Tools to analyze protein characteristics
3-D fold model
Identification of
conserved regions
-Family member
-Multiple alignments
Protein
sequence
Protein sorting and
sub-cellular localization
Some
Signal sequence
(tags)
Evolutionary
relationship (Phylogeny)
Protein
modifications
Anchoring into
the membrane
nascent proteins contain a specific signal, or targeting sequence
that directs them to the correct organelle. (ER, mitochondrial, chloroplast,
lysosome, vacuoles, Golgi, or cytosol)
Questions
Can
we train the computers:
To detect signal sequences and predict protein destination?
To identify conserved domains (or a pattern) in proteins?
To predict the membrane-anchoring type of a protein?

(Transmembrane domain, GPI anchor…)
To predict the 3D structure of a protein?
Learning
algorithms are good for solving problems in pattern
recognition because they can be trained on a sample data set.
Classes
of learning algorithms:
-Artificial neural networks (ANNs)
-Hidden Markov Models (HMM)
Artificial neural networks (ANN)
Machine
learning algorithms that mimic the
brain. Real brains, however, are orders of
magnitude more complex than any ANN.
ANN
is composed of a large number of
highly interconnected processing elements
(neurons) working simultaneously to solve
specific problems.
like people, learn by example.
ANNs cannot be programmed to perform a
specific task.
ANNs,
The
first artificial neuron was developed
in 1943 by the neurophysiologist Warren
McCulloch and the logician Walter Pits.
Hidden Markov Models (HMM)
Used
to answer questions like:
What is the probability of obtaining a particular outcome?
What is the best model from many combinations?

HMM
is a probabilistic process over a set of states, in which the
states are “hidden”. It is only the outcome that visible to the
observer. Hence, the name Hidden Markov Model.
HMM
has many uses in genomics:
Gene prediction (GENSCAN)
SignalP
Finding periodic patterns

The ExPASy (Expert Protein Analysis System)
Expasy
server (http://au.expasy.org)
is dedicated to the analysis of
protein sequences and structures.
Sequence
analysis tools include:
DNA -> Protein [Translate]
Pattern and profile searches
Post-translational modification and
topology prediction
Primary structure analysis
Structure prediction (2D and 3D)
Alignment


PredictProtein: A service for sequence analysis, and structure prediction
http://www.predictprotein.org/newwebsite/submit.html

TMpred:

TMHMM: Predicts transmembrane helices in proteins (CBS; Denmark)
http://www.ch.embnet.org/software/TMPRED_form.html
http://www.cbs.dtu.dk/services/TMHMM-2.0/

big-PI : Predicts GPI-anchor site:http://mendel.imp.univie.ac.at/sat/gpi/gpi_server.html

DGPI: Predicts GPI-anchor site: http://129.194.185.165/dgpi/index_en.html

SignalP: Predicts signal peptide: http://www.cbs.dtu.dk/services/SignalP/

PSORT: Predicts sub-cellular localization:

TargetP: Predicts sub-cellular localization: http://www.cbs.dtu.dk/services/TargetP/

NetNGlyc: Predicts N-glycosylation sites:http://www.cbs.dtu.dk/services/NetNGlyc/

PTS1: Predicts peroxisomal targeting sequences
http://www.psort.org/
http://mendel.imp.univie.ac.at/mendeljsp/sat/pts1/PTS1predictor.jsp

MITOPROT: Predicts of mitochondrial targeting sequences
http://ihg.gsf.de/ihg/mitoprot.html
Hydrophobicity: http://www.vivo.colostate.edu/molkit/hydropathy/index.html
http://www.cbs.dtu.dk/services/: prediction server
NetNGlyc: Predicts N-glycosylation sites: http://www.cbs.dtu.dk/services/NetNGlyc/
NetPhos: Predicts phosphorylation of residues: http://www.cbs.dtu.dk/services/NetPhos/
NetPhosK: Predicts recognition sites for specific kinases:
http://www.cbs.dtu.dk/services/NetPhosK/
NetAcet: N-terminal acetylation in eukaryotic proteins:
http://www.cbs.dtu.dk/services/NetAcet/
NetCGlyc: C-mannosylation sites in mammalian proteins
Multiple alignment
Used
to do phylogenetic analysis:
Same protein from different species
Evolutionary relationship: history

Used
to find conserved regions
Local multiple alignment reveals conserved regions
Conserved regions usually are key functional regions
These regions are prime targets for drug developments
Protein domains are often conserved across many species

Algorithm

for search of conserved regions:
Block maker: http://blocks.fhcrc.org/blocks/make_blocks.html
Multiple alignment tools
Free
programs:
Phylip and PAUP: http://evolution.genetics.washington.edu/phylip.html
Phyml: http://atgc.lirmm.fr/phyml/

The
most used websites :
http://align.genome.jp/
http://prodes.toulouse.inra.fr/multalin/multalin.html
http://www.ch.embnet.org/index.html (T-COFFEE and ClustalW)

ClustalW:

Standard popular software

It aligns 2 and keep on adding a new sequence to the alignment

Problem: It is simply a heuristics.
Motif

discovery: use your own motif to search databases:
PatternFind: http://myhits.isb-sib.ch/cgi-bin/pattern_search
http://meme.nbcr.net/meme4_6_0/intro.html
Phylogenetic analysis
Phylogenetic
Describe
Major
trees
evolutionary relationships between sequences
modes that drive the evolution:
Point
mutations modify existing sequences
Duplications (re-use existing sequence)
Rearrangement
Two
most common methods
Maximum
parsimony
Maximum likelihood
The
most useful software:
http://www.megasoftware.net/mega4/m_con_select.html
Definitions
Homologous:Have
Orthologous:
Paralogous:
a common ancestor. Homology cannot be measured.
The same gene in different species . It is the result of
speciation (common ancestral)
Related genes (already diverged) in the same species. It is
the result of genomic rearrangements or duplication
Determining protein Structure-Function

Direct measurement of structure
X-ray crystallography
NMR spectroscopy



Site-directed mutagenesis
Computer modeling
Prediction of structure
Comparative protein-structure modeling

Comparative protein-structure modeling

Goal:Construct 3-D model of a protein of unknown
structure (target), based on similarity of sequence to
proteins of known structure (templates)

Procedure:
Template selection
Template–target alignment
Model building
Model evaluation

Blue: predicted model by PROSPECT
Red: NMR structure
The Protein 3-D Database
The
Protein DataBase (PDB) contains 3-D structural data
for proteins
Founded
in 1971 with a dozen structures
As
of June 2004, there were 25,760 structures in the database.
All structures are reviewed for accuracy and data uniformity.
80% come from X-ray crystallography
16% come from NMR
2% come from theoretical modeling

Structural
data from the PDB can be freely accessed at
http://www.rcsb.org/pdb/
High-throughput methods
Most used websites for 3-D structure prediction
Protein
Homology/analogY Recognition Engine (Phyre) at
http://www.sbg.bio.ic.ac.uk/phyre/html/index.html
PredictProtein
at
http://www.predictprotein.org/newwebsite/submit.html
UCLA
Fold Recognition at
http://www.doe-mbi.ucla.edu/Services/FOLD/
Commercial bioinformatics softwares
CLC Genomics Workbench
Genomics:
454, Illumina Genome Analyzer and SOLiD sequencing data;
De novo assembly of genomes of any size;
Advanced visualization, scrolling, and zooming tools;
SNP detection using advanced quality filtering;
Transcriptomics:
RNA-seq including paired data and transcript-level expression;
Small RNA analysis;
Expression profiling by tags;
Epigenetics:
Chromatin immunoprecipitation sequencing (ChIP-seq) analysis;
Peak finding and peak refinement;
Graph and table of background distribution;
false discovery rate;
Peak table and annotations;
VectorNTI:
Sequence analysis and illustration;
restriction mapping;
recombinant molecule design and cloning;
in silico gel electrophoresis;
synthetic biology workflows
AlignX:
BioAnnotator:
ContigExpress:
GenomBench
The bioinformatics not covered in this class
Comparative
genomics and Genome browser:
http://genome.lbl.gov/vista/index.shtml
http://www.sanger.ac.uk/resources/software/artemis/
Genome
annotation:
http://linux1.softberry.com/berry.phtml
http:// rast.nmpdr.org/
Metagenomics:
http://metagenomics.anl.gov/
System
biology tools.