Protein Secondary Structure Prediction

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Chapter 14 Protein Secondary Structure Prediction
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Refresher

• Proteins have secondary structures
• These structures are essential to maintain the 3D
  structure of the protein
• Secondary structure can be either of
• ?-helix
• ?-strand
• Coil
• ?-helix H-bond between CO and N-H of every 4ith
  residue
• 3.6 aa per turn
• 1.5 Å / aa ( 5.4 Å per turn)
• (fully extended peptide backbone 3.5 Å / aa)
• ?-strand H-bond between CO and N-H of distant
  regions
• Parallel or anti-parallel
• Coiled coil
• Hydrophobic amino acids interact

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Secondary Structure Predictions

• Prediction of conformation of each amino acid
• H ?-helix
• E ?-strand
• C Coil (no defined 2 structure)
• Used for classification of proteins
• Defining domains and motifs
• Intermediary step towards 3 structure prediction
• Globular and trans-membrane proteins are
  structurally very different
• Required different algorithms to predict these
  two classes of proteins

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• Problem is not trivial
• ?-helix based on short distance (4i
  interactions)
• ?-strand based on long distance (5 50
  residues)
• Long range interaction predictions less accurate
• Accuracy about 75
• Ab initio based
• Statistical calculation of residues in single
  query sequence
• Homology-based
• Common 2 structure patterns in homologous
  sequences

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Ab initio Methods
Chou-Fasman Intrinsic property of residue to be
in helix, strand or turn structure A, E, M common
in ?-helices N residues in all protein
structures M residues in ?-helices Y Total Ala
in protein structures X Ala in
?-helices Propensity Ala in ?-helix
(X/Y)/(M/N) Value 1 same distribution as
average Value gt 1 more often in ?-helix than
average Value lt 1 less often in ?-helix than
average 6 residue window of which 4 is H ?
?-helix Window extended bidirectionally until P
lt 1.0 5 residue window of which 3 is E ? ?-strand
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http//fasta.bioch.virginia.edu/fasta_www2/fasta_w
ww.cgi?rmmisc1
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Example Chou-Fasman
10 20 30 40
50 60 SRRSASHPTY SEMIAAAIRA
EKSRGGSSRQ SIQKYIKSHY KVGHNADLQI KLSIRRLLAA
70 80 90 GVLKQTKGVG
ASGSFRLAKS DKAKRSPGKK
HELIX 1 HA1 SER A 29 ALA A 38 HELIX 2
HA2 ARG A 47 SER A 56 HELIX 3 HA3 ALA A
64 ALA A 78 SHEET 1 SA 3 SER A 45 SER A
46 SHEET 2 SA 3 GLY A 91 ARG A 94 SHEET
3 SA 3 LEU A 81 GLY A 86
. . . .
. . SRRSASHPTYSEMIAAAIRAEKSRG
GSSRQSIQKYIKSHYKVGHNADLQIKLSIRRLLAA helix
lt--------gt lt-----gt
lt----------------- sheet EEEEEEEEE
EEEEEE EEEEEEEEEEEEE turns T T
T T T
. . .
GVLKQTKGVGASGSFRLAKSDKAKRSPGKK helix -------gt
lt-------gt sheet EEEEEEEEE
turns T T TT T
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Garnier-Osguthorpe-Robson (GOR)

• Makes use of distant influences on propensity
• Uses 17 residue window
• Adds propensity for four 2º structure states (H,
  E, T, C)
• Highest value defines 2º structure state of
  central residue in window

. 10 . 20 . 30 . 40 . 50
. 60 SRRSASHPTYSEMIAAAIRAEKSRGGSSRQSIQKY
IKSHYKVGHNADLQIKLSIRRLLAA helix
HHHHHHHHHHH HHHHHH
HHHH sheet EEEEEEEE
E EEEEEE turns TTTT
TTTTT T TTTT coil C
CCCCC CCC C
. 70 . 80 . 90
GVLKQTKGVGASGSFRLAKSDKAKRSPGKK helix HHHH
HHHHHHHHHHH sheet EEEEE E
turns TTT
coil CCCC C C Residue
totals H 36 E 21 T 17 C 16
percent H 48.6 E 28.4 T 23.0 C 21.6
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Expansion using larger crustal structure databases

• Algorithms based on a larger database of crystal
  structure information
• GOR II, III and IV
• SOPM
• http//npsa-pbil.ibcp.fr/cgi-bin/npsa_automat.pl?p
  age/NPSA/npsa_server.html

SRRSASHPTYSEMIAAAIRAEKSRGGSSRQSIQKYIKSHYKVGHNADLQI
KLSIRRLLAAGVLKQTKGVG cccccccchhhhhhhhhhhhtccttcccc
hhhhhhhhhtcccccccthhhhhhhhhhhhhhhhhttttcc ASGSFRL
AKSDKAKRSPGKK cccceeeecccccccccccc
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Homology based methods
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Neural Network programs

• A neural net has an input layer, hidden layers
  composed of nodes given different weights, and an
  output layer
• Neural net trained with multiply aligned
  sequences
• Accuracy gt75
• PHD
• BLASTP
• MAXHOM (sequence alignment)
• Neural Net
• Layer one 13 residue window
• Layer two 17 residue window
• Layer three Jury layer removes very short
  stretches
• PSIPRED
• PSI-BLAST
• Neural net
• SSpro
• PROTER
• PROF

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Predictions with Multiple Methods

• No single prediction program is correct, and it
  is generally good practice to use the output from
  several programs
• Some web servers do this
• JPred
• PHD, PREDATOR, DSC, NNSSP, Inet and ZPred
• First submitted to PSI-BLAST
• Multiple alignment
• Submitted to above 6 programs
• Consensus returned
• No consensus, uses PHD
• SRRSASHPTYSEMIAAAIRAEKSRGGSSRQSIQKYIKSHYKVGHNADLQI
  KLSIRRLLAAGVLKQTKGVGASGSFRLAKSDKAKRSPGKK
• ---------HHHHHHHHHHH--------HHHHHHHHHH-------HHHHH
  HHHHHHHH---EEEEE------EEEE--------------

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How accurate?
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Trans-membrane proteins

• Two types of trans-membrane proteins
• ?-helix
• ?-barrel
• Many consists solely of ?-helix and are found in
  the cytoplasmic membrane
• ?-barrel normally found in outer-membrane of gram
  negative bacteria
• Difficult to get X-ray or NMR structure

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• ?-helix perpendicular to membrane 17-25 residues
• Hydrophobic residues separated by hydrophilic
  loops (lt60 residues)
• Residues bordering hydrophobic module is
  generally charged
• Inner cytosolic region most often highly charged
  (orientation info)
• Positive inside rule
• Scan window 17-25 residues calculate
  hydrophobicity score
• Many false positives
• Signal peptide sequences confuse algorithm

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• TMHMM
• Trained with 160 known TM sequences
• Probability of having an ?-helix is given
• Orientation of ?-helix based on positive inside
  rule
• Phobius
• Incorporates distinct HMM models for signal
  peptides and TM helices
• Signal peptide sequence ignored
• Can use sequence homologs and multiply aligned
  sequences

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Prediction of ?-barrel proteins

• ?-strand forming trans-membrane section is
  amphipatic
• 10-22 residues
• Alternating hydrophobic and hydrophilic sequence
  arrangement
• ?-helix TM prediction programs thus not
  applicable to ?-barrel proteins
• TBBpred
• Neural net trained with ?-barrel protein
  sequences

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Coiled coil prediction

• Two or more ?-helices winding around each other
• For every 7 residues, 1 and 4 are hydrophobic,
  facing central core
• Coils
• Scan window of 14, 21 or 28 residues
• Compares residues to probability matrix based on
  known coiled coils
• Accurate for left-handed coil, but not
  right-handed coil
• Multicoil
• Scoring matrix based on 2-strand and 3-strand
  coils
• Used in several genome-wide studies
• Leucine zippers
• sub-class of coiled coils
• L-X6-L-X6-L-
• Found in transcription factors
• Anti-parallel ?-helices stabilized by leucine
  core

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Chapter 13 Protein Tertiary Structure Prediction
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• The need for predicting 3D structures
• X-ray crystallography is extremely tedious
• DNA sequences and therefore protein sequences are
  rapidly generated
• A gap between sequence and structure is widening
• Protein structure often provides insight info
  function
• Thee main methods for 3D prediction
• Homology modeling
• Threading
• Ab initio

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Homology Modeling
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Template Selection

• Search PDB for homologous sequences with BLAST or
  FASTA
• Should have gt30 sequence identity (20 at a
  stretch)
• In case of multiple hits, choose
• Highest identity
• Highest resolution
• Most appropriate co-factors

Sequence Alignment
Critical Incorrectly aligned residues will give
an incorrect model Use Praline or T-Coffee for
alignment Inspect visually to confirm alignment
of key residues
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Backbone Model Building

• Copy the backbone atoms of the query sequence to
  that of the corresponding aligned residue
• If the residues are identical, the coordinates of
  the whole residue can be copied
• If the residues are different, only the ?C are
  copied
• The remaining atoms of the residue are modeled
  later

Loop Modeling

• It often happens that there are gaps in the
  aligned sequences
• Two techniques to connect the protein on either
  side of the gap
• Database
• Search database for fragments that fit the gap
• Measure coordinates and orientation of backbone
  on either side of gap
• Search for fragments that can fit
• Best loop gives no steric clash with structure
• Ab Initio
• Generate random loop No clash with nearby
  side-chains
• ? And ? angles in acceptable region of
  Ramachandran plot

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Side Chain Refinement

• Need to model side-chains where these differ from
  aligned template sequence
• Search database for all occurrences of given
  side-chain in backbone conformation and minimal
  clash with neighbouring residues
• Computationally prohibitive
• Library of rotamers
• Collection of conformations for each residue that
  is most often observed in structure database
• Select rotamer with conformation that best fits
  backbone
• Minimal interference with neighbouring
  side-chains
• SCWRL

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Model Refinement using Energy Function

• After loop modeling and side-chain refinement the
  follwing remain
• Unfavourable torsion angles
• Unacceptable proximity of atoms
• Use energy minimization to alleviate such
  problems
• Limit number of iteration (lt100) to ensure that
  the entire model does not change form the
  template
• Molecular Dynamic can be used to search for a
  global minimum

Model Evaluation

• Check consistency in ?-? angles
• Bond lengths
• Close contacts
• Flag regions below acceptability threshold
• Procheck
• WHATIF
• ANOLEA
• Verify3D

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Comprehensive Modeling Programs

• Modeler
• Swiss-Model
• 3D-Jigsaw

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Threading and Fold Recognition

• Pairwise Energy Method
• Fit sequence to each fold in database
• Use local alignment to improve fit
• Calculate energies
• Pairwise residue interaction
• Solvation Hydrophobic
• Profile Method
• Fit sequence to fold
• Calculate propensity of each amino acid to be
  present at each profile position
• Secondary structure types
• Solvent exposure
• Hydrophobicity
• Use structure fold that best fits profile of
  parameters

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Ab Initio Prediction
Protein fold into a native, low-energy native
state The mechanism driving this process is
poorly understood Computationally untenable to
explore all possible states and calculate
energies A 40 residue peptide will require 1020
years to calculate all states using a 11012
FLOPS computer Not realistic approach currently