The document discusses several techniques for automated protein classification including ProtoMap and PRODISTIN. ProtoMap clusters proteins based on sequence similarity scores, while PRODISTIN clusters based on protein-protein interaction data. Both techniques have limitations as biological networks, such as protein and domain interaction networks, have been found to have scale-free topologies. Any improved protein classification algorithm needs to account for this scale-free network structure.

1. SURYABHAN
SINGH RAWAT

2. Protein Classification
A comparison of function
inference techniques

3. Why do we need automated
classification?
 Sequencing a genome is only the first
step.
 Between 35-50% of the proteins in
sequenced genomes have no assigned
functionality.
 Direct observation of function is costly,
time consuming, and difficult.

4. Protein Domains
The tertiary structure of many proteins is built from
several domains.
Often each domain has a separate function to
perform for the protein, such as:
•binding a small ligand (e.g., a peptide in the
molecule shown here)
•spanning the plasma membrane (
transmembrane proteins)
•containing the catalytic site (enzymes)
•DNA-binding (in transcription factors)
•providing a surface to bind specifically to another
protein
In some (but not all) cases, each domain in a
protein is encoded by a separate exon in the gene
encoding that protein.

5. Inference through sequence
similarity
ProtoMap: Automatic Classification
of Protein Sequences, a Hierarchy
of Protein Families, and Local
Maps of the Protein Space (1999)

6. Final Goal

7. Observations
 Sometimes you don’t know where the
domains are.
 It is generally accepted that two
sequences with over 30% identity are
likely to have the same fold.
 Homologous proteins have similar
functions.
 Homology is a transitive relationship.

8. Departures
 Authors do not attempt to define protein
domains or motifs.
 Not dependant on predefined groups or
classifications.
 Chart the space of all proteins in
SWISSPROT, as opposed to individual
families
 Produce global organization of sequences.

9. Algorithm Overview
 We construct a weighted graph where
the nodes are protein sequences and
the edges are similarity scores.
 Cluster the network considering only
those edges above some threshold.
 Decrease similarity threshold and
repeat.

10. Measuring Sequence Similarity
 Expectation value used. This the
normalized probability of the similarity
occurring at random.
 Lower value implies logarithmically
stronger similarity.
λS − ln K
S'=
ln 2
E = N /2 S'

11. Blosum62 Scoring Matrix

12. Finding Homologies
 Very difficult to distinguish a clear
threshold between homology and
chance similarity.
 Authors chose e = .1, .1, and .001 for
SW, FASTA, and BLAST, respectively.
 Spent a lot of time empirically
determining these thresholds.

13. Clustering
Clustering is done
iteratively.
Start with a threshold
of E < 10-100
Cluster and increase
threshold by a factor
of 105
Sublinear threshold
prevents the collapse
of sequence space

14. ProtoMap: Results
 Produces well-defined groups which
correlate strongly to protein families in
PROSITE and Pfam.

15. Results:
Immunoglobin Superfamily

16. ProtoMap: Limitations
 Analysis performs poorly by families
dominated by short/local domains (PH,
EGF, ER_TARGET, C2, SH2, SH3, ect…)
 High scoring, low complexity segments can
lead to nonhomogeneous clusters.
 “Hard” clustering vs. “Soft” clustering
 Has difficulty classifying multidomain
proteins.

17. ProtoMap: Future Directions
 3D structure/fold
 Biological function
 Domain content
 Cellular location
 Tissue specificity
 Source organism
 Metabolic pathways

18. Inference through protein
interaction networks
Functional Classification of
Proteins for the Prediction of
Cellular Function from a Protein-
Protein Interaction Network
(2003)

19. PRODISTIN
• Very similar to ProtoMap,
only the data used to
produce the graph is a list
of binary protein-protein
interactions instead of
sequence similarity scores
• Sequence similarity not a
dominating factor in
PRODISTIN clusters

22. PRODISTIN Results

23. Problems with PRODISTIN
• Paucity of
protein-protein
interaction data
(average # of
connections =
2.6)
• Either very
robust or very
indiscriminant

24. Problems: Multidomain and
Nonlocal Proteins
• protein kinases
• hydrolases
• ubiquitin…
PRODISTIN: Present problems in clustering by
biochemical function
ProtoMap: Can create undesired connection among
unrelated groups

25. Scale-Free Networks
• Node connection probability follows
a power law distribution
• Maximum degree of separation
grows as O(lg n)
• Highly robust under noise, except
at hubs and superhubs.
ki
P(linking to node i) ~
∑kj j

26. The Internet

27. Social Networks

28. Metabolic Networks
• The E. coli metabolic network is scale-free.
• Actually, the metabolic networks of all organisms in
all three domains of life appear to be scale-free (43
examined)
• The network diameter of all 43 metabolic networks is
the same, irrespective of the number of proteins
involved.
• Is this counter-intuitive? Yes.
http://biocomplexity.indiana.edu/research/bionet/

29. Protein Domain Networks
• Protein Domains – Nature’s take on writing
modular code
• Reconciles apparent paradox of a fixed network
diameter across species – despite vast differences in
complexity (some human proteins have 130
domains)
• Occurrence of specific protein domains in
multidomain proteins is scale-free.
http://mbe.oupjournals.org/cgi/content/full/18/9/1694

30. Protein Domain Graphs
• Prosite domains have a distribution following the
power-law function f(x) = a(b + x)-c, with c = .89.
There are few highly connected domains and many
rarely connected ones.
• ProDom and Pfam domains follow the power
function P ( k ) ≈ k − γ
y = 2.5 for ProDom
y = 1.7 for Pfam

31. Hub Domains in Signaling
Pathways

32. Conclusions
• The accuracy of both ProtoMap and PRODISTIN is
limited because they make the tacit assumption of a
random network topology.
• Protein-Protein interaction networks have scale-
free topology, foiling PRODISTIN
• Protein Domain networks have scale-free topology,
foiling ProtoMap
• Any protein classification algorithm that performs
better than ProtoMap is probably going to have to
address this issue.