Proteiinianalyysi 7

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Proteiinianalyysi 7

• Kolmiulotteisen rakenteen ennustaminen
• http//www.bioinfo.biocenter.helsinki.fi/downloads
  /teaching/spring2006/proteiinianalyysi

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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

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Motivation

• Protein structure determines protein function
• For the majority of proteins the structure is not
  known

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• Curve fitted to data
• for homologous
• families
• Divergence of
• common cores
• fraction in core
• decreases with
• increasing sequence
• divergence

Chothia Lesk (1986)
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Steps in comparative modelling

• Find suitable template(s)
• Build alignment between target and template(s)
• Build model(s)
• Replace sidechains
• Resolve conflicts in the structure
• Model loops (regions without an alignment)
• Evaluate and select model(s)

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State of the art in homology modelling

• Template search
• (iterative) sequence database searches (PSIBLAST)
• Alignment step
• multiple alignment of close to fairly distant
  homologues
• Modelling step
• rigid body assembly
• segment matching
• satisfaction of spatial constraints

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An alignment defines structurally equivalent
positions!
Template structure
Template sequence
Alignment
Target sequence
Model
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The crucial importance of the alignment
Template sequence
Template structure
Alignment
Target sequence
Model
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Modelling by spatial restraints

• Generate many constraints
• Homology derived constraints
• Distances and angles between aligned positions
  should be similar
• Stereochemical constraints
• Bond lengths, bond angles, dihedral angles,
  nonbonded atom-atom contacts
• Model derived by minimizing restraints

Modeller Sali Blundell (1993)
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Loop modelling

• Exposed loop regions usually more variable than
  protein core
• Often very important for protein function
• Loops longer than 5 residues difficult to built
• Mini-protein folding problem

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Model evaluation

• Check of stereochemistry
• bond lengths angles, peptide bond planarity,
  side-chain ring planarity, chirality, torsion
  angles, clashes
• Check of spatial features
• hydrophobic core, solvent accessibility,
  distribution of charged groups,
  atom-atom-distances, atomic volumes, main-chain
  hydrogen bonding
• 3D profiles/mean force potentials
• residue environment

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Knowledge-based mean force potentials

• Compute typical atomic/residue environments based
  on known protein structures

Melo Feytmanns (1997)
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Modelling a transcription factor

• Sequence from different species
• Is binding to ligand conserved?

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Ligand binding domain
hydrogen bonds to ligand
homo-serine lactone moiety binding
acyl moiety binding
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DNA binding domain
DNA binding domain
Linker
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New Loop
Template
Target
Variable loops
MODELLER output
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Ligand binding pocket
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Errors in comparative modelling

1. Side chain packing
2. Distortions and shifts
3. Loops
4. Misalignments
5. Incorrect template

True structure
Template
Model
Marti-Renom et al. (2000)
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Modelling accuracy
Marti-Renom et al. (2000)
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Applications of homology modelling
Marti-Renom et al. (2000)
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Structural genomics

• Post-genomics
• many new sequences, no function
• Aim a structure for every protein
• High-throughput structure determination
• robotics
• standard protocols for cloning/expression/crystall
  ization

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Structural coverage
high quality models
Complete models
Total 43
Vitkup et al. (2001)
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Target selection
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Fold recognition - Assumption

• Native structure is the global minimum energy
  conformation
• So, need
• Discriminating energy function
• Conformation generator
• Backbone from homologous template (comparative
  modelling)
• Backbone from analogous template (fold
  recognition)
• Comprehensive sampling (ab initio)

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Fold recognition steps

• Template library
• Known structures from Protein Data Bank
• Fold classification suggests a limited number of
  fold types
• Score sequence-structure fitness
• Environmental preferences of amino acids
• Boltzmann engine
• Search problem alignment
• Complicated with pair potentials
• Significance of best score in database search
• Reference state

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Potentials of mean force

• Boltzmann engine
• In thermodynamic equilibrium, particles are
  partitioned between states proportionally to
  exp(-DG)
• Effective energy negative logarithm of the
  equilibrium constant
• Count occurrences per state
• Radial distribution of aa pairs (Sippl)

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Structural environment

• Single-residue preferences 20 x 3 x 3 x 3
• Helix, strand, coil
• Accessibility
• Contact area (indirectly codes for aa type)
• Contact pair potentials
• Atomic contacts within 4 A
• C-beta atoms within 7 A
• Secondary structure of residues i and j
• 3 x 20 x 3 x 20 3600 preferences

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Information content
Arg-Asp helix-helix (dashed) Arg-Asp
strand-strand (solid) Arg-Asp (dotted)
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Threading algorithms

• Dynamic programming
• Simple
• frozen approximation
• Read sequence-dependent environment from template
  (1st round), then from aligned target sequence
• Stochastic optimization (Monte Carlo)
• Pair potentials
• Exhaustive search
• Simplify search space (e.g., ignore loops)

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Prospect model (Xu Xu)
Etotal vmutateEmutate x vsingleEsingle x
vpairEpair x vgapEgap Weights v optimized on
training set
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Prospect - segmentation

• - Finds optimal threading fairly efficiently
• Topological complexity
• No gaps in secondary structure elements
• Pair energy term only evaluated between
• secondary structure elements

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Prospect- observations

• Mutation energy is the most important
• Single-residue terms with profile information
  generate reasonably good alignments for 2/3 of
  test cases
• The pairwise energy term can thus be ignored
  during the search for optimal alignment, but is
  used in evaluating the fold recognition

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Performance comparison
Method Family only Superfamily Fold only Top
1 Top 5 Top 1 Top 5 Top 1 Top 5 Using pair
potential PROSPECT 84.1 88.2 52.6 64.8 27.7 50.3
Using dynamic programming, structural
environment FUGUE 82.2 85.8 41.9 53.2 12.5 26.8 T
HREADER 49.2 58.9 10.8 24.7 14.6 37.7 Using
sequence similarity only PSI-BLAST 71.2 72.3 27.4
27.9 4.0 4.7 HMMER 67.7 73.5 20.7 31.3
4.4 14.6 SAMT98 70.1 75.4 28.3 38.9
3.4 18.7 BLASTLINK 74.6 78.9 29.3 40.6
6.9 16.5 SSEARCH 68.6 75.7 20.7 32.5 5.6 15.6
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Threading score - significance

• Target sequence fold library
• Each threading aligns a different sub-sequence
• Compute Z-score for each by ungapped threading on
  large decoy (Sippl)
• Reverse threading
• Design optimal sequence for a given fold

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Incorrect self-threading
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Fold recognized
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Fold recognized Poor alignment of residues
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Ab initio prediction

• HMMSTR/I-sites/RosettaHMMSTR is a Hidden Markov
  Model based on protein STRucture. Each Markov
  state in this model represents a position in one
  of the I-sites motifs. HMMSTR can predict local
  structure (as backbone angles), secondary
  structure, and supersecondary structure (edge
  versus middle strand, hairpin versus diverging
  turn).
• I-sites LibraryI-sites is a library of folding
  initiation site motif, which are sequence motifs
  that correlate with particular local structures
  such as beta hairpins and helix caps. I-sites can
  be used to predict local structure, or to predict
  which parts of a protein are likely to fold
  early, initiating folding.

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Intermediates are not observed, but
Folding is 2-state
Unfolded
Folded
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Nucleation sites
something happens first...
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Early folding events might be recorded in the
database
Short, recurrent sequence patterns could be
folding Initiation sites
recurrent part
HDFPIEGGDSPMQTIFFWSNANAKLSHGY
CPYDNIWMQTIFFNQSAAVYSVLHLIFLT IDMNPQGSIEMQTIFFGYA
ESAELSPVVNFLEEMQTIFFISGFTQTANSD
INWGSMQTIFFEEWQLMNVMDKIPSIFNESKKKGIAMQTIFFILSGR
PPPMQTIFFVIVNYNESKHALWCSVD
PWMWNLMQTIFFISQQVIEIPS
MQTIFFVFSHDEQMKLKGLKGA
Non-homologous proteins
Nature has selected for these patterns because
they speed folding.
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How to read an I-sites motif profile
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Backbone angles and sequence pattern for
Amphipathic alpha-helix
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Superposition of the top scoring 30
true-positives
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Conserved polar (green) and non-polar (purple)
sidechains
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Serine alpha-N-cap
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HMMSTR
A Markov state. A hidden Markov model consists of
Markov states connected by directed transitions.
Each state emits an output symbol, representing
sequence or structure. There are four categories
of emission symbols in our model b, d, r, and c,
corresponding to amino acid residues, three-state
secondary structure, backbone angles (discretized
into regions of phi-psi space) and structural
context (e.g. hairpin versus diverging turn,
middle versus end-strand), respectively. Bystroff
C, Thorsson V Baker D. (2000). HMMSTR A
hidden markov model for local sequence-structure
correlations in proteins. Journal of Molecular
Biology 301, 173-90.
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Merging of two I-sites motifs to form an HMM.
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Sequence Profiles
Sequence alignment
VIVAANRSA
VIVSAARTA
VIASAVRTA
VIVDAGRSA
VIASGVRTA
VIVAAKRTA
VIVSAVRTP
Sequence profile
VIVSAARTA
VIVSAVRTP
aa
VIVDAGRTA
VIVDAGRTA
VIVSGARTP


VIVDFGRTP
VIVSATRTP
VIVSATRTP
VIVGALRTP
VIVSATRTP
VIVSATRTP
VIASAARTA
VIVDAIRTP
Red high prob ratio (LLRgt1)Green background
prob ratio (LLR0)Blue low prob ratio (LLRlt-1)
VIVAAYRTA
VIVSAARTP
VIVDAIRTP
VIVSAVRTA
VIVAAHRTA
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I-sites motifs
Backbone angles ygreen, fred
Amino acids arranged from non-polar to polar
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Why do I-sites exist?
1. They are ancient conserved regions? 2. They
fold independently?
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Patterns of conservation suggest independent
folding
2. sidechain contacts
1. backbone angle constraints
3. negative design
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NMR structures confirm independent folding
diverging turn motif
NMR structure of a 7-residue I-sites motif in
isolation (Yi et al, J. Mol. Biol, 1998)
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Fold prediction Rosetta method

• Knowledge based scoring function

Bayes' law
P(structure) P(sequencestructure)
P(structuresequence)
P(sequence)
P(sequencestructure) f(residue contacts in
native structures)
near-native structures
protein-likestructures
sequence consistentlocal structure
P(structure) probability of a protein-like
structure (no clashes, globular shape)
Simons et al. (1997)
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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.
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The Folding Problem
Two parts (1) The Search Problem Is the true
structure one of my 2 million guesses? (2) The
Discrimination Problem If its one of these 2
million, which one is it?
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Fragment insertion Monte Carlo
Rosetta
backbone torsion angles
accept or reject
moveset
Energy function
Choose fragment from moveset
change backbone angles
Convert angles to 3D coordinates
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Backbone angles are restrained in I-sites regions
Rosetta
regions of high-confidence I-sites prediction
backbone torsion angles
moveset
Fragments that deviate from the paradigm (gt90 in
f or y) are removed from the moveset.
Generally, about one-third of the sequence has an
I-sites prediction with confidence gt 0.75, and is
restrained.
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Sequence dependent features
Rosetta
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Sequence-independent features
Rosetta
Probabilities from the database
Current structure
The energy score for a contact between secondary
structures is summed using database statistics.
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CASP4 predictions
Rosetta
31 target sequences. Ab initio prediction i.e.
Sequence homolog data was ignored if present. 61
topologically correct 60 locally correct 73
secondary structure correct
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T0116 262-322 (61 residues)
Rosetta
prediction
true structure
Topologically correct (rmsd5.9Å) but helix is
mis-predicted as loop.
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T0121 126-199 (66 residues)
Rosetta
prediction
true structure
Topologically correct (rmsd5.9Å) but loop is
mis-predicted as helix.
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T0122 57-153 (97 residues)
Rosetta
prediction
true structure
...contains a 53 residue stretch with max
deviation 96
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T0112 153-213
Rosetta
prediction
true structure
Low rmsd (5.6Å) and all angles correct ( mda
84), but topologically wrong!
(this is rare)
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Rosetta
What needs to be fixed?
Turns
8 of the residues in the targets have f gt
0. 44 of these are at Glycine residues. 7 of
the residues in the predictions have f gt 0. but
only 16 of these are at Glycines.
Contact order
True structure 0.252
Predictions 0.119
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Prediction of protein structure

• ROSETTA program most famous
• different models to treat the local and nonlocal
  interactions.
• sequence-dependent local interactions bias
  segments of the chain to sample distinct sets of
  local structures
• turn to in known three-dimensional structures as
  an approximation to the distribution of
  structures sampled by isolated peptides with the
  corresponding sequences.
• nonlocal interactions select the lowest
  free-energy tertiary structures from the many
  conformations compatible with these local biases.
• The primary nonlocal interactions considered are
  hydrophobic burial, electrostatics, main-chain
  hydrogen bonding and excluded volume.
• minimizing the nonlocal interaction energy in the
  space defined by the local structure
  distributions using Monte Carlo simulated
  annealing.

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Using NMR to guide Rosetta

• We have extended the ROSETTA ab initio structure
  prediction strategy to the problem of generating
  models of proteins using limited experimental
  data. By incorporating chemical shift and NOE
  information and more recently dipolar coupling
  information into the Rosetta structure generation
  procedure, it has been possible to generate much
  more accurate models than with ab initio
  structure prediction alone or using the same
  limited data sets with conventional NMR structure
  generation methodology. An exciting recent
  development is that the Rosetta procedure can
  also take advantage of unassigned NMR data and
  hence circumvent the difficult and tedious step
  of assigning NMR spectra.

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Rosetta in comparative modelling

• We have also developed a method for comparative
  modeling that was one of the top performing
  methods in the CASP4 experiment. The method
  utilizes a new protein sequence structure
  alignment method and structurally variable
  regions such as long loops not present in the
  structure of a homologue are built using a
  modification of the rosetta ab initio structure
  prediction methodology. Both the ab initio and
  the comparative modeling methods have been
  implemented in a server called ROBETTA which was
  one of the best all around fully automated
  structure prediction servers in the CASP5 test.

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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.
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Summary

• Most prediction methods depend on sequence
  homology.(Darwin)
• Folding predictions combine statistics and
  simulations.
• Putative folding initiation sites can be found
  using database statistics.
• Knowledge-based energy functions are derived from
  database statistics.
• The folding problem is really two problems the
  search problem and the discrimination problem.
• If we knew how proteins fold, we could predict
  their structures.
• We dont know how proteins fold.

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CASP6 current status

• Comparative modelling extended to distant
  homologues
• Easy PSI-Blast neighbours
• Hard indirect PSI-Blast neighbours
• Fold recognition merged with comparative
  modelling
• Ab initio methods based on fragment assembly
  generate models (among top N predictions) that
  have some resemblance to the real structure