Transcript Secondary structure prediction

Secondary structure prediction
from amino acid sequence
Homology: Paralogs and orthologs
a
duplication
a
Paralogs =
gene families
b
in same species
speciation
a
b
species 1
a
b
species 2
orthologs
DNA sequence
Automatic
translation
Physico-chemical properties
Amino acid primary sequence
1. Search for sequence homologue(s)
and construct an alignment
2. Homologue(s) with known 3D
structure?
3. Motif recognition: Search
secondary databases
Secondary structure prediction
Fold assignment
(e. g., using EMBOSS suite)
Primary db searches
FASTA, BLAST
Homology modelling
available
Chou-Fasman Parameters
• Amino acid
propensities
Accuracy of prediction
• Q3 score
Q3 = qa+qb+qcoil
total no. of residues
X 100%
Recent improvements
• The availability of large families of homologous sequences
has greatly enhanced secondary structure prediction.
• The combination of sequence data in multiple alignments
with sophisticated computing techniques such as neural
networks has lead to accuracies well in excess of 70 %.
• The limit of 70-80% may be a function of secondary
structure variation within homologous proteins.
Stereochemical analysis
Patterns of residue conservation
are indicative of particular
secondary structure types.
Alpha helices have a periodicity of
3.6. Many alpha helices in proteins
are amphipathic, meaning that one
face is pointing towards the
hydrophobic core and the other
towards the solvent.
Patterns of hydrophobic residue
conservation showing the i, i+3,
i+4, i+7 pattern are highly
indicative of an alpha helix.
XOOXXOOX
Stereochemical analysis
The geometry of beta
strands means that
adjacent residues have
their side chains pointing
in oppposite directions.
Beta strands that are half
buried in the protein core
will tend to have
hydrophobic residues at
positions i, i+2, i+4, i+8
etc, and polar residues at
positions i+1, i+3, i+5,
etc.
XOXOXOXOXO
Stereochemical analysis
Beta strands that are completely buried (as is often the
case in proteins containing both alpha helices and beta
strands) usually contain a run of hydrophobic residues.
XXXXXXXXXXXX
Helical transmembrane proteins
+
• Strong
hydrophobicity signal from membrane
spanning regions, each ~25 residues in length
• Predominance of positively charged amino acid
residues on cytoplasmic side
•Prediction accuracy with multiple alignment = 95%
Helical transmembrane proteins
• ~30% of top 100 drugs bind to membrane
proteins
• Difficult to determine experimentally
• But much easier to predict than globular
proteins!
• TMpred – based on statistical analysis of
transmembrane proteins
• TMHMM – based on Hidden Markov Model
Protein Structure Classification
Class(C) secondary structure content – mainly alpha, mainly beta,
alpha/beta, few secondary structures (type)
Architecture(A) gross arrangement of sec. structure elements
(type and number of SS elements)
Topology(T) shape and connectivity of SS (type, number and
order of SS elements)
Homologous superfamily (H)
http://www.cathdb.info/latest/index.html
Topology
Fold families
Class
Architecture
Topology
Homologous
domains,
share
common
ancestor
H-level
In CATH, the assignments of
structures to fold groups and
homologous superfamilies are
made by sequence and structure
comparisons.
Fold families
Class
Architecture
Topology
Homologous
domains,
share
common
ancestor
H-level
Homologous
domain family ?
Architecture: ‘Barrel’
9 Topologies : type of SS, number and order
Secondary structure prediction
methods
• PSI-pred (PSI-BLAST profiles used for prediction; David
Jones, Warwick)
• JPRED Consensus prediction (includes many of the
methods given below; Cuff & Barton, EBI)
• DSC King & Sternberg
• PREDATORFrischman & Argos (EMBL)
• PHD home page Rost & Sander, EMBL, Germany
• ZPRED server Zvelebil et al., Ludwig, U.K.
• nnPredict Cohen et al., UCSF, USA.
• BMERC PSA Server Boston University, USA
• SSP (Nearest-neighbor) Solovyev and Salamov, Baylor
College, USA.
http://speedy.embl-heidelberg.de/gtsp/secstrucpred.html
Consensus prediction method
hydrophobic
highly conserved
b= buried, e = exposed
Consensus prediction method -JPRED
hydrophobic
highly conserved
hydrophobic
b= buried, e = exposed
amphipathic
Neural network prediction - PHD
Multiple alignment
of protein family
SS profile for
window of
adjacent residues
Hidden Markov Models-HMMSTR
amino acid
secondary structure element
structural context
Markov state
• Recurrent local features of protein sequences
• Accuracy of 74%
Bystroff et al., 2000