Transcript Medicago Genomics and Bioinformatics

PLPTH 890 Introduction to Genomic Bioinformatics
Lecture 23
Protein Structure Analysis - II
Liangjiang (LJ) Wang
[email protected]
April 10, 2005
Outline
• Protein structure alignment (DALI and
VAST).
• Protein secondary structure prediction
(PHDsec, PSIPRED, etc).
• Prediction of 3-D protein structures:
– Homology modeling.
– Threading.
– Ab initio prediction.
• Protein structural genomics.
Protein Structure Comparison
• Why is structure comparison important?
– To understand structure-function relationship.
– To study the evolution of many key proteins
(structure is more conserved than sequence).
• Comparing 3-D structures is much more
difficult than sequence comparison.
• Protein structure classification:
– SCOP: Structure Classification Of Proteins.
– CATH: Class, Architecture, Topology and
Homology.
• Protein structure alignment: DALI and VAST.
Protein Structure Alignment
• Positions of atoms in two or more 3-D protein
structures are compared.
• Must first determine which atoms to align. At
least two sets of three common reference
points should be identified.
• Atoms in structures are
matched to minimize the
average deviation.
• Computers are NOT good
at comparing 3-D objects
(an NP-hard problem).
(Baxevanis and Ouellette, 2005)
How to Compare Structures?
Structure 1
Structure 2
Feature extraction
Description 1
Description 2
Comparison
Scores
Statistical analysis
Similarity, classification
DALI
• DALI is for Distance matrix ALIgnment.
• Each structure is represented as a twodimensional array (matrix) of distances
between all pairs of C atoms.
– Remember what a C atom is?
• Assume that similar 3-D structures have
similar inter-residue distances.
• DALI uses distance matrices to align protein
structures.
• DALI is available at http://www.ebi.ac.uk/dali/.
VAST
• VAST is for Vector Alignment Search Tool.
• Each structure is represented as a set of
secondary structure elements (SSEs).
– SSEs:  helices or  strands.
• VAST scores pairs of SSEs based on their type,
orientation and connectivity.
• The SSE matches of statistical significance are
then extended (similar to BLAST).
• Structures in MMDB have been pre-computed,
and organized as structure neighbors in Entrez.
• VAST can be accessed at
http://www.ncbi.nlm.nih.gov/Structure/VAST/vast.shtml.
Secondary Structure Prediction
• Given the sequence of a polypeptide,
secondary structures are predicted.
• Assume that secondary structures are fully
determined by local interactions among
neighboring residues.
• Early analysis were based on the frequencies
of amino acid found in different types of
secondary structures.
– For example, proline occurs at turns, but not in  helices.
• Modern approaches use machine learning
techniques and multiple sequence alignments.
Machine Learning Approach
QEALDAAGDKLVVVDF
HHHHHHLLLLEEEEEE
Training Dataset
H – Helix
E – Sheet
L – Loop
Test Dataset
Training
Testing
Classifier (Model)
No
Yes
Performance?
Prediction
PHDsec
• For a given protein sequence:
– Search for homologous sequences.
– Produce a multiple sequence alignment.
– Generate a profile (evolutionary information).
• PHDsec uses a feed-forward artificial neural
network to predict the secondary structures.
Input layer
S
P
A
R
S
H
E
L
K
Y
Hidden layer
Output layer
(PHDsec can be accessed at http://www.predictprotein.org/)
PSIPRED
• For a given protein sequence:
– Perform a PSI-BLAST search.
– Create a profile that conveys the
evolutionary information at each position.
– Feed the profile into a system of neural
networks (or support vector machines).
• PSIPRED can be accessed at
http://bioinf.cs.ucl.ac.uk/psipred/.
How to Evaluate the Performance?
• EVA: an independent server for evaluation of
protein structure prediction methods.
• The best tool
for three-state
per-residue
secondary
structure
prediction
now reaches
the accuracy
of about 78%.
(http://cubic.bioc.columbia.edu/eva/)
Prediction of 3-D Protein Structures
• There are about 30,000 structures in PDB, but
more than 1.8 million non-redundant protein
sequences in UniProt (Swiss-Prot + TrEMBL).
• Computational structure prediction may
provide valuable information for most of the
protein sequences derived from genome
sequencing projects.
• Three predictive methods:
– Homology (or comparative) modeling.
– Threading (or fold recognition).
– Ab initio structure prediction.
Sequence - Structure Relationship
• In cells, protein folding is determined by the
amino acid sequence. But, protein structures
can also be affected by post-translational
modifications and the cellular environment.
• Proteins with ≥ 30% sequence identity tend to
have similar structures. However, exceptions
do exist …
80-residue stretch
(yellow) with 40%
sequence identity
(Bourne, 2004)
(Viral capsid protein, 1PIV:1)
(Glycosyltransferase, 1HMP:A)
Homology Modeling
• Probably the most accurate method for
protein structure prediction.
• Five different steps:
– Find a known structure related to the query
sequence by sequence comparison.
– Align the query sequence with the known
structure (template).
– Build a model by modifying the backbone and
side chains of the template.
– Refine the model using energy minimization.
– Validate the model using visual inspection or
software tools.
Homology Modeling (Cont’d)
• Accuracy of structure prediction depends on
the percent amino acid sequence identity
shared between the query and template.
• For >50% sequence identity, RMSD (Root
Mean Square Deviation) is only 1 Å for mainchain atoms, which is comparable to the
accuracy of a medium-resolution NMR
structure or a low-resolution X-ray structure.
• Homology modeling may not be used for
predicting protein structures if the sequence
identity is less than 30%.
Homology Modeling Servers
• SWISS-MODEL (http://swissmodel.expasy.org/):
A popular site for structure homology modeling.
• SDSC1 (http://cl.sdsc.edu/hm.html):
the #1 ranked
server for
homology
modeling on
the EVA site.
SDSC1
http://cubic.bioc.columbia.edu/eva/
Threading
(Baxevanis and Ouellette, 2005)
Threading (Cont’d)
• Threading takes a query sequence and passes
(threads) it through the 3-D structure of each
protein in a fold database (known structures).
• As a sequence is threaded, the fit of the
sequence in the fold is evaluated using some
functions of energy or packing efficiency.
• Threading may find a common fold for proteins
with essentially no sequence homology.
• Structures predicted from threading techniques
often are not of high quality (RMSD > 3 Å).
• Based on EVA results, 3D-PSSM is the best
threading server (http://www.sbg.bio.ic.ac.uk/~3dpssm/).
Ab Initio Structure Prediction
• Ab initio prediction can be used when a protein
sequence has no detectable homologues in PDB.
• Protein folding is modeled based on global freeenergy minimization.
• Since the protein folding problem has not yet been
solved, the ab initio prediction methods are still
experimental and can be quite unreliable.
• One of the top ab initio prediction methods is called
Rosetta, which was found to be able to successfully
predict 61% of structures (80 of 131) within 6.0 Å
RMSD (Bonneau et al., 2002).
• The HMMSTR/Rosetta Server can be accessed at
http://www.bioinfo.rpi.edu/~bystrc/hmmstr/server.php.
Comparing Structure
Prediction Methods
A – C: homology
modeling with 60%
(A), 40% (B) and 30%
(C) sequence identity.
D and E: ab initio
protein structure
prediction.
Predicted structures
are in red, and actual
structures are in blue.
(Baker and Sali, 2000)
Example: Cysteine-Rich Peptides
Signal helix and cleavage site
C
C
C
C
C
C
C
C
NCR: Nodule-specific Cysteine Rich genes in legumes.
Avr9: fungal avirulence protein from Cladosporium fulvum.
Defensin: antimicrobial peptides.
Proteinase inhibitor: Serine proteinase inhibitors.
SCR6: S-locus of Brassica, SI, interact with SRK6.
Ab Initio Prediction of Cys Rich Peptides
LSG-TC51151
PsENOD3
Defensin (AAG40321, M. sativa)
Avr9 (Cladosporium fulvum)
Protein Structural Genomics
• A worldwide initiative aimed at determining a
large number of protein structures in a high
throughput mode.
• In the US, nine structural genomics centers
have been funded by the National Institutes of
Health (NIH).
• More information may be found at
http://www.rcsb.org/pdb/strucgen.html.
• TargetDB (http://targetdb.pdb.org/): a
centralized registration database for target
sequences from the worldwide structural
genomics projects.
A Target Selection Pipeline from JCSG
Methods
TMHMM
Protein size
(7 - 80 kDa)
Low complexity
Redundancy
BLAST against
PDB sequences
Summary
• Fast and accurate structure alignment is still
a very hard problem to be solved.
• Machine learning techniques are widely used
in protein secondary structure prediction.
• Homology modeling is probably the most
reliable method for structure prediction.
• The protein folding problem has not yet been
solved.
Prediction of Solvent Accessibility
• Solvent accessibility: the relative area of a
residue’s surface that is exposed to the
surrounding solvent.
• The solvent-accessible residues may be part of
an active site or a binding site, while the buried
residues may play an important role in stabilizing
the protein structure.
• PHDacc (http://www.predictprotein.org/): a neural
network-based method (similar to PHDsec).
• Jpred (http://www.compbio.dundee.ac.uk/~www-jpred/):
a neural network system that predicts both
secondary structure and solvent accessibility.
Predicting Transmembrane Segments
• Transmembrane segments share common
biophysical features (e.g., hydrophobicity).
• PHDhtm (http://www.predictprotein.org/):
– Part of the PredictProtein services.
– Transmembrane helices are predicted using a
neural network system.
• TMHMM (http://www.cbs.dtu.dk/services/TMHMM/):
– A set of known transmembrane segments are
represented as HMMs.
– A query sequence is matched to a known
transmembrane pattern.
Signal Peptide Prediction
• Extracellular proteins or proteins targeted to
subcellular compartments contain short
signal peptides (often at the N-terminal).
• PSORT (http://psort.ims.u-tokyo.ac.jp/): A
rule-based expert system for predicting subcellular
localization of proteins from their amino acid
sequences. The algorithm of k-nearest neighbors
is used for reasoning.
• SignalP (http://www.cbs.dtu.dk/services/SignalP/):
predicts the presence and location of signal
peptide cleavage sites using a combination of
neural networks and HMMs.