linear motifs and phosphorylation sites
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Short sequence of amino acids encoding a particular molecular function
…a first taste
We need a more accurate definition!
Linear Motifs Functional sites
What are you going to learn about Linear Motifs?
Why are they important?
Where can we find them?
How can we discover them?
When and how can we use them?
What are tools and resources to handle them?
Can we classify them?
How can we represent them?
What are you going to learn about Linear Motifs?
Why are they important?
Where can we find them?
How can we discover them?
When and how can we use them?
What are tools and resources to handle them?
Can we classify them?
How can we represent them?
Tyrosine kinsase Src has several functional sitesTyrosine kinsase Src has several functional sites
CSK phosphorylation (Y527) &CSK phosphorylation (Y527) &SH2 ligandSH2 ligand
SH3 ligandSH3 ligand
Auto phosphorylation site (Y416)Auto phosphorylation site (Y416)
Myristoylation siteMyristoylation site
MDM2
TAFII31
P300
P300NLS
CYCLIN
CBPNES
S100BSIR2
phosphorylation
Pin1 P-Ser-Pro isomerisation
Acetylation
SUMO
Ubiquitinylation
p53 is full of functional sitesp53 is full of functional sites
The sequences of many proteins contain short, conserved motifs that are involved in recognition and targeting activities, often separate from other functional properties of the molecule in which they occur.
Tim Hunt (TIBS 1990)
These motifs are linear, in the sense that three-dimensional organization is not required to bring distant segments of the molecule together to make the recognizable unit.
Tim Hunt (TIBS 1990)
The conservation of these motifs varies: some are highly conserved while others, for example, allow substitutions that retain only a certain pattern of charge across the motif.
A more accurate definition
• short, common stretches of polypeptide chains (~ 3-10 amino acid residues long)
• embody a distinct molecular function independent of a larger sequence/structure context.
• are nearly always involved in regulation
• are involved in protein/domain-protein/domain interactions
• often reside in disordered or low-complexity regions
• often become ordered upon binding to another protein or domain
• bind with low affinity (1.0-150 M). Mediate transient interactions.
• occurrences of LMs seem to arise or disappear as a result of point mutations
What are you going to learn about Linear Motifs?
Why are they important?
Where can we find them?
How can we discover them?
When and how can we use them?
What are tools and resources to handle them?
Can we classify them?
How can we represent them?
Evolutionary unrelated protein sharing a functional feature are likely to contain similar linear motifs
This may be the result of - convergent evolution- evolutionary conservation in a divergent evolution process
Why are they important?
In any case, linear motifs are indicative of functions
With the appropriate tools, they can be used to identify:•protein functions•functional regions (in a protein sequence and on its three-dimensional structure, if available)
They are made up of the amino acid residues encoding a functional site
In other words…
What are you going to learn about Linear Motifs?
Why are they important?
Where can we find them?
How can we discover them?
When and how can we use them?
What are tools and resources to handle them?
Can we classify them?
How can we represent them?
PRACTICE: Let’s find linear motifs in human p53…
Go to the UniProt website: http://www.uniprot.org/
Type p53 in the Query text box and select P04637
or
Type directly either P04637 or P53_HUMAN in the Query text box
Work in groups and analyse the p53 entry record:
- how many LMs can you identify?- which function(s) are they indicative of?- are they always annotated as “motif”?- can you classify them according to the 4 categories?
What are you going to learn about Linear Motifs?
Why are they important?
Where can we find them?
How can we discover them?
When and how can we use them?
What are tools and resources to handle them?
Can we classify them?
How can we represent them?
How can we represent LMs?
Regular expression: Regular expression: [RK].L.{0,1}[FLIV][RK].L.{0,1}[FLIV]
inhibitorsinhibitors
Alignment of cyclin ligands
How can we represent LMs?
Regular expression: Regular expression: [RK].L.{0,1}[FLIV][RK].L.{0,1}[FLIV]
inhibitorsinhibitors
Alignment of cyclin ligands
Regular Expression (regexp)
L: single amino acid “L” = Leucine [KR]: different amino acids allowed at this position x or .: wildcard {0,1}: variable length
Before we describe what regexp are useful for, let’s briefly see how to discover de novo motifs
In some cases, the structure and function of an unknown protein which is too distantly related to any protein of known structure to detect its affinity by overall sequence alignment may be identified by its possession of a particular cluster of residues types classified as a motifs. The motifs, or templates, or fingerprints, arise because of particular requirements of binding sites that impose very tight constraint on the evolution of portions of a protein sequence
Arthur Lesk, 1988
What are you going to learn about Linear Motifs?
Why are they important?
Where can we find them?
How can we discover them?
When and how can we use them?
What are tools and resources to handle them?
Can we classify them?
How can we represent them?
In contrast to domains, which are readily detectable by sequence comparison, linear motifs are difficult to discover due to their short length, a tendency to reside in disordered regions in proteins, and limited conservation outside of closely related species.
Neduva et al. PLoS Biology 2005
Study literature paper(s)/review(s) on a group of unrelated proteins sharing a function
Build an alignment of these proteins
Add to the alignment other sequences relevant to the subject under consideration
Pay attention to the residues and regions thought or proved to be important to the biological function of that group of proteins:
• enzyme catalytic sites• PTM sites• regions involved in binding
Try to find a short conserved sequence which includes functionally important residues
De novo Linear Motif discovery
Discovery of de novo Linear Motif
There are algorithms that do it automatically
Neduva et al. PLoS Biology 2005
Discovery of de novo Linear Motif
Neduva et al. PLoS Biology 2005
Our central hypothesis is that proteins with a common interaction partner will share a feature that mediates binding, either a domain or a linear motif. In the absence of a shared domain, a linear motif could well be the only common sequence feature and might thus be detectable simply by virtue of over-representation, which is the basis of our approach.
Edwards et al. PLoS ONE 2007
A probabilistic method for identifying over-represented, convergently evolved, short linear motifs in proteins.
PRACTICE: Discovery of de novo Linear Motifs
http://dilimot.russelllab.org/
http://www.southampton.ac.uk/~re1u06/software/slimfinder/
Dilimot
SLIMFinder
What are you going to learn about Linear Motifs?
Why are they important?
Where can we find them?
How can we discover them?
When and how can we use them?
What are tools and resources to handle them?
Can we classify them?
How can we represent them?
Linear Motif Databases
PROSITE ELM
1632 documentation entries (domains and functional sites) 174 manually annotated motifs
16-03-2012
R-x-[RK]-x(1,2)-R R.[RK]{1,2}.R
How can we use regular expressions?
Regular expressions can be used to search for motif occurrences in (uncharacterised) protein sequences
There are algorithms that do this for us
A motif (a regexp) can have many instances
We call the occurrence of a motif in a sequence an INSTANCE of that motif
What regular expressions are useful for?
KKVAVVRTPPKSPSSAKSRLISPPTPKPRPPRPLPVAPGSEDQILKKPLPPEPAAAPVSTSHRKTKKPLPPTPEEDQILKTRICKIYDSPCLPEAEAMFA
[RKY]..P..P
TAU_HUMANP85A_HUMANBTK_HUMANBTK_HUMANBTK_HUMANBTK_HUMANRAD51_HUMAN
SH3 ligand motif
Prediction of new instances of Linear Motifs
ScanProsite
Scansite
ELM
MiniMotifMiner
Allows the search for user-defined regular expressions
INPUT: a protein sequenceOUTPUT: PROSITE or user-defined motif matches in the input sequence
INPUT: a protein sequenceOUTPUT: scansite motif matches in the input sequence
INPUT: a protein sequenceOUTPUT: ELM motif matches in the input sequence
INPUT: a protein sequenceOUTPUT: MiniMotifMiner motif matches in the input sequence
PRACTICE: Prediction of new instances of Linear Motifs
http://prosite.expasy.org/scanprosite/
Go to the ScanProsite website and search for the RGD motif in the SwissProt database
How many hits? How many hits are expected by chance?
R-G-D
Select database
Regular expression pros and cons
Advantages Disadvantages
Memorable to humans Over determined
Computationally fast Motif may vary in other lineages
Standardised in scripting languages (Python, Perl)
Do not capture weaker preferences
Often, they can descrive a motif very well
Easy to make a poor representation
Unfortunately matches to these motifs are not significant, providing a signal-to-noise problem for bioinformatics tools
Functional sites only work in proper contextFunctional sites only work in proper context
The cell knows how to discriminate TP from FP !!!The cell knows how to discriminate TP from FP !!!
The site must be in the correct The site must be in the correct cellular contextcellular context (subcellular localisation)(subcellular localisation)
The site is only relevant in a specific The site is only relevant in a specific taxonomy rangetaxonomy range
Knowledge of context can provide the basisKnowledge of context can provide the basisfor filters for improved prediction offor filters for improved prediction offunctional sitesfunctional sites
The site must be in correct molecular The site must be in correct molecular contextcontext - accessible- accessible - usually not in globular domains,- usually not in globular domains, - often together with certain types of co-domains- often together with certain types of co-domains
Motifs are mostly found in disordered regions
Globular domain filter
Src kinase
The disordered regions are proving to be rich in Linear Motifs
We can exploit this observation and filter out motif matches inside domains
When inside a domain, a motif match is more likely to be a True Positive (TP) if it occurs in a flexible (i.e. loop, turn or linker) and accessible region of the domain
Structural Filter
Inside domains they are unlikely unless in surface loops
Motif matches are not ALWAYS outside domains
An exposed instance of the RGD motif in a domain
An instance of the RGD motif in a region outside a domain
The RGD motif is recognized by different members of the integrin family
We can think to implement a filter that is based on the three-dimensional features of motifs (i.e. their accessibility and secondary structure types)
If the match is not accessible
If the match is in -helix
If the match is in -strand
low score
low score
low score
Structural Filter
Other features that can be used to filter out FPs:
•Taxonomy•Cellular compartment•Evolutionary conservation
Davey NE et al. Mol Biosyst 2011
Improve the prediction of LM instances by discarding those matches that are unlikely to be functional because they have not been conserved during the evolution of the protein sequences
Why is a Conservation Score useful for linear motif prediction?
There is a resource which implements these filters
It associates a score to occurrences of motifs based on
•Cellular context•Molecular context•Domain context•Disorder•Taxonomy •Evolutionary conservation
The Eukaryotic Linear Motif (ELM) Resource implements a logical filtering system to reduce false matches
The Eukaryotic Linear Motif (ELM) Resource
• Repository of information about functional sites (including experimentally reported instances)
• A motif-based query tool to find possible new functional sites
• A logical filtering system to reduce false matches
PRACTICE: The ELM server (http://elm.eu.org/)Go to the ELM server
Search for motif matches in the EH domain-binding mitotic phosphoprotein
Output 1
annotated instance
Instance in unfavourable context
instance in structurally unfavourable context
highly conserved instance
Browse the ELMs page for the Clathrin Box motif in Endocytosis cargo adaptor proteins (ELM: LIG_AP2alpha_2)
Phosphorylation is the addition of a phosphate group (PO4) to a protein molecule or small molecule.
The hydroxyl groups (-OH) of SER, THR or TYR residues side chain are the most common targets
A protein kinase moves a phosphate group from ATP to the protein
A protein phosphatase removes the phosphate and the protein reverts to its original state.
ATP (adenosine triphosphate) is the energy currency of the living world. Every cellular process that requires energy gets it from ATP
•It is rapid (few seconds)•It is easily reversible
Reversible protein phosphorylation
It is involved in regulation of metabolism, motility, growth, division, differentiation, trafficking, membrane transport, learning, memory
~ one third of cellular proteins could undergo phosphorylation
Even subtle changes in the activity of protein kinases can lead to a variety of diseases (cancer)
Reversible protein phosphorylation regulates most aspects of cell life
Phosphorylation is a Post Translational Modification (PTM)
A kinase recognises its substrate and adds a phosphate group (PO4) to one of its residues, typically a Serine (Ser, S), Threonine (Thr, T), or Tyrosine (Tyr, Y)
Amino acid phosphorylation is probably the mostabundant of the intracellular PTMs used to regulate the state of eukaryotic cells, with estimates ranging up to 500,000 phosphorylation sites in the human proteome
Substrate recognition is specific
Each kinase is capable of recognising its substrate(s) in the cell
In other words…
Nevertheless…
Even though the determinants of specificity are still unclear
In fact, the enzymes must be specific and act only on a defined subset of cellular targets to ensure signal fidelity.
Substrate recruitment is one of the known specificity mechanisms The protein composition around the phosphorylatable site is another factor
Kinases are capable of recognising the region surrounding the phosphoacceptor residue (in sequence and/or in structure)
In fact, kinases do not phosphorylate every Ser, Thr, Tyr they encounter in the cell Kreegipuu et al, NAR 1998
A phosphorylation site can be represented by a phosphorylation motif
Experimentally verified phosphorylation motifs can be used to predict new phosphorylation sites and characterise kinase substrates
There are many resources collecting P-sites and many tools to predict P-sites in user-defined protein sequences
Collection of instances of P-sites Prediction of new instances of P-sites
Phospho.ELM
phospho.elm.eu.org/
Phospho.ELM
phospho.elm.eu.org/
PhosphoSitePlus
www.phosphositePlus.org/
Scansite
scansite.mit.edu/
PHOSIDA
www.phosida.com/
NetPhos
www.cbs.dtu.dk/services/NetPhos/
PHOSPHORYLATION SITE DATABASE
www.phosphorylation.biochem.vt.edu/
NetPhosK
www.cbs.dtu.dk/services/NetPhos/
Phospho.3D
www.phospho3d.org/
NetworKIN
networkin.info/search.php
KinasePhos
KinasePhos.mbc.nctu.edu.tw/
Predikin
predikin.biosci.uq.edu.au/
Current release contains: •42,914 instances (fully linked to literature references) • 299 kinases • 11,224 sequences • 8,698 substrates
Phospho.ELMphospho.elm.eu.org
Database of experimentally verified phosphorylation sites in eukaryotic proteins
Phospho.3D
http://www.phospho3d.org/
PRACTICEGo to the Phospho.3D website and search all the substrates of the Src kinase
MEESQSDISLELPLSQETFSGLWKLLPPEDILPSPHCMDDLLLPQDVEEFFEGPSEALRVSGAPAAQDPVTETPGPVAPAPATPWPLSSFVPSQKTYQGNYGFHLGFLQSGTAKSVMCTYSPPLNKLFCQLAKTCPVQLWVSATPPAGSRVRAMAIYKKSQHMTEVVRRCPHHERCSDGDGLAPPQHLIRVEGNLYPEYLEDRQTFRHSVVVPYEPPEAGSEYTTIHYKYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRDSFEVRVCACPGRDRRTEEENFRKKEVLCPELPPGSAKRALPTCTSASPPQKKKPLDGEYFTLKIRGRKRFEMFRELNEALELKDAHATEESGDSRAHSSYLKTKKGQSTSRHKKTMVKKVGPDSD
Suggestions to predict P-sites in unknown sequences
?
• Go to UniProt (or Blast your sequence against the UniProt database) and explore the sequence annotation
• Go to Phospho.ELM and scan the sequence
• Go to PHOSIDA and PhosphoSitePlus and do the same
• Use different predictors and select only high scoring sites
• Use structural information if available: - is the site exposed?- is it in a flexible region?
• Use domain (SMART and Pfam) databases:- is the site inside a domain?
• Use evolutionary information: - is the site conserved?
Exploring unknown protein sequences
When all information is collected, only retain sites predicted by more than one tool
•Not inside domain(s)•Not in secondary structure elements (helices and strands)•Accessible to the solvent•Evolutionary conserved
Amongst these, for further experimental tests, preferably choose sites that are:
Exploring unknown protein sequences
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