Simple Rearrangements

Reversals

• Blocks represent conserved genes.

1 32

4

10

56

8

9

7

1, 2, 3, 4, 5, 6, 7, 8, 9, 10

Reversals1 32

4

10

56

8

9

7

1, 2, 3, -8, -7, -6, -5, -4, 9, 10

Blocks represent conserved genes. In the course of evolution or in a clinical context, blocks

1,…,10 could be misread as 1, 2, 3, -8, -7, -6, -5, -4, 9, 10.

Types of Rearrangements

Reversal1 2 3 4 5 6 1 2 -5 -4 -3 6

Translocation1 2 3 44 5 6

1 2 6 4 5 3

1 2 3 4 5 6

1 2 3 4 5 6

Fusion

Fission

Sorting by reversals: 5 stepsStep 0: p 2 -4 -3 5 -8 -7 -6 1Step 1: 2 3 4 5 -8 -7 -6 1Step 2: 2 3 4 5 6 7 8 1Step 3: 2 3 4 5 6 7 8 -1Step 4: -8 -7 -6 -5 -4 -3 -2 -1Step 5: g 1 2 3 4 5 6 7 8

Sorting by reversals: 4 stepsStep 0: p 2 -4 -3 5 -8 -7 -6 1Step 1: 2 3 4 5 -8 -7 -6 1Step 2: -5 -4 -3 -2 -8 -7 -6 1Step 3: -5 -4 -3 -2 -1 6 7 8Step 4: g 1 2 3 4 5 6 7 8

Sorting by reversals: 4 stepsStep 0: p 2 -4 -3 5 -8 -7 -6 1Step 1: 2 3 4 5 -8 -7 -6 1Step 2: -5 -4 -3 -2 -8 -7 -6 1Step 3: -5 -4 -3 -2 -1 6 7 8Step 4: g 1 2 3 4 5 6 7 8

What is the reversal distance for this permutation? Can it be sorted in 3 steps?

From Signed to Unsigned Permutation (Continued)

0 5 6 10 9 15 16 12 11 7 8 14 13 17 18 3 4 1 2 19 20 22 21 23

• Construct the breakpoint graph as usual

• Notice the alternating cycles in the graph between every other vertex pair

• Since these cycles came from the same signed vertex, we will not be performing any reversal on both pairs at the same time; therefore, these cycles can be removed from the graph

Reversal Distance with Hurdles

• Hurdles are obstacles in the genome rearrangement problem

• They cause a higher number of required reversals for a permutation to transform into the identity permutation

• Taking into account of hurdles, the following formula gives a tighter bound on reversal distance:

d(π) ≥ n+1 – c(π) + h(π)

• Let h(π) be the number of hurdles in permutation π

Median Problem

Goal: find M so that DAM+DBM+DCM is minimized

NP hard for most metric distances

Genome Enumeration for Multichromosome Genomes

$

1

-1

3

-3

2

-3

2 3 $

-3

.

$3

-3

...

...

...

...

.

.

.

.

.

.

.

.

.

.

.

...$-3

3 $

‹ 1, 2, 3 ›

‹ 1, 2, -3 ›

‹ 1, 2 › ‹ 3 ›

‹ 1, 2 › ‹ -3 ›

Genome Enumeration

For genomes on gene {1,2,3}

2

-2

2

-2

2

-2

Rearrangement Phylogeny

Compute A Given Tree (Start)

Compute A Given Tree (First Median)

Compute A Given Tree (Second Median)

Compute A Given Tree (Third Median)

Compute A Given Tree (After 1st Iteration)

Binary Encoding

MLBE Sequences

Experimental Results (Equal Content)

80% inversion, 20% transposition

An Example—New Genomes1 2 3 4 5 6 7 8 9 10

1 -4 5 2 8 10 9 -7 -6 3

…

1 3 5 7 9

1 5 9 -7 3

…

Jackknifing Rate

Support Value Threshold - FP

Up to 90% FP can be identified with 85% as the threshold

Jackknife Properties

• Jackknifing is necessary and useful for gene order phylogeny, and a large number of errors can be identified

• 40% jackknifing rate is reasonable• 85% is a conservative threshold, 75% can

also be used• Low support branches should be examined

in detail

Protein

In-silico Biochemistry

• Online servers exist to determine many properties of your protein sequences• Molecular weight• Extinction coefficients• Half-life

• It is also possible to simulate protease digestion• All these analysis programs are available on

• www.expasy.ch

Analyzing Local Properties• Many local properties are important for the function of

your protein• Hydrophobic regions are potential transmembrane domains• Coiled-coiled regions are potential protein-interaction

domains• Hydrophilic stretches are potential loops

• You can discover these regions• Using sliding-widow techniques (easy)• Using prediction methods such as hidden Markov Models

(more sophisticated)

Sliding-window Techniques• Ideal for identifying strong

signals• Very simple methods

• Few artifacts• Not very sensitive

• Use ProtScale on www.expasy.org

• Make the window the same size as the feature you’re looking for

www.expasy.org/cgi-bin/protscale.pl

www.expasy.org/cgi-bin/protscale.pl

www.expasy.org/cgi-bin/protscale.pl

www.expasy.org/cgi-bin/protscale.pl

Hphob. / Eisenberg

Transmembrane Domains

• Discovering a transmembrane domain tells you a lot about your protein

• Many important receptors have 7 transmembrane domains

• Transmembrane segments can be found using ProtScale

• The most accurate predictions come from using TMHMM

Using TMHMM

• TMHMM is the best method for predicting transmembrane domains

• TMHMM uses an HMM• Its principle is very different from that of ProtScale• TMHMM output is a prediction

TMHMM vs. ProtScale

>sp|P78588|FREL_CANAX Probable ferric reductase transmembrane component OS=Candida albicans GN=CFL1 PE=3 SV=1 MTESKFHAKYDKIQAEFKTNGTEYAKMTTKSSSGSKTSTSASKSSKSTGSSNASKSSTNA HGSNSSTSSTSSSSSKSGKGNSGTSTTETITTPLLIDYKKFTPYKDAYQMSNNNFNLSIN YGSGLLGYWAGILAIAIFANMIKKMFPSLTNNLSGSISNLFRKHLFLPATFRKKKAQEFS IGVYGFFDGLIPTRLETIIVVIFVVLTGLFSALHIHHVKDNPQYATKNAELGHLIADRTG ILGTFLIPLLILFGGRNNFLQWLTGWDFATFIMYHRWISRVDVLLIIVHAITFSVSDKAT GKYKNRMKRDFMIWGTVSTICGGFILFQAMLFFRRKCYEVFFLIHIVLVVFFVVGGYYHL ESQGYGDFMWAAIAVWAFDRVVRLGRIFFFGARKATVSIKGDDTLKIEVPKPKYWKSVAG GHAFIHFLKPTLFLQSHPFTFTTTESNDKIVLYAKIKNGITSNIAKYLSPLPGNTATIRV LVEGPYGEPSSAGRNCKNVVFVAGGNGIPGIYSECVDLAKKSKNQSIKLIWIIRHWKSLS WFTEELEYLKKTNVQSTIYVTQPQDCSGLECFEHDVSFEKKSDEKDSVESSQYSLISNIK QGLSHVEFIEGRPDISTQVEQEVKQADGAIGFVTCGHPAMVDELRFAVTQNLNVSKHRVE YHEQLQTWA

Search with Accession number P78588http://www.uniprot.org/uniprot/

www.cbs.dtu.dk/services/TMHMM-2.0

www.cbs.dtu.dk/services/TMHMM-2.0

Predicting Post-translational Modifications

• Post-translational modifications often occur on similar motifs in different proteins

• PROSITE is a database containing a list of known motifs, each associated with a function or a post-translational modification

• You can search PROSITE by looking for each motif it contains in your protein (the server does that for you!)

• PROSITE entries come with an extensive documentation on each function of the motif

Searching for PROSITE Patterns

• Search your protein against PROSITE on ExPAsy• www.expasy.org/tools/scanprosite

• PROSITE motifs are written as patterns• Short patterns are not very informative by themselves• They only indicate a possibility• Combine them with other information to draw a conclusion

• Remember: Not everything is in PROSITE !

www.expasy.org/tools/scanprosite

P12259

www.expasy.org/tools/scanprosite

Interpreting PROSITE Patterns• Check the pattern function: Is it compatible with the protein?

• Sometimes patterns suggest nonexistent protein features • For instance : If you find a myristoylation pattern in a prokaryote, ignore

it; prokaryotic proteins have no myristoylation !

• Short patterns are more informative if they are conserved across homologous sequences

• In that case, you can build a multiple-sequence alignment• This slide shows an example

Patterns and Domains

• Patterns are usually the most striking feature of the more general motifs (called domains)

• Domains are less conserved than patterns but usually longer

• In proteins, domain analysis is gradually replacing pattern analysis

Protein Domains

• Proteins are usually made of domains

• A domain is an autonomous folding unit

• Domains are more than 50 amino acids long

• It’s common to find these together:

• A regulatory domain• A binding domain• A catalytic domain

Discovering Domains

• Researchers discover domains by• Comparing proteins that have similar functions• Aligning those proteins• Identifying conserved segments

• A domain is a multiple-sequence alignment formulated as a profile

• For each column, a domain indicates which amino acid is more likely to occur

Domain Collections• Scientists have been discovering and characterizing protein

domains for more than 20 years

• 8 collections of domains have been established• Manual collections are very precise but small

• Automatic collections are very extensive but less informative

• These collections• Overlap

• Have been assembled by different scientists

• Have different strengths and weaknesses

• We recommend using them all!

The Magnificent 8

• Pfam is the most extensive manual collection• Pfam is often used as a reference

Searching Domain Collections

• Domains in Pfam often include known functions

• A match between your protein and a domain is desirable• A match is a potential indication of a function• This is VERY informative for further research!

• Three servers exist to compare proteins and domain collections:

• InterProScan www.ebi.ac.uk/interproscan• CD-Search (conserved Domain) www.ncbi.nih.nlm.gov• Motif Scan www.ch.embnet.org

Using InterProScan• InterProScan is the most

comprehensive search engine for domain databases

• Makes it possible to compare alternative results on most collections

• Does not provide a statistical score

>sp|P53539|FOSB_HUMAN Protein fosB OS=Homo sapiens GN=FOSB PE=1 SV=1 MFQAFPGDYDSGSRCSSSPSAESQYLSSVDSFGSPPTAAASQECAGLGEMPGSFVPTVTA ITTSQDLQWLVQPTLISSMAQSQGQPLASQPPVVDPYDMPGTSYSTPGMSGYSSGGASGS GGPSTSGTTSGPGPARPARARPRRPREETLTPEEEEKRRVRRERNKLAAAKCRNRRRELT DRLQAETDQLEEEKAELESEIAELQKEKERLEFVLVAHKPGCKIPYEEGPGPGPLAEVRD LPGSAPAKEDGFSWLLPPPPPPPLPFQTSQDAPPNLTASLFTHSEVQVLGDPFPVVNPSY TSSFVLTCPEVSAFAGAQRTSGSDQPSDPLNSPSLLAL

www.ebi.ac.uk/InterProScan

www.ebi.ac.uk/InterProScan

The CD-Search Output• CD search is less extensive than that of InterProScan• Results come with a a statistical evaluation (E-value)

• 10e-15 Low E-value Good match• 2.1 High E-value Bad match

www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi

www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi

www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi

Predicting Functions with Domains

• Finding a match with a domain having a catalytic function is good news . . . but what, exactly, does it mean?

• A match indicates that your sequence has the domain structure . . . but does it also have the function?

• You cannot say before looking into these details:• Where are the catalytic residues on the domain?• Does your sequence have the right residues at these positions?

Looking into the Details• Catalytic residues are normally highly conserved in

domains• Motif Scan makes it possible to check whether these

important residues are conserved in your sequence• High bar above 0 = Highly conserved residues• Green = Your sequence has an expected residue• Red = Your sequence has an unexpected residue

Looking into the Details (cont’d.)

R (Arginine) is highly expected at this positionHigh barPotential active site

If your protein has an arginine on this position . . .Bar is filled with greenYour protein could be active

myhits.isb-sib.ch/cgi-bin/motif_scan

Protein 3D Structure

Primary, Secondary and Tertiary Structures

• Proteins are made of 20 amino acids• Proteins are on average 400 amino acids

long• Protein structure has 3 levels:

• The primary structure is the sequence of a protein

• The secondary structure is the local structure • The tertiary structure is the exact position of

each atom on a 3D model

Secondary Structures

• Helix• Amino acid that twists like a spring

• Beta strand or extended• Amino acid forms a line without

twisting• Random coils

• Amino acid with a structure neither helical nor extended

• Amino-acid loops are usually coils

Guessing the Secondary Structure of Your Protein

• Secondary structure predictions are good• If your protein has enough homologues, expect

80% accuracy• The most accurate secondary structure prediction

server is PSIPRED

PSIPRED Output• Conf = Confidence

• 9 is the best, 0 the worst

• Pred = Every amino acid is assigned a letter:• C for coils

• E for extended or beta-strand

• H for helix

>gi|15892329|ref|NP_360043.1| translocation protein TolB [Rickettsia conorii str. Malish 7] MRNIIYFILSLLFSVTSYALETINIEHGRADPTPIAVNKFDADNSAADVLGHDMVKVISNDLKLSGLFRP ISAASFIEEKTGIEYKPLFAAWRQINASLLVNGEVKKLESGKFKVSFILWDTLLEKQLAGEMLEVPKNLW RRAAHKIADKIYEKITGDAGYFDTKIVYVSESSSLPKIKRIALMDYDGANNKYLTNGKSLVLTPRFARSA DKIFYVSYATKRRVLVYEKDLKTGKESVVGDFPGISFAPRFSPDGRKAVMSIAKNGSTHIYEIDLATKQL HKLTDGFGINTSPSYSPDGKKIVYNSDRNGVPQLYIMNSDGSDVQRISFGGGSYAAPSWSPRGDYIAFTK ITKGDGGKTFNIGIMKACPQDDENSERIITSGYLVESPCWSPNGRVIMFAKGWPSSAKAPGKNKIFAIDL TGHNEREIMTPADASDPEWSGVLN

bioinf.cs.ucl.ac.uk/psipred//?program=psipred

bioinf.cs.ucl.ac.uk/psipred//?program=psipred

bioinf.cs.ucl.ac.uk/psipred//?program=psipred

bioinf.cs.ucl.ac.uk/psipred//?program=psipred

Predicting Other Secondary Features

• It is also possible to predict these accurately:• Transmembrane segments• Solvent accessibility• Globularity• Coiled/coil regions

• All these predictions have an expected accuracy higher than 70%

Servers

• www.predictprotein.org• cubic.bioc.columbia.edu/predictprotein• www.sdsc.edu/predicprotein• www.cbi.pku.edu.cn/predictprotein

Predicting 3D Structures• Predicting 3D structures from sequences only is almost impossible

• The only reliable way to establish the 3D structure of a protein is to make a real-world experiment in

• X-ray crystallography• Nuclear magnetic resonance (NMR)

• Structures established this way are conserved in the PDB database

• “The PDB of my protein” is synonymous with “The structure of my protein”

Retrieving Protein Structures from PDB

• All PDB entries are 4-letter words!• 1CRZ, 2BHL . . .

• Sometimes the chain number is added: • 1CRZA, 1CRZB . . .

• To access all PDB entries, go to www.rcsb.org • PDB contains 42,000 entries• PDB contains the structure of 16,000 unique proteins or RNAs

• You can download the coordinates and display the structure

www.rcsb.org

www.rcsb.org

Displaying a PDB Structure• You can use any of the online

viewers to display the structure

• They will let you rotate the structure, zoom in and out, or color it

• PDB files themselves are not human-readable

Predicting the Structure of Your Protein

• The bad news: • It is very hard to predict protein 3D structures

• The good news:• Similar proteins have similar structures

• If your favorite protein has a homologue with a known structure . . .

• You can do homology modeling

• How?• Start with a BLAST (more about that in the next slide)

ncbi.nlm.nih.gov/BLAST

ncbi.nlm.nih.gov/BLAST

BLASTing PDB for Structures• BLAST your protein against

PDB

• If you get a very good hit, it means PDB contains a protein similar to yours

• Your protein and this hit probably have the same structure

Be Careful! • Sometimes only one of the domains contained in your protein has

been characterized• If that’s the case, the PDB will only contain this domain• Always check the alignments

• Red line = full protein in PDB• Blue line = one domain only in this entry

Structures and Sequences

• Highly conserved sequences are often important in the structure

• Make a multiple-sequence alignment to identify these important positions

• Highly conserved positions are either in the core or important for protein/protein interactions

3D Predictions• If you want to predict the structure of your protein

automatically, try the Swiss Model• Swiss Model makes the BLAST for you

• The program does a bit of homology modeling

• The process delivers a new PDB entry

• You can access it at swissmodel.expasy.org

• Swiss Model gives good results for proteins having homologues in PDB

zhanglab.ccmb.med.umich.edu/I-TASSER/

zhanglab.ccmb.med.umich.edu/I-TASSER/

3D-BLAST• Use this technique if you have a structure and you

want to find other similar structures

• Use VAST or DALI to look for proteins having the same 3D shape as yours• www.eb.ac.uk/dali• www.ncbi.nlm.nih/vast

3D Movements• Most proteins need to move to do their job

• Predicting protein movement is possible using molecular dynamics• Check out this site: molmolvdb.mbb.yale.edu

• Good molecular dynamics requires extremely powerful computers• Don’t expect miracles from standard online resources