Linear motifs and phosphorylation sites

Linear motifs and phosphorylation sites

What is a linear motif? (in molecular biology)

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?









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









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


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…









Can we classify LMs? How?

Can we classify LMs? How?

Functional group Functional site (Linear Motif)

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?









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

Regular Expression: Examples

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









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


Discovery of de novo Linear Motif


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









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

Overprediction and context information

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

For example…

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

Two MOD_N-GLC_1 motifs in a domain

MOD_N-GLC_1 (.(N)[^P][ST]..) is a motif for N-glycosilation site

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

The ELM Resource - An overview

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

Output 2

Output 2

Browse the ELMs page for the Clathrin Box motif in Endocytosis cargo adaptor proteins (ELM: LIG_AP2alpha_2)

Link to reported instances

Exploring unknown protein sequences

Phosphorylation sites

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

PRACTICEGo to the Phospho.ELM website and search P-sites for p53

ELM and Phospho.ELM are interconnected

PhosphoBlast

Structural information on P-sites and 3D scan

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?


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:


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Linear motifs and phosphorylation sites

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