This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. Article Cite This: ACS Omega 2019, 4, 3726−3731 http://pubs.acs.org/journal/acsodf Simple Coordination Geometry Descriptors Allow to Accurately Predict Metal-Binding Sites in Proteins Giuseppe Sciortino,†,‡ Eugenio Garribba,‡ Jaime Rodríguez-Guerra Pedregal,*,† and Jean-Didier Mareć hal*,† † Departament de Química, Universitat Autònoma de Barcelona, Cerdanyola del Vallés, 08193 Barcelona, Spain ‡ Dipartimento di Chimica e Farmacia, Università di Sassari, Via Vienna 2, I-07100 Sassari, Italy * S Supporting Information ABSTRACT: With more than a third of the genome encoding for metal-containing biomolecules, the in silico prediction of how metal ions bind to proteins is crucial in chemistry, biology, and medicine. To date, algorithms for metal-binding site prediction are mainly based on sequence analysis. Those methods have reached enough quality to predict the correct region of the protein and the coordinating residues involved in metal-binding, but they do not provide three-dimensional (3D) models. On the contrary, the prediction of accurate 3D models for protein−metal adducts by structural bioinformatics and molecular modeling techni- ques is still a challenge. Here, we present an update of our multipurpose molecular modeling suite, GaudiMM, to locate metal-binding sites in proteins. The approach is benchmarked on 105 X-ray structures with resolution lower than 2.0 Å. Results predict the correct binding site of the metal in the biological scaffold for all the entries in the data set. Generated 3D models of the protein−metal coordination complexes reach root-mean- square deviation values under 1.0 Å between calculated and experimental structures. The whole process is purely based on finding poses that satisfy metal-derived geometrical rules without needing sequence or fine electronic inputs. Additional post- optimizations, including receptor flexibility, have been tested and suggest that more extensive searches, required when the host structures present a low level of pre-organization, are also possible. With this new update, GaudiMM is now able to look for metal-binding sites in biological scaffolds and clearly shows how explicitly considering the geometric particularities of the first coordination sphere of the metal in a docking process provides excellent results. ■ INTRODUCTION With more than 30% of the genome encoding for metal- panoply of molecular modeling methods struggles for this kind of predictions. containing biomolecules, the study of the interactions between To the best of our knowledge, the available methods for metal ions and proteins bares a fundamental role in biology, predicting metal-binding sites are based on sequence analysis pharmacy and medicine.1 Metals provide life with unique or structural motif analogies between the query and well- structural and catalytic properties for transport, storage, or characterized metal-binding in proteins.4 Those relying on enzymatic functions. Humans have taken advantage of this sequence homology tend to be limited to a specific subset of complementarity and now apply it in many fields like therapy metal elements and protein residues, e.g., MetalDetector v2.05 (metallodrugs) and diagnosis (biosensors) of many diseases considers transition metals and Cys or His residues; Metal- ranging from cancer to ulcer.2 Predator6 is focused on iron−sulfur proteins; SeqCHED Experimental techniques like X-ray crystallography or NMR Server7 supports Zn, Fe, Ni, Cu, Co, Mn, Mg, and Ca, and spectroscopy can provide accurate or partial three-dimensional Cys, His, Glu, or Asp residues; and ZincFinder8 is available for (3D) information of the metal-bound structure of a protein, Zn−proteins only. Structural motif analogies have been used but when those data are unreachable, the determination of the by software such as mFASD,9 FINDSITE-metal,10 MIB,11 region where a metal ion could bind must be addressed TEMSP,12 and FEATURE metal scanning,13 the latter two computationally.3 A complete simulation of the metal-binding process should take into account: (1) the vast conformational limited to Zn; however, they are restricted to a reduced subset space that must be explored to detect metal-binding sites in a of all possible metal elements. protein; (2) the intrinsic properties of the first coordination sphere of the metal during binding, i.e., the directionality of the Received: December 10, 2018 metal−ligand interactions or the possible changes in the Accepted: February 5, 2019 number of chemical groups bound to metal. Nowadays, the Published: February 19, 2019 © 2019 American Chemical Society 3726 DOI: 10.1021/acsomega.8b03457 ACS Omega 2019, 4, 3726−3731 ACS Omega Article − To design a metal-binding predictor with a broader scope, OOCGlu, SMet, OCamide, −Namide, etc.; see Table S1 of the we thought of using a strategy inspired by protein−ligand Supporting Information for further details). dockings, since these are designed to explore wide conforma- Setup. All structures of the data set were prepared using a tional spaces and provide fast predictions of ligand-binding PyChimera20 script with the following instructions: (1) remove modes to proteic receptors. Although docking is mainly water and other nonproteic molecules present in the PDB focused on small organic species,14 in principle, the simplified structure, (2) add hydrogen atoms with UCSF Chimera force fields make the modifications of energetic terms (scoring v1.1114a (“addh” command), and (3) extract the coordinates of functions) more approachable than in Molecular Mechanics, the bare metal ions and proteins in new, separate Mol2 files. It which opens some opportunities for metal implementations. must be highlighted that for the largest proteins only one However, attempts to predict metal-binding sites with subunit chain, the one containing the binding site, was chosen protein−ligand dockings have only reached partial success.3c,15 for the simulation. Some stand on pure electrostatic models, which, despite their Calculation Protocol. GaudiMM provides a modular step forward, fail to respect coordination rules (e.g., providing optimization framework for molecular modeling in which the metal with an octahedral environment). Others, like those multiple competing evaluation criteria can be set up we recently developed inside the GOLD framework, stand on simultaneously. In GaudiMM, each iteration of the optimiza- introducing some directionality to the vacant sites of the metal tion is divided in two stages: exploration and evaluation. In the by explicitly simulating coordination bonds but are still exploration stage, genes generate new possible solutions by restricted in terms of first coordination sphere description.16 assigning different values to the structural properties. In the Indeed, the user needs to define how the metal−protein evaluation stage, one or more objectives compute a score for interaction should take place in terms of angle and direction. each of those candidate solutions. Here, we present a completely different approach to the The GaudiMM genes used for this benchmark were (1) a problem. It is a new molecular descriptor (or objective) for our molecule gene for the isolated protein structure, (2) another GaudiMM platform,17 which reproduces coordination geo- molecule gene for the isolated metal ion, and (3) a search gene metries of metal moieties. Unlike existing strategies, this instructed to move the metal ion within 20 Å of the objective does not rely on structural templates extracted from crystallographic binding site position. This search radius large protein data sets but on pure coordination rules (mainly covers, on average, ca. 55% of the volume of the selected geometrical) that are structure agnostic. A benchmark of 105 proteins. structures shows that the algorithm can find the correct The objectives to be minimized were (1) the evaluation of binding site in 100% of the entries of the data set and generate clashes (interatomic unfavorable contacts) and (2) the 3D models with root-mean-square deviation (RMSD) values coordination evaluator set to identify any possible binding under 1.0 Å from the crystallographic pose. The best solutions site of the ion, with at least three donors within 3.5 Å of the are always in the top 5 results, according to the coordination metal, compatible with an octahedral geometry. The possible score detailed below. Post-analysis including protein flexibility donor atom types considered by the coordination objective maintains an excellent trend. were Npl (trigonal planar sp2 nitrogen with a lone pair, e.g., ■ COMPUTATIONAL SECTION Data Set. To validate the new GaudiMM objective,17 a data His), O3 (tetrahedral sp3 oxygen, neutral with two lone pairs or negatively charged with three lone pairs, e.g., R−OH/R−O− of Tyr, Ser, or Thr), O2 (trigonal sp2 oxygen with two lone pairs, set of 105 high-quality X-ray structures was built. All were e.g., ketone group of the backbone), O2− (trigonal sp2 selected via the MetalPDB web server18 and downloaded from negatively charged oxygen in a carboxylic group, e.g., COO− the Protein Data Bank (PDB).19 They feature a bare metal of Asp or Glu), and S3 (tetrahedral sp3 sulfur, neutral with two cation bound to one or more side chains of a proteic host. lone pairs and negatively charged with 3, e.g., R−S−R/R−S− Whereas the objective supports any arbitrary geometry that can of Met or Cys). All calculations were performed with 150 be provided as a set of origin-centered vectors, the data set is generations and an initial population size of 100 individuals. focused on octahedral or derived geometries (i.e., square The complete input configuration used in our benchmark can pyramidal or T-shaped) having at least one vacant coordina- be found in the Supporting Information. The bare ions, tion position (to consider possible water occupancy). extracted from the crystallographic structure, were re-docked Tetrahedral geometries are easier to model: a single angle to reproduce the experimental binding sites without any kind can be used for all four vertices. Our chosen subset includes of geometrical constrains or energy restrains. The protein was nontrivial coordination geometries commonly found in treated as a rigid body. The GaudiMM solutions were analyzed biological scaffolds. The vacancies could be already present by means of GaudiView,17,21 a UCSF Chimera extension in the original crystallographic structure or correspond to developed to browse through docking solutions coming from nonproteic ligands (e.g., water or carbonate), which are GOLD, GaudiMM and others. removed before running the calculation. The rationale behind Coordination Objective. The coordination objective this decision is to develop a general method able to find any discovers sets of donor residues able to coordinate a metal potential coordination sites with a minimum number of donors ion (bare in this study, but it could be complexed by some and a compatible orientation according to the chosen ligands, if desired). Based on an early work,22 the method geometry. Labile ligands like water molecules are, therefore, evaluates the immediate surroundings of different metal ion considered implicitly. Two additional criteria were taken into positions, both chemically and geometrically, as generated by account to select the entries: (1) it must be a high-resolution the exploration stage of the algorithm. For each candidate, this structure (<2.0 Å when possible) and (2) no co-factors are GaudiMM objective first analyzes the presence of suitable bound to the metal. The resulting data set includes a wide donor atom types within a user-defined distance (3.5 Å by range of biologically relevant metals (Mg, Ca, Mn, Fe, Co, Ni, default) from the metal ion; if the number of atoms found is Cu, Zn) and donor types (NHis, −SCys, −OSer, −OTyr, −OOCAsp, high enough to fulfill the minimum number of atoms requested 3727 DOI: 10.1021/acsomega.8b03457 ACS Omega 2019, 4, 3726−3731 ACS Omega Article Figure 1. Given a metal ion M, potential donors (D) are searched in its surroundings. If the distance M−D is smaller than the r threshold for more than three potential donors, the RMSD between the donor atom positions and the chosen ideal polyhedron (an octahedron in this figure) is computed. If the test is successful and D is bound to another atom (1st_neighbor), the M−D−1st_neighbor angle α is computed. If 1st_neighbor has another nonterminal neighbor (2nd_neighbor), the M−D−1st_neighbor−2nd_neighbor dihedral angle θ is also computed. The absolute sines of the differences between the calculated angles and their ideal counterparts (according to chimera.bondGeom routines) are then calculated. Since the polyhedron matching procedure is scalable, the absolute difference between the M−D distances and the expected ideal coordination distances for those elements are also computed. All the obtained terms are then summed together, and the sum should be zero for an ideal coordination geometry. (three, at least), a series of geometrical aspects are also of the algorithm, along with discarded alternative strategies, calculated. can be found in the Supporting Information (Figure S1). ■ First, a RMSD calculation between the ideal polyhedron and the one obtained from the candidate’s coordination sphere is RESULTS AND DISCUSSION performed with a 3D rigid implementation of the Coherent Point Drift (CPD) algorithm.23 This term tells if the positions The capabilities to predict metal-binding sites with our of the potential coordinating ligand atoms are adequate: a upgraded GaudiMM with coordination-aware docking features value of zero will report a perfect overlap of the atoms with the were evaluated considering the Coord_Fitness value and chosen coordination polyhedron. However, the directionality computing the RMSD of each docked metal ion versus the of the neighbor bonds is equally important. To account for crystallographic coordinates. The binding site was considered that, for each donor found, the angles and dihedrals formed by fully reproduced if the Coord_Fitness value is below 5.0 and (1) the metal center (probe), (2) the donor atom (donor), (3) the obtained RMSD is below 1.0 Å. In the docking benchmarks its immediate neighbor (1st_neighbor), and (4) a nonterminal reported in literature, the RMSD threshold applied to identify neighbor of 1st_neighbor (2nd_neighbor) are computed and a successful solution usually ranges from 2.0 to 3.0 Å.3c,14b,24 In our case, working with a single atom, it is more appropriate to the absolute sines of the difference between the found angles consider a smaller value. In combination with the Coord_Fit- and the expected ideal values (as calculated out of the ideal ness score, this criterion can help avoid false-positive atom positions suggested by the chimera.bondGeom module) geometries. Steric clashes, measured as the volumetric overlap are summed (see Figure 1). Thus, a sum equal to zero would of the van der Waals spheres of close atoms by the contacts correspond to an ideal directionality. Since the CPD algorithm objective, were always below 3.0 Å3. scales the ideal polyhedron to fit the candidate vertices, the A detailed analysis of the GaudiMM solutions for the entire hypothetical coordination bonds can end up being accidentally data set is summarized in Table 1, which shows that the larger or shorter than expected. We add one more term to crystallographic binding sites are reproduced with a success control this error by computing the ideal distance deviation as the absolute difference between the ideal element−element distance reported by the chimera.bondGeom routine and the Table 1. Summary of RMSD between the Experimental and calculated probe−donor distance. It must be noted that no the Predicted Binding Sites (First Column) and assumptions are made on the coordinating structure: the Coord_Fitness Distribution of the Data Set algorithm simply looks for suitable coordinating atoms RMSDa,b total Coord_Fitness ≤ 3.5c 3.5 < Coord_Fitness ≤ 5.0c arranged in a particular geometry; the atoms can belong to <0.5 67 27 28 protein residues, but this does not constitute a requirement. 0.5−1.0 38 16 15 Finally, the total score (Coord_Fitness) is calculated by >1.0 summing the RMSD, distance deviation, and directionality a terms. Smaller values will correspond to better geometries Value reported in Å. bRMSD computed via UCSF Chimera. cValue (closer to the ideal polyhedron). A more detailed description calculated by GaudiMM using eq S1 in the Supporting Information. 3728 DOI: 10.1021/acsomega.8b03457 ACS Omega 2019, 4, 3726−3731 ACS Omega Article Figure 2. Comparison between the GaudiMM solution (in orange) and the original X-ray diffraction (XRD) structure (in dots surface) for the PDB structures (a) 1EJJ, (b) 414A, and (c) 2Y12. rate of 100% with a RMSD ≤ 1.0 Å by structures contained in potential metal-binding sites by probing the protein space for the top 5 results. The mean of the Coord_Fitness score is close accessible regions in which the center of mass has β-carbon of to 4.0 units. The mean RMSD was 0.519 Å, with an associated three or more potential coordinating amino acids (Glu, His, standard deviation value of 0.175 Å, highlighting an error Asp, etc.) within a threshold distance of 3.5 Å. The resulting distribution very close to the mean of the set. In Figure 2, as an script, called multisite.py, and the accompanying documenta- example, a comparison between three simulated structures is tion are included in the Supporting Information. reported. The three best ranked metal-binding sites predicted with the Although for each structure, the solution with the best value multisite.py script were consequently processed with Gau- of the coordination objective (lowest Coord_Fitness score) diMM exploring the rotameric states of the surrounding amino correctly predicts the metal binding site, only in 86.7% of the acids (using the Dunbrack rotamer database25 included in the cases the same solution presented the best RMSD value of the rotamers gene) and using two objectives: clashes and run. The polyhedron models considered in our objective are coordination. It is important to highlight that (1) from the likely too ideal when it comes to cases where the metal three best ranked metal centers, the crystallographic one was geometry is skewed or lacks one or more coordination vertices. systematically identified, showing that the multisite.py script Also, the ideal distances and angles parametrized in could already be used in a standalone manner and (2) prior to chimera.bondGeom are measured for generic element atom the rotameric exploration, the orientation of all residues of the types and do not account for partial charges, polarizabilities, potential binding sites were randomly modified to avoid any etc. Since some ligands were removed prior to the calculation possible bias in the pre-organization of the receptor. (e.g., water or carbonate ions, see Computational Section), The results, detailed in Table 2, successfully found 87.5% of deviations from the ideal polyhedra are expected in these the metal centers of this reduced data set with RMSD ≤ 1.2 Å. systems and can explain why low-RMSD solutions can exhibit In 85.7% of the correctly found sites, three coordinating relatively high Coord_Fitness scores (see Table 1). Even so, donors were correctly described, with a mean RMSD of 0.79 ± the proposed geometric terms are enough in most situations: 0.34 Å. they still reach a 100% prediction power within the top 5. The high success rate of this reduced data set indicates that To conclude this part of the study, our updated version of the coordination objective in our metal docking approach has GaudiMM with metal coordination prediction capabilities an excellent predictive power even for explorations accounting provides the correct binding site of the ions with an average for the flexibility of the receptor. The slightly worse result with success rate ranging from ca. 87 to 100% (best RMSD value in respect to the rigid receptor exercise is likely related to the top 1 or top 5 scores, respectively), even if not all the wider search space dimensionality of considering side-chain coordination positions are occupied. flexibility. Improving the exploration algorithm could poten- Dealing with Receptor Flexibility and Rearrange- tially improve this success rate, and we are now working at ment. The previous benchmark successfully reproduced the possible updates. We mainly aim to implement a smarter coordination geometries of the metal ions after exploring a search procedure and a heuristics-guided side-chain explora- tion gene. rigid protein structure. Even if the possibility of vacant sites of the metal is considered, it could be argued that the calculation benefitted from the existing structural pre-organization of the protein. In this second assessment, we studied the performance ■ CONCLUSIONS The in silico prediction of the binding sites of metal ions in of the coordination objective when considering full side-chain proteic scaffolds offers many possible applications at the flexibility. Since this obviously implies tremendously wider chemical biology interface, like decoding the molecular computational time, we limited the test to a subset processes that allowed life to take advantage of the inorganic corresponding to the highest coordinated entry for each realm or generating alternative metal-binding sites in proteins metal, for a total of eight structures from the complete data set to provide them with novel functions or activities (e.g., artificial (1AVW, 1AX1, 1B8C, 1B71, 1FX7, 1J5Y, 1XJS, and 3AWS). enzymes). Despite its interest, standard molecular modeling Moreover, we slightly changed the search strategy to save the approaches fail to generate ab initio prediction of 3D models computational time and avoid possible bias (i.e., starting the for metal-binding poses. Conceptually, the multiobjective search too close to the X-ray structure). To do so, we applied genetic algorithm at the core of GaudiMM presents a suitable an automated, structure-agnostic strategy that first locates environment to advance along this line: each important 3729 DOI: 10.1021/acsomega.8b03457 ACS Omega 2019, 4, 3726−3731 ACS Omega Article descriptor involved in metal−protein interactions could be RMSDa encoded as an objective. This work presents a new objective 87.5% 0.556 1.238 0.792 0.268 0.889 1.023 0.391 designed to reproduce geometric features without explicitly resorting to fine electronic details and offers an encouraging c degree of predictiveness. We believe that this approach provides computational chemists and molecular modelers with a novel tool to handle metal ions in their computational RMSD value computed via UCSF Chimera, reported in Å. bBidentate coordination of the carboxylate group. cThe correct binding site was not identified during the calculation. (−OOC)Asp90; (−OOC)Asp92; (−OOC)Asp101; (OCamide)Lys96 (−OOC)Asp129; (−OOC)Asp136; (−OOC)Glu127; (N)His142 workflows. ■ ASSOCIATED CONTENT (N)His79; (−OOC)Glu83; (N)His98; (−OOC)Glu172 * S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsome- Table 2. Summary of Reproduced Binding Sites, Donors, and RMSD Values Obtained after the Flexible Side Chain Docking Simulation (−OOC)Glu94; (−OOC)Glu128; (N)His131 ga.8b03457. (−S)Cys128; (−S)Cys41; (−OOC)Asp43b donorscalcd Data set specification (Table S1); coordination objective flowchart including supporting eqs S1 and S2 (Figure OOC(Glu80); OCamide(Val75) S1); strategies for increasing performance, RMSD deviations and Coord_Fitness values (Tables S2 and (−OOC)Glu67; (N)His82 S3), RMSD distribution of the GaudiMM benchmarks (Table S4), and supporting eqs S3 and S4; benchmark input details; Multisite.py script (PDF) ■ success rate AUTHOR INFORMATION Corresponding Authors *E-mail: jaime.rodriguezguerra@uab.cat (J.R.-G.P.). − c *E-mail: jeandidier.marechal@uab.cat (J.-D.M.). ORCID Giuseppe Sciortino: 0000-0001-9657-1788 His79, Glu83, His98, Glu172, Gln175 Asp90, Asp92, Asp94, Ile96, Asp101 Glu70, Glu80, Glu77, Val75, Asn72 Eugenio Garribba: 0000-0002-7229-5966 Asp129, Asp136, Glu127, His142 Jaime Rodríguez-Guerra Pedregal: 0000-0001-8974-1566 Glu94, Glu128, Glu53, His131 His148, His79, His146, Glu87 Cys66, Cys128, Cys41, Asp43 Jean-Didier Maréchal: 0000-0002-8344-9043 Notes rotamers The authors declare no competing financial interest. His82, Glu67, His68 ■ ACKNOWLEDGMENTS J.-D.M., G.S., and J.R.-G.P. are thankful for the support given by the Spanish grant CTQ2017-87889-P and the Generalitat de Catalunya grant 2017SGR1323. Support of COST Action CM1306 is kindly acknowledged. 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