Download file3

Protein-protein interactions
Ia. A combined algorithm for genome-wide
prediction of protein function.
Edward M. Marcotte, Matteo Pellegrini, Michael
J. Thompson, Todd O. Yeates, David
Eisenberg(1999) Nature 402,83-86.
• Protein function in the post-genomic era.
David Eisenberg, Edward M. Marcotte, Ioannis
Xenarios & Todd O. Yeates(2000) Nature 405,
823-826
FUNCTIONAL RELATIONSHIPS
AMONG PROTEINS:
• GENOME-WIDE PREDICTION
(FUNCTIONAL GENOMICS)
• Does not rely on DIRECT SEQUENCE
HOMOLOGY
• 3 independent predictions methods &
available experimental data.
STRATEGIES USED TO “FUNCTIONALLY
LINK” PROTEINS:
6217 yeast proteins
• Correlated Evolution: Related Phylogenetic Profiles
(pattern of presence or absence of a particular protein
across a set of organisms whose genomes have been
sequenced): proteins, which operate together in a common
pathway or complex, are inherited together.
• Correlated mRNA Expression Patterns: Correlated
mRNA Expression Patterns under different growth
conditions
• Correlated Patterns Of Domain Fusion: Link 2 proteins
whose homologs are fused into a single gene (Rosetta
stone sequences) in another organism.
STRATEGIES USED TO “FUNCTIONALLY LINK”
PROTEINS:(continued)
• Gene Neighbour Method: if in several genomes, the
genes that encode 2 proteins are neighbors on the
chromosome, the proteins tend to be functionally linked
• Experimental Evidence: Mass spectrometry,
Coimmunoprecipitaion, Yeast 2-hybrid data (DIP, MIPS
yeast genome db)
• Metabolic pathway neighbours: Proteins, which
participate in same metabolic pathway, common structural
complex or biological process or closely related
physiological function: BLAST homology searches and
pairwise links were defined between yeast proteins whose
E.Coli homologs catalyse sequential reactions in a
metabolic pathway (EcoCyc db)
RESULTS:
• Phylogenetic profiles: 20,749 links
• mRNA expression patterns: 26,013 links
• Domain fusion method: 45,502 links
• 93,750 pairwise functional links among 76% (4,701)
of yeast proteins
• 4130: “HIGHEST CONFIDENCE” links
(experimental proof, valid by 2 of 3 prediction
methods)
• 19,251: “HIGH CONFIDENCE”links:
(predicted by phylogenetic profiles)
• Remainder predicted by domain fusion or correlated
mRNA expression patterns
VALIDATION:
• Excellent reliability if 2 or more prediction methods agreed on
a link.
• These methods link many proteins that are already known to
function together on the basis of experiments.
(Ribosomal proteins, proteins from flagellar motor apparatus and
metabolic pathways)
• “Keyword recovery”: Prediction could be compared to the
actual annotation: compare keyword annotation on SwissPDB, for
both members of each pair of proteins, linked by one of the
methods-possible when the members have known function.
“Keyword recovery”: if keywords match.
Average signal to noise ratio for “Keyword recovery”:
• Phylogenetic profiles: 5
• mRNA expression patterns: 2
• When 2 prediction methods gave same linkage: 8
• Direct experimental data: 8
OUTCOME:
• Functional links between proteins of unknown function:
• General function assigned to more than half of 2557 previously
uncharacterized yeast proteins: 15% from high and highest
confidence links, 62% using all links.
• Functional Links Between Non-Homologous Proteins: beyond
traditional “sequence matching”: Sup35, MSH6
• Discovery of potential interactions within and across cellular
processes and compartments.
• Connections represent a “gold mine” for experimentally testing
specific hypotheses about gene function.
• Viewing protein-protein interactions globally as a network and not
as binary data sets, increases the confidence levels for individual
interactions: inspection of interaction web at different steps
identifies “unexpected” links between previously unconnected
cellular processes.
• Ib. A network of protein-protein interactions in
yeast.
Schwikowski B, Uetz P, Fields S. (2000). Nat
Biotechnol. 18, 1257-61
DATA SOURCE:
• MIPS site
• YPD
• DIPS
Yeast-2-hybrid studies
Biochemical experimental data
Prediction of function:
• Annotated functions of all neighbors of P are
ordered in a list, from the most frequent to the
least frequent.
• Functions that occur the same number of times are
ordered arbitrarily.
• Everything after the third entry in the list is
discarded, and the remaining three or fewer
functions are declared as predictions for the
function of P.
• Evaluation of the quality of the links: For
unknown protein, test predicted function
RESULTS:
• Analyzed 2,709 published interactions involving 2,039 yeast
proteins
• Single large network containing 2,358 links among 1,548
individual proteins.Other networks had few proteins.
• 65% of the interactions in the complete set of networks occur
among proteins with at least one common functional
assignment.
• 78% of the 1,432 interactions between proteins of known
localization, the proteins share one or more compartments.
• Correctly predicted a functional category for 72% of 1393
characterised proteins, with at least one partner of known
function.
• Cross-talk between and within functional groups/subcellular
compartments.
• Local function vs Contextual/cellular function (extended web
of interacting molecules)
• Predicted functions of 364 uncharacterised proteins.
Reliability of the generated networks:
• 1,393 of the 2,039 proteins were annotated with
some function and had at least one neighbor
annotated with a function.
• In 1,005 of these 1,393 cases (72.1%), at least one
annotated function was predicted correctly by the
above method.
• Performed the same prediction algorithm 100
times on the basis of randomly generated
interactions.
• Only 12.2% of the predictions yielded a prediction
that agreed with the known annotation.
PROBLEMS…
• Interactions of membrane proteins
underrepresented: Y2H data
• Y2H data: lots of false positives.
• Only 15% agreement between this
interaction data and Marcotte’s “high
quality” prediction data.
• Uncertainities remain that WILL require
additional experimentation.
CHALLENGES:
• Protein complexes are not static: change
with metabolic state of cell, external stimuli
etc.
• Protein chip technology: used to study
transient interactions: amenable to variety
of assays like nucleotide-binding, enzymatic
activity etc.
II. Mapping protein family interactions:
intramolecular and intermolecular protein
family interaction repertoires in the PDB and
yeast.
Park J, Lappe M, Teichmann SA. (2001). J Mol
Biol. 307, 929-38.
• Protein DOMAIN interactions:
interactions between whole structural families of
evolutionarily related domains as opposed to
interactions between individual proteins.
• Types of domain interactions:
• 1)
Domain-domain(intra-chain) interactions
in multi-domain polypeptide chains
• 2)
Inter-chain protein interactions in multisubunit protein complexes.
3)
In transient complexes between proteins,
which can also exist independently
METHODS:
• Protein superfamilies from SCOP db
• Interactions between families in the PDB: (domains of known
3D structure)
coordinates of each domain were parsed to check whether there are
5 or more contacts with 5A to another domain
• Interactions between families in the yeast genome: by
homology:
-Protein structures assigned to the yeast proteins using the domains
from SCOP as queries in PSI-BLAST.
-Yeast sequences also compared to the PDB-ISL with FASTA
• Assumption: Within polypeptide chains, structural domains
interact if there are less than 30 amino acids separating them.
• If one family F has 2 domains, a and b, and each of these interacts
with a domain from a different family, then the number of
interaction families for F will be 2.
RESULTS:
• 1st attempt at classifying interactions between all the known
structural protein domains according to their families.
• Could classify 8151 interactions between individual domains in the
PDB and the yeast in terms of 664 types of interactions between
pairs of protein families.
• Scale free network: Most protein families only interact with 1 or 2
other families.
A few families are extremely versatile in their interactions and are
connected to many families (Hubs in the graph)-functional reasons.
Eg: -Immunoglobulins, P-loop nucleotide triphosphate hydrolases
• In 45% of all families in the PDB, domains interact with other
domains from the same family: internal duplication and domain
oligomerisation is favourable.
• Pairs of families that interact both within and between polypeptide
chains belong mostly to 2 types of domains: enzyme domains and
domains from the same family.
PROBLEMS:
• Multi-domain proteins: cannot resolve exactly
which domains are interacting: not used
• Members of 2 families can sometimes interact in
different ways, using different types of interface
(different modes of oligomerisation of nucleoside
diphosphate kinases)
• Does not take account of symmetric
homooligomers, of which only one monomer is in
the PDB entry and hence the number of
homomultimeric family interactions may be
underestimated.
FUTURE:
• 51 new interactions between superfamilies:
potential targets for structure elucidation and
experimental investigation of these interacting
polypeptides that do not have analogs in the PDB.
• For interactions in which one partner does not
have a structural assignment, possible structures
can be picked up from the set of known family
interactions
• Database of domain-domain interfaces