predicción de estructura de proteínas. modelado por...
TRANSCRIPT
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/
Paulino Gómez Puertas Bioinformática.
Virtual docking:
carnitine
Paulino Gómez Puertas Bioinformática.
Protein structure determination
Xray crystallography NMR
By D. Devos
Paulino Gómez Puertas Bioinformática.
EMBL
PDB
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.
MAKEFGIPAA VAGTVLNVVE AGGWVTTIVS ILTAVGSGGL SLLAAAGRES IKAYLKKEIKKKGKRAVIAW
Physical principles: not in the next few years? Protein structure prediction
Paulino Gómez Puertas Bioinformática.
“AB INITIO ”
Paulino Gómez Puertas Bioinformática.
Molecular dynamics simulation.
ab initio methods
Paulino Gómez Puertas Bioinformática.
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
Paulino Gómez Puertas Bioinformática.
Paulino Gómez Puertas Bioinformática.
PROTEIN UNFOLDING
Paulino Gómez Puertas Bioinformática.
http://folding.stanford.edu/
Paulino Gómez Puertas Bioinformática.
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.
Paulino Gómez Puertas Bioinformática.
protein structure evolution
Paulino Gómez Puertas Bioinformática.
DnaK
FtsA
Actin
Hexokinase
Hsc70
MreB
Paulino Gómez Puertas Bioinformática.
Structural alignment
Paulino Gómez Puertas Bioinformática.
Paulino Gómez Puertas Bioinformática.
Lesk & Chothia, 1986
Structure & sequence similarity
Paulino Gómez Puertas Bioinformática.
Sander & Schneider, 1991
Paulino Gómez Puertas Bioinformática.
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
Paulino Gómez Puertas Bioinformática.
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
Paulino Gómez Puertas Bioinformática.
Quality check: the Ramachandran plot.
Paulino Gómez Puertas Bioinformática.
Paulino Gómez Puertas Bioinformática.
Paulino Gómez Puertas Bioinformática.
Quality check: Prosa II energy plots
Paulino Gómez Puertas Bioinformática.
http://modbase.compbio.ucsf.edu
Paulino Gómez Puertas Bioinformática.
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.)
Paulino Gómez Puertas Bioinformática.
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
Paulino Gómez Puertas Bioinformática.
ALGUNOS SERVIDORES DE THREADINGFUGUE: http://www-cryst.biob.cam.ac.uk/~fugue/
Paulino Gómez Puertas Bioinformática.
ALGUNOS SERVIDORES DE THREADINGPSIPRED: http://bioinf.cs.ucl.ac.uk/psipred/
Paulino Gómez Puertas Bioinformática.
Cuestiones…