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Lesson 5Lesson 5

Protein Prediction and Protein Prediction and ClassificationClassification

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Learning about a proteinLearning about a protein

What does a protein do??What does a protein do?? Post-translational modifications – Post-translational modifications –

phosphorylation, glycosylation, etc.phosphorylation, glycosylation, etc. Identifying patterns, motifsIdentifying patterns, motifs Secondary structureSecondary structure Tertiary/quaternary structureTertiary/quaternary structure Protein-protein interactionsProtein-protein interactions

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Domains & MotifsDomains & Motifs

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DomainsDomains

An analysis of known 3-D protein An analysis of known 3-D protein structures reveals that, rather than structures reveals that, rather than being monolithic, many of them being monolithic, many of them contain multiple folding unitscontain multiple folding units. .

Each such folding unit is a domain Each such folding unit is a domain (>50 aa, < 500 aa)(>50 aa, < 500 aa)

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calcium/calmodulin-dependent protein kinase

SH2 domain: interact with phosphorylated tyrosines, and are thus part of intracellular signal-transuding proteins. Characterized by specific sequences and tertiary structure

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What is a motif??What is a motif??

A sequence motifA sequence motif = a certain = a certain sequence that is widespread and sequence that is widespread and conjectured to have biological conjectured to have biological significancesignificance

Examples:Examples:KDELKDEL – ER-lumen retention signal – ER-lumen retention signalPKKKRKVPKKKRKV – an NLS (nuclear – an NLS (nuclear localization signal)localization signal)

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More loosely defined motifsMore loosely defined motifs

KDEL (usually)KDEL (usually)++

HDEL (rarely) HDEL (rarely) ==

[HK]-D-E-L:[HK]-D-E-L:H H oror K at the first position K at the first position

This is called a pattern (in Biology), or This is called a pattern (in Biology), or a regular expression (in computer a regular expression (in computer science)science)

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Syntax of a patternSyntax of a pattern

Example:Example: W-x(9,11)-[FYV]-[FYW]-x(6,7)-[GSTNE].W-x(9,11)-[FYV]-[FYW]-x(6,7)-[GSTNE].

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PatternsPatterns

W-x(9,11)-[FYV]-[FYW]-x(6,7)-[GSTNE].W-x(9,11)-[FYV]-[FYW]-x(6,7)-[GSTNE].

Any amino, between 9-

11 times

F or Y or

V

WOPLASDFGYVWPPPLAWSROPLASDFGYVWPPPLAWSWOPLASDFGYVWPPPLSQQQ

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Patterns - syntaxPatterns - syntax

The standard IUPAC one-letter codes. The standard IUPAC one-letter codes. ‘‘x’x’ : any amino acid. : any amino acid. ‘‘[]’[]’ : residues allowed at the position. : residues allowed at the position. ‘‘{}’{}’ : residues forbidden at the position. : residues forbidden at the position. ‘‘()’()’ : repetition of a pattern element are indicated in : repetition of a pattern element are indicated in

parenthesis. X(n) or X(n,m) to indicate the number or parenthesis. X(n) or X(n,m) to indicate the number or range of repetition. range of repetition.

‘‘-’-’ : separates each pattern element. : separates each pattern element. ‘‹’‘‹’ : indicated a N-terminal restriction of the pattern. : indicated a N-terminal restriction of the pattern. ‘›’‘›’ : indicated a C-terminal restriction of the pattern. : indicated a C-terminal restriction of the pattern. ‘‘.’.’ : the period ends the pattern. : the period ends the pattern.

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Pattern ~ motif ~ signaturePattern ~ motif ~ signature

A A patternpattern (similarly to consensus and (similarly to consensus and profile) is a way to represent a profile) is a way to represent a conserved sequenceconserved sequence

Whereas a profile and consensus Whereas a profile and consensus usually relate to the entire sequence, usually relate to the entire sequence, a pattern usually relates to a a few a pattern usually relates to a a few tens of amino-acidstens of amino-acids

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Profile-pattern-consensusProfile-pattern-consensus

AAAACCTTTTGG

AAAAGGTTCCGG

CCAACCTTTTCC

1122334455

AA0.660.66110000..

TT00000011..

CC0.330.33000.660.6600..

GG00000.330.3300..

AAAACCTTTTGG

[AC-]A-[GC]-T-[TC]-[GC]

multiple alignment

consensus

pattern

profile

•Information:

consensus<pattern<profile

NNAANNTTNNNN

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InterproInterpro

Interpro: a collection of many protein Interpro: a collection of many protein signature databases (Prosite, Pfam, signature databases (Prosite, Pfam, Prints…) integrated into a Prints…) integrated into a hierarchical classifying systemhierarchical classifying system

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Interpro exampleInterpro example

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PTM – Post-PTM – Post-Translational Translational ModificationModification

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PTM – Post-Translational PTM – Post-Translational ModificationModification

PhosphorylationPhosphorylationTyr, Ser, ThrTyr, Ser, Thr

GlycosylationGlycosylation(addition of sugars)(addition of sugars)Asn, Ser, ThrAsn, Ser, Thr

Addition of fatty acids (e.g. N-Addition of fatty acids (e.g. N-myristoylation, S-Palmitoylation)myristoylation, S-Palmitoylation)

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So how to predictSo how to predict

Take into account:Take into account:

1.1. Context (motif):Context (motif):PKC (a kinase) recognizes PKC (a kinase) recognizes X S/T X R/KX S/T X R/KN-Myristoylation at M G X X X S/TN-Myristoylation at M G X X X S/TSeveral times – we don’t know the exact Several times – we don’t know the exact motif!motif!

2.2. ConservationConservationIs the motif found (for instance, in Is the motif found (for instance, in human) also conserved in related human) also conserved in related organisms (for instance, in chimp)?organisms (for instance, in chimp)?

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Prediction problemsPrediction problems

Signal for detection is very shortSignal for detection is very short Not enough biological knowledge for Not enough biological knowledge for

characterizing the signalcharacterizing the signal Tertiary structureTertiary structure

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Prediction will be more efficient if Prediction will be more efficient if more information is availablemore information is available

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Secondary StructureSecondary Structure

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Secondary StructureSecondary Structure

Reminder- Reminder- secondary structure is usually secondary structure is usually divided into three categories:divided into three categories:

Alpha helix Beta strand (sheet)Anything else –

turn/loop

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Secondary StructureSecondary Structure

An easier question – what is the An easier question – what is the secondary structure when the 3D secondary structure when the 3D structure is known?structure is known?

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DSSPDSSP

DSSPDSSP (Dictionary of Secondary (Dictionary of Secondary Structure of a Protein) – assigns Structure of a Protein) – assigns secondary structure to proteins secondary structure to proteins which have a crystal structurewhich have a crystal structure

H = alpha helix

B = beta bridge (isolated residue)

E = extended beta strand

G = 3-turn helix

I = 5-turn helix

T = hydrogen bonded turn

S = bend

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Predicting secondary structure from Predicting secondary structure from primary sequenceprimary sequence

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Chou and Fasman (1974)Chou and Fasman (1974)Name P(a) P(b) P(turn)

Alanine 142 83 66Arginine 98 93 95Aspartic Acid 101 54 146Asparagine 67 89 156Cysteine 70 119 119Glutamic Acid 151 037 74Glutamine 111 110 98Glycine 57 75 156Histidine 100 87 95Isoleucine 108 160 47Leucine 121 130 59Lysine 114 74 101Methionine 145 105 60Phenylalanine 113 138 60Proline 57 55 152Serine 77 75 143Threonine 83 119 96Tryptophan 108 137 96Tyrosine 69 147 114Valine 106 170 50

The propensity of an amino acid to be part of a certain secondary structure (e.g. – Proline has a low propensity of being in an alpha helix or beta sheet breaker)

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Chou-Fasman predictionChou-Fasman prediction

Look for a series of >4 amino acids which all have Look for a series of >4 amino acids which all have (for instance) alpha helix values >100(for instance) alpha helix values >100

Extend (…)Extend (…) Accept as alpha helix if Accept as alpha helix if

average alpha score > average beta scoreaverage alpha score > average beta score

Ala Pro Tyr Phe Phe Lys Lys His Val Ala Thr

α 142 57 69 113 113 114 114 100 106 142 83

β 83 55 147 138 138 74 74 87 170 83 119

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Chou and Fasman (1974)Chou and Fasman (1974)

Success rate of 50%Success rate of 50%

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Improvements in the 1980’sImprovements in the 1980’s

Conservation in MSAConservation in MSA Smarter algorithms (e.g. HMM, neural Smarter algorithms (e.g. HMM, neural

networks).networks).

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AccuracyAccuracy

Accuracy of prediction seems to hit a Accuracy of prediction seems to hit a ceiling of 70-80% accuracyceiling of 70-80% accuracy

MethodMethodAccuracyAccuracy

Chou & FasmanChou & Fasman50%50%

Adding the MSAAdding the MSA69%69%

MSA+ sophisticated MSA+ sophisticated computationscomputations

70-80%70-80%

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Gene OntologyGene Ontology

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GOGO

GGeneene O Ontology – a project for ntology – a project for consistent descriptionconsistent description of gene of gene products in products in different databasesdifferent databases. .

Consistent descriptionConsistent description - Common key - Common key definitions. definitions.

Example:Example: ‘protein synthesis’ or ‘protein synthesis’ or ‘translation’‘translation’

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GOGO

GO - GO describes proteins in terms of :GO - GO describes proteins in terms of :

biological processbiological process

cellular componentcellular component

molecular functionmolecular function

GO is GO is notnot::

– A sequence database.A sequence database.

– A portal for sequence informationA portal for sequence information

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GO – structureGO – structure

nucleus

Nuclear chromosome

cellcellular componentcellular component

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GO exampleGO example

Links from the swissprot entry of human protein kinase C alpha

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Examples for use of GOExamples for use of GO

Enrichment for a GO category:Enrichment for a GO category:1.1. Do all up regulated genes in a Do all up regulated genes in a

microarray you built belong to the microarray you built belong to the same GO “molecular function” same GO “molecular function” category?category?

2.2. You have predicted a new You have predicted a new transcription factor binding site. Do transcription factor binding site. Do all genes with this site belong to the all genes with this site belong to the same GO biological process?same GO biological process?

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Evaluation of prediction Evaluation of prediction methodsmethods

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Evaluation of prediction methodsEvaluation of prediction methods

Comparing our results to experimentally Comparing our results to experimentally verified sitesverified sites

Positive (hit)Positive (hit)NegativeNegative

TrueTrueTrue-positiveTrue-positive

True-negativeTrue-negative

FalseFalseFalse-positiveFalse-positive(false alarm)(false alarm)

False-negativeFalse-negative(miss)(miss)

Our prediction gives:

Is t

he

pre

dic

tio

n c

orr

ect

?

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Method evaluationMethod evaluation

Positive (hit)Positive (hit)NegativeNegative

TrueTrueTrue-positiveTrue-positive

True-negativeTrue-negative

FalseFalseFalse-False-positivepositive

(false alarm)(false alarm)

False-negativeFalse-negative(miss)(miss)

A good method will be one with a high level of A good method will be one with a high level of true-positives and true-negatives, and a low true-positives and true-negatives, and a low level of false-positives and false-negativeslevel of false-positives and false-negatives

Our prediction gives:

Is t

he

pre

dic

tio

n c

orr

ect

?

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Calibrating the methodCalibrating the method

All methods have a parameter (or a All methods have a parameter (or a score) that can be calibrated to score) that can be calibrated to improve the accuracy of the method.improve the accuracy of the method.

For example: the E-value cutoff in For example: the E-value cutoff in BLASTBLAST

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Calibrating E-value cutoffCalibrating E-value cutoff

Reminder: the lower the E-value, the Reminder: the lower the E-value, the more ‘significant’ the alignment more ‘significant’ the alignment between the query and the hit.between the query and the hit.

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Calibrating the E-valueCalibrating the E-value

What will happen if we raise the E-value What will happen if we raise the E-value cutoff (for instance – work with all hits with cutoff (for instance – work with all hits with an E-value which is < 10) ?an E-value which is < 10) ?

Positive (hit)Positive (hit)NegativeNegative

TrueTrueTrue-positiveTrue-positive

True-negativeTrue-negative

FalseFalseFalse-positiveFalse-positive(false alarm)(false alarm)

False-negativeFalse-negative(miss)(miss)

Our prediction gives:

Is t

he

pre

dic

tio

n c

orr

ect

?

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Calibrating the E-valueCalibrating the E-value

On the other hand – if we lower the E-value On the other hand – if we lower the E-value (look only at hits with E-value < 10(look only at hits with E-value < 10-8-8))

Positive (hit)Positive (hit)NegativeNegative

TrueTrueTrue-positiveTrue-positive

True-negativeTrue-negative

FalseFalseFalse-positiveFalse-positive(false alarm)(false alarm)

False-negativeFalse-negative(miss)(miss)

Our prediction gives:

Is t

he

pre

dic

tio

n c

orr

ect

?

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Improving predictionImproving prediction

Trade-off between Trade-off between specificityspecificity and and sensitivitysensitivity

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Sensitivity vs. specificitySensitivity vs. specificity

Sensitivity = Sensitivity =

Specificity =Specificity =

True positive

True positive + False negative

Represent all the proteins which are

really phosphorylated

True negative

True negative + False positive

Represent all the proteins which are

really NOT phosphorylated

How good we hit real

phosphorylations

How good we avoid real non-

phosphorylations

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Raising the E-value to 10:Raising the E-value to 10:sensitivitysensitivityspecificityspecificity

Lowering the E-value to 10Lowering the E-value to 10-8-8

sensitivity sensitivity specificityspecificity

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Over-predictions: exampleOver-predictions: example

Many PTM-predictors tend to Many PTM-predictors tend to over-over-predictpredict high level of false high level of false positives positives low specificity low specificity

WHY?WHY?

1.1. Tertiary structure! (buried/exposed, Tertiary structure! (buried/exposed, tertiary motifs)tertiary motifs)

2.2. The phosphorylation recognition The phosphorylation recognition mechanism is not completely clear!mechanism is not completely clear!

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Next time on: Next time on:

Biological Sequences Biological Sequences AnalysisAnalysis

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The Human GenomeThe Human Genome

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Horizontal (Lateral) Gene TransferHorizontal (Lateral) Gene Transfer

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Alternative splicingAlternative splicing

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Repetitive ElementsRepetitive Elements