We use the same coding scheme as Sodhi’s, but we choose different radii for defining non-site residues. Instead of using artificial neural network for training and testing, we use SVM to perform the classification tasks. Our results from the 21Å training model achieves a 97.4% overall prediction accuracy compared to 94.5% and 46.2% TPR at 5% FPR compared to 39.2%. The comparison is listed in Table 6.
4 DISCUSSIONS
In identifying functional relevance residues in proteins, previous studies11,21,24 have shown that the machine learning approach is an effective way to discriminate the site residues from non-site residues. The SVM method is used in this study to predict the metal-binding site residues and gives consistent results. By comparing the performance of each coding feature, it is obvious that the ability to identify the site residues with a high accuracy comes from the sequence information as well as the structure information. The combination of structural and sequence information improves the quality of prediction11,24. Using SVM, this work is comparable with the recent work using artificial neural network11.
When doing n-fold cross validation, the dataset is divided into n subsets, and the evaluation process repeats n times. Each time, one of the n subsets is taken as the test set, and the other n-1 subsets are taken as one training set as a whole. The division of the dataset is usually by random18,19. In this study we divide the dataset according to the super-family of each instance in the dataset. The reason for using this approach is that we can detect the site in proteins as if they are in new super-families since the training and testing set do not have proteins in common super-families11. We compared these two approaches for dividing the dataset. The one with random division has the better results because of the knowledge of the homologs. In practice, since we already have a library for all proteins with known structures, the homology information can be utilized to improve the prediction accuracy; if it fails to have the homologs, it is just the case to predict the proteins in new super-families. Therefore the performance resulting from grouping by super-families can be regarded as the worst case situation.
The site or non-site residues are defined as within or without a certain distance to the metal ion. The value of this distance has a direct impact on the amount of site or non-site residues.
The site residues in this study are defined to be within the sphere of radius 7Å centered at the metal ion. We try several different cut-off values to define the non-site residues and find that not only the training time but also the TPR improves as the radius increases. By using a small radius, more residues close to the binding site (but still outside the 7Å radius) are taken as non-site residues. These residues resemble the real site residues in position and nature. This may cause the SVM difficult to distinguish the site residues from non-site residues. Choosing the limited number of non-site residues as the control group10 may prevent the classifier from over-fitting and improve the sensitivity to detect site residues. The results showed in Table 5 confirmed this point. The best prediction results do not occur at the training model produced by 7Å cut-off value for non-site residues nor at that by 35Å. The 35Å radius is so large an area such that few non-site residues can be included in the training set, resulting in the scarcity of training data.
From the results we notice that the prediction accuracy and TPR of calcium ion is much lower compared to other metal ions. This trend is consistent with the results by Sodhi, et al.
(2004). The ionic radius of calcium is 0.95Å, 0.46Å for manganese, 0.645Å for iron, 0.65Å for magnesium, 0.73Å for copper, and 0.75Å for zinc25-27. As a result, the ion-oxygen bond length is longer for calcium ion than the other ions listed here28. They also show that calcium ions have a strong binding affinity to oxygen atoms, resulting in the constrained choice of ligands. Due to the same decision radius of 7Å for all metal ions, the longer ion-oxygen bond length of calcium ion may prevent the ligands from been included in the site residues. This situation may lead to the lower prediction accuracy and also the longer training time.
As a conclusion, it has been shown that the coding scheme that combines sequence profile and structural features is capable of represent the characteristics of particular site patterns.
Protein structures can be viewed as residue interaction graphs (RIGs)29, a simplified model to represent the complex protein 3D structures. This model is used to identify active sites, ligand-binding and evolutionary conserved residues. Bagley and Altman (1995) use a fixed number of concentric shells to collect the features stored in the vicinity of the calcium ion.
This raises a possibility to encode the spatial distribution of properties of residues in the shells centered at particular residue. This new direction may lead to more efficient calculations and more accurate predictions.
REFERENCES
1. Ibers JA, Holm RH. Modeling coordination sites in metallobiomolecules. Science 1980;209(4453):223-235.
2. Babor M, Greenblatt HM, Edelman M, Sobolev V. Flexibility of metal binding sites in proteins on a database scale. Proteins 2005;59(2):221-230.
3. Lu Y, Berry SM, Pfister TD. Engineering novel metalloproteins: design of
metal-binding sites into native protein scaffolds. Chem Rev 2001;101(10):3047-3080.
4. Thompson KH, Orvig C. Boon and bane of metal ions in medicine. Science 2003;300(5621):936-939.
5. Dobson PD, Doig AJ. Distinguishing enzyme structures from non-enzymes without alignments. J Mol Biol 2003;330(4):771-783.
6. Chothia C. Proteins. One thousand families for the molecular biologist. Nature 1992;357(6379):543-544.
7. Dobson PD, Doig AJ. Predicting enzyme class from protein structure without alignments. J Mol Biol 2005;345(1):187-199.
8. Charniak E. Bayesian networks without tears. AI Magazine 1991;12(4):50-63.
9. Vapnik V. The Nature of Statistical Learning Theory. New York: Springer-Verlag;
1995.
10. Bagley SC, Altman RB. Characterizing the microenvironment surrounding protein sites. Protein Sci 1995;4(4):622-635.
11. Sodhi JS, Bryson K, McGuffin LJ, Ward JJ, Wernisch L, Jones DT. Predicting metal-binding site residues in low-resolution structural models. J Mol Biol 2004;342(1):307-320.
12. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein Data Bank. Nucleic Acids Res 2000;28(1):235-242.
13. Zhang C, Kim SH. Environment-dependent residue contact energies for proteins. Proc Natl Acad Sci U S A 2000;97(6):2550-2555.
14. Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 1995;247(4):536-540.
15. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990;215(3):403-410.
16. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983;22(12):2577-2637.
17. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ.
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;25(17):3389-3402.
18. Hua S, Sun Z. A novel method of protein secondary structure prediction with high
segment overlap measure: support vector machine approach. J Mol Biol 2001;308(2):397-407.
19. Yu CS, Wang JY, Yang JM, Lyu PC, Lin CJ, Hwang JK. Fine-grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple-parameter sets. Proteins 2003;50(4):531-536.
20. Chen YC, Lin YS, Lin CJ, Hwang JK. Prediction of the bonding states of cysteines using the support vector machines based on multiple feature vectors and cysteine state sequences. Proteins 2004;55(4):1036-1042.
21. Yan C, Dobbs D, Honavar V. A two-stage classifier for identification of protein-protein interface residues. Bioinformatics 2004;20 Suppl 1:I371-I378.
22. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. Software available from http://www.csie.ntu.edu.tw/~cjlin/libsvm. 2001.
23. Metz CE. Basic principles of ROC analysis. Semin Nucl Med 1978;8(4):283-298.
24. Gutteridge A, Bartlett GJ, Thornton JM. Using a neural network and spatial clustering to predict the location of active sites in enzymes. J Mol Biol 2003;330(4):719-734.
25. Pauling L. The nature of the chemical bond. Cornell University Press. Ithaca, NY.
1960.
26. Brown ID. What factors determine cation coordination numbers? Acta Cryst 1988;B44:545-553.
27. Barbalace K, Barbalace R, Barbalace J. Periodic Table of Elements Sorted by Ionic Radius. http://environmentalchemistrycom/ 1995.
28. Katz AK, Glusker JP, Beebe SA, Bock CW. Calcium Ion Coordination: A Comparison with That of Beryllium, Magnesium, and Zinc. J Am Chem Soc 1996;118:5752-5763.
29. Amitai G, Shemesh A, Sitbon E, Shklar M, Netanely D, Venger I, Pietrokovski S.
Network analysis of protein structures identifies functional residues. J Mol Biol 2004;344(4):1135-1146.
TABLES Table 1. The statistics of the datasets used in this work Ion No. PDB
chainsa No. SCOP
super-families No. metal ions
Zn2+ 372 202 613
Ca2+ 273 144 576
Mg2+ 261 129 357
Mn2+ 110 66 173
Cu2+ 47 28 74
Fe3+ 51 25 66
aThese data are collected at the time of Nov 2004.
Table 2. Comparison of the two grouping methods: by randomness and by super-families
By randomness By super-families
Ion Q2 accuracy (%) TPR (%) Q2 accuracy (%) TPRa (%)
Zn2+ 94.8 79.4 93.8 73.8
Ca2+ 89.8 55.0 86.3 34.1
Mg2+ 95.3 50.0 94.7 41.3
Mn2+ 94.5 71.6 93.2 61.5
Cu2+ 95.6 84.4 91.5 62.6
Fe3+ 93.7 86.1 85.9 54.0
Average 94.0 71.1 90.9 54.6
The cutoff radius for non-site residues is 35Å.
aThis is the true positive rate (TPR) at 5% false positive rate (FPR).
Table 3. Comparison of the two training options of SVM: weight and parameter adjustment
Parameter adjustment Weight adjustment
Ion Q2 accuracy (%) TPR (%) Q2 accuracy (%) TPR (%)
Zn2+ 94.6 79.0 93.8 73.8
Ca2+ 87.4 41.0 86.3 34.1
Mg2+ 95.0 48.9 94.7 41.3
Mn2+ 93.5 65.3 93.2 61.5
Cu2+ 91.9 66.4 91.5 62.6
Fe3+ 86.8 56.5 85.9 54.0
Average 91.5 59.5 90.9 54.6
The cutoff radius for non-site residues is 35Å.
Table 4. Comparison among different buffer sizes
Q2 accuracy (%) TPR (%) / 5% FPR Buffer
Size (Å) Zn Ca Mg Mn Cu Fe Avg Zn Ca Mg Mn Cu Fe Avg 35 94.0 92.7 98.1 94.9 92.9 81.0 92.3 67.3 28.7 36.1 55.9 35.6 41.4 44.2 28 96.4 95.6 98.4 96.8 95.4 92.8 95.9 67.7 30.4 36.3 55.3 36.3 45.1 45.2 21 97.1 96.2 98.6 97.9 97.5 96.8 97.4 66.6 30.2 35.2 55.8 40.1 49.4 46.2 14 97.3 96.4 98.6 98.3 98.0 98.0 97.8 66.9 29.5 33.5 55.8 37.4 51.1 45.7 7 97.5 96.5 98.7 98.4 98.2 98.5 98.0 64.0 29.1 30.7 52.4 35.3 49.4 43.5 The testing set is of 7Å buffer size. The training sets are of different buffer sizes ranging from 7Å to 35Å.
Table 5. Comparison among different coding features
Q2 accuracy (%) TPR (%) / 5% FPR Featurea
Zn Ca Mg Mn Cu Fe Avg Zn Ca Mg Mn Cu Fe Avg
ACDE 97.1 96.2 98.6 97.9 97.5 96.8 97.4 66.6 30.2 35.2 55.8 40.1 49.4 46.2 CDE 96.9 96.8 98.7 98.5 98.2 98.4 97.9 19.2 7.1 10.9 10.6 6.3 14.7 11.5 A 96.8 96.7 98.6 97.9 97.5 96.6 97.4 64.0 26.4 31.7 52.5 39.1 50.6 44.1 B 96.8 96.6 98.6 97.9 97.8 97.2 97.5 58.8 27.1 32.0 48.3 33.9 40.7 40.1 C 96.9 96.8 98.7 98.5 98.3 98.5 98.0 5.7 7.5 6.3 5.5 4.8 3.0 5.5
aA: Site PSSM, B: Sequence PSSM, C: Secondary Structure, D: Solvent Accessibility, E:
Distance Matrix
Table 6. Comparison with Sodhi’s results
Sodhi JJLina
Ion Q2 accuracy (%) TPR (%) Q2 accuracy (%) TPR (%)
Zn2+ 94.6 47.8 97.1 66.6
Ca2+ 93.9 30.4 96.2 30.2
Mg2+ 94.2 32.4 98.6 35.2
Mn2+ 94.7 38.8 97.9 55.8
Cu2+ 94.9 36.2 97.5 40.1
Fe3+ 94.9 48.8 96.8 49.4
Average 94.5 39.1 97.4 46.2
aThe cutoff radius for non-site residues is 21Å.
FIGURE CAPTIONS
Figure 1. PDBid 1A8A, with coordination number 4. The sphere in magenta is the calcium ion, and the sphere in red is the oxygen atom. The smaller figure at upper-left corner is the full-view of the protein, and the larger figure is the magnified view of one of the metal-binding site.
Figure 2. PDBid 1G4Y, with coordination number 5.
Figure 3. PDBid 1I76, with coordination number 6.
Figure 4. PDBid 1GCI, with coordination number 7.
Figure 5. PDBid 1ARU, with coordination number 8.
Figure 6. The data pre-processing procedure. We obtain the original sequences from PDB.
The chain that is DNA or RNA is filtered out. The length of the chain must be at least 50 residues. The chain must contain at least one metal ion. The information of SCOP super-families, DSSP secondary structure, and solvent accessibility is retrieved and processed.
The DSSP sequence is used to generate the PSSM of the sequence. Finally the dataset contains 1063 protein chains.
Figure 7. Definition of the site residues. Residues are the site residues if their main chain atoms (N, Cα, C) lie within the 7Å radius sphere centered at the metal ion. The figure shows the Benzoylformate Decarboxylase structure with PDBid 1BFD.
Figure 8. Definition of buffer zone and the non-site residues. The buffer zone is located between the inner and outer sphere. The radius of the inner sphere is 7Å, and the radius of the outer sphere ranges from 7Å to 35Å. The non-site region is located outside the outer sphere.
Residues located at the non-site region are the non-site residues. The figure shows the Benzoylformate Decarboxylase structure with PDBid 1BFD.
Figure 9. Coding information of PSSM. The 20 scores of the next residue are appended to the rear of the scores of the present residue.
Figure 10. Coding information of DSSP. The 40 scores of the DSSP are appended to the rear
of the PSSM scores.
Figure 11. Coding information of distance matrix of Cβ atoms of residue neighbors. The 45 distances are appended to the rear of the DSSP scores.
Figure 12. System flowchart
Figure 13. Comparison of the accuracy among different buffer sizes. The 7Å testing set is predicted by all the training models of different radii.
Figure 14. Comparison of the true positive rate (TPR) among different buffer sizes.
Figure 15. ROC curves of different classifiers. The results are from buffer size of 21Å.
Figure 16. ROC curves of each feature. A: Site PSSM, B: Sequence PSSM, C: Secondary structure, D: Solvent accessibility, E: Distance matrix. (a) Fe3+ ion. (b) Cu2+ ion. (c) Mn2+ ion.
(d) Mg2+ ion. (e) Ca2+ ion. (f) Zn2+ ion.
FIGURES Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Original
sequence Filter out the chain that is DNA or RNA
Filter out the chain with sequence length less than 50
Filter out the chain without metal ion
Identifying DSSP secondary structure and solvent accessibility Identifying SCOP classification at the superfamily level
The dataset
Figure 7
Non-binding site residue
Binding site residue
Figure 8
Non-binding site residue
Binding site residue Buffer Zone
Figure 9
Figure 10
Figure 11
23 G 19 F 25 G 27 I 17 S 16 F 21 K 31 E 26 T 20 D 4.692 5.131 5.397 5.416 5.691 5.743 6.288 6.403 6.806 23 G 9.599 5.532 9.512 7.258 9.342 7.031 9.127 8.386 19 F 9.824 4.559 7.418 6.090 7.315 5.662 9.515 25 G 7.815 9.153 7.907 10.673 10.212 4.656 27 I 10.125 7.459 8.684 5.156 5.665 17 S 5.333 9.062 10.924 12.066 16 F 11.222 10.690 9.782 21 K 4.848 10.825 31 E 8.423 26 T
Distance matrix of Cβ atoms
Total: 10 * 9 / 2 = 45 (distances)
Figure 12
PDB
Data pre-processing
Dataset
Encoding
SVM input file
SVM training
Fivefold cross-validation
DSSP (SS, ACC)
PSSM
Distance Matrix 25% sequence identity
clustering
Performance Evaluation
Figure 13
80 82 84 86 88 90 92 94 96 98 100
7 14 21 28 35
Buffer radius (Å)
Accuracy (%)
Zn Ca Mg Mn Cu Fe
Figure 14
20 25 30 35 40 45 50 55 60 65 70
7 14 21 28 35
Buffer radius (Å)
TPR (%)
Zn Ca Mg Mn Cu Fe
Figure 15
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0
FPR (%)
TPR (%)
Fe Cu Mn Mg Ca
.45 0.5
Zn
Figure 16
Fe
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
FPR (%)
TPR (%)
A B C C+D+E A+C+D+E
Figure 17
Cu
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
FPR (%)
TPR (%)
A B C C+D+E A+C+D+E
Figure 18
Mn
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
FPR (%)
TPR (%)
A B C C+D+E A+C+D+E
Figure 19
Mg
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
FPR (%)
TPR (%)
A B C C+D+E A+C+D+E
Figure 20
Ca
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
FPR (%)
TPR (%)
A B C C+D+E A+C+D+E
Figure 21
Zn
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
FPR (%)
TPR (%)
A B C C+D+E A+C+D+E
APPENDIX Appendix 1. Protein chains containing Fe3+ ions
Fe
1EO2:B 1COJ:A 1NF6:F 1SUV:E 1B13:A 1RSV:B 1B0L:A 1OHV:A 1KBP:A 1GUP:A 1QGH:A 2PAH:A 1DO6:A 1NDO:A 1OQU:C 1DLM:A 1OQ4:A 1MMO:D 1NX4:B 1NMO:A 1B71:A 1O9R:A 1HU9:A 1D9Y:A 1UN7:A 1Q0C:A 1EIQ:A 1BK0:_ 1RCW:B 1GV8:A 1K6W:A 1PHZ:A 1M63:A 1O2D:B 1QHW:A 1GY9:A 1LTV:A 1RXG:_ 2AHJ:C 1J3Q:A 1LM4:A 1DYT:A 1FRF:L 1GP5:A 1LNB:E 1GVG:A 1XSM:_ 1J2C:A 1LM6:A 1FHA:_ 1DFX:_
Appendix 2. Protein chains containing
u
Cu2+ ions
C
1EQW:B 1CC3:A 1ET7:A 1BAW:A 1OAC:B 1M56:A 1H1I:B 1AV4:_ 1BQK:_
1ID2:A 1M56:B 1IBY:A 1A2V:A 1AOZ:A 1HCY:_ 1A65:A 1GY2:A 1THO:_
1ODB:A 1OV8:A 1N9E:A 1NOL:_ 1FSR:A 1GMW:A 1N68:A 1LNL:A 1OPM:A 1LCF:_ 1GOF:_ 1GW0:A 2OXI:A 1BUG:A 1A8V:B 1JER:_ 1KCW:_ 1GSK:A 7ICJ:A 1ARM:_ 1KYR:A 1AND:_ 1IAA:_ 1QD0:A 1AQP:_ 1OT4:A 1UKU:A 1OQ6:A 1FVS:A
Appendix 3. Protein chains containing Mn2+ ions
Mn
1BJQ:B 1AZD:A 1HQF:A 1KFL:A 1GV3:A 1F3W:A 1F1H:A 1DID:A 1HHS:A 1BFR:A 1GQ2:A 1ZQL:A 1G8O:A 1FSA:A 1M6V:A 1E6A:A 1HX3:B 1FQW:A 1N51:A 1CHR:A 1O0R:B 1K20:A 1MHP:B 1NHX:A 1HO5:A 1J2T:A 1FUI:A 1FJM:A 1N1H:A 1ITW:A 1QMG:A 1O98:A 1A0D:A 1DE6:A 1JPR:B 1KGP:A 1IMC:B 1G0I:A 1RF7:_ 1GX1:A 1C39:A 1JFZ:B 1ON2:A 1M0D:A 1UW8:A 1GQ6:B 1NOM:_ 1G5B:B 1I50:A 1L5G:A 1JQN:A 1KSI:A 1LV5:A 1L5G:B GX6:A 1FA0:B 1N1P:A 1KHW:B 1ECC:A 1MM8:A 1ELS:_ 1LWD:A 1OLX:A 1CNZ:A 1CDK:A 1II7:A 1I0B:A 1KGZ:B 1IPS:A 1O6K:A 1CPO:_ 1IG1:A 1OF2:A 1UT5:B 1QH3:A 1KWS:A 1M4Z:B 1MQW:A 1J53:A 1GN8:A 1A5V:_
1VJ2:A 1NR0:A 1EF2:A 1HK8:A 1AQ2:_ 1F5A:A 3UAG:A 1IR6:A 1A6Q:_
1MNP:_ 1FOA:A 1LNC:E 1A76:_ 1RZD:_ 1LL2:A 1H7Q:A 1K23:D 1DAH:_
1ZIP:_ 1FI2:A 1INO:_ 1JAI:_ 1NCY:_ 1NZ5:A 1G15:A 1JLK:A 1J25:A 2PAL:_ 1IGV:A
1
Appendix 4. Protein chains containing Mg2+ ions
Mg
1BGL:A 1AN0:A 1DIE:A 1BWV:A 1N0H:A 1BR1:A 1MAB:A 1EW2:A 1MFR:A 1A49:A 1EBG:A 1OHH:D 1EYI:A 1AGR:A 1HUR:A 1N1Z:A 1IV2:A 1NUE:A 1EC9:D 1M34:E 1BYU:B 1CG0:A 1ATR:_ 1ZPD:A 1F0J:A 1CJB:C 1E2F:A 1FRF:L 1AF6:A 1G69:B 1OFH:G 1EFT:_ 1F4V:A 1OCC:A 1F8I:A 1M3U:A 1P8F:A 1MXA:_ 1G3U:A 1JGT:B 1ECB:C 13PK:A 1GOJ:A 1GZG:A 1JQV:A 1QMZ:A 1J7L:A 1HBM:C 1F5S:A 1PT6:B 1NFS:B 1HI0:P 1AON:A 1B0P:A 1II0:B 1MX0:C 1MIV:A 1NMP:A 2AKY:_ 1QEZ:A 1DY3:A 1F7D:A 1IQ8:A 1HBN:A 1BWF:O 1AUK:_ 1GKI:B 1DTN:_ 1GQC:A 1N8W:A 1OHF:C 1N5Y:A 1K3C:A 1YVE:I 1NJ1:A 1HBN:B 1KCZ:A 1EYZ:A 1BXZ:A 1UR2:A 1ESN:A 1H7U:A 1RK2:B 1M1B:A 1E0J:B 1OES:A 1GS5:A 1FEZ:A 1DXE:A 1DEK:A 1QL0:A 1IW7:A 1NR9:A 1BJY:B 1OE0:A 1KO5:A 1GRV:B 1B8C:A 1GUS:A 1ITZ:A 1EQR:A 1H7A:A 1GPM:C 1EHI:A 1K9Y:A 1UBW:_ 1GXB:D 1JX4:A 1PFK:A 1IOV:_ 1FYD:A 1H65:A 1H1D:A 1L4Y:A 1HTW:A 1Q9S:A 1IW7:D HQM:D 1IW7:C 1GL9:B 1G8X:A 1L8A:A 1FW6:A 1H16:A 1GQI:A 1NUG:B 3PMG:A 1UN9:A 1AMU:A 1SH3:B 1M0W:A 1GQY:B 1EWK:B 1BIF:_ 1HYO:B 1JBV:A 1OD5:A 1FNN:A 1J58:A 1J1C:B 1OC7:A 1N6M:A 1NI4:A 1JMS:A 1GOL:_ 1IXY:B 1IW7:F 1NE9:A 8ICI:A 1GKZ:A 1E19:A 1OBG:A 1H3I:A 1MC3:A 1MUM:A 2PRN:_ 1VFN:_ 1F1Z:A 1G6H:A 1J9J:A 1IRU:G 1NUI:A 1H7Q:A 1GKB:A 1A82:_ 1LVH:A 1N9K:A 1UUT:A 1IPP:A 1H56:A 1L5Y:B 1KQM:B 1L0O:A 1KTG:A 1CMC:A 1NLQ:A 1UN6:C 1I3Q:A 1KC7:A 1D5A:A 1N52:A 1GM5:A 1FNM:A 1F5N:A 1NQE:A 1H1L:B 1KA2:A 1BPM:_ 1H1L:A 1O6T:A 1MXG:A 2UAG:A 1AQF:E 1QS0:A 1INP:_ 1HMV:B 1IIR:A 1E4G:T 1OLU:A 1BG0:_ 1QLG:A 1EZW:A 1L3R:E 1GS6:X 1H3J:A 1B62:A 1LP4:A 1L8Q:A 1FHV:A 1ODS:A 1A77:_ 1GSA:_ 1JP4:A 1NQ6:A 1J1U:A 1IZC:A 1H74:B 1CSN:_ 1TFR:_ 1I44:A 1JWY:B 1IAH:B 1JHZ:A 1K77:A 1O6Y:A 1HW6:A 1EYE:A 1LDF:A 1NXQ:A 1HK7:A 1NSF:_ 1DQN:A 1QR0:A 1KYR:A 1IRU:I 1IRU:1 1OPR:_ 1N0W:A 1OUO:A 1IRU:X 1KHZ:B 1MH9:A 1NRJ:B 1H6P:A 1ETU:_ 1IWL:A 1NQZ:A 1QF8:A 1NZI:A 1KWO:C 1R67:A 1ID0:A 1VSD:_ 1GMI:A 1F7R:A 1CS3:A 1HXI:A 1HZ1:A 1IW7:O 1IG5:A 1MOG:A 1
Appendix 5. Protein chains containing Ca2+ ions
Ca
1BJQ:B 1CVW:H 1BM6:_ 1AZD:A 1AYP:A 1OMR:A 1FJT:A 1A4G:A 1BGP:_
1CDG:_ 1EA7:A 1HTN:_ 1B2Y:A 1QLS:A 3LHM:_ 1N73:C 1B90:A 1B09:A 1BU4:_ 1EGI:B 1PVA:A 1DR1:_ 1AXN:_ 1MN1:_ 1OAC:B 1STA:_ 1JKU:A 1AY0:A 1AKL:_ 1GA1:A 2MSB:B 1HQD:A 1Q5C:C 1LW5:D 1IZJ:A 1M34:A 1PK8:A 1ALV:A 1UMN:G 1C0F:S 1LRW:A 1FWX:A 1DJW:B 1OAH:B 1L9M:B 1E3X:A 1JDA:_ 1A0S:P 1J6Z:A 1O88:A 1LVU:D 1DTH:A 5STD:A 1HL5:C 1UYY:A 1TNQ:_ 1HP0:B 2MAS:A 1LNQ:A 1FZD:A 1HCU:B 1E8U:B 1LHN:A 1B0P:A 1F9D:A 1HM8:A 1C9U:B 1HPL:A 2CEL:A 1OLP:A 1E5N:A 1G0H:A 1CB8:A 1LPM:_ 1EAK:A 1GTT:A 1CVM:A 1EB7:A 1GCA:_ 1E5J:A 1G42:A 1F2O:A 1FF5:A 1BYH:_ 1I9B:A 1H6X:A 1O8P:A 1L7L:A 1GZT:A 1FI5:A 1MPX:A 1B4N:A 1UP8:A 1M56:A 1E77:A 1R64:A 2TAA:A 1O6V:A 1KTW:A 1LWH:A 1HYO:B 1JI3:A 1MNZ:A 1H1V:G 1ZQC:A 1KXR:B 2CBL:A 1FID:_
1BMO:A 1QU0:C 1OH4:A 1BYF:A 1HT9:A 1F4N:A 1BG3:A 1UVN:A 1K1X:A 1DE4:C 1GXD:A 1G21:B 1J1N:A 1N48:A 1D0K:A 1TAD:C 1EGZ:A 1NZY:A 1I82:A 1J83:A 1AG9:A 2FHA:_ 1UZJ:A 1DSY:A 2PSR:_ 1C07:A 1LMJ:A 1H0H:A 1J0M:A 1KB0:A 1M9I:A 1CJY:A 1NBW:A 1H3G:A 1NQH:A 1KFQ:A 1N7U:A 1HDH:A 1N2L:A 1MIO:B 1IA7:A 1DYK:A 1OC6:A 1G9U:A 1FMJ:A 1DCT:A 1IME:A 1UOC:A 1H6G:A 1L6R:A 1CPM:_ 1OUP:A 1NNL:B 1OTN:A 1RTG:_ 4SBV:A 1H1A:A 1I40:A 1PRR:_ 1JTG:B 1E7D:A 1NZI:B 1NBC:A 1GN1:F 1NL2:A 1EXZ:A 1C7K:A 1RLW:_ 1IT4:A 1NQD:B 1MJ2:B 1FZP:B 1HDF:A 1K9U:A 1GUN:A 1SU4:A 1JV2:A 1KIT:_ 1BF2:_ 1ACC:_ 1N7D:A 1NOL:_ 1C8D:A 1CLC:_ 1JV2:B 1BFD:_ 1C7I:A 1FSU:_ 1MU5:A 5ENL:_
1CVR:A 1LWS:A 1BAG:_ 1IKA:_ 1H30:A 1JHN:A 1QH4:D 1NGI:_ 1FBL:_
1OKG:A 1KA1:A 1JX6:A 1GXR:A 1E54:A 1V04:A 2APR:_ 1OBR:_ 1GXO:A 1RDR:_ 1E1A:A 1GQ3:C 1BOB:_ 2POR:_ 1EZM:_ 1NRW:A 1URX:A 1JTD:A 1B2L:A 1O7L:D 1ZQV:_ 1J1T:A 1SLM:_ 1DAF:_ 4SBV:C 1PEX:_ 1QV1:A 2SAS:_ 1M1U:A 2STV:_ 1JE5:B 1HQV:A 1XYN:_ 1NYA:A 1B2V:A 1V67:A 1B1C:A 1LF0:A 1K12:A 1ESL:_ 1GUI:A 1JB0:L 1SRA:_ 1K5W:A 1VSI:_
1TN3:_ 1POC:_ 1J24:A 1GR3:A 1PK6:B 1AYO:B 5CHY:_ 1J5U:A 1DFX:_
1F7L:A 2MCM:_ 1IU9:A 1WAD:_ 1SVY:_ 1O6S:B 2PLT:_ 1H4B:A 1HJ7:A 1N7S:C 1GUA:B 1OHZ:B
Appendix 6. Protein chains containing Zn2+ ions
Zn
1E3E:A 1EQW:B 1ZNC:A 1BM6:_ 1EW2:A 1FJT:A 1EZZ:B 1LBC:B 1A8T:A 1F57:A 1K1D:A 1Q2R:A 1N4P:B 1F0J:A 1A6Y:B 1GT7:A 1JDI:A 1STE:_
1JD5:A 1GYT:A 1NVB:B 1H7R:A 1Q3K:A 1H4S:A 1HR6:D 1KOG:A 1NNJ:A 1TSR:A 1FBX:A 1IS8:A 1G2D:C 1DWV:A 1KZO:B 1NLX:A 1OCC:F 1CA1:_
1AKL:_ 1KBP:A 1DE5:A 1GUP:A 1F30:A 1ADD:_ 1HZY:A 1DTH:A 1PWU:A 1R3N:A 1ZQT:A 1IX1:A 1E4M:M 1M2G:A 2NLL:B 1EPW:A 1J36:A 1IQ8:A 1CNQ:A 1BH5:B 1LR5:A 1HZ5:A 1I50:A 1I3Q:C 1ENR:_ 1FSJ:B 1I3Q:I 1I3Q:J 1SFO:B 1JQG:A 1UMY:D 1ANV:_ 1J8F:A 1IA9:B 1SML:A 1NJG:A 1B71:A 1DY1:A 1G5C:A 1E67:A 2HAP:C 1PFV:A 1EAK:A 1ODZ:A 1FN9:A 1DOS:A 1AMP:_ 1F2O:A 1HI9:A 1QRL:A 1G12:A 1F35:A 3LVE:_ 1Q08:A 1M2V:B 1HBM:D 2USH:B 1KAH:B 1EI6:A 1CG2:A 1LI7:A 1ITQ:A 1JQ5:A 7MDH:B 1L9H:A 1Q74:A 1QH3:A 1BKC:E 1AH7:_ 1QR2:A 1EKJ:G 1K6Y:B 1A74:A 1F1M:A 1E7D:A 1GX1:A 1IE0:A 1RB7:A 1JOE:A 1B6Z:A 1ETE:A 1JTK:A 1A2P:A 1OI0:A 1L3E:B 1AJY:A 4GAT:A 1A7I:_ 1A1T:A 1WJB:A 1JZQ:A 1M2O:A 1EKM:A 1GXD:A 1GW6:A 1H7A:A 4ENL:_ 1MXD:A 1UWY:A 1C3R:A 1JR3:A 1ET8:A 1UDT:A 1ALN:_ 1TON:_ 1YEI:L 1ZIN:_ 1BI0:_
1I6O:B 1LHN:A 1EH6:A 1BYF:A 1FWQ:A 1BAW:A 1JCC:C 1HQM:D 1HWW:A 1GAX:A 1H3N:A 1KFI:A 1I7W:C 1IRX:B 1UQW:A 1DDZ:A 1K2Y:X 1IA7:A 1F83:A 1KOL:A 1XLL:A 1JI3:A 1P9W:A 1M63:A 1N25:A 1V33:A 1J79:A 1PV9:A 1JR3:E 1IBQ:A 1JAZ:A 1GUD:A 1NVT:A 1FXU:A 1TOA:A 1GVF:B 1UT8:B 1MVH:A 1DK4:A 1ML9:A 1NUI:A 1JW9:B 1L0Y:A 1M65:A 1H1Z:A 1I9R:H 1EU3:A 1L7O:B 2AHJ:C 1M55:A 1LQW:A 1TF6:D 1L1O:C 1H1O:A 1QF8:A 1BWN:A 1BT7:_ 1VSH:_ 1EB0:A 1MBX:A 2HRV:A 1E31:B 1ODG:A 1C7K:A 2CUA:B 1JOC:A 1M3V:A 1DVF:D 2A0B:_ 1I3O:E 1NN7:A 3CAO:A 1D0Q:A 1NO5:B 1MWQ:A 1MR1:C 2PSR:_ 1OQJ:A 1IQB:A 1LDJ:B 1LLM:C 1UN6:C 1L0I:A 1I27:A 1TBN:_ 2GAT:A 1HYI:A 2DRP:D 1MM2:A 1YUI:A 1FAQ:_ 1MFT:A 1PTQ:_ 1TAQ:_ 1OAO:C 1DMT:A 1I1I:P 1KWG:A 1KLN:A 1CLC:_ 1FSS:A 1A8H:_ 1OAH:A 1M7J:A 1LFW:A 1QE3:A 1LML:_ 1DQ3:A 1PMI:_ 1CVR:A 1EUC:B 1HP7:A 1FBL:_ 1NL5:A 1HKK:A 1K9Z:A 1ZAP:_
1NKX:A 1JK0:A 1H2M:A 1GR0:A 1D8D:A 1OBR:_ 1CKO:_ 1QHW:A 1EZM:_
1C8M:1 2EBN:_ 1I6N:A 1GL4:A 1NZJ:A 1AK0:_ 1VK6:A 1CI3:M 1HW7:A AOL:_ 1A8L:_ 1SLM:_ 1KYS:A 1GPC:_ 1DVP:A 1KEA:A 1LBU:_ 1PEG:B 1FD9:A 1JR9:A 1AST:_ 1GEN:_ 1OCY:A 4TSS:_ 1B8T:A 1FIO:A 1J3G:A 1NKU:A 1OEK:A 1UVQ:B 1IM5:A 1JWQ:A 1EB6:A 1IWL:A 1OHT:A 1DKH:A 258L:A 1FUK:A 1G73:B 1ODH:A 1VHH:_ 1R4V:A 1F3Z:_ 4KMB:1 1LBA:_
1UV0:A 1MZB:A 1CPR:_ 1Q9U:B 1LR0:A 1HML:_ 1IJL:A 1E87:A 1G3F:A 1RMD:_ 1UBD:C 1A6F:_ 1AA0:_ 1XPA:_ 1M4M:A 1JM7:A 1M2A:A 1XER:_
1G73:D 1QBH:A 1ZFP:E 1JM7:B 1I3J:A 1CY5:A 1EYF:A 1CIT:A 1HFE:S 1F81:A 1CTL:_ 1R79:A 1EXK:A 1E4U:A 1IML:_ 1BT0:A 1MWZ:A 1DX8:A 1G47:A 1RGO:A 1A7W:_ 1CHC:_ 1UW1:A 1G25:A 1LV3:A 4MT2:_ 1H7V:A 2ADR:_ 1E53:A 1DL6:A 1UN6:D 1BBO:_ 1BOR:_ 1EF4:A 1IYM:A 1IRN:_
1JJD:A 1LPV:A 1F62:A 1