This document discusses classifying and characterizing proteins of unknown function in the fission yeast Schizosaccharomyces pombe. It begins by showing the progress that has been made in characterizing known proteins, but that the number of unknown proteins is decreasing only gradually. It then discusses classifying proteins as unknown if their broad cellular role is not known. The rest of the document proposes different methods for predicting the potential functions of unknown proteins, such as identifying informative features, clustering unknown proteins based on similar features, and predicting potential functions by finding the best matching known proteins. It emphasizes the need for high-quality curation and experimental data to make accurate predictions.

1. Hidden in plain sight
Classifying the known and the unknown
proteome
Valerie Wood,
PomBase

2. We tend to study what we know
5054 protein coding
2154 published, small scale (blue),
2050 inferred from orthologs (red)
Steady progress in characterizing proteins
already studied in other organisms
‘Unknowns’ decreasing only gradually
509 conserved (green)
321 Schizosaccharomyces specific (purple)
Similar situation in other organisms (other
organisms checked for annotation)
Progress in characterising proteins since
2006

3. Classifying as unknown
• Concept of “known function” is vague/arbitrary- we may
know the molecular function (oxidase/protease) but
nothing about the broader cellular role
• For fission yeast we use ‘unknown’ if there is no
information about the broad cellular role in which they
participate, and thus cannot be assigned to a ‘biological
process’ in the ‘GO slim’ (i.e. transcription, translation,
replication, amino acid metabolism)
• People tend to work on processes, this makes them
accessible as candidates for follow up

4. vertebrate, eukaryote only
vertebrate, bacteria
} 179 (have clear
human ortholog)
Taxonomic conservation
Unknown 830
Schizosaccharomyces only 178
321 fission yeast specific
Schizosaccharomyces pombe
only 143
other non-fungal eukaryote, no vertebrate
fungi only 186
horizontal transfer 19
509 conserved in other organismsfungi and bacteria

5. • To make useful inferences for ‘unknowns’ we
need a clear and accurate picture of what we
know
• A “GO slim” is a way of summarizing the
biological roles of an organisms gene products
• This “matrix” shows genes co-annotated to
pairs of GO slim terms
• Many GO slim terms do not share annotations
with other slim terms
• Used for QC, identify ontology and annotation
errors (especially electronic annotation)
What is known ?

6. DNA replication, recombination, repair frequently
intersect with each other and with
Chromosome organization, mitotic cell cycle regulation
Rarely (currently never) intersect with:
carbohydrate metabolism
amino acid metabolism
Cytokinesis
Lipid metabolism
Nucleocytoplasmic transport
Protein glycosylation

7. Unknown 830
TOTAL 5054
cytoskeleton
org 206
nuclear DNA
replication,
recombination,
repair
305
mitotic
chromosome
segregation
184 regulation of mitotic
cell cycle 232
10
CELL DIVISION 751
27
cytokinesis
110
0
39 1
46
3
4. MITOCHONDRIAL
ORG/EXP
280
4
cell wall
org 1303
4
1
MEMBRANES, TRAFFICKING, CELL SURFACE 787
14
lipid met
222 vesicle
Mediated
transport
324
6
glycosylation
polysacc met
140membrane
org 199
75
0
6
74
10
33
0
detox
SMALL MOLECULE TM
TRANSPORT
288
13
9
0
AA &
sulfur
met
220
vitamin
cofactor
met
9
5 nucleo-base/
side/tide met
219
small
sugar met
77
CENTRAL MET,
ENERGY
AND BUILDING
BLOCKS 549
Nitrogen
15
25
174
54
3430
other energy
generation
25
23
signalling
404
sexual reproductive
process 262
(Many intersections)
Other 290
No intersections.
Includes adhesion,
many proteases,
peroxions
EXPRESSION 1294
````
EXPRESSION submod 863
4
1
3
ribosome
biogenesis
317
RNA
metabolism
772cytoplasmic
translation
249
189
c
nucleocyto
transport
110
5
34
26
2
Transcription
479
32
18
PROTEIN ASSEMBLY/STABILITY 765
protein
catabolism
& autophagy
251
ubiquitination
192
63
folding
102
complex
Assembly
325
1
3
4
1
All cardiolipin synthesis

8. MITOCHONDRIA
L ORG/EXP
280
MEMBRANES, TRAFFICKING,
CELL SURFACE 787
signalling
404
sexual
reproductive
process 262
Other 290
TOTAL 5054
PROTEIN ASSEMBLY
/STABILITY 765
CELL DIVISION 751
SMALL MOLECULE
TM TRANSPORT
288
CENTRAL MET, ENERGY
AND BUILDING
BLOCKS 549
EXPRESSION 1294
````
EXPRESSION submod 863
c
Transcription
479
Unknown 830
This covers the known “process options” for a single-celled eukaryote
- First step for the unknowns , assign to broad process (bring to researchers of interest
notice)
- If we can predict strong association with some module or submodule is unlikely to be
associated with others (caveat)
- Provides a ‘framework’ to begin to partition “unknowns” based on general or specific
non-process characteristics (constrain predictions, and evaluate them, based on
existing knowledge)
New biology?

9. Function prediction
1. Find features informative for known processes
• Phenotypes
• Taxonomic distribution
• Location
• Catalytic grouping
2. Identify these informative features in unknown protein:
3. Cluster similar features
4. Ask “which known genes best match these profiles?”
5. Look for matching processes

10. Classification/clustering of unknowns
(conserved to human subset)
1. Identify informative features
for each unknown protein:
Phenotypes
Location
Taxonomic distribution
Catalytic grouping
2. Group by similar features
For 100/179 of the conserved to
human subset see poster 144

11. 1. ER localization
2. >1 <4 TM domain
3. Absent from S. cerevisiae
4. Conserved in vertebrates
Ask “which known genes best
match these profiles?”

12. Query using PomBase advanced search tool:

13. Look for matching processes
“What are these genes
enriched for?”

14. 1. Present in nucleus
2. Methyltransferase domain
3. Conserved in bacteria
4. Conserved in vertebrates

15. 11/15 are tRNA metabolism,
… there are orphan tRNA enzymes11/15 tRNA met, 3/15 rRNA met

16. Adding another feature “HU sensitivity”, increases specificity for tRNA metabolism

17. Real example
SPCC1840.09 was recently characterised as coq11 in S. cerevisiae
4/9 with these phenotypes are ubiquinone biosynthesis
1 transcription
1 (SPAC823.10c) indirect (reannotated to heme transport)

18. Guilt by association
All unknowns in STRING (http://string-db.org/)
Human AMMECR1
Human MEMO1

19. Using AnGELI
AMMECR subnetwork has connections to meiotic cell cycle (7/8), and are upregulated in
response to caffeine and rapamycin and stress

20. What do we need to make good
predictions?
CURATION
• Accurate predictions requires high quality curation. Continual removal of known false positive
annotations (via ontology errors, incorrect experiments, manual curation errors and incorrect
automated mapping) from the ‘true positives training set’
FUNCTION PREDICTION METHODS
• Many pipelines for function prediction produce lots of false positives because not fully constrained
by existing knowledge
• Integrations of all methods, integrated approach to prediction, different methods will suit different
processes (no one size fits all)
• To identify more informative features (e.g. phenotypes) which correlate strongly and specifically
with known processes (i.e. some phenotypes ‘abnormal shape may be enriched for some
processes but are non-specific)
EXPERIMENTAL DATA
• Require more datasets which provide more strong positive and negative discriminators
• More high quality physical interactions
ACCESS
• Make predictions prominently accessible to validate in small scale follow up

21. The future

22. Acknowledgements
• Midori Harris
• Antonia Lock
• Jurg Bahler
• Danny Bitton (AnGeli)
• Steve Oliver

23. Spare slides

24. 167
No biological role:
3436
831 Biological Process
e.g.
Cell cycle
Transcription
DNA replication
Transport
Regulation of process
Molecular Function
e.g
transporter
enzyme
-protein kinase
-ubiquitin ligase
-oxidoreductase
-protease
binding functions
enzyme regulator (direct)
Cellular Component
(location or complex)
455
15 1842
Total 5054
All 3 aspects unknown
717, unknown role
Plus 113 where the process
Is not very informative total 830
90

25. Find “non process” features that
correlate with processes
e.g. “Mitochondrial organization”
(A GO slim term)
More likely to be:
Location
Phenotype
Phenotype
Expression
Less likely to be:
Protein
feature
Using AnGeLi http://bahlerweb.cs.ucl.ac.uk/cgi-bin/GLA/GLA_input
275/280 genes involved in mitochondrion
organization are mitochondrial, BUT not
all mitochondrial genes (732) are involved in
mitochondrial organization
10/10 genes which show decreased
population growth on galactose are mitochondrial organization
Less likely to be periodic 8/497 or abnormal cell cycle 4/695
`

26. Between module intersections
Numbers of between module
intersections are low

27. Between module intersections
Excluding signalling co-annotation the
intersections between modules are
minimal (mainly Ub mediated protein
degradation)