v9 from protein complexes to networks and back

9. Lecture WS 2006/07

Softwarewerkzeuge 1

V9 From Protein Complexes to Networks and back

Protein interaction could be defined in a number of ways

(1) Proteins that form permanent supracomplexes = „protein machines“

(2) Proteins that bind each other transiently

(signal transduction, bioenergetics ... )

(3) Co-regulated expression of genes/proteins

(4) Proteins participating in the same metabolic pathways

(5) Proteins sharing substrates

(6) Proteins that are co-localized

Techniques: Experimental methods + computational methods.


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1 Protein-Protein Complexes

It has been realized for quite some time that cells don‘t work by random

diffusion of proteins,

but require a delicate structural organization into large protein complexes.

Which complexes do we know?


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RNA Polymerase II

RNA polymerase II is the

central enzyme of gene

expression and synthesizes all

messenger RNA in

eukaryotes.

Cramer et al., Science 288, 640 (2000)


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RNA processing: splicesome

Structure of a cellular editor that "cuts and pastes" the first draft of RNA straight

after it is formed from its DNA template. It has two distinct, unequal halves

surrounding a tunnel. The larger part appears to contain proteins and the short

segments of RNA, while the smaller half is made up of proteins alone. On one

side, the tunnel opens up into a cavity, which the researchers think functions as

a holding space for the fragile RNA waiting to be processed in the tunnel itself.

Profs. Ruth and Joseph Sperlinghttp://www.weizmann.ac.il/


Softwarewerkzeuge 5

Protein synthesis: ribosome

The ribosome is a complex

subcellular particle composed of

protein and RNA. It is the site of

protein synthesis,

http://www.millerandlevine.com/chapter/12/cryo-em.html

Model of a ribosome with a

newly manufactured protein

(multicolored beads) exiting

on the right.


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Signal recognition particle

40S small ribosomal subunit

(yellow) 60S large ribosomal

subunit (blue), P-site tRNA

(green), SRP (red).

Halic et al. Nature 427, 808 (2004)

Cotranslational translocation of proteins across or into membranes is a vital process in all kingdoms of life. It requires that the translating ribosome be targeted to the membrane by the signal recognition particle (SRP), an evolutionarily conserved ribonucleoprotein particle. SRP recognizes signal sequences of nascent protein chains emerging from the ribosome. Subsequent binding of SRP leads to a pause in peptide elongation and to the ribosome docking to the membrane-bound SRP receptor. SRP shows 3 main activities in the process of cotranslational targeting: first, it binds to signal sequences emerging from the translating ribosome; second, it pauses peptide elongation; and third, it promotes protein translocation by docking to the membrane-bound SRP receptor and transferring the ribosome nascent chain complex (RNC) to the protein-conducting channel.


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Nuclear Pore ComplexA three-dimensional image of the

nuclear pore complex (NPC),

revealed by electron microscopy.

A-B The NPC in yeast.

Figure A shows the NPC seen

from the cytoplasm while figure B

displays a side view.

C-D The NPC in vertebrate

(Xenopus).

http://www.nobel.se/medicine/educational/dna/a/transport/ncp_em1.htmlThree-Dimensional Architecture of the Isolated Yeast Nuclear Pore Complex: Functional and Evolutionary Implications, Qing Yang, Michael P. Rout and Christopher W. Akey. Molecular Cell, 1:223-234, 1998

NPC is a 50-100 MDa protein assembly that

regulates and controls trafficking of

macromolecules through the nuclear envelope.

http://www.nobel.se/medicine/educational/dna/a/transport/ncp_em1.html

http://www.nobel.se/medicine/educational/dna/a/transport/ncp_em1.html


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GroEL: a chaperone to assist misfolded proteins

Schematic Diagram of GroEL Functional States(a) Nonnative polypeptide substrate (wavy black line) binds to an open GroEL ring. (b) ATP binding to GroEL alters its conformation, weakens the binding of substrate, and permits the binding of GroES to the ATP-bound ring. (c) The substrate is released from its binding sites and trapped inside the cavity formed by GroES binding. (d) Following encapsulation, the substrate folds in the cavity and ATP is hydrolysed. (e) After hydrolysis in the upper, GroES-bound ring, ATP and a second nonnative polypeptide bind to the lower ring, discharging ligands from the upper ring and initiating new GroES binding to the lower ring (f) to form a new folding active complex on the lower ring and complete the cycle.

http://people.cryst.bbk.ac.uk/~ubcg16z/chaperone.html

Ransom et al., Cell 107, 869 (2001)


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proteasome

The proteasome is the central

enzyme of non-lysosomal protein

degradation. It is involved in the

degradation of misfolded proteins

as well as in the degradation and

processing of short lived regulatory

proteins.The 20S Proteasome

degrades completely unfoleded

proteins into peptides with a

narrow length distribution of 7 to

13 amino acids.

http://www.biochem.mpg.de/xray/projects/hubome/images/rpr.gifLöwe, J., Stock, D., Jap, B., Zwickl, P., Baumeister, W. and Huber, R. (1995). Crystal structure of the 20S proteasome from the archaeon T. acidophilum at 3.4 Å resolution. Science 268, 533-539.

http://www.biochem.mpg.de/xray/projects/hubome/images/rpr.gif

http://www.biochem.mpg.de/xray/projects/hubome/images/rpr.gif


Softwarewerkzeuge 10

Apoptosome

(A) Top view of the apoptosome along the 7-fold

symmetry axis.

(B) Details of the spoke.

(C) A side view of the apoptosome reveals the

unusual axial ratio of this particle. The scale bar is

100 Å.

(D) An oblique bottom view shows the puckered

shape of the particle. The arms are bent at an

elbow (see asterisk) located proximal to the hub.

Acehan et al. Mol. Cell 9, 423 (2002)

Apoptosis is the dominant form of programmed cell death during embryonic development and normal tissue turnover. In addition, apoptosis is upregulated in diseases such as AIDS, and neurodegenerative disorders, while it is downregulated in certain cancers. In apoptosis, death signals are transduced by biochemical pathways to activate caspases, a group of proteases that utilize cysteine at their active sites to cleave specific proteins at aspartate residues. The proteolysis of these critical proteins then initiates cellular events that include chromatin degradation into nucleosomes and organelle destruction. These steps prepare apoptotic cells for phagocytosis and result in the efficient recycling of biochemical resources.In many cases, apoptotic signals are transmitted to mitochondria, which act as integrators of cell death because both effector and regulatory molecules converge at this organelle. Apoptosis mediated by mitochondria requires the release of cytochrome c into the cytosol through a process that may involve the formation of specific pores or rupture of the outer membrane. Cytochrome c binds to Apaf-1 and in the presence of dATP/ATP promotes assembly of the apoptosome. This large protein complex then binds and activates procaspase-9.



Experiment

Start from 232 purified complexes from Tandem Affinity Purification (TAP) strategy.

Select 102 that gave samples most promising for EM from analysis of gels and

protein concentrations.

Take EM images.

Theory

Make list of components.

Assign known structures of individual proteins.

Assign templates of complexes-If complex structure available for this pair- if complex structure available for homologous protein- if complex structure available for structurally similar protein (SCOP)

2 Aim: generate structures of protein complexes

Bettina Böttcher (EM)Rob Russell (Bioinformatics)



How transferable are interactions?interaction similariy (iRMSD) vs. %

sequence identity for all the available

pairs of interacting domains with

known 3D structure.

Curve shows 80% percentile (i.e. 80%

of the data lies below the curve), and

points below the line (iRMSD = 10 Å)

are similar in interaction. Aloy et al. Science, 303, 2026 (2004)



Bioinformatics Strategy

Illustration of the methods and concepts

used. How predictions are made within

complexes (circles) and between them

(cross-talk). Bottom right shows two

binary interactions combined into a three-

component model

Aloy et al. Science, 303, 2026 (2004)



Successful models of yeast complexes

(A) Exosome model on PNPase fit into

EM map.

(B) RNA polymerase II with RPB4

(green)/RPB7 (red) built on

Methanococcus jannaschii equivalents,

and SPT5/pol II (cyan) built with IF5A.

(C and D) Views of CCT (gold) and

phosphoducin 2/VID27 (red) fit into EM

map.

(E) Micrograph of POP complex, with

particle types highlighted.

(F) Ski complex built by combination of

two complexes.




Future?

Structural genomics (X-ray) may soon generate enough templates of individal

folds.

Structural genomics may be expanded to protein complexes.

Interactions between proteins of the same fold tend to be similar when the

sequence identity is above approximately 30% (Aloy et al.).

Hybrid modelling of X-ray/EM will not be able to answer all questions- problem of induced fit- transient complexes cannot be addressed by these techniques

Essential to combine large variety of hybrid + complementary methods

Russell et al. Curr. Opin. Struct. Biol. 14, 313 (2004)



3 Bioinformatic identification of interface patches

Statistical analysis of protein-protein interfaces in crystal structures of

protein-protein complexes: residues at interfaces have significantly different

amino acid composition that the rest of the protein.

predict protein-protein interaction sites from local sequence information ?

Conservation at protein-protein interfaces: interface regions are more conserved

than other regions on the protein surface

identify conserved regions on protein surface e.g. from solvent accessibility

Patterns in multiple sequence alignments: Interacting residues on two binding partners

often show correlated mutations (among different organisms) if being mutated

identify correlated mutations

Structural patterns: surface patterns of protein-protein interfaces: interface often

formed by hydrophobic patch surrounded by ring of polar or charged residues.

identify suitable patches on surface if 3D structure is known



4 NOXClass: Distinguish Permanent / Transient Complexes

Aim:

(1) distinguish different types of biological interactions (X-ray structures of protein-

protein complexes).

(2) develop automatic classification scheme.

Zhu, Domingues, Sommer, Lengauer, BMC Bioinformatics 7, 27 (2006),



Dataset



Interface properties considered




Distribution of interface area

Crystal packing contacts have

very small interfaces.

Obligate interfaces are on average

larger than non-obligate interfaces.

Interface area =

abba

SASASASASASA 2

1




Dataset

ba

SASASASA ,min

Area InterfaceRatio Area Interface

The distributions of obligate and non-obligate interfaces are quite similar, but

very different from crystal packing contacts.




Hydrophobic residues (FLIV) contribute twice as much to obligate interfaces as

to crystal packing contacts.

Aromatic residues (FWY) tend to be more abundant in biological interfaces.Zhu, Domingues, Sommer, Lengauer, BMC Bioinformatics 7, 27 (2006),



Good Performance




5 Correlated mutations at interface

Pazos, Helmer-Citterich, Ausiello, Valencia J Mol Biol 271, 511 (1997):

correlation information is sufficient for selecting the correct structural arrangement of

known heterodimers and protein domains because the correlated pairs between the

monomers tend to accumulate at the contact interface.

Use same idea to identify interacting protein pairs.



Correlated mutations at interface

Correlated mutations evaluate the similarity in variation patterns between positions in

a multiple sequence alignment.

Similarity of those variation patterns is thought to be related to compensatory

mutations.

Calculate for each positions i and j in the sequence a rank correlation coefficient (rij):

Pazos, Valencia, Proteins 47, 219 (2002)

lkjjkl

lkiikl

lkjjkliikl

ij

SSSS

SSSS

r

,

2

,

2

,

where the summations run over every possible pair of proteins k and l in the multiple

sequence alignment.

Sikl is the ranked similarity between residue i in protein k and residue i in protein l.

Sjkl is the same for residue j.

Si and Sj are the means of Sikl and Sjkl.



i2h method

Schematic representation of the i2h method.

A: Family alignments are collected for two

different proteins, 1 and 2, including

corresponding sequences from different

species (a, b, c, ).

B: A virtual alignment is constructed,

concatenating the sequences of the probable

orthologous sequences of the two proteins.

Correlated mutations are calculated.

C: The distributions of the correlation values

are recorded. We used 10 correlation levels.

The corresponding distributions are

represented for the pairs of residues internal

to the two proteins (P11 and P22) and for the

pairs composed of one residue from each of

the two proteins (P12). Pazos, Valencia, Proteins 47, 219 (2002)



Predictions from correlated mutationsResults obtained by i2h in a set of 14 two domain

proteins of known structure = proteins with two

interacting domains. Treat the 2 domains as different

proteins.

A: Interaction index for the 133 pairs with 11 or more

sequences in common. The true positive hits are

highlighted with filled squares.

B: Representation of i2h results, reminiscent of those

obtained in the experimental yeast two-hybrid system.

The diameter of the black circles is proportional to the

interaction index; true pairs are highlighted with gray

squares. Empty spaces correspond to those cases in

which the i2h system could not be applied, because they

contained <11 sequences from different species in

common for the two domains.

In most cases, i2h scored the correct pair of protein

domains above all other possible interactions.Pazos, Valencia, Proteins 47, 219 (2002)



6 Coevolutionary Analysis

Idea: if co-evolution is relevant, a ligand-receptor pair should occupy related

positions in phylogenetic trees.

Goh & Cohen, 2002 showed that within correlated phylogenetic trees,

the protein pairs that bind have a higher correlation between their phylogenetic

distance matrices than other homologs drawn drom the ligand and receptor

families that do not bind.

Other Idea: analyze occurrence of proteins that can functionally substitute for

another in various organisms.

Detect analogous enzymes in thiamin biosynthesis



Detect analogous enzymes in thiamin biosynthesis Gene names are applied according to the first gene

described from a group of orthologs.

Solid black arrows represent known or proposed

reaction steps and dashed black arrows indicate

unknown reactions. In addition, significant

anticorrelations in the occurrence of genes across

species (red arrows), and relevant in silico predicted

protein-protein interactions (blue dashed arrows) are

illustrated.

Distinct precursors have been proposed for different

species (indicated in gray). Genes with orthologous

sequences in eukaryotes and prokaryotes are in

green; genes assumed to be prokaryote-specific are

black. Interestingly, significant 'one-to-one'

anticorrelations usually involve a prokaryote-specific

and a 'ubiquitous' gene.

Abbreviations: AIR, 5-aminoimidazole ribonucleotide;

Cys, cysteine; Gly, glycine; His, histidine; HMP, 2-

methyl-4-amino-5-hydroxymethylpyrimidine; THZ, 4-

methyl-5- -hydroxyethylthiazole; Tyr, tyrosine; Vit. B6,

Vitamin B6.

Morett et al. Nature Biotech 21, 790 (2003)



THI-PP biosynthesis pathway: analogous genesNegatively correlating gene

occurrences are highlighted using the

same colors. Species having at least

two genes with a role unique to THI-

PP biosynthesis are predicted to

possess the functional pathway. The

column 'STRING score' shows the

most significant interaction for each

gene, predicted using the STRING

server. Predicted interaction partners

are listed in the column 'Interact. with'.

COG id: „id in groups of orthologous

proteins server“

(a) Essential THI-PP biosynthesis

enzymes, which are unique to the

pathway.

(b) Essential THI-PP biosynthesis

enzymes, which have been implicated

in more than one biological process.

The thiO gene, suggested to play a

role in the pathway, was also added to

that list.

(c) Proteins predicted in silico to be

involved in the pathway.


4 analogies detected:thiE can be replaced by MTH861thiL by THI80thiG by THI4thiC by tenA



Interpretation

Proteins that functionally substitute eachother

have anti-correlated distribution pattern across organisms.

allows discovery of non-obvious components of pathways

and function prediction of uncharacterized proteins

and prediction of novel interactions.




7 Construct complete network of gene association

Network reconstructions have largely focused on physical protein interaction and so

represent only a subset of biologically important relations.

Aim: construct a more accurate and extensive gene network by considering functional,

rather than physical, associations, realizing that each experiment, whether genetic,

biochemical, or computational, adds evidence linking pairs of genes, with associated error

rates and degree of coverage.

In this framework, gene-gene linkages are probabilistic summaries representing functional

coupling between genes. Only some of the links represent direct protein-protein interactions;

the rest are associations not mediated by physical contact, such as regulatory, genetic, or

metabolic coupling, that, nonetheless, represent functional constraints satisfied by the cell

during the course of the experiments.

Working with probabilistic functional linkages allows many diverse classes of

experiments to be integrated into a single coherent network which enables the linkages

themselves to be more reliably

Lee, ..., Marcotte, Science 306, 1555 (2004)



Method for integrating functional genomics data

Benchmark functional genomics data sets for their

relative accuracies.

Several raw data sets already have intrinsic

scoring schemes, indicated in parentheses (e.g.,

CC, correlation coefficients; P, probabilities, and

MI, mutual information scores).

These data are rescored with LLS, then integrated

into an initial network (IntNet).

Additional linkages from the genes’ network

contexts (ContextNet) are then integrated to create

the final network (FinalNet), with È34,000 linkages

between 4681 genes (ConfidentNet) scoring

higher than the gold standard (small-scale assays

of protein interactions).

Hierarchical clustering of ConfidentNet defined

627 modules of functionally linked genes spanning

3285 genes (‘‘ModularNet’’), approximating the set

of cellular systems in yeast.




Scoring scheme for linkages

Unified scoring scheme for linkages is based on a Bayesian statistics approach.

Each experiment is evaluated for its ability to reconstruct known gene pathways

and systems by measuring the likelihood that pairs of genes are functionally

linked conditioned on the evidence, calculated as a log likelihood score:

P(L|E) and P(L|E) : frequencies of linkages (L) observed in the given

experiment (E) between annotated genes operating in the same pathway and in

different pathways

P(L) and P(L): the prior expectations (i.e., the total frequency of linkages

between all annotated yeast genes operating in the same pathway and operating

in different pathways).

Scores > 0 indicate that the experiment tends to link genes in the same pathway,

with higher scores indicating more confident linkages.




Benchmarks

As scoring benchmarks, the method was tested against two primary annotation

references:

(1) the Kyoto-based KEGG pathway database and

(2) the experimentally observed yeast protein subcellular locations determined by

genome-wide green fluorescent protein (GFP)–tagging and microscopy.

KEGG scores were used for integrating linkages, with the other benchmark

withheld as an independent test of linkage accuracy.

Cross-validated benchmarks and benchmarks based on the Gene Ontology (GO)

and COG gene annotations provided comparable results.




Functional inference from interaction networks

Benchmarked accuracy and extent of functional genomics data sets and the integrated networks. A critical point is the comparable performance of the networks on distinct benchmarks, which assess the tendencies for linked genes to share (A) KEGG pathway annotations or (B) protein subcellular locations.x axis: percentage of protein-encoding yeast genes provided with linkages by the plotted data;y axis: relative accuracy, measured as the of the linked genes’ annotations on that benchmark. The gold standards of accuracy (red star) for calibrating the benchmarks are smallscale protein-protein interaction data from DIP. Colored markers indicate experimental linkages; gray markers, computational. The initial integrated network (lower black line), trained using only the KEGG benchmark, has measurably higher accuracy than any individual data set on the subcellular localization benchmark; adding context-inferred linkages in the final network (upper black line) further improves the size and accuracy of the network.




Features of integrated networks

At an intermediate degree of clustering that maximizes cluster size and functional coherence, 564 (of

627) modules are shown connected by the 950 strongest intermodule linkages.

Module colors and shapes indicate associated functions, as defined by Munich Information Center for

Protein Sequencing (MIPS), with sizes proportional to the number of genes, and connections inversely

proportional to the fraction of genes linking the clusters.




Features of integrated networks

Portions of the final, confident gene network are shown for (C) DNA damage

response and/or repair, where modularity gives rise to gene clusters, indicated by

similar colors, and (D) chromatin remodeling, with several uncharacterized

genes (red labels).

Networks are visualized with Large Graph Layout (LGL).




zusätzliche Folien (nicht benutzt)



1. Methods for the structural characterization of macromolecular assemblies


(a) Electron diffraction map and 3D X-ray protein structure. X-ray provides atomic-resolution structures. (b) 3D protein structure and plot showing chemical shifts determined by NMR. NMR spectroscopy extracts distances between atoms by measuring transitions between different nuclear spin states within a magnetic field. These distances are then used as restraints to build 3D structures. NMR spectroscopy also provides atomic-resolution structures, but is generally limited to proteins of about 300 residues. It plays an increasingly important role in studying interaction interfaces between structures determined independently. (c) EM micrograph and 3D reconstruction of a virus capsid. EM is based on the analysis of images of stained particles. Different views and conformations of the complexes are trapped and thus thousands of images have to be averaged to reconstruct the three-dimensional structure. Classical implementations were limited to a resolution of 20 Å. More recently, single-particle cryo techniques, whereby samples are fast frozen before study, have reached resolutions as high as approximately 6 Å. EM provides information about the overall shape and symmetry of macromolecules. (d) Slice images and rendered surface of a ribosome-decorated portion of endoplasmic reticulum. In electron tomography, the specimen studied is progressively tilted upon an axis perpendicular to the electron beam. A set of projection images is then recorded and used to build a 3D model. This technique can tackle large organelles or even complete cells without perturbing their physiological environment. It provides shape information at resolutions of approximately 30 Å. (e) Yeast two-hybrid array screen and small network of interacting proteins. Interaction discovery comprises many different methods whose objective is to determine spatial proximity between proteins. These include techniques such as the two-hybrid system, affinity purification, FRET, chemical cross-linking, footprinting and protein arrays. These methods provide very limited structural information and no molecular details. Their strength is that they often give a quasi-comprehensive list of protein interactions and the networks they form.



Putative structure through modeling and low-resolution EM


(a) Exosome subunits. The top of the panel shows the domain organization of two subunits

present in the complex, but lacking any detectable similarity to known 3D structures. The

model for the nine other subunits (bottom) was constructed by predicting binary interactions

using InterPReTS and building models based on a homologous complex structure using

comparative modeling.

(b) EM density map (green mesh) with the best fit of the model shown as a gray surface

and the predicted locations of the subunits labeled. The question marks indicate those

subunits for which no structures could be modeled.



5 Pairwise docking: Katchalski-Kazir algorithm; FTDOCK

Gabb et al. J. Mol. Biol. (1997)

Discretize proteins A and B on a grid.

Every node is assigned a value

Use FFT to compute correlation efficiently.

Output: solutions with best surface

complementarity.



2. Yeast 2-Hybrid Screen

Data on protein-protein

interactions from

Yeast 2-Hybrid Screen.

One role of bioinformatics is to

sort the data.



Protein cluster in yeast

Schwikowski, Uetz, Fields, Nature Biotech. 18, 1257 (2001)

Cluster-algorithm

generates one large

cluster for proteins

interacting with each

other based on

binding data of

yeast proteins.



Annotation of function


After functional annotation:

connect clusters of

interacting proteins.



Annotation of localization




3. Systematic identication of large protein complexesYeast 2-Hybrid-method can only identify binary complexes.

Cellzome company: attach additional protein P to particular protein Pi ,

P binds to matrix of purification column.

yields Pi and proteins Pk bound to Pi .

Gavin et al. Nature 415, 141 (2002)

Identify proteins

by mass spectro-

metry (MALDI-

TOF).



Analyis of protein complexes in yeast (S. cerevisae)


Identify proteins by

scanning yeast protein

database for protein

composed of fragments

of suitable mass.

Here, the identified

proteins are listed

according to their

localization (a).

(b) lists the number of

proteins per complex.



Validation of methodology


Check of the method: can the same

complex be obtained for different

choice of attachment point

(tag protein attached to different

coponents of complex)? Yes (see gel).

Method allows to identify components

of complex, not the binding interfaces.

Better for identification of interfaces:

Yeast 2-hybrid screen (binary interactions).

3D models of complexes are important

to develop inhibitors.

- theoretical methods (docking) - electron tomography



Potential errors in biochemical interaction discovery


(a) Indirect interactions between

cyclin-dependent kinase regulatory

subunit (CKS) and cyclin A detected

by the Y2H system.

Several interactions between CKS

domains and cyclins were reported in

genome-scale two-hybrid studies.

However, analysis of 3D structures

suggests that the endogenous cyclin-

dependent kinase 2 (CDK2) probably

mediates the interaction, as

combining the CDK2–CKS and

CDK2–cyclin A structures places the

CKS and cyclin domains 18 Å apart.

(b) An example of an interaction that is not detected by any screen, possibly because molecular labels (e.g. affinity purification tags, or two-hybrid DNA binding or activation domains) are interfering with the interaction. The X-ray structure of the actin–profilin complex reveals that the actin C terminus (C-t) lies at the interaction interface (the other N and C termini are also labeled).



Cross talk between complexes

(Top) Triangles show

components with at least one

modelable structure and

interaction; squares, structure

only; circles, others.

Lines show predicted

interactions: thick lines imply a

conserved interaction interface;

red, those supported by

experiment.

(Bottom) Expanded view of

cross-talk between transcription

complexes built on by a

combination of two complexes.




3. In silico studies to predict protein protein contactsField of studying protein interactions is split into two areas:

(1) on the macro level: map networks of protein interactions

(2) on the micro level: understand mechanisms of interaction

to predict interaction sites

Growth of genome data stimulated a lot of research in area (1).

Fewer studies have addressed area (2).

Constructing detailed models of the protein-protein interfaces is important

for comprehensive understanding of molecular processes, for drug design and

for prediction the arrangement into macromolecular complexes.

Also: understanding (2) should facilitate (1).

Therefore, this lecture focusses on linking area (2) to area (1).



Analysis of interfaces

1812 non-redundant protein

complexes from PDB

(less than 25% identity).

Results don‘t change

significantly if NMR structures,

theoretical models, or

structures at lower resolution

(altogether 50%) are excluded.

Most interesting are the results

for transiently formed

complexes.

Ofran, Rost, J. Mol. Biol. 325, 377 (2003)

permanent

transient



Amino acid composition of interface types

The frequencies of all residues found in SWISS-PROT were used as background

when the frequency of an amino acid is similar to its frequency in SWISS-PROT, the

height of the bar is close to zero. Over-representation results in a positive bar, and

under-representation results in a negative bar. Ofran, Rost, J. Mol. Biol. 325, 377 (2003)



Pairing frequencies at interfacesred square: interaction occurs more

frequently than expected;

blue square: it occurs less frequently than

expected.

(A) Intra-domain: hydrophobic core is clear

(B) domain–domain,

(C) obligatory homo-oligomers,

(D) transient homo-oligomers,

(E) obligatory hetero-oligomers, and

(F) transient hetero-oligomers.

The amino acid residues are ordered

according to hydrophobicity, with isoleucine

as the most hydrophobic and arginine as the

least hydrophobic.

propensities have been successfully used

to score protein-protein docking runs. Ofran, Rost, J. Mol. Biol. 325, 377 (2003)



Predicted interactions for E. coli

Number of predicted interactions for E. coli.

The bars represent the number of

predicted interactions obtained from the

67,238 calculated pairs (having at least 11

homologous sequences of common

species for the two proteins in each pair),

depending on the interaction index cutoff

established as a limit to consider

interaction.

Pazos, Valencia, Proteins 47, 219 (2002)

Among the high scoring pairs are many cases of known interacting proteins.

v9 from protein complexes to networks and back

Documents

proteinprotein complexesit

protein machines2 proteins

protein translocation

large protein complexes

site of protein synthesis

ribosome docking

translating ribosome

ribosomethe ribosome