COMPONENT OF MICROVILLI OF INTESTINAL EPITHELIAL CELLS.

Thefoldingtime is on the order of 10 micro-seconds.

3 Phe

1 Trp

1 Phe

Folding@home: Simulations of the villin headpiece

Paulino Gómez Puertas Bioinformática.

MAKEFGIPAA VAGTVLNVVE AGGWVTTIVS ILTAVGSGGL SLLAAAGRES IKAYLKKEIKKKGKRAVIAW

Physical principles: not in the next few years?

Informatics (copying from known cases)

- Homology modeling

Score = 85.1 bits (208), Expect = 6e-19Identities = 27/56 (48%), Positives = 42/56 (74%), Gaps = 1/56 (1%)

Query: 2 FIAIYDYKAETEEDLTIK KGEKLEIIEKEGD-WWKAKAIGSGEIGYIPANYIAAAE 56F+A+YDY+A TE+DL+ KGEK +I+ WW+A+++ +GE GYIP+NY+A +

Sbjct: 8 FVALYDYEARTEDDLSFHKGEKFQILNSSEGDWWEARSLTTGETGYIPSNYVAPVD 63

- Threading

MAKEFGIPAA VAGTVLNVVE AGGWVTTIVS ILTAVGSGGL SLLAAAGRES IKAYLKKEIKKKGKRAVIAW

Pseudo-Energy level?

PDB

Fold ranking

By D. Devos

Protein structure prediction

Paulino Gómez Puertas Bioinformática.

Protein structure prediction. Flow chart.

Paulino Gómez Puertas Bioinformática.

Modelado por homología.

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protein structure evolution

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DnaK

FtsA

Actin

Hexokinase

Hsc70

MreB

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Structural alignment

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Paulino Gómez Puertas Bioinformática.

Lesk & Chothia, 1986

Structure & sequence similarity

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Sander & Schneider, 1991

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Paulino Gómez Puertas Bioinformática.

alignment

AHPLTSDFGGHTERDLHA

AHTLTSEGGGHTEADVHA|| |||: ||||| |:||

template (1ndb)

model (hCPTI)

Paulino Gómez Puertas Bioinformática.

Classical homology modeling algorithm

Generate alignment query - target

Replace conserved amino acid side-chains

Replace other amino acids

- use most common rotamers

- keep as many atoms in positions as possible

Model loops regions (insertions and deletions)

Optimize packing

Quality check

Paulino Gómez Puertas Bioinformática.

AlignmentsThe quality of the alignment is the key step in homology modeling

pairwise alignments

multiple sequence alignments

profile based alignments

Match of secondary structure elements *observed/predicted

Match structural environments and residue properties

Optimal structural position of gaps in the alignments

minimal number of gaps

better place them in loops / exposed regions

and close in structure (possible to model)

Paulino Gómez Puertas Bioinformática.

Alignments and introduced errors

Paulino Gómez Puertas Bioinformática.

Modeling protein cores

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Modeling loops (deletions & insertions)

by Luis Sanchez-Pulido, 2000

Paulino Gómez Puertas Bioinformática.

Modeling side chains (rotamer libraries)

Paulino Gómez Puertas Bioinformática.

Rotamers

Paulino Gómez Puertas Bioinformática.

Similarity Search

Database of 3D structures

�Substitution of side-chains of internal residues

�Substitution of other side-chains

3D Model of the

protein core

Protein query

Sequence - StructureAlignment

Query-Target Structures

Basic approach in homology modeling

Structuralalignment of target structures

Protein core

Full coordinates 3D Model

�Modeling based onrotamer databases

�Space restriction for the conformational search

Refined 3D Model

Loop search

MD and EM tech.

Rotamer library

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Quality check: the Ramachandran plot.

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Paulino Gómez Puertas Bioinformática.

Paulino Gómez Puertas Bioinformática.

Quality check: Prosa II energy plots

Paulino Gómez Puertas Bioinformática.

http://modbase.compbio.ucsf.edu

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http://swissmodel.expasy.org/repository

Paulino Gómez Puertas Bioinformática.

Protein structure prediction. Flow chart.

Paulino Gómez Puertas Bioinformática.

Threading. Reconocimiento de plegamiento. Modelado por homología remota.

Paulino Gómez Puertas Bioinformática.

Homology Modelling vs Fold Recognition

Fold Recognition Homology Modelling

% seq. ID

0 30 100

Application

Model Quality

Any Sequence >= 30-50% IDwith template

Fold Level Atomic Level

If the sequence is similar to a known structure (>30-50% identity) you can usually move straight onto generating an all atom model by homology modelling.

Target Sequence

Paulino Gómez Puertas Bioinformática.

Structural space

Sequence space

Homology Modelling Targets

Fold Recognition Targets

Sequence Space vs. Structure Space

The development of fold recognition methods came from the observation that many apparently unrelated sequences had very similar 3-dimensional structures (folds).

Paulino Gómez Puertas Bioinformática.

Superfolds(Orengo et al.)

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Algoritmos de threading. General.

Secuenciaproblema

Paulino Gómez Puertas Bioinformática.

Count pairs of each residue type at different separations

Algoritmos de threadingPotenciales de contacto

Energy of interaction = -KT ln (frequency of interactions)Boltzmann principle

d

Eco

unts

d

Jones, 1992; Sippl, 1995

Paulino Gómez Puertas Bioinformática.

Prediction-basedthreading

Algoritmos de threadingCoincidencia de estructura secundaria y accesibilidad

Rost, 1995 http://cubic.bioc.columbia.edu/predictprotein

secondary structure prediction

Paulino Gómez Puertas Bioinformática.

ALGUNOS SERVIDORES DE THREADING3D-PSSM: http://www.sbg.bio.ic.ac.uk/3dpssm

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ALGUNOS SERVIDORES DE THREADINGFUGUE: http://www-cryst.biob.cam.ac.uk/~fugue/

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ALGUNOS SERVIDORES DE THREADINGPSIPRED: http://bioinf.cs.ucl.ac.uk/psipred/

Paulino Gómez Puertas Bioinformática.

Cuestiones…