Metalloproteins play crucial biochemical roles in our body and are essential across all domains of life. The structural environment around a metal ion, especially the coordination geometry (CG), is both sequentially and functionally relevant. Studies of the metalloprotein’s CG will greatly help alleviate the imbalance between the ample sequence data available and the insufficient knowledge on protein functions. Current methodologies in characterizing metalloproteins’ CG consider only previously reported CG (canonical CG) models based primarily on nonbiological chemical context. Exceptions to these canonical CG models can greatly hamper the ability to characterize metalloproteins both structurally and functionally

Flight, Robert M.

Moseley, Hunter N. B.

Rouchka, Eric C.

Yao, Sen

English

University of Kentucky

Figure 3. Metal-O bond length distribution after average bond length deviation filtering. The average deviation filter can detect potential misassigned metal ion, and removes the skewed long tails. Aberrant coordination geometries discovered in most abundant metalloproteinsSen Yao1,2,4,5,6, Robert M. Flight4,5,6, Eric C. Rouchka1,2,3, and Hunter N.B. Moseley4,5,6,7*1School of Interdisciplinary and Graduate Studies, 2Department of Computer Engineering and Computer Science, 3KBRIN Bioinformatics CoreUniversity of Louisville, Louisville, KY 40292, USA4Department of Molecular and Cellular Biochemistry, 5Markey Cancer Center, 6Center for Environmental and Systems Biochemistry7Institute for Biomedical Informatics, University of Kentucky, Lexington KY 40356, USA¨ Methods¨ References¨ Introduction ¨ Results¨ Conclusion• Various filters were applied to ensure high quality of the data being analyzed, including removing of metal binding sites with abnormal normalized bond-length deviation and correcting standard deviations based on x-ray resolution. The ligand detection method is statistically rigorous, producing an estimated false positive rate of ~0.11% and an estimated false negative rate of ~1.2%. • The detected bond length modes agree very well with studies based on Cambridge Structural Database (CSD) [7], and are specific to the functional group and multidentation.• The existence of compressed angles are universal among the top five metals bound by proteins, but especially pronounced in Ca. They caused significant misclassification of metal binding sites into canonical CGs in all previous studies. Aberrant CG models were identified after separating the metal binding sites that have compressed vs. normal angles.• The universal existence of aberrant CG clusters is further supported by the electron density maps.• Distinct biochemical functions are associated with aberrant CGs versus non-aberrant CGs• The ability to detect the structure-function correlation are greatly affected by the sample size (see Yao et al 2017 [5]). Lack of adequate data could be the reason that metal binding sites with compressed angles have never been treated separately as their own independent CGs before.¨ Future Directions• Develop methods in assessing the quality of a metal binding sites in terms of its electron density fitness.• Use information of functional group and multidentation bond length modes to improve the ligand detection method.• Develop better representation for angle-space description of CGs, especially for 7-and 8-ligand CGs.• Develop methods to better relate overlapping protein regions and improve association between functions and specific clusters.• Generalize and apply the methodology to all other metal binding sites.1. Andreini et al., Acc Chem Res 2009; 42(10): 1471-1479.2. Alberts et al., Protein Sci 1998; 7(8):1700-1716.3. Andreini et al., Bioinformatics 2012; 28(12):1658-1660.4. Yao et al., Proteins 2015; 83(8): 1470-87.5. Yao et al., Proteins 2017; 85(5): 885-907.6. Yao et al., Proteins 2017; 85(5): 938-944.7. Harding, Acta Cryst 2006; D62, 678–682.Table	1.	The	number	of	top	10	metalloproteins	in	wwPDB	as	of	Feb	2015.	Metal	Number	of	PDB	entries	Number	of	total	metal	sites	Metal	Number	of	PDB	entries	Number	of	total	metal	Zn	 9360	 26788	 K	 1673	 5306	Mg	 9145	 53896	 Cu	 1134	 4397	Ca	 7762	 24335	 Ni	 935	 2252	Fe	 6359	 27514	 Co	 915	 2087	Na	 4888	 16527	 …	 	 	Mn	 2266	 8138	 Total	 47,527	 187,587		Figure 2.  The overall workflow.¨ Acknowledgement • NSF 1252893, NIH 1U24DK097215-01A1, and P20GM103436 for KBRIN.Figure 1.  The canonical coordination geometries (CGs) of metalloproteins. The magenta ball represents the metal ion, and the white balls represent coordination ligands.  For each row, a major CG (red) is followed by its associated minor CGs (black), which can be viewed as missing ligands from the major one. The abbreviations are in parenthesis. From the left to the right, the CGs are separated by lines to have 8, 7 ,6 ,5, and 4 ligands respectively.Figure 5. Bond length distributions and statistics of all bond types involving elements O, N, and S, which have greater than 5% occurrence.Figure 7. PDB structure and electron density maps of examples for highly aberrant CGs. Structures are shown in balls and sticks and featured by bidentated compressed angles. These structures are also supported by their fitness to the electron density maps. All structures were generated in LiteMol Viewer, with 2Fo–Fc at 1.5 s and Fo – Fc at -3s (red) and 3s (green), except for panel E with 2Fo – Fc at 1.01 s.  Metal ions are put at the center of each subgraph with larger size, where Zn is represented as light blue, Fe as purple, and Mg and Ca as green. The cluster identifier, PDB metal site ID, and its resolutions are as follows: A, 5-ligand Zn, cluster 3, 2B13.B.401, resolution 1.55Å; B, 5-ligand, Ca, cluster 6, 3RYD.C.267, resolution 2.37Å; C, 5-ligand Fe, cluster 1, 4AM4.A.1161, resolution 1.68Å; D, 6-ligand Mg, cluster 8, 3ETH.A.402, resolution 1.60Å; E, 6-ligand Ca, cluster 3, 4P99.B.509 resolution 1.80Å; F, 6-ligand Fe, cluster 2, 2GYQ.B.404, resolution 1.40Å Figure 6. Chemical functional group and multidentation specific bond length modes. On the left is the overall bidentation short and long arms for each metal and some specific functional groups that contributing to the overall bond length histogram. On the right is a breakdown of the most abundant functional groups in the bidentation and multidentation, as they often exhibit distinct modes.Table	2.	Ligand	counts	and	error	rates	by	metal.MetalNumber	of	metal	clustersNumber	of	usable	metal	sites	(>3-ligand)Number	of	unusable	metal	sites	(<=3-ligand)4-ligand5-ligand6-ligand7-ligand8-ligand9-ligand TotalEstimated	Ligand	Detection	Error	rateNon-redundant	setZn 572 21,257 4,959 11,380 2,365 a750 b2 - - 14,497 0.000443 4,800Mg 691 29,859 23,346 3,595 2,941 a5,674 b69 b2 - 12,281 0.002113 2,813Ca 196 21,057 3,082 918 1,490 4,485 5,399 a1,258 b18 13,568 0.001760 4,080Fe 11,287 14,990 1,237 1,071 3,929 a5,804 b2 - - 10,806 0.000057 2,370Na 240 11,475 4,812 703 1,557 1,840 186 a17 - 4,303 0.000000 1,184Overall 0.001128aHighest coordination	number	considered	valid	for	the	given	metal.bCoordination numbers	considered	erroneous	and	thus	used	in	ligand	detection	error	estimation.Table 3: Enriched GO terms, having corrected p-value <= 0.05 and consistent enrichment patterns in combineLig and individual ligand enrichments.Normal CompressedID Description Type P-adjust Metal % Sig. P-adjust Metal % Sig. GO:0044249 Cellular biosynthetic process BP 6.595e-06 Zn 0.4264 TRUE 1.000e+00 Mg 0.3297 FALSE GO:0072524 Pyridine-containing compound metabolic process BP 4.483e-02 Mg 0.4430 TRUE 1.000e+00 Mg 0.5000 FALSE GO:0034641 Cellular nitrogen compound metabolic process BP 2.359e-08 Zn 0.4413 TRUE 1.000e+00 Mg 0.3211 FALSE GO:0015077 Monovalent inorganic cation transmembrane transporter activity MF 3.796e-02 Fe 0.4242 TRUE 1.000e+00 Ca 0.7500 FALSE GO:0072593 Reactive oxygen species metabolic process BP 9.952e-03 Fe 0.4821 TRUE 1.000e+00 Zn 1.0000 FALSE GO:1901566 Organonitrogen compound biosynthetic process BP 1.320e-03 Mg 0.3933 TRUE 1.000e+00 Mg 0.4865 FALSE GO:0016053 Organic acid biosynthetic process BP 3.090e-02 Mg 0.3543 TRUE 1.000e+00 Mg 0.3077 FALSE GO:0044283 Small molecule biosynthetic process BP 1.108e-03 Mg 0.4000 TRUE 1.000e+00 Mg 0.3333 FALSE GO:0046394 Carboxylic acid biosynthetic process BP 3.090e-02 Mg 0.3543 TRUE 1.000e+00 Mg 0.3077 FALSE GO:0050794 Regulation of cellular process BP 1.459e-04 Zn 0.4454 TRUE 1.000e+00 Zn 0.4545 FALSE GO:0050896 Response to stimulus BP 1.530e-04 Mg 0.3825 TRUE 1.000e+00 Ca 0.4860 FALSE GO:0065008 Regulation of biological quality BP 1.000e+00 Fe 0.2778 FALSE 6.807e-03 Fe 0.3878 TRUE GO:0008484 Sulfuric ester hydrolase activity MF 1.000e+00 Ca 0.3333 FALSE 2.187e-02 Ca 0.4286 TRUE GO:0006811 Ion transport BP 1.000e+00 Mg 0.3889 FALSE 6.392e-06 Ca 0.4364 TRUE Metalloproteins play crucial biochemical roles in our body and are essential across all domains of life [1]. The structural environment around a metal ion, especially the coordination geometry (CG), is both sequentially and functionally relevant. Studies of the metalloprotein’s CG will greatly help alleviate the imbalance between the ample sequence data available and the insufficient knowledge on protein functions. Current methodologies in characterizing metalloproteins’ CG consider only previously reported CG (canonical CG) models based primarily on non-biological chemical context [2,3]. Exceptions to these canonical CG models can greatly hamper the ability to characterize metalloproteins both structurally and functionally.We have developed a less-biased method that directly handles potential exceptions without pre-assuming any canonical CG models[4-6]. Figure 4. Smallest angle distributions broken down by ligand type and metal. bi is bidentation, aa is amino acid, nonaa means that at least one of the composing ligands is not the 20 standard amino acids, H2O-aa means that one of the composing ligand is the 20 standard amino acids and the other is water, and H2O-both means that both of the composing ligands are water molecules.CSD Average 2.39 2.17 2.08 2.28 2.05 2.07 2.41 2.01 2.07 2.28 	

Aberrant Coordination Geometries Discovered in Most Abundant Metalloproteins

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Aberrant Coordination Geometries Discovered in Most Abundant Metalloproteins

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