Transcript Proteiinianalyysi 5

Proteiinianalyysi 5
Rakenteen ennustaminen
Funktion ennustaminen
http://www.bioinfo.biocenter.helsinki.fi/downlo
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Sekvenssistä rakenteeseen
• komparatiivinen mallitus
• 1-ulotteinen tilan (luokan) ennustaminen
sekvenssistä
• 3-ulotteisen rakenteen tunnistaminen
annetusta kirjastosta (fold recognition)
• 3-ulotteisen rakenteen ennustaminen ab
initio
– ROSETTA
The “Folding Problem”
Two parts:
(1) The “Search Problem”
Is the true structure one of my 2 million guesses?
Fragment assembly
(2) The “Discrimination Problem”
If it’s one of these 2 million, which one is it?
Empirical pseudopotential
Rosetta
(1) A stone with three ancient languages on it.
(2) A program (David Baker) that simulates the folding of a
protein, using statistical energies and moves.
Fold prediction – Rosetta
method
• Knowledge based scoring function
Bayes' law:
P(structure) * P(sequence|structure)
P(structure|sequence) =
P(sequence)
P(sequence|structure) = f(residue contacts in native structures)
sequence consistent
local structure
protein-like
structures
near-native structures
P(structure) = probability of a protein-like structure
(no clashes, globular shape)
Simons et al. (1997)
Collection of putative backbone
conformations
Protein sequence
Library of small segments
...
...
For each window of 9 residues:
lookup 25 closest (sequence)
neighbours in library
...
sequences
structures
Simons et al. (1997)
Intermediates are not observed,
but
Folding is 2-state
Unfolded
Folded
Nucleation sites
something
happens
first...
Early folding events might be
recorded in the database
Non-homologous proteins
Short, recurrent sequence patterns could be folding Initiation sites
recurrent
part
HDFPIEGGDSPMQTIFFWSNANAKLSHGY
CPYDNIWMQTIFFNQSAAVYSVLHLIFLT
IDMNPQGSIEMQTIFFGYAESA
ELSPVVNFLEEMQTIFFISGFTQTANSD
INWGSMQTIFFEEWQLMNVMDKIPS
IFNESKKKGIAMQTIFFILSGR
PPPMQTIFFVIVNYNESKHALWCSVD
PWMWNLMQTIFFISQQVIEIPS
MQTIFFVFSHDEQMKLKGLKGA
Nature has selected for these patterns because they
speed folding.
I-sites motifs
diverging type-2
turn
Serine
hairpin
Proline helix C-cap
Backbone
angles:
y=green,
f=red
Amino
acids
arranged
from nonpolar to
polar
alpha-alpha corner
Type-I
hairpin
Frayed
helix
glycine helix N-cap
Rosetta
Fragment insertion Monte
Carlo
backbone torsion angles
moveset
accept or
reject
Choose fragment
from moveset
change backbone
angles
Convert angles to 3D
coordinates
Energy
function
Rosetta
Backbone angles are restrained in I-sites regions
regions of highconfidence I-sites
prediction
moveset
backbone torsion angles
Fragments that deviate from the paradigm (>90° in
f or y) are removed from the moveset.
Generally, about one-third of the
sequence has an I-sites prediction with
confidence > 0.75, and is restrained.
Rosetta
Sequence dependent features
Rosetta
Sequence-independent features
Current structure
vector representation
Probabilities from the database
The energy score for a contact between secondary structures
is summed using database statistics.
MC-SA optimization
• for each random position
– pick a random neighbour
– replace backbone conformation
– calculate probability of new structure
• MC: Monte-Carlo
– accept up-hill moves with a certain
probability that depends on temperature
• SA: simulated annealing
– Gradual cooling of temperature: first allow
many changes, later fewer changes
Simons et al. (1997)
Results
• Small molecules:
ok
• Proteins with
mostly
α-helices: ok
• Proteins with
mostly
β-sheets: not so ok
Simons et al. (1997)
Rosetta
What needs to be fixed?
Turns
8% of the residues in the targets have f > 0.
44% of these are at Glycine residues.
7% of the residues in the predictions have f > 0.
but only 16% of these are at Glycines.
Contact order
N
1
CO 
Sij

LN
True structure: 0.252
Predictions: 0.119
Prediction algorithms have
underlying principles
Darwin = protein evolution.
Principle: Proteins that evolved from common
ancestor have the same fold.
Boltzmann = protein folding
Principle: Proteins search conformational space,
minimizing the free energy (empirical pseudo-potential)
Geenin funktion määrittäminen
• fenotyyppi
• biokemiallinen aktiivisuus (in vitro)
• ilmentyminen
• GO, Gene Ontology
– molekulaarinen funktio
– biologinen prosessi
– solunsisäinen lokalisaatio
Homologia  sama funktio?
Paralogia: geenien kahdentumisen tulos
Vaihtoehtoinen silmukointi: yksi geeni, monta proteiinia
Pleiotropia: yksi geeni, monta funktiota
Redundanssi: yksi funktio, monta geeniä
Heteromeria: kompleksien muodostus
“Crosstalk”: signalointireitit vaikuttavat toisiinsa
Protein functional shifts are
common
• COG0044
– Dihydroorotase
• CAD (fusion protein)
– Dihydropyriminidase
– D-hydantoinase
– Allantoinase
– Rudimentary protein (involved in
developmental programs)
COG0044 functions
Urease superfamily functions
Fast evolution ~ functional shift
rat lung isoform
rat liver isoform,
functional shift
CYP2 family (cytochrome P450)
“Druggable genome”
• Property filters
– Likelihood of functional shift
– Degree and nature of paralogy
– Factors reflecting pleiotropy
•
•
•
•
Size
Breadth of expression
Interaction potential
Evolutionary rates
Funktion siirto
• Nearest neighbour (lähin homologi)
– esim. Blast-haku
– Fylogeneettinen lähin naapuri
• Post-genomiset menetelmät
– riippumattomia homologiasta
– Proteiini-proteiini-interaktioiden vertailu
• Guilt By Association
• Hahmontunnistus
Funktion siirto
•
Hypoteettinen sekvenssi  funktio?
– Karakterisoitu homologi
• Blast / PSI-Blast
– Fylogenia!
• evoluutionopeus riippuu perheestä
• monen sekvenssin linjaus
•
Virheelliset funktion määritykset kertautuvat tietokannoissa!
– Väärä funktio
• liittyy domeeniin, jota ei esiinny hakusekvenssissä
– Väärä homologiapäätelmä
– Liian yksityiskohtainen funktion kuvaus
• funktion muuttuminen evoluutiossa
• biokemiallinen vs. fysiologinen funktio
– esim. eukaryoottispesifiset funktiot eivät voi esiintyä bakteerissa
– Sekvenssilinjaus
• funktionaalisten aminohappojen säilyminen
• esimerkki:
– atratsiiniklorohydrolaasi vs. melamiinideaminaasi: 4 mutaatiota (98 % identtisyys)
– Esim. GO liputtaa funktion määrityksen lähteen
Guilt by association
• Prediction of subcellular localization based
on classification of neighbours
Non-homology protein identification
using network context
Query pattern
Interactome
Ref: Lappe M, Park J, Niggemann O, Holm L (2001) Bioinformatics Suppl 1, S149-S156
Natural selection
• Functional coupling leads to correlations
– E.g. co-occurrence of sets of genes in species
• Residues required for molecular function
– Functional conservation above general
sequence divergence of a family
Pancreatic
trypsin
inhibitor
(2ptc)
Approaches
• Evolutionary Trace
– Lichtarge et al. 1996
• Sequence Space
– Casari et al. 1995
• Ortholog / paralog discriminants
– Mirny & Gelfand 2003
Evolutionary Trace
• The branchpoints separating subclades of
a phylogenetic tree can specify molecular
speciation events, and hence evolutionary
selection of amino acids
• Map trace residues to 3D structures
Evaluation of Evolutionary Trace
• Trace residues determined at many ranks
– Trace residue sets are nested
• Test of significance of trace residue at any
rank
– Overlap with otherwise defined functional
sites
• Bound ligands in 3D structures (~20 residues)
• Annotated sites (~4 residues)
ET assessment
• Detects 3D clusters
• Manual filtering and pruning of the data
– Decide which subclades of the protein family
to use in analysis
– Exclude fragments
– Original method was based on strict
invariance within subclade
• Automatic implementations
– But manually optimized traces score higher
Sequence Space
• Aligned protein sequences represented as
vectors in a high-dimensional space
– Each amino acid type at each column of the MSA is a
unique point in Sequence Space
• Dimension reduction by Principal Components
Analysis
• Cluster proteins
– Based on their sequence identity
• Map residues in the same space
– Direction points to association with protein group
A 3D object
PCA projection of the 3D object
New axes are
linear combination
of original axes
Coding of amino acids
Sequence vector representation
Interpretation
1st axis represents the whole family
2nd, 3rd , …, 6th axes represent subclassifications
Subfamily-specific residues are found at the
tips of a polygon
Common residues shared by several subfamilies
are found along the edges of a polygon
Many unspecific residues at origin
Protein clustering
Residue clustering
Selection of residues & proteins
Ortologit ja paralogit
Malliorganismien käyttö: identtinen fysiologia?
Summary
• Functional groupings of proteins
– Phylogenetic lineage
• Orthologs / paralogs
– Clustering by general sequence similarity
• Residues associated with above groupings
– Intra-group conservation
– Inter-group variation
– Neutral residues behave randomly
Function = interactions
• Protein-protein interactions
• Co-evolution of interacting proteins
• Comparative genomics
Experimental methods
• Y2H = yeast-two-hybrid
–
–
–
–
Ex vivo, binary interactions
Interaction must occur in the nucleus
Autoactivation (5-10 % of random ORFs)
Posttranslational modifications
• AP/MS = affinity purification / Mass Spectrometry
– Purified complexes
• PChips = protein microarrays
– In vitro
– Covalent attachment to solid support
– Screening with fluorescently labelled probes (e.g. proteins or
lipids)
Small part of an interaction network
NewScientist, 13. April 2002,
David Cohen about the work by
Barabasi, Albert et al.
Interaktioiden ennustaminen
• ko-evoluutio
• genomien vertailu
– geenien järjestys kromosomissa
– fylogeneettiset profiilit
– geenifuusio
Ko-evoluutio
monen sekvenssin linjaus, etsi korreloivat
mutaatiot
• proteiinit, joilla on paljon interaktioita,
muuttuvat hitaammin
• kaksi fylogeniapuuta, etsi parit
•
Comparative genomics
• Correlated genomic context between
orthologous genes reveal functional
couplings
– Conserved gene order (conserved synteny)
– Coupled gene loss / preservation
(phylogenetic profiles)
– Gene fusion events
Conserved synteny
• Chromosomal rearrangements randomize
gene order over the course of evolution
• Groups of genes that have a similar
biological function tend to remain localized
in a group or cluster
• Bacterial operons allow coordinated
regulation of gene expression from a
common promoter
• Eukaryotic clusters observed, too
Phylogenetic profiling
p1
p4
p5
p1 p2 p3
p5 p6 p8
yeast
H. influenzae
p2 p3 p4
p5 p7
E. coli
ye
P1 1
P2 1
P3 1
P4 0
P5 1
P6 1
P7 0
P8 1
hi
1
0
0
1
1
0
0
0
ec
0
1
1
1
1
0
1
0
ye
P7 0
P4 0
P6 1
P8 1
P2 1
P3 1
P1 1
P5 1
hi
0
1
0
0
0
0
1
1
ec
1
1
0
0
1
1
0
1
Observations - phyloprofiles
• Bit-vectors sensitive to noise in gene
status assignment
• Specific patterns generated mainly from
bacterial gene loss / horizontal transfer
• Eukaryotic species have larger genomes
and large numbers of eukaryote-specific
protein families
Gene fusion
Domain swapping
Some details
• 6,809 interactions predicted for E. coli
based on gene fusions
– 321 (~5 %) overlap with predictios by
phylogenetic profile method
– Eight times more than random
• Promiscuous modules (SH2, SH3, etc.)
– 5 % of domains made more than 25 links to
other proteins
– Fusions counted within remaining set of 95 %
Observations – gene fusion
• Marcotte et al. (Science 285:751-753,
1999) predicted novel interactions for 50
% of yeast proteins using gene fusion
information in any homologous proteins
• Enright et al. (Nature 402:86-90, 1999)
considered orthologs with higher signal-tonoise ratio but only 7 % coverage
Integrated predictions
• Predictions by conserved synteny, phylogenetic
profiles and gene fusion are largely additive
– small overlap
• Combined score
– Calibrated against same / different KEGG map
• STRING server
– Predictions for about 50 % of genes from complete
genomes
– http://www.bork.embl-heidelberg.de/STRING/
Functional association maps
• Noisy
• Different types of interaction
– Physical interaction (complex formation)
– Transient interactions
• Dependent on post-translational modification state,
e.g. phosphorylation
– Functional linkage
• Successive steps of a metabolic pathway
• Involvement in related biological processes
Tentti
• Tentti 28.4.
• Uusinta 3.5. yleinen tenttipäivä
• Tenttiin tulee
– Päättelytehtäviä
– Esseekysymyksiä