Prediction of metal ion-binding sites in proteins using the fragment transformation method.

Abstract

The structure of a protein determines its function and its interactions with other factors. Regions of proteins that interact with ligands, substrates, and/or other proteins, tend to be conserved both in sequence and structure, and the residues involved are usually in close spatial proximity. More than 70,000 protein structures are currently found in the Protein Data Bank, and approximately one-third contain metal ions essential for function. Identifying and characterizing metal ion-binding sites experimentally is time-consuming and costly. Many computational methods have been developed to identify metal ion-binding sites, and most use only sequence information. For the work reported herein, we developed a method that uses sequence and structural information to predict the residues in metal ion-binding sites. Six types of metal ion-binding templates- those involving Ca(2+), Cu(2+), Fe(3+), Mg(2+), Mn(2+), and Zn(2+)-were constructed using the residues within 3.5 Å of the center of the metal ion. Using the fragment transformation method, we then compared known metal ion-binding sites with the templates to assess the accuracy of our method. Our method achieved an overall 94.6 % accuracy with a true positive rate of 60.5 % at a 5 % false positive rate and therefore constitutes a significant improvement in metal-binding site prediction.

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Prediction of Metal Ion–Binding Sites in Proteins Using
the Fragment Transformation Method
Chih-Hao Lu1*, Yu-Feng Lin1,2, Jau-Ji Lin2,3,4, Chin-Sheng Yu5,6
1Graduate Institute of Molecular Systems Biomedicine, China Medical University, Taichung, Taiwan, 2Institute of Bioinformatics and Systems Biology, National Chiao
Tung University, Hsinchu, Taiwan, 3Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan,
4Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan, 5Department of Information Engineering and Computer Science, Feng Chia
University, Taichung, Taiwan, 6Master’s Program in Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan
Abstract
The structure of a protein determines its function and its interactions with other factors. Regions of proteins that interact
with ligands, substrates, and/or other proteins, tend to be conserved both in sequence and structure, and the residues
involved are usually in close spatial proximity. More than 70,000 protein structures are currently found in the Protein Data
Bank, and approximately one-third contain metal ions essential for function. Identifying and characterizing metal ion–
binding sites experimentally is time-consuming and costly. Many computational methods have been developed to identify
metal ion–binding sites, and most use only sequence information. For the work reported herein, we developed a method
that uses sequence and structural information to predict the residues in metal ion–binding sites. Six types of metal ion–
binding templates– those involving Ca2+, Cu2+, Fe3+, Mg2+, Mn2+, and Zn2+–were constructed using the residues within 3.5 A˚
of the center of the metal ion. Using the fragment transformation method, we then compared known metal ion–binding
sites with the templates to assess the accuracy of our method. Our method achieved an overall 94.6 % accuracy with a true
positive rate of 60.5 % at a 5 % false positive rate and therefore constitutes a significant improvement in metal-binding site
prediction.
Citation: Lu C-H, Lin Y-F, Lin J-J, Yu C-S (2012) Prediction of Metal Ion–Binding Sites in Proteins Using the Fragment Transformation Method. PLoS ONE 7(6):
e39252. doi:10.1371/journal.pone.0039252
Editor: Beata G Vertessy, Institute of Enzymology of the Hungarian Academy of Science, Hungary
Received September 29, 2011; Accepted May 21, 2012; Published June 18, 2012
Copyright: ? 2012 Lu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by grants from the National Science Council(NSC 99-2113-M-039-002-MY2) and China Medical University(CMU98-N2-19),
Taiwan to CHL and the National Science Council(NSC 98-2627-B-035-001-), Taiwan to CSY. The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: chlu@mail.cmu.edu.tw
Introduction
The structure of a protein determines its function and its
interaction(s) with other components, e.g., other proteins and
cofactors, including metal ions. Approximately one-third of all
proteins bind at least one metal ion [1,2,3], and many different
types of metal ion–binding proteins are found in humans [4,5].
Metal ions help stabilize protein structure, may induce a
conformational change upon binding, and/or participate in
catalysis. Metal ions found in proteins include those of the alkali
metals, alkaline earth metals and transition metals, with the most
common being sodium and potassium ions, calcium and
magnesium ions, and iron, manganese, copper and zinc ions,
respectively. For the metal ion–binding proteins found in the
Protein Data Bank (PDB http://www.rcsb.org/pdb/), ,66 %
contain transition metal ions, ,37 % contain alkaline earth metal
ions, and ,6 % contain alkali metal ions [6].
In humans, hemoglobin transports oxygen in the blood from the
lungs to peripheral tissues. Hemoglobin contains four heme groups
that reversibly bind Fe2+. Fe2+coordinates four heme nitrogens
and, reversibly, one oxygen. In the absence of an oxygen, a water
molecule is bound. Urease, expressed by the Gram-negative
microaerophilic bacterium Helicobacter pylori, requires Ni2+for its
function. Urease hydrolyses urea into carbon dioxide and
ammonia to produce an alkaline environment that protects the
bacterium from acidic gastric juice during its infection of the
stomach. Thus, in both prokaryotes and eukaryotes, metal ion–
binding proteins are extensively involved in many different
biochemical reactions. Identifying metal ion–binding sites is,
therefore, key to understanding the functional mechanisms of
metal ion–binding proteins.
Experimentally, metal ion–binding proteins are identified and/
or characterized using nuclear magnetic resonance spectroscopy
[7], gel electrophoresis [8], metal-affinity column chromatography
[9], electrophoretic mobility shift assay [9], absorbance spectros-
copy [10], and mass spectrometry [8]. Most of these methods
require complex steps and specialized equipment, making them
unsuitable for unknown targets. There is considerable demand,
therefore, for other ways to identify metal ion–binding sites.
Computational methods have been used to identify metal ion–
binding sites, e.g., support vector machines [6,11,12], neural
networks [6,13], the FoldX force field [14], the CHED algorithm
[15,16], graph theory and geometry algorithms [17,18]. Some of
these methods use only sequence information [6,11,12], whereas
others use both sequence and structure information [17,18].
However these previous attempts to predict metal ion–binding
sites have often had low sensitivities; clearly, predictive accuracy
must be improved.
On average, the members of the Structural Genomics Initiative
solve 20 new protein structures each week. Currently, the PDB
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contains more than 70,000 protein structures. In general the
regions in proteins that interacts with ligands, substrates, or other
proteins tends to be structurally conserved [19] and the residues
involved are in close spatial proximity even though they may be
distant in sequence. Such residues constitute , 10–30 % of a
protein sequence [20,21,22]. The residues that most often bind
metal ions are CYS, HIS, GLU and ASP [23,24] because the
atoms of their polar or charged side chains can coordinate metal
ions. For the work reported herein, we used the fragment
transformation method [25] to identify residues in proteins that
bind Ca2+, Cu2+, Fe3+, Mg2+, Mn2+, or Zn2+. This method
combines sequence and structural information contained within
spatially local fragments. Given that the three-dimensional
structure and residue type are often conserved, similar binding
regions can be found by comparing the types of residues and their
relative locations with those of computationally constructed metal
ion–binding residue templates.
Methods
Overview
First, the structures of known metal ion–binding proteins were
extracted from the PDB. Next, a database containing metal ion–
binding sites templates was constructed. Each template included
residues at least partially within 3.5 A˚of the metal ion center. The
structure of the protein being queried for a metal ion–binding site
(query protein) was then compared with each template using a
‘‘leave-one-out’’ comparison method. The fragment transforma-
tion method [25] attempts to structurally align fragments of the
query protein and the metal ion–binding residue template. After
each comparison, each residue in the query protein was assigned
an alignment score that is composed of two functions for the
evaluation of sequence and structure conservation. The sequence
similarity is calculated by using the BLOSUM62 substitution
matrix [26], and the structure similarity is taken by measuring the
root mean square deviation (RMSD) of the Ca carbons of the
Figure 1. Schematic of the metal ion–binding prediction method.
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local structural alignments. Residues that score above the assigned
alignment-score threshold are predicted to bind metal ions. This
method is illustrated in Figure 1.
Dataset containing the metal ion–binding proteins
The proteins in the final dataset were extracted from the PDB
and contain at least one Ca2+, Cu2+, Fe3+, Mg2+, Mn2+, or Zn2+
ion. At the time of our study, approximately one-fourth of all PDB
entries (20094 of 77294 proteins) contained a metal ion(s). The
following criteria were applied to these proteins as filters. If the
structures did not contain any polypeptide chain, those structures
were excluded. For proteins containing more than one polypeptide
chains, we included only the chains with residues involved in metal
ion–binding. The length of the polypeptide chain was required to
be more than 50 residues. DNA and/or RNA components were
removed, leaving only the polypeptide chain.
To ensure that many different types of proteins were included in
the dataset, proteins were grouped according to their superfamily
by SCOP (version 1.67) [27]. Proteins that could not be classified
by in this manner were removed. Finally, BLASTClust, in the
standalone BLAST package (version 2.2.10) [28], was used to align
the sequences in a pairwise fashion so that the remaining proteins
could be sorted into groups that had sequence identities $ 25%.
This step was performed to remove the redundant structures from
the dataset because sequences with at least 25 % identity usually
have similar conformations. For each cluster we retained the first
entry as representative of the cluster. The final dataset is composed
of 1,109 polypeptides representing 361 SCOP superfamilies.
Figure 2. Metal ion–binding residues. All residues at least partially within 3.5 A˚of a metal ion are defined as metal ion–binding residues.
doi:10.1371/journal.pone.0039252.g002
Table 1. The types and number of metal ion–binding
polypeptides and metal ion–binding residue template.
Metal ion
Number of
polypeptidesNumber of templates
Ca2+
Cu2+
Fe3+
Mg2+
Mn2+
Zn2+
273407
4774
5177
256209
110144
372499
Total1109 1410
doi:10.1371/journal.pone.0039252.t001
Figure 3. The fragment transformation method. siand skare two
arbitrary triplet units in the query protein S, and tjand tlare two
arbitrary triplet units in the template T. In the illustration, the triplet siis
transformed onto tjvia application of the transformation matrix Mi,j.
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Metal ion–binding residue templates
Figure 2 shows an example of a local structure containing metal
ion–binding residues, i.e., those at least partially within 3.5 A˚of a
metal ion center as judged by their PDB coordinates. To be
considered as a template, a site needed contain more than two
metal ion–binding residues. In total, 1,410 templates were
generated from the 1,109 polypeptides. Table 1 list the statistics
for each kind of metal ion–binding polypeptide and metal ion–
binding template.
The fragment transformation method
In general, the fragment transformation method [25] aligns
similar local fragments that contain residues that are discontinuous
in sequence but spatially close; for our study, the method was
modified to align metal ion–binding residues. The fragment
transformation method treats each binding residue as an
individual unit. The structural unit used to align the query protein
and the templates is a triplet formed by the backbone N{Ca{C
atoms of a given residue. S denotes the query protein of length m,
T denote template of n residues. The N{Ca{C triplets of S and
T be given by (xN,xCa,xC) and (yN,yCa,yC) respectively, where
x and y are the PDB coordinates for that atom. S and T can
therefore beexpressedinterms of thetripletsas
S~ s1,s2,???,sm
fg and T~ t1,t2,???,tm
fg, where
si~ xN,xCa,xC
ðÞandtj~ yN,yCa,yC
ðÞ:
Note that the information contained in the peptide bonds
preceding and following a residue is not used, meaning that s and
t are not representative of the backbone torsion angles, w and Q,
whichrequirethe coordinates
N{Ca{C{N00, respectively, where C’ is the carbonyl carbon
preceding the residue and N’’ is the amide nitrogen of the next
residue. Thus, the fragment unit do not contain information
concerning the torsion angles.
A matrix of dimensions m|n is then constructed for the
residues of S and T as:
of
C0{N{Ca{C
and
M~D
M1,1
M2,1
...
M1,2
M2,2
...
...
...
M1,n
M2,n
... ...
...
Mm,1
Mm,2
Mm,n
D
ð1Þ
where the element Mijis a rigid-body transformation matrix that
Figure 4. Frequency of each amino acid in the metal ion–binding sites. Frequencies of each amino acid in a given type of metal ion–binding
site (black) and in the corresponding protein (grey). A, Ca2+. B, Cu2+. C, Fe3+. D, Mg2+. E, Mn2+. F, Zn2+. For this study, 1,109 metal ion–binding
polypeptides were used and the metal ion–binding sites were defined as residues partially within 3.5 A˚of the metal ion.
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transforms the triplet sito tj, i.e., Mijsi~tj. Each transformation
matrix Mijcontains three rotations around and three translations
along the x, y and z Cartesian axes (Figure 3).
Performing triplet clustering
Dij
kl, defined as the Cartesian distance between the target tland
the transformed triplet Mijsk, provides a measure of how similar
the orientation of the triplet pairs (si,tj) and (sk,tl) is, which
Figure 5. Frequency of atom types in the metal ion–binding sites. Frequency of each type of atom in the backbone (black) and in the side
chain (grey). A, Ca2+. B, Cu2+. C, Fe3+. D, Mg2+. E, Mn2+. F, Zn2+.
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allows us to cluster the triplet fragments using the single-linkage
algorithm [29] as follows. If for two triplet pairs,(si,tj) and (sk,tl),
Dij
and G2be two clusters, the first containing (si,tj) and (sk,tl) and
the second containing (si0,tj0) and (sk0,tl0). If Dij
and G2are merged to form a new cluster G3, where G3~G1|G2.
The procedures are carried out iteratively until no new clusters
can be formed. For each final cluster Gm, we obtain the aligned
substructurepairSm
and
Tm,
[
klvD0, and i=k and j=l, then the triplets are clustered. LetG1
k0l0vD0, then G1
where
Sm~
[
sk[Gm
sk
and
Tm~
tk[Gm
tk.
Scoring function
The metal ion–binding score, Ci, for each residue i, is defined as
Ci~MAX
si[Gm
em|CR
m|CB
m
??
ð2Þ
where emis the number of triplets of Sm, i.e., the aligned residues of
the query structure. The alignment scores CR
m, CB
mare defined as:
CR
m~
1
1zRMSD(Sm,Tm)
ð3Þ
and
CB
m~BLOSUM(Sm,Tm)
BLOSUM(Tm,Tm)
ð4Þ
where RMSD(Sm,Tm) is the root mean square deviation of allCa
atoms between Sm and Tm;BLOSUM(Sm,Tm) is the sequence
alignment score between Sm and Tm, calculated using the
BLOSUM62 [26] substitution matrix, and BLOSUM(Tm,Tm) is
Figure 6. Metal ion–binding site prediction as functions of the metal ion–binding threshold scores. Accuracy (black solid line), true
positive rate (dashed line), and false positive rate (grey line) as functions of the threshold values. A, Ca2+. B, Cu2+. C, Fe3+. D, Mg2+. E, Mn2+. F, Zn2+.
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the maximum sequence alignment score of Tm. The value of
RMSD(Sm,Tm) should be less than 3 A˚, and CB
than CB
0which can be adjusted to obtain the best result for each
type of metal ion. Finally, the normalized metal ion–binding score,
ZC
i, is calculated as:
mshould be greater
ZC
i~Ci{C
SDC
ð5Þ
where C and SDCdenote the mean and the standard deviation,
respectively, of the metal ion–binding score.
Performance assessment
The performance of the metal ion–binding site prediction
method, i.e., the prediction accuracy (ACC), was defined as the
number of true positive and true negative and evaluated using a
leave-one-out approach. The accuracy (ACC), the true positive
rate (TPR) and false positive rates (FPR) were calculated using the
true positive (TP), true negative (TN), false positive (FP), and false
negative (FN) values as follows:
ACC~
TPzFN
TPzTNzFPzFN
ð11Þ
TPR~
TP
TPzFN
ð12Þ
FPR~
FP
FPzTN
ð13Þ
Results
Metal ion–binding residue profiles
Spheres each with a 3.5 A˚radius from the center of a metal ion
were constructed for each metal ion–site in our dataset. We
assessed the frequency that each of the 20 amino acids coordinated
a metal ion (Fig. 4); those metal ions were found to preferentially
bind certain residues, as follows: for Ca2+, ASP, GLU, ASN, and
GLY; for Cu2+, HIS; for Mg2+ASP and GLU; for Fe3+, HIS,
GLU, ASP, CYS, and TYR; for Mn2+, ASP, HIS, and GLU; and
for Zn2+, CYS and HIS. Notably, each type of metal ion favors
specific residues.
The preferred types of atoms surrounding the metal ions are as
follows (Figure 5): for Ca2+, backbone and side-chain oxygens; for
Mg2+and Mn2+, side-chain oxygens; for Cu2+, Fe3+, and Zn2+,
oxygen, nitrogen, and sulfur. Each metal ion appears to
preferentially bind certain atoms in certain residues.
Predictive performance
For each metal ion, we set the threshold of the normalized metal
ion–binding score so that the FPR was #5% (Fig. 6). For Ca2+–
binding proteins, the threshold was 1.6, which gave a 94.1 %
Figure 7. Receiver operating characteristic curves generated from the metal ion–binding site prediction. The performance of the
method was assessed by measuring the areas under the receiver operating characteristic curves. The x axis reports the false positive rate (FPR), and
the y axis reports the true positive rate (TPR).
doi:10.1371/journal.pone.0039252.g007
Table 2. Comparison of the results for the fragment
transformation and the artificial neural network methods.
Metal ion
ANNThis work
Accuracy (%) TPR (%) Accuracy (%)TPR (%)
Ca2+
Cu2+
Fe3+
Mg2+
Mn2+
Zn2+
93.9 30.494.1 48.9
94.9 36.294.9 85.6
94.948.894.985.4
94.2 32.494.6 37.0
94.738.8 95.061.4
94.647.8 94.871.1
Overall94.539.1 94.6 60.5
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accuracy and a TPR of 48.9 %; for Cu2+– and Mg2+–binding
proteins, the threshold was 1.8, which yielded 94.9 % accuracy
and a TPR of 85.6 %, and 95.0 % accuracy and a TPR of
61.4 %, respectively; for Fe2+– and Mn2+–binding proteins, the
threshold was 1.0 for 94.9 % accuracy and a TPR of 85.4 %, and
94.6 % accuracy and a TPR of 37.0 %, respectively. The best
performance was obtained for Zn2+–binding proteins, for which a
threshold of 2.2 gave 94.8 % accuracy and a TPR of 71.1 %. The
performance of the predictions as a function of the threshold
values for six types of metal ion–binding proteins is shown as
receiver operating characteristic plot (TPR values vs. FPR values,
Fig. 7). The predictive performance was excellent for Cu2+– and
Fe3+–binding proteins and very good for Mn2+– and Zn2+–
binding, but less so for Mg2+– and Ca2+–binding proteins.
Comparison with published methods
We compared our results with those obtained using the artificial
neural network (ANN) method [30] and the geometric subgraph
method [18]. The same types of metal ion–binding sites were used
in the three studies, and the methods were each designed to
predict every residue within a metal ion–binding protein as a
binding or a non-binding residue. When the FPR was # 5 %, our
method was more accurate and had greater TPR values than did
the ANN method (Table 2). Given the similar accuracies (61 %),
the larger TPR values were especially noticeable for the Cu2+– and
Fe3+–binding proteins (TPR=85.6 % and 85.4 % for our
method, and 36.2 % and 48.8 % for the ANN method, for the
two types of proteins, respectively). The TPR values for Mn2+and
Zn2+also dramatically improve–from 38.8 % to 61.4 % for Mn2+
Figure 8. Identification of Ca2+–binding sites. A. human cytosolic phospholipase A2 (PDB ID:1RLW) as the query protein. B. Template
constructed from chain A of synaptotagmin I C2B-domain (PDB ID:1K5W).
doi:10.1371/journal.pone.0039252.g008
Figure 9. Identification of Cu2+–binding sites. A. Chain A of plastocyanin from the cyanobacterium Phormidium laminosum (PDB ID:1BAW) as
the query protein. B. Template constructed from plastocyanin (PDB ID:1KCW).
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and from 47.8 % to 71.1 % for Zn2+. The TPR for Ca2+also
increased from 30.4 % to 48.9 %; however, the improvement was
much smaller for Mg2+, from 32.4 % to 37.0 %. The average
TPR for the six classes of proteins for our study was 60.5 %
(FPR# 5 %), which is an improvement compared with the results
obtained using the geometric subgraph method (TPR, 46.9 %;
FPR, 11.9 %).
Template matching
Figures 8,9,10,11,12,13 show examples of an alignment for each
type of metal ion–binding protein and the corresponding template.
The structures were drawn by PyMOL [31]. For human cytosolic
phospholipase A2 (PDB ID: 1RLW; Fig. 8) [32], which has two
Ca2+–binding sites, seven binding residues were found, all with
large normalized metal ion–binding scores. The template that best
Figure 10. Identification of Fe3+–binding sites. A. Desulfoferrodoxin (PDB ID:1DFX) as the query protein. B, C. Templates constructed from (B)
chain A of superoxide reductase (PDB ID:1DO6:A) and (C) chain A of rubrerythrin (PDB ID:1B71).
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Figure 11. Identification of Mg2+–binding sites. A. Chain A of human mitochondrial deoxyribonucleotidase (PDB ID:1MH9) as the query protein.
B. Template constructed from chain B of transglutaminase 3 (PDB ID:1NUG).
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aligned with the Ca2+–binding sites in phospholipase A2 was the
chain A of synaptotagmin I C2B-domain (PDB ID:1K5W) [33]
(Fig. 8). The template for the Cu2+–binding protein, human
ceruloplasmin (PDB ID:1KCW) [34], almost perfectly aligned
with the Cu2+–binding site in the A chain of plastocyanin
(PDBID:1BAW) [35] (Fig. 9), although a few FP metal ion–
binding residues were also identified. The best predictive
performance was found for Fe3+–binding proteins. For desulfo-
ferrodoxin (PDB ID:1DFX) [36], two templates derived from two
different proteins, superoxide reductase chain A (PDB ID:1DO6)
[37] and rubrerythrin chain A(PDBID:1B71) [38], matched its two
binding sites, and the nine binding residues, plus an FP, were
identified (Fig. 10). Although the identification of Mg2+–binding
sites was not as successful, because many FPs were associated with
high scores, the Mg2+–binding site of human mitochondrial
deoxyribonucleotidase chain A (PDB ID:1MH9) [39] was found to
be similar to the template constructed from transglutaminase 3
chain B (PDB ID:1NUG) [40] (Fig. 11). In cytochrome b1 chain A
(PDB ID:1BFR) [41], two Mn2+–binding sites were in close
proximity and involved the same two glutamic acids (Fig. 12).
These binding sites were found using the template from
ribonucleotide reductase chain A (PDB ID:1KGP) [42], even
though a reorientation of the template was required during the
fragment transformation procedure. For Zn2+–binding proteins, a
near perfect match was found for chain A of the inhibitor of
apoptosis protein DIAP1 (PDB ID:1JD5) [43] and the template
from chain E of the baculoviral IAP repeat-containing protein 4,
BIR 2 (PDB ID:1I3O) [44] (Fig. 13).
Discussion
In this study, we developed and used a structure comparison
method to predict metal ion–binding sites in proteins. During
development, we combined conserved structure and sequence
information to identify metal ion–binding residues, which are
extremely important design elements as they substantially affect
the prediction. Our prediction method performed much better for
Cu2+, Fe,
possibly because there are fewer types of residues that bind the
transition metal ions compared with those that bind the alkaline
earth ions. Thus, the residues and structures of the Ca2+– and
Mg2+–binding sites may be less specific. In particular, we observed
that backbone carbonyl oxygens, rather side-chain oxygens,
frequently bind Ca2+and Mg2+, which indicates that the type of
residue is less important–at least for an interaction involving a
carbonyl oxygen. Conversely, interactions between backbone
atoms and Cu2+, Fe3+, Mn2+, and Zn2+are rare; instead, side-
chain atoms bind these ions; causing steric and chemical
limitations imposed by the particular side-chain. These two
factors, i.e., residue and atom–binding patterns, probably result
in smaller sequence alignment scores for the metal ion–binding
residues. As such, the final metal ion–binding scores for certain
residues may in fact be lower than the threshold value set for metal
ion–binding residues.
Our approach yielded excellent predictions for Cu2+– and Fe3+–
binding sites, and very good predictions for Zn2+– and Mn2+–
binding sites. Although the method gave poorer results for Ca2+–
and Mg2+–binding sites, it nonetheless performed better than did
3+Mn2+, and Zn2+than it did for Ca2+and Mg2+,
Figure 12. Identification of Mn2+–binding sites. A. Chain A of cytochrome b1 (PDB ID:1BFR) as the query protein. B, C. Both templates
constructed from chain A of ribonucleotide reductase (PDB ID:1KGP) but oriented differently.
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the geometric subgraph and ANN methods. Ultimately, for an
FPR threshold of 5 % our method achieved an overall 94.6 %
accuracy with a TPR of 60.5 %, which is a substantial
improvement over other prediction methods currently available.
Therefore, our method may find use as a predictor of putative
metal ion–binding proteins and their binding. The Linux binary
codes for our method are available upon request.
Acknowledgments
We gratefully thank Jenn-Kang Hwang and Yeong-Shin Lin, National
Chiao Tung University, Taiwan for their invaluable comments.
Author Contributions
Conceived and designed the experiments: CHL. Performed the experi-
ments: CHL YFL JJL. Analyzed the data: CHL YFL CSY. Contributed
reagents/materials/analysis tools: CHL CSY. Wrote the paper: CHL YFL.
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