Structure-based Analysis of
Protein Function
PTPs and Serine Hydrolases
Jacquelyn S. Fetrow
Reynolds Professor of Computational Biophysics
Departments of Physics and Computer Science
Wake Forest University
Jacquelyn S. Fetrow
Wake Forest University
Need for Improved Proteome
Analyses
• Powerful genomics and proteomics
methods identify large numbers of protein
sequences
• Need to identify biochemical function and
functional state accurately
• Need to increase quality of annotations:
decrease false positive and false negative
identifications
Knowing the Sequence is Not
Enough to Determine the Function
• Except in model organisms, over
50% of all proteins identified by
large-scale sequencing projects
are annotated as “function
unknown”
• Annotations are inadequate and
do not adequately describe
functional complexity of proteins
• Annotation transfer methods can
assign incorrect function in a
significant number of cases
S. cerevisiae Fsh3p
S.pombe DYR_SCHPO
S. cerevisiae DHFR
Structural proteomics approach to
function annotation
• Most common
method:
– structural
superposition
– function annotation
transfer based on
structural similarity
COX-1 (1cqe)
COX-2 (1cx2)
But, Knowing the Structure is Not
Enough to Predict the Function
Similar Structure,
Analysis of high
Different Function resolution structures
Similar Structure,
Similar Function
48%
released in 1998
compared to pre1998 PDB structures
27%
23%
Different Structure,
Different Function
1.5%
Different Structure,
Similar Function
See also:
Martin, et al. 1998, Structure 6:875-884
Hegyi and Gerstein, 1999, J. Mol. Biol. 288:147-164.
Koppensteiner, W., Lackner, P.
Wiederstein, M., & Sippl, M. J.
Mol. Biol 2000 296:1139.
But, then, what do we really mean by
function?
• Two isoforms of human
cyclooxygenase, COX-1 &
COX-2
• COX-1 is expressed in healthy
tissues; COX-2 is induced in
inflammatory response
• COX-1 and COX-2 have ~60%
sequence identity, very similar
overall structures, and identical
catalytic residues
COX-1 (1cqe)
COX-2 (1cx2)
But, then, what do we really
mean by function?
• Aspirin/NSAIDs inhibit both
isoforms; COX-1 inhibition can
lead to gastrointestinal side effects
1cqe: P RLVLTVRSNLI AQ TF –EFNQLYHWH –R FGM Y- GESMIEMGAPFSLK
COX-1 (1cqe)
1cx2: P –YVLTSRSYLI AQ TF SEFNTLYHWH YR FSL YL GETMVELGAPFSLK
COX-2 (1cx2)
• Newer COX-2 selective inhibitors
(VioxxTM, CelebrexTM) have antiinflammatory and pain killing
benefits of NSAIDs with reduced
side effects
Goal: accurate identification of active sites and their similarities and differences
Fuzzy Functional Forms and Active
Site Profiling
• Advantage: computational
method based on structure
– Use of structural (not just
sequence) information
– Identification of key functional
features (not annotation
transfer via global sequence
alignment)
– Fast; can be globally applied
to protein sequences
• Disadvantage:
computational method
– Scoring function cutoffs
– False positive and negative
rates
– Size of FFF library
Fetrow & Skolnick. J. Mol. Biol. (1998) 282: 949-968.
Cammer, Hoffman, Speir, Canady, Nelson, Knutson, Gallina, Baxter, Fetrow. J. Mol. Biol.
(2003) 334:387-401.
Geometric definition of an FFF
• Defined by three metrics
– Key residues (and their
identity) involved in active
site chemistry
– Geometric constraints
(distances between alpha
carbons)
– Allowed variability for
geometric constraints
• Training
– Against all PDB structures
– Relax constraints to identify
all true positive structures,
but no false positives
– Cross validation
Fetrow & Skolnick. J. Mol. Biol. (1998) 282: 949-968.
Fetrow, Godzik & Skolnick. (1998) J. Mol. Biol. 282:703-711.
B
A
C
Advantages of the FFF approach
FFF for redox regulatory site
• Use of structural information
enables:
–
Function annotation farther into
“twilight zone”
–
Identification of similar
functional sites in proteins of
different structure
• Functional complexity
–
–
Identification of multiple
chemistries within a single
functional site
Identification of multiple
functions within a protein
domain
Serine-threonine phosphatase
FFF 1=metal binding site
FFF 2=metal binding site
FFF 3=phosphatase catalytic residues
Fetrow, Siew, Skolnick. FASEB J (1999) 13:1866-74
Comparison of putative redox
active site residues
PP1
P30366
P48487
P48486
P48483
P48488
P48481
P48480
P48484
P48489
P22198
P48485
P48482
P23880
Q05547
P12982
P48461
P36874
P36873
P37139
P08128
P08129
P48462
P37140
P13681
P32598
P20654
P23777
P48490
P23733
P23734
P20604
C
C
C
C
C
C
C
C
C
C
T
S
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
T
T
V
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
F
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
A
P48726
P32838
P32345
P23595
P48580
P23696
P11493
P11611
P11082
P48463
P13353
P05323
P48577
P48579
Q07099
Q07098
P23778
Q06009
Q07100
P48578
P23635
P23636
P23594
V
I
V
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
F
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
PP2A
V
F
K
I
I
L
L
L
L
L
L
L
I
I
I
I
V
V
V
V
V
V
I
P48456
Q27889
P16299
P20651
P48453
Q08209
P20652
P48452
P48455
P48454
Q12705
O42773
P48457
Q05681
P23287
P14747
A
A
A
A
A
A
A
A
T
T
S
S
S
S
S
S
C
C
S
S
S
C
C
C
C
C
A
N
A
C
V
N
PP2B
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
Cluster analysis of PP1, PP2A,
and PP2B subfamilies
PP1
PP2A
PP2B
Comparison of putative redox
active site residues
P30366
P48487
P48486
P48483
P48488
P48481
P48480
P48484
P48489
P22198
P48485
P48482
P23880
Q05547
P12982
P48461
P36874
P36873
P37139
P08128
P08129
P48462
P37140
P13681
P32598
P20654
P23777
P48490
P23733
P23734
C
C
C
C
C
C
C
C
C
C
T
S
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
T
T
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P48726
P32838
P32345
P20604
P48580
P23696
P11493
P11611
P11082
P48463
P13353
P05323
P48577
P48579
Q07099
Q07098
P23778
Q06009
Q07100
P48578
P23635
P23636
P23594
P23595
V
I
V
V
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
Y
Y
Y
F
Y
Y
Y
Y
Y
Y
Y
Y
F
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
V
F
K
A
I
L
L
L
L
L
L
L
I
I
I
I
V
V
V
V
V
V
I
I
PP2A
PP1
P48456
Q27889
P16299
P20651
P48453
Q08209
P20652
P48452
P48455
P48454
Q12705
O42773
P48457
Q05681
P23287
P14747
A
A
A
A
A
A
A
A
T
T
S
S
S
S
S
S
C
C
S
S
S
C
C
C
C
C
A
N
A
C
V
N
PP2B
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
Limitations of the FFF Approach
• FFFs only uses identities of three residues
– Leads to false positive identifications
• FFF hit is only yes/no
– Does not have a score or confidence
associated with it
• FFFs only identify key residues
– Does not identity specificity—substrate or
small molecule specificity
Active site signature: first step in
active site profiling
• Use FFF to identify key
functional residues
• Extract fragments in
structural proximity to
FFF residues
• Arrange fragments to
form a linear
sequence—active site
signature
Cammer, Hoffman, Speir, Canady, Nelson,
Knutson, Gallina, Baxter, Fetrow. J. Mol. Biol.
(2003) 334:387-401.
Align signatures to create
active site profile
Profile
segments for 7
enzymes
identified by
one FFF
1mucA_A
2mucA_B
1bkhC_C
1bkhB_D
3mucA_E
1chrA_F
2chr__G
Examples of
residues identical across
family
Examples of
residues different
between family
members—possible
specificity determinants?
RHRVFKLKIGA-ASIFALKIAKNGGPVTA--GLYGGTMLEGSIGTLASAHAF--LTWGTELFGPLLL
RHRVFKLKIGA-ASIFALKIAKNGGPVTA--GLYGGTMLEGSIGTLASAHAF--LTWGTELIGPLLL
RHRVFKLKIGA-ASIFALKIAKNGGPVTA—-GLYGGTMLEGSIGTLASAHAF--LTWGTELFGPLLL
RHRVFKLKIGA-ASIFALKIAKNGGPVTA—-GLYGGTMLEGSIGTLASAHAF--LTWGTELFGPLLL
RHRVFKLKIG--ASIFALKIAKNGGPVTA—-GLYGGTMLEGSIGTLASAHAF--LTWGTELFGPLLL
RHNRFKVKLGF-VDVFSLKLCNMG—VTIA--ASYGGTMLDGSIGTLASAHAF-SLPFGCELIGPFVL
RHNRFKVKLGF-VDVFSLKLCNMGGVTIA--ASYGGTMLDSTIGTSVALQLYS-LPFGCELIGPFVL
Active Site Profile Score
1cozA_1
GTFDLLHWGHIKLLEAYRTISTTKIKEE
1cozB_1
GTFDLLHWGHIKLLEAYRTISTTKIKEE
BS002557__1cozA
GTFDPPHNGHLLMANDYREVSSTMIRER
****
• Empirically derived function
takes into account
sequence similarity
• Enables approaches based
on active site information
–
–
Clustering of functional
families (profile score)
Novel sequence family and
subfamily assignment
(pairwise score)
* **: : : ** :*:* *:*.
Identity
Strong
Weak
0.2
0.1
1.0
n
Score 
S
1
I
m
k
l
1
1
1
  S S   SW   S g
N
Validation of Active Site Profile
Score
•
193 real functional families
–
–
–
–
•
193 FFFs applied to known
structures from PDB to identify
functional families
For each protein in each family,
extract active site signature
Align all signatures in a given family
to create profile
Calculate profile score
193 decoy functional families
–
–
–
Geometric criteria “relaxed” slightly
to identify first “false positive”
(Automatically identified as part of
training procedure)
Extract signatures, align to create
profile, calculate score
B
A
C
B
A
C
Validation of the active site profile
score
Active site profile for serine carboxypeptidases
Profile score=0.42
1ac5_
1ivyA
1ysc_
1ivyB
1cpy_
LNGGPC-GESYAGQY-IGNGWI-----NMYNFN-NGDKDLICNN-NASHMVPFD
LNGGP--GESYAGIYIVGNGLSLFNIYNLY--N-NGDVDMACNF-GAGHMVPTD
LNGGP--GESYAGHY-IGNGLTMAGE-NVYDIRKAGDKDFICNWLNGGHMVPFD
LNGGP--GESYAGIYIVGNGLSLFNIYNLYA-N-NGDVDMACNF-GAGHMVPTD
LNGGP-AGASYAGHYIIGNGLTMAG--NVYDIR-AGDKDFICNWLNGGHMVPFD
***** * **** * :***
*:* . ** *: ** ...**** *
Validation of Active Site Profile
Score
Serine
carboxypeptidase
profile
Score=0.42
1ac5_
1ivyA
1ysc_
1ivyB
1cpy_
LNGGPC-GESYAGQY-IGNGWI-----NMYNFN-NGDKDLICNN-NASHMVPFD
LNGGP--GESYAGIYIVGNGLSLFNIYNLY--N-NGDVDMACNF-GAGHMVPTD
LNGGP--GESYAGHY-IGNGLTMAGE-NVYDIRKAGDKDFICNWLNGGHMVPFD
LNGGP--GESYAGIYIVGNGLSLFNIYNLYA-N-NGDVDMACNF-GAGHMVPTD
LNGGP-AGASYAGHYIIGNGLTMAG--NVYDIR-AGDKDFICNWLNGGHMVPFD
***** * **** * :***
*:* . ** *: ** ...**** *
Serine
carboxypeptidase
decoy profile
Score=0.14
1ac5_
1ivyA
1ysc_
1ivyB
1cpy_
1c4xA
LNGGPC-GESYAGQY--IGNGWI-----NMYNFN-NGDKDLICNN---NASHMVPFD
LNGGP--GESYAGIYI-VGNGLSLFNIYNLY--N-NGDVDMACNF---GAGHMVPTD
LNGGP--GESYAGHY--IGNGLTMAGE-NVYDIRKAGDKDFICNWL--NGGHMVPFD
LNGGP--GESYAGIYI-VGNGLSLFNIYNLYA-N-NGDVDMACNF---GAGHMVPTD
LNGGP-AGASYAGHYI-IGNGLTMAG--NVYDIR-AGDKDFICNWL--NGGHMVPFD
LHGAG--GNSMGGAVTLMGSVG-----SFVY----HGRQDRIVPLTLDRCGHWAQLE
*:*.
* * .*
:*.
:*
* *
.* . :
Validation of Active Site Profile Score
Profile Score
1.2
True profiles
1
0.8
0.6
0.4
0.2
0
-0.2 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171
Decoy profiles
-0.4
FFF Functional Family
B
1.2
Profile Score
• Profile score
compared to decoy
profile score shows
clear separation for
most families
• Separation less
distinct when decoy
is functionally
related to FFF family
• Profile score ≥0.25
considered
significant
A
1
0.8
0.6
0.4
0.2
0
173
176
179
182
185
188
FFF Functional Family
191
194
Prospective validation of the method
• Human protein tyrosine
phosphatases (PTPs)
– PTPs are important signal
transduction proteins
– Analysis demonstrates
accuracy and throughput
• Yeast serine hydrolases
– Serine hydrolases are
crucial for many cellular
processes
– Analysis demonstrates
experimental validation of
sensitivity and accuracy of
function annotations
– Performance compared to
other tools
Method for genome analysis
•
•
•
Download protein sequences encoded by human or yeast genome
Run Prospector (Skolnick, et al) fold recognition program
For any protein sequence that aligns with structure used to create FFF:
– Take top 20 alignments (top five hits for four scoring functions)
– Determine if FFF residues conserved
•
If yes:
– Predict FFF function
– Identify active site signature
– Align and calculate pairwise profile score
PTP Functional Family
• Catalytic site is found in multiple protein
structures
• Active site structure is conserved
2hnp, a classical PTP
1vhr, a dual specificity PTP
1phr, a low molecular weight PTP
Annotation of human genome
sequences for PTP function
• Identified over 150 human PTPs
–
Comparison to experimentally-verified
PTPs shows that over 95% of known
PTPs identified:
false negative rate < 5%
• Over 40 unique PTPs identified
–
Sequences that are not recognized as
PTPs by any other method (including
BLAST, Blocks, Prints and Pfam)
100%
80%
60%
40%
20%
0%
Unique to FFF
FFF + other tools
How good are these function assignments?
Functional Characterization of
PTP Proteins
• Clone, express, and purify
• Progress (before termination of
project)
–
–
400
300
200
100
49 soluble PTP domains
purified
5
37 PTPs active in vitro
Four active PTPs that were not
previously recognized by other
methods (including no
recognizable similarity to any
PTP in the public databases)
10
PNPP (mM)
15
180
20
Target Set
160
Number of Proteins
–
V (A405nm/sec)
• Test PTPs for biochemical function
-6
500x10
Hydrolysis of pNPP by
PTP #1
Soluble Protein
140
Active in vitro
120
100
80
60
30%
75%
40
35%
65%
15%
66%
20
0
TOTAL
Structure-based
novels
Clean novels
Proteins in Target Set
Functional Characterization of
PTP Proteins
• False positive rate cannot be absolutely
determined; PTP project shows:
– Total PTP proteins: 49 soluble proteins, with 37 active
in pNPP hydrolysis assay (~25% not validated in
assay)
– PTP proteins unrecognized by other methods: 6
soluble proteins, with 4 active in pNPP hydrolysis
assay (~33% not validated in assay)
– Maximum false positive rate: ~25-33%
• Why a maximum?
– Only one substrate and assay condition tested
– Small sample set
Active Site Profiling of Human PTPs:
Identification of Sub-families
• Identified over 150 human
PTPs
• Identify active site
signature from each PTP
sequence
• Align to create active site
profile for PTP family
• Cluster to identify
subfamilies of PTPs
100%
80%
60%
40%
20%
0%
Unique to FFF
FFF + other tools
Active Site Profiling of Human PTPs:
Identification of Sub-families
--Novel PTP#5
Subfamily 1
--Blast (global
sequence similarity)
indicates that PTP#5 is
dual specificity PTP
Subfamily 2
All PTPs
--Clustering of active site
profile indicates “PTP#5”
falls into class 1
Classical
PTPs
Subfamily 3
Subfamily 4
Dual specificity
PTPs and PTEN
Subfamily 5
Subfamily 6
Subfamily 7
Subfamily 8
Low molecular
weight PTPs
Summary of human PTP
annotation project
• 150 PTPs identified in human genome
– Over 95% of previously annotated PTPs identified
(false negative rate <5%)
– Of those tested in our lab, 75% exhibited PTP
function
• 40 proteins not identified by other methods
(BLAST, Blocks, Pfam)
– Of those tested, 66% exhibited PTP function
• Maximum false positive rate: 25-33%
• Active site profiling subclassifies proteins
differently than global sequence alignment
FFFs for Serine Hydrolases
• 35 serine hydrolase FFFs describing 25 EC-defined
functions
– Nucleophilic serine in active site
– Protease, lipase, esterase, amidase or transacylase function (FADindependent-S-hydroxynitrile lyase, too)
– Several “family” FFFs, including a/b hydrolase “family” FFF
• 35 FFFs cover approximately 63% of known structural
space and 23% of potential functional space
Total Structural Space
Function
Serine Proteases
Serine Lipases
Serine Esterases
Serine Amidases
Serine Transacylases
(S) Hydroxynitrile Lyases
Identification of Yeast Serine
Hydrolases by FFFs and Profiling
• 6946 yeast protein sequences (NCBI and SGD)
• Threading with PROSPECTOR against PDB
structures
• Analysis of top 20 threads (top five scores, four
scoring functions) with serine hydrolase FFFs
• If thread is “hit” by FFF, sequence is identified as
a serine hydrolase (yes or no)
• Active site profile scoring provides rank ordering
of identified serine hydrolases; ≥0.25 is
considered significant
Skolnick & Kihara. (2001) Proteins 42:319-331.
DiGennaro, Siew, Hoffman, Zhang, Skolnick, Neilson, Fetrow. (2001) J. Struct. Biol. 134:232-245.
Fetrow, Godzik & Skolnick. (1998) J. Mol. Biol. 282:703-711.
Annotation of yeast genome for
serine hydrolase functions
• 147 proteins identified by combination of PROSPECTOR
and serine hydrolase FFFs
• 52 of 147 proteins identified by more than one serine
hydrolase FFF
• 55 of 147 proteins identified with significant active site
profile score (≥0.25)
• 7 proteins were previously identified* as serine
hydrolases (“knowns”)
– Profile score≥0.25: Dap2, Kex1, Prb1, Prc1, Ste13, and Yjl068c
– Profile score=0.23: Ppe1
How good are these function assignments?
*Previously identified in SGD
(http://genome-www.stanford.edu/Saccharomyces/)
Activity-based Probe Technology
• Advantage: probe
chemistry
– Identifies functional
proteins in complex
mixtures
– Fractionates proteome on
basis of chemical reactivity
(not protein abundance)
Biological
Samples
Activity
Probes
• Disadvantage: probe
chemistry
– Specific for serine
hydrolases?
Patricelli, Giang, Stamp, Burbaum. (2001) Proteomics 1:1067-1071.
Kidd, Liu & Cravatt. (2001) Biochemistry 40:4005-4015.
Cravatt & Sorenson. (2000) Curr. Opin. Chem. Biol. 4:663-668.
High
Throughput
Screening
Identification of Serine Hydrolases
by ABPs
• Yeast grown under four culture
conditions
• Cultures lysed, centrifuged,
fractions labeled with ABP
• Affinity chromatography;
separation of labeled proteins
by 1D PAGE
• In-gel tryptic digest and LC-MS
identification of peptides
• High quality identifications:
More than one peptide
identified for a given protein
Results of ABP labeling
experiments
• 80 proteins uniquely labeled by ABP
• 23 of 80 proteins identified with high quality
mass spec data
– 8 of 23 proteins were previously identified* as
serine hydrolases (“knowns”): Dap2, Kex1,
Ppe1, Prb1, Prc1, Ste13, Yjc068c and Amd2
– “unknowns”: Ygl039w, Ygl157w, Yml059c,
Fas2, Ydr428c, Ynl123w, Yor084w, Eht1, Yju3,
Ybr139w, Ybr204c, Yhr049c, Ylr118c,
Ymr222c, and Yor280c
*Previously identified in Saccharomyces Genome Database (SGD)
(http://genome-www.stanford.edu/Saccharomyces/)
Comparison of computational and
experimental results
• Chemical proteomics: 23 high quality
identifications
• Computational/structural proteomics: 55
proteins identified with significant active
site profile score (≥0.25)
• 15 proteins identified by both methods
(high quality identifications by both
methods)
How well did the FFFs identify
ABP-labeled proteins?
• If all 23 proteins identified by ABP labeling
are correct, then:
– FFF identification: 15/23=65%
– FFF coverage of structure space (“the best
we could expect to do”): 65%
– FFF coverage of biological function space
(“the worst we could expect to do”): 23%
• But, are all the ABP identifications actually
serine hydrolases?
What did the FFFs miss?
• 8 proteins identified by high quality ABP data, but
not serine hydrolase FFFs
– Amd2 (“8th known”) identified by ABP, but not FFF
because no amidase FFF had been constructed
– 3 proteins identified by dehydrogenase FFFs, not
serine hydrolase FFFs (discussed subsequently)
– 3 proteins with significant threading scores, no FFF hit
• Yor084w (1a8uA): chloroperoxidase T (known serine
hydrolase)
• Fas2 (1kas): 3-oxo-ACP-reductase/synthase
• Ynl123w (1pysB): tRNA synthetase
– 1 protein (Ydr428c) yields no computational results
Advantages of Combining Methods:
Clarification of ABP identifications
• 3 proteins identified by high quality ABP data, but not
serine hydrolase FFFs
– Ygl039w, Ygl157w, and Yml059c
– All three labeled by another family of FFFs (UDP-galactose-4epimerase, estradiol-17-beta dehydrogenase, and 3-alpha, 20beta-hydroxysteroid dehydrogenase)
– Proteins in this family all have active site serine and tyrosine:
possible site of ABP labeling
• If these protein functions are correctly identified by the
FFFs AND if other five ABP identifications are correct,
then:
– FFF identification: 18/23=78% (better than expected)
What about the “unknowns”?
• 15 proteins identified by both methods
– 7 of 8 “knowns” identified by both methods
(Dap2, Kex1, Ppe1, Prb1, Prc1, Ste13, and
Yjl068c)
– 8 novel annotations of proteins as serine
hydrolases (Eht1, Yju3, Ybr139w, Ybr204c,
Yhr049w, Ylr118c, Ymr222c, and Yor280c)
• All 8 annotated as “function unknown” or
“hypothetical protein” in SGD
• High confidence in novel annotations (two
independently applied methods)
What about the “unknowns”?
• 15 proteins identified by both methods
– 7 of 8 “knowns” identified by both methods
(Dap2, Kex1, Ppe1, Prb1, Prc1, Ste13, and
Yjl068c)
– 8 novel annotations of proteins as serine
hydrolases (Eht1, Yju3, Ybr139w, Ybr204c,
Yhr049w, Ylr118c, Ymr222c, and Yor280c)
• All 8 annotated as “function unknown” or
“hypothetical protein” in SGD
• High confidence in novel annotations (two
independently applied methods)
New Family of Eukaryotic Serine
Hydrolases (FSH)
• 3 yeast proteins (Yhr049w, Ymr222c, and
Yor280c) identified by both ABP and FFFs
• 3 sequences related by sequence
similarity
• All annotated as “function unknown” at
SGD
• None annotated with confidence by other
computational methods (Prints, Pfam or
Blocks)
New Family of Eukaryotic Serine
Hydrolases (FSH)
• These 3 proteins related to proteins from other eukaryotic
proteomes (human, mouse, worm, fruit fly, mosquito, plant)
• No NCBI biochemical annotations for any of these proteins
(except one—see next slide)
Cautionary Tale for Annotation
Transfer
• One FSH protein, DYR_SCHPO, from S. pombe was
annotated as a dihydrofolate reductase (DHFR)
• Sequence analysis indicates a multidomain protein:
contains both DHFR and serine hydrolase function
– Possible biological connection between serine hydrolase and
DHFR functions?
• Annotation transfer methods would have assigned
incorrect function to FSH family of proteins
S. cerevisiae Fsh1p
S.cerevisiae Fsh2p
S. cerevisiae Fsh3p
S.pombe DYR_SCHPO
S. cerevisiae DHFR
Comparison to other computational methods:
How much information does structure add?
• ABPs identified 23 proteins with high confidence
• FFFs identified 15 (65%) as serine hydrolases
• Pfam identified 10 (43%) as serine hydrolases
25
20
Total
15
FFF
10
Pfam
5
0
All Experimental Hits
Experimental Hits with SGD
"molecular function
unknown"
Summary of yeast serine hydrolase
annotation project
• 15 serine hydrolase sequences identified by
both methods
– 7 of 8 known serine hydrolases identified by both
methods (all eight identified by ABP labeling)
– 8 new serine hydrolases identified (formerly
annotated as “function unknown”)
– New family of eukaryotic serine hydrolases (FSH)
• FFF annotation clarifies molecular function of the
three proteins identified by ABP labeling
• More accurately identify limits of FFF and active
site profiling accuracy
– If 23 ABP identifications are correct, FFF correctly
identifies function of 78%
Baxter, et al. (2004) Mol. Cell Prot.
Structure-based annotation of
protein function
• Prospective experimental validation of
predictions demonstrates accuracies (and
limitations) of current methods
• Mis-annotation of function continues to be a
problem—found in all databases
• Results suggest that a significant number of
proteins will exhibit well-studied functions, but
are not identified by current computational
methods
• Profiling of sequences around functional site
provides additional information on function and
specificity
Acknowledgements
(now Cengent Therapeutics)
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Susan Baxter (NCGR)
Melanie Nelson (SAIC)
Stephen Cammer (SDSC)
Brian Hoffman (Scitegic)
Jen Montimurro (Wadsworth Ctr)
Stacy Knutson (Wake Forest)
Jeff Speir (Scripps)
Jeannine DiGennaro (GeneVault)
Steve Betz (Neurocrine)
Marijo Galina
Susan Okuley
Chris Scott
ActivX
– Jonathan Burbaum
– Jonathan Rosenblum
– Dan Giang