Domains, their prediction and domain databases

Lecture 16:

Introduction to Bioinformatics

CENTR

FORINTEGRATIVE

BIOINFORMATICSVU

E

Sequence

Structure

Function

Threading

Homology searching (BLAST)

Ab initio prediction and folding

Function prediction from structure

Sequence-Structure-Function

impossible but for the smallest structures

very difficult

TERTIARY STRUCTURE (fold)TERTIARY STRUCTURE (fold)

Genome

Expressome

Proteome

Metabolome

Functional Genomics – Systems Functional Genomics – Systems BiologyBiology

Metabolomics

fluxomics

Systems Biologyis the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behaviour of that system (for example, the enzymes and metabolites in a metabolic pathway). The aim is to quantitatively understand the system and to be able to predict the system’s time processes

• the interactions are nonlinear• the interactions give rise to emergent properties, i.e. properties

that cannot be explained by the components in the system • Biological processes include many time-scales, many

compartments and many interconnected network levels (e.g. regulation, signalling, expression,..)

Systems Biologyunderstanding is often achieved through modeling and simulation of the system’s components and interactions.

Many times, the ‘four Ms’ cycle is adopted:

Measuring

Mining

Modeling

Manipulating

‘The silicon cell’

(some people think ‘silly-con’ cell)

A system response

Apoptosis: programmed cell death

Necrosis: accidental cell death

This pathway diagram shows a comparison of pathways in (left) Homo sapiens (human) and (right) Saccharomyces cerevisiae (baker’s yeast). Changes in controlling enzymes (square boxes in red) and the pathway itself have occurred (yeast has one altered (‘overtaking’) path in the graph)

We need to be able to do automatic pathway comparison (pathway alignment)

Human Yeast

‘Comparative metabolomics’

Important difference with human pathway

Experimental

• Structural genomics

• Functional genomics

• Protein-protein interaction

• Metabolic pathways

• Expression data

Issue when elucidating function experimentally

• Partial information (indirect interactions) and subsequent filling of the missing steps

• Negative results (elements that have been shown not to interact, enzymes missing in an organism)

• Putative interactions resulting from computational analyses

Protein function categories• Catalysis (enzymes)

• Binding – transport (active/passive)– Protein-DNA/RNA binding (e.g. histones, transcription factors)

– Protein-protein interactions (e.g. antibody-lysozyme) (experimentally determined by yeast two-hybrid (Y2H) or bacterial two-hybrid (B2H) screening )

– Protein-fatty acid binding (e.g. apolipoproteins)

– Protein – small molecules (drug interaction, structure decoding)

• Structural component (e.g. -crystallin)

• Regulation

• Signalling

• Transcription regulation

• Immune system

• Motor proteins (actin/myosin)

Catalytic properties of enzymes

[S]

Mo

les/

s

Vmax

Vmax/2

Km

Michaelis-Menten equation:

Km kcat

E + S ES E + P• E = enzyme• S = substrate• ES = enzyme-substrate complex (transition state)• P = product

• Km = Michaelis constant

• Kcat = catalytic rate constant (turnover number)

• Kcat/Km = specificity constant (useful for comparison)

Vmax × [S]V = ------------------- Km + [S]

Protein interaction domains

http://pawsonlab.mshri.on.ca/html/domains.html

Energy difference upon binding

Examples of protein interactions (and of functional importance) include:

• Protein – protein (pathway analysis);

• Protein – small molecules (drug interaction, structure decoding);

• Protein – peptides, DNA/RNA 

The change in Gibb’s Free Energy of the protein-ligand binding interaction can be monitored and expressed by the following equation: 

G = H – T S       

(H=Enthalpy, S=Entropy and T=Temperature)

Protein-protein interaction networks

Protein function • Many proteins combine functions

• Some immunoglobulin structures are thought to have more than 100 different functions (and active/binding sites)

• Alternative splicing can generate (partially) alternative structures

Protein function & Interaction

Active site / binding cleft

Shape complementarity

Protein function evolution

Chymotrypsin

How to infer function• Experiment• Deduction from sequence

– Multiple sequence alignment – conservation patterns

– Homology searching

• Deduction from structure– Threading– Structure-structure comparison– Homology modelling

Cholesterol Biosynthesis:

Cholesterol biosynthesis primarily occurs in eukaryotic cells. It is necessary for membrane synthesis, and is a precursor for steroid hormone production as well as for vitamin D. While the pathway had previously been assumed to be localized in the cytosol and ER, more recent evidence suggests that a good deal of the enzymes in the pathway exist largely, if not exclusively, in the peroxisome (the enzymes listed in blue in the pathway to the left are thought to be at least partly peroxisomal). Patients with peroxisome biogenesis disorders (PBDs) have a variable deficiency in cholesterol biosynthesis

Mevalonate plays a role in epithelial cancers: it can inhibit EGFR

Cholesterol Biosynthesis: from acetyl-Coa to mevalonate

Epidermal Growth Factor as a Clinical Target in Cancer

A malignant tumour is the product of uncontrolled cell proliferation. Cell growth is controlled by a delicate balance between growth-promoting and growth-inhibiting factors. In normal tissue the production and activity of these factors results in differentiated cells growing in a controlled and regulated manner that maintains the normal integrity and functioning of the organ. The malignant cell has evaded this control; the natural balance is disturbed (via a variety of mechanisms) and unregulated, aberrant cell growth occurs. A key driver for growth is the epidermal growth factor (EGF) and the receptor for EGF (the EGFR) has been implicated in the development and progression of a number of human solid tumours including those of the lung, breast, prostate, colon, ovary, head and neck.

Energy housekeeping:Adenosine diphosphate (ADP) – Adenosine triphosphate (ATP)

Chemical Reaction

Add Enzymatic Catalysis

Add Gene Expression

Add Inhibition

Metabolic Pathway: Proline Biosynthesis

Proline as end product effects a negative feedback loop

Transcriptional Regulation

Methionine Biosynthesis in E. coli

Shortcut Representation

High-level Interaction representation

Levels of Resolution

SREBP Pathway

Signal Transduction

Important signalling pathways: Map-kinase (MapK) signalling pathway, or TGF- pathway

Transport

Phosphate Utilization in Yeast

Multiple Levels of Regulation

• Gene expression

• Protein posttranslational modification

• Protein activity

• Protein intracellular location

• Protein degradation

• Substrate transport

Graphical Representation – Gene Expression

Protein interaction domains

Protein Interaction Domains

    

     

 

   

      

          

 

     

   

 

     

 

     

      

 

     

     

 

      

       

      

      

    

      

     

       

      

    

     

   

 

      

   

     

 

    

 

    

     

      

     

       

      

     

     

     

       

     

     

      

         

         

       

     

                  

http://pawsonlab.mshri.on.ca/index.php?option=com_content&task=view&id=30&Itemid=63

Domain function

Active site / binding cleft

Protein-protein (domain-domain) interaction

Shape complementarity

A domain is a:

• Compact, semi-independent unit (Richardson, 1981).

• Stable unit of a protein structure that can fold autonomously (Wetlaufer, 1973).

• Recurring functional and evolutionary module (Bork, 1992).“Nature is a tinkerer and not an inventor” (Jacob, 1977).

• Smallest unit of function

Delineating domains is essential for:• Obtaining high resolution structures (x-ray but

particularly NMR – size of proteins)• Sequence analysis • Multiple sequence alignment methods• Prediction algorithms (SS, Class, secondary/tertiary

structure)• Fold recognition and threading• Elucidating the evolution, structure and function of

a protein family (e.g. ‘Rosetta Stone’ method)• Structural/functional genomics• Cross genome comparative analysis

Domain connectivity

linker

Pyruvate kinasePhosphotransferase

barrel regulatory domain

barrel catalytic substrate binding domain

nucleotide binding domain

1 continuous + 2 discontinuous domains

Structural domain organisation can be nasty…

Domain size•The size of individual structural domains varies widely

– from 36 residues in E-selectin to 692 residues in lipoxygenase-1 (Jones et al., 1998)

– the majority (90%) having less than 200 residues (Siddiqui and Barton, 1995)

– with an average of about 100 residues (Islam et al., 1995).

•Small domains (less than 40 residues) are often stabilised by metal ions or disulphide bonds.•Large domains (greater than 300 residues) are likely to consist of multiple hydrophobic cores (Garel, 1992).

Analysis of chain hydrophobicity in multidomain proteins

Analysis of chain hydrophobicity in multidomain proteins

Domain characteristicsDomains are genetically mobile units, and multidomain families are found in all three kingdoms (Archaea, Bacteria and Eukarya) underlining the finding that ‘Nature is a tinkerer and not an inventor’ (Jacob, 1977). The majority of genomic proteins, 75% in unicellular organisms and more than 80% in metazoa, are multidomain proteins created as a result of gene duplication events (Apic et al., 2001). Domains in multidomain structures are likely to have once existed as independent proteins, and many domains in eukaryotic multidomain proteins can be found as independent proteins in prokaryotes (Davidson et al., 1993).

Protein function evolution- Gene (domain) duplication -

Chymotrypsin

Active site

Pyruvate phosphate dikinase

• 3-domain protein

• Two domains catalyse 2-step reaction

A B C

• Third so-called ‘swivelling domain’ actively brings intermediate enzymatic product (B) over 45Å from one active site to the other

/

Pyruvate phosphate dikinase

• 3-domain protein

• Two domains catalyse 2-step reaction

A B C

• Third so-called ‘swivelling domain’ actively brings intermediate enzymatic product (B) over 45Å from one active site to the other

/

The DEATH Domain• Present in a variety of Eukaryotic proteins involved with cell death.• Six helices enclose a tightly packed hydrophobic core.• Some DEATH domains form homotypic and heterotypic dimers.

http

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Detecting Structural Domains• A structural domain may be detected as a compact,

globular substructure with more interactions within itself than with the rest of the structure (Janin and Wodak, 1983).

• Therefore, a structural domain can be determined by two shape characteristics: compactness and its extent of isolation (Tsai and Nussinov, 1997).

• Measures of local compactness in proteins have been used in many of the early methods of domain assignment (Rossmann et al., 1974; Crippen, 1978; Rose, 1979; Go, 1978) and in several of the more recent methods (Holm and Sander, 1994; Islam et al., 1995; Siddiqui and Barton, 1995; Zehfus, 1997; Taylor, 1999).

Detecting Structural Domains

•However, approaches encounter problems when faced with discontinuous or highly associated domains and many definitions will require manual interpretation.

•Consequently there are discrepancies between assignments made by domain databases (Hadley and Jones, 1999).

Detecting Domains using Sequence only

• Even more difficult than prediction from structure!

SnapDRAGON

Richard A. George

George R.A. and Heringa, J. (2002) J. Mol. Biol., 316, 839-851.

 

Integrating protein multiple sequence alignment, secondary and tertiary structure

prediction in order to predict structural domain boundaries in sequence data

Protein structure hierarchical levels

VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

PRIMARY STRUCTURE (amino acid sequence)

QUATERNARY STRUCTURE

SECONDARY STRUCTURE (helices, strands)

TERTIARY STRUCTURE (fold)

Protein structure hierarchical levels

VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

PRIMARY STRUCTURE (amino acid sequence)

QUATERNARY STRUCTURE

SECONDARY STRUCTURE (helices, strands)

TERTIARY STRUCTURE (fold)

Protein structure hierarchical levels

VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

PRIMARY STRUCTURE (amino acid sequence)

QUATERNARY STRUCTURE

SECONDARY STRUCTURE (helices, strands)

TERTIARY STRUCTURE (fold)

Protein structure hierarchical levels

VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

PRIMARY STRUCTURE (amino acid sequence)

QUATERNARY STRUCTURE

SECONDARY STRUCTURE (helices, strands)

TERTIARY STRUCTURE (fold)

SNAPDRAGONDomain boundary prediction protocol using sequence information

alone (Richard George)

1. Input: Multiple sequence alignment (MSA) and predicted secondary structure

2. Generate 100 DRAGON 3D models for the protein structure associated with the MSA

3. Assign domain boundaries to each of the 3D models (Taylor, 1999)

4. Sum proposed boundary positions within 100 models along the length of the sequence, and smooth boundaries using a weighted windowGeorge R.A. and Heringa J.(2002) SnapDRAGON - a method to delineate protein structural

domains from sequence data, J. Mol. Biol. 316, 839-851.

SnapDragonFolds generated by Dragon

Boundary recognition

(Taylor, 1999)Summed and Smoothed Boundaries

CCHHHCCEEE

Multiple alignment

Predicted secondary structure

SNAPDRAGONDomain boundary prediction protocol using sequence information

alone (Richard George)

1. Input: Multiple sequence alignment (MSA)

1. Sequence searches using PSI-BLAST (Altschul et al., 1997)

2. followed by sequence redundancy filtering using OBSTRUCT (Heringa et al.,1992)

3. and alignment by PRALINE (Heringa, 1999)

• and predicted secondary structure4. PREDATOR secondary structure prediction

programGeorge R.A. and Heringa J.(2002) SnapDRAGON - a method to delineate protein structural domains from sequence data, J. Mol. Biol. 316, 839-851.

Distance Regularisation Algorithm for Geometry OptimisatioN

(Aszodi & Taylor, 1994)

Domain prediction using DRAGON

•Fold proteins based on the requirement that (conserved) hydrophobic residues cluster together.

•First construct a random high dimensional C distance matrix.

•Distance geometry is used to find the 3D conformation corresponding to a prescribed target matrix of desired distances between residues.

SNAPDRAGONDomain boundary prediction protocol using sequence information

alone (Richard George)

2. Generate 100 DRAGON (Aszodi & Taylor, 1994) models for the protein structure associated with the MSA– DRAGON folds proteins based on the requirement that

(conserved) hydrophobic residues cluster together– (Predicted) secondary structures are used to further

estimate distances between residues (e.g. between the first and last residue in a -strand).

– It first constructs a random high dimensional C (and pseudo C) distance matrix

– Distance geometry is used to find the 3D conformation corresponding to a prescribed matrix of desired distances between residues (by gradual inertia projection and based on input MSA and predicted secondary structure)

DRAGON = Distance Regularisation Algorithm for Geometry OptimisatioN

•The C distance matrix is divided into smaller clusters.

•Separately, each cluster is embedded into a local centroid.

•The final predicted structure is generated from full embedding of the multiple centroids and their corresponding local structures.

3NN

NN

C distancematrix

Targetmatrix

N

CCHHHCCEEE

Multiple alignment

Predicted secondary structure100 randomised

initial matrices

100 predictions Input data

Lysozyme 4lzm

PDB

DRAGON

Methyltransferase 1sfe

DRAGON

PDB

Phosphatase 2hhm-A

PDB DRAGON

Taylor method (1999)DOMAIN-3D

3. Assign domain boundaries to each of the 3D models (Taylor, 1999)

• Easy and clever method• Uses a notion of spin glass theory (disordered magnetic

systems) to delineate domains in a protein 3D structure• Steps:

1. Take sequence with residue numbers (1..N)2. Look at neighbourhood of each residue (first shell)3. If (“average nghhood residue number” > res no) resno = resno+1

else resno = resno-1 4. If (convergence) then take regions with identical “residue

number” as domains and terminate

Taylor,WR. (1999) Protein structural domain identification. Protein Engineering 12 :203-216

Taylor method (1999)

41

5

6

89

56

78

repeat until convergence

if 41 < (5+6+56+78+89)/5

then Res 41 42 (up 1)

else Res 41 40 (down 1)

Taylor method (1999)

continuous

discontinuous

SNAPDRAGONDomain boundary prediction protocol using sequence information

alone (Richard George)

4. Sum proposed boundary positions within 100 models along the length of the sequence, and smooth boundaries using a weighted window (assign central position)

Window score = 1≤ i ≤ l Si × Wi

Where Wi = (p - |p-i|)/p2 and p = ½(n+1).

It follows that l Wi = 1George R.A. and Heringa J.(2002) SnapDRAGON - a method to delineate protein structural domains from sequence data, J. Mol. Biol. 316, 839-851.

i

Wi

SNAPDRAGONStatistical significance:• Convert peak scores to Z-scores using

z = (x-mean)/stdev • If z > 2 then assign domain boundary

Statistical significance using random models:• Test hydrophibic collapse given distribution of

hydrophobicity over sequence• Make 5 scrambled multiple alignments (MSAs) and predict

their secondary structure• Make 100 models for each MSA• Compile mean and stdev from the boundary distribution

over the 500 random models• If observed peak z > 2.0 stdev (from random models) then

assign domain boundary

SnapDRAGON prediction assessment

• Test set of 414 multiple alignments;183 single and 231 multiple domain proteins.

• Boundary predictions are compared to the region of the protein connecting two domains (maximally 10 residues from true boundary)

SnapDRAGON prediction assessment• Baseline method I:

• Divide sequence in equal parts based on number of domains predicted by SnapDRAGON

• Baseline method II: • Similar to Wheelan et al., based on domain length

partition density function (PDF)• PDF derived from 2750 non-redundant structures

(deposited at NCBI) • Given sequence, calculate probability of one-

domain, two-domain, .., protein• Highest probability taken and sequence split equally

as in baseline method I

Continuous set Discontinuous set Full set

SnapDRAGONCoverage 63.9 (± 43.0) 35.4 (± 25.0) 51.8 (± 39.1)

Success 46.8 (± 36.4) 44.4 (± 33.9) 45.8 (± 35.4)

Baseline 1Coverage 43.6 (± 45.3) 20.5 (± 27.1) 34.7 (± 40.8)

Success 34.3 (± 39.6) 22.2 (± 29.5) 29.6 (± 36.6)

Baseline 2Coverage 45.3 (± 46.9) 22.7 (± 27.3) 35.7 (± 41.3)

Success 37.1 (± 42.0) 23.1 (± 29.6) 31.2 (± 37.9)

Average prediction results per protein

Coverage is the % linkers predicted (TP/TP+FN)Success is the % of correct predictions made (TP/TP+FP)

Average prediction results per protein