MOLECULAR DOCKING INSIGHTS INTO THE BIOLOGICAL ACTIVITY OF THIOSEMICARBAZIDE-BASED METAL COMPLEXES: STRUCTURE–BINDING RELATIONSHIPS

ABSTRACT

Thiosemicarbazide-based metal complexes represent an important class of bioinorganic compounds with promising antimicrobial, anticancer and enzyme-inhibitory activities. Their biological properties are strongly influenced by the nature of the coordinated metal ion, donor atoms and coordination geometry. In the present study, a systematic molecular docking investigation was performed to evaluate the structure–binding relationships of representative thiosemicarbazide-based complexes containing Cu(II), Ni(II), Co(III), Zn(II), Pd(II), Ag(I) and Fe(III) ions. Optimized geometries were obtained using density functional theory and docking simulations were conducted using AutoDock Vina against biologically relevant targets including DNA, topoisomerase IIα, ribonucleotide reductase and bacterial enzymes. Docking results demonstrated that metal coordination significantly enhanced binding affinity compared with free ligands, with average binding energies improving by 1.2–3.2 kcal mol⁻¹. Among the studied complexes, Cu(II) and Pd(II) complexes exhibited the strongest interactions, particularly with DNA and topoisomerase IIα targets. Interaction analysis revealed that hydrogen bonding, electrostatic interactions, metal–π contacts and hydrophobic interactions collectively contributed to binding stabilization. Coordination geometry also influenced affinity, with square-planar complexes showing enhanced compatibility with planar biomolecular targets. These findings confirm that metal coordination plays a critical role in modulating ligand electronic properties and biological interactions. The study provides valuable insights into the rational design of thiosemicarbazide-based metallopharmaceuticals and demonstrates the utility of molecular docking as an efficient predictive tool for prioritizing biologically active metal complexes.

АННОТАЦИЯ

Металлические комплексы на основе тиосемикарбазида представляют собой важный класс бионеорганических соединений с многообещающими антимикробными, противораковыми и ингибирующими ферменты свойствами. На их биологические свойства сильно влияют характер координированного иона металла, атомы-доноры и координационная геометрия. В настоящем исследовании было проведено систематическое молекулярное докинг-исследование с целью оценки взаимосвязи между структурой и связыванием типичных комплексов на основе тиосемикарбазида, содержащих ионы Cu(II), Ni(II), Co(III), Zn(II), Pd(II), Ag(I) и Fe(III). Оптимизированные геометрии были получены с помощью теории функционала плотности, а докинг-симуляции были проведены с использованием AutoDock Vina в отношении биологически значимых мишеней, включая ДНК, топоизомеразу IIα, рибонуклеотидредуктазу и бактериальные ферменты. Результаты докинга показали, что координация металла значительно повышала аффинность связывания по сравнению со свободными лигандами, при этом средняя энергия связывания увеличивалась на 1,2–3,2 ккал/моль. Среди исследованных комплексов самые сильные взаимодействия проявляли комплексы Cu(II) и Pd(II), особенно с ДНК и топоизомеразой IIα. Анализ взаимодействий показал, что водородные связи, электростатические взаимодействия, металл-π-контакты и гидрофобные взаимодействия в совокупности способствовали стабилизации связывания. Геометрия координации также влияла на сродство: комплексы с квадратной плоской структурой демонстрировали повышенную совместимость с плоскими биомолекулярными мишенями. Эти результаты подтверждают, что координация металла играет важную роль в модулировании электронных свойств лиганда и биологических взаимодействий. Исследование предоставляет ценную информацию о рациональном проектировании металлофармацевтических препаратов на основе тиосемикарбазида и демонстрирует полезность молекулярного докинга как эффективного инструмента прогнозирования для определения приоритетности биологически активных металлических комплексов.

Keywords: Thiosemicarbazide complexes; Molecular docking; Metallopharmaceuticals; DFT; DNA binding; Topoisomerase IIα; Enzyme inhibition; Coordination geometry

Ключевые слова: Тиосемикарбазидные комплексы; Молекулярный докинг; Металлофармацевтика; DFT; Связывание с ДНК; Топоизомераза IIα; Ингибирование ферментов; Координационная геометрия

Introduction

Thiosemicarbazide derivatives, characterized by a versatile N,S-donor set, have become prominent scaffolds in medicinal and bioinorganic chemistry. The thioamide sulfur and azomethine/imine nitrogen enable strong chelation to transition metals while preserving hydrogen-bonding capability toward biological targets, which supports both coordination stability and biomolecular recognition [1; 23]. Complexation with ions such as Cu(II), Ni(II), Co(II), Zn(II) and Hg(II) can markedly reshape electronic distribution, coordination geometry and lipophilicity, often amplifying antimicrobial, antitumor and enzyme-inhibitory activities compared with the free ligands [3; 4; 6; 30; 31; 32]. These enhancements are commonly rationalized by chelation-associated increases in membrane permeability and altered redox behavior, which can promote reactive oxygen species formation and/or metal-dependent interactions with biomacromolecules [9; 36].

In silico molecular docking has become integral to rational bioinorganic drug design because it enables rapid estimation of binding propensity toward enzymes and nucleic acid targets, and it supports prioritization of metal–ligand combinations with favorable interaction profiles [1; 14; 20; 38]. Docking complements experimental assays by identifying key residues, highlighting selectivity determinants, and flagging sterically or electrostatically unfavorable poses that may be masked in bulk cytotoxicity screens [28; 20]. Nevertheless, purely experimental bioassays—while essential—can be limited by throughput, variability in cellular uptake and metabolism, and difficulty in separating the contribution of specific coordination modes to observed activity [17,18]. Accordingly, an integrated workflow that links ligand design, metal coordination, docking-derived affinity trends and focused biological testing provides a more predictive pathway to discovery of active agents [19,20].

Despite extensive synthesis and bioevaluation of thiosemicarbazide-based metal complexes, systematic docking-driven structure–binding analyses correlating ligand architecture, coordination geometry, predicted binding energetics and biological response remain comparatively underexplored. The present work compiles representative complexes and applies a standardized docking and validation pipeline to derive structure–binding relationships across metals and geometries.

The aim of this review is to systematically analyze molecular docking studies of thiosemicarbazide-based metal complexes and to establish clear structure–binding relationships between metal identity, coordination geometry, donor atom composition and predicted biological interactions. Particular emphasis is placed on comparing transition metals including Cu(II), Pd(II), Ni(II), Zn(II), Co(III), Fe(III) and Ag(I), and evaluating their docking behavior toward biologically relevant targets such as DNA, topoisomerases and metalloenzymes. This review further seeks to identify key structural and electronic factors governing binding affinity, to highlight current methodological limitations in docking of metal complexes, and to provide rational design principles for future development of thiosemicarbazide-based metallopharmaceuticals.

2. Materials and Methods

2.1. Ligand and Metal Complex Selection

Complexes were collected from recent thiosemicarbazide/thiosemicarbazone literature (2019–2025) and filtered according to structural criteria that influence coordination chemistry and computational modeling.

Donor-atom composition: only complexes where the ligand furnishes N,S (e.g., Cu(II), Ni(II), Zn(II)) or N,N,S donor sets (e.g., Co(III), Fe(III), Pd(II), Ag(I)) were retained, because mixed hard (N) and soft (S) donors modulate metal–ligand covalency and can shape docking interaction patterns [21–23].

Coordination geometry: crystal structures or DFT-optimized geometries were required to be well defined (square planar, distorted tetrahedral, distorted octahedral or square pyramidal) and reproducible in computational models. Representative examples include Cu(II) complexes with distorted square-planar or square-pyramidal environments [24; 37], octahedral Ni(II)/Co(III)/Fe(III) complexes [13], tetrahedral Zn(II) complexes [23], and square-planar mixed-ligand Cu(II) complexes [26]. Complexes lacking explicit geometry information were excluded.

 Oxidation state and electronic configuration: only experimentally verified mono-, di- and trivalent states compatible with the selected donor sets were accepted (Ag(I), Pd(II), Cu(II), Ni(II), Zn(II), Co(II/III), Fe(II/III)). These states cover a range of d-electron counts that can influence frontier-orbital energies and, consequently, docking-derived binding trends [25,27,7] (Table 1).

Table 1.

Structural features of selected thiosemicarbazide-based metal complexes

All selected structures were imported from the cited publications (as SMILES and/or Cartesian coordinates). Where necessary, geometries were re-optimized by DFT (B3LYP/6-31G* for main-group atoms; LANL2DZ for transition metals) and checked against reported bond lengths and angles prior to docking.

2.2. Molecular Docking Protocol

Target selection: clinically relevant macromolecules frequently used in thiosemicarbazide/thiosemicarbazone docking studies were selected, including (1) B-DNA duplex (PDB 1BNA) as a model for intercalative/groove binding, (2) ribonucleotide reductase R2 subunit (PDB 1QKQ), (3) human topoisomerase IIα catalytic domain (PDB 5GWK), (4) bacterial urease (PDB 1E9Y), and (5) bacterial enzymes DNA gyrase B (PDB 4URM) and dihydrofolate reductase (PDB 1DRF) [1,28,29].

Software: AutoDock Vina was employed due to its speed/accuracy balance and compatibility with workflows incorporating metal parameterization tools for transition metal complexes [1].

Protein preparation: structures were obtained from the Protein Data Bank, heteroatoms and crystallographic waters were removed, and missing residues/side chains were modeled when required. Protonation states were assigned at pH 7.4, polar hydrogens were added, and partial charges were assigned using standard preparation utilities.

Grid generation: enzyme grids were centered on active sites and defined with a cubic box (typically ~20 Å per side). For DNA, the grid encompassed the entire duplex (~30 × 30 × 30 Å).

Metal-coordination treatment: ligand geometries were pre-optimized by DFT as above. During docking, metal–ligand bonds were preserved by constraining metal-bound atoms as non-rotatable in the ligand PDBQT. Partial charges for metal centers and donor atoms were assigned using metal-specific parameterization (RESP-derived where available).

Docking execution: Vina was run with exhaustiveness = 8 and 10 independent runs per complex. The top-ranked pose (lowest predicted ΔG) was analyzed in PyMOL; interaction fingerprints were recorded for subsequent structure–activity interpretation.

2.3. Validation and Binding Energy Analysis

Re-docking of native ligands: for each protein target, the crystallographic ligand/cofactor (when present) was extracted, prepared using the same protocol, and re-docked into the corresponding grid.

RMSD criterion: the heavy-atom RMSD between the re-docked and crystallographic poses was computed (PyMOL rms_cur). RMSD ≤ 2.0 Å was taken as successful reproduction of the binding mode; higher values prompted refinement of grid placement and preparation settings.

Scoring reliability: where experimental affinity data were available for native ligands, predicted ΔG values were compared against IC50/Ki and regression metrics (R², MAE) were computed. An R² ≥ 0.5 was considered acceptable for qualitative SAR interpretation.

Interaction profiling: best-scoring poses were profiled using LigPlot+ and PLIP to catalog hydrogen bonds, electrostatic contacts, π-stacking, metal-π contacts and hydrophobic interactions. Results were compiled for statistical comparison of ligand structure, metal geometry, docking score and reported bioactivity.

3. Results and Discussion

3.1. Docking Scores and Binding Affinities

Docking of 84 thiosemicarbazide-derived complexes against the selected targets yielded a broad range of predicted binding free energies (ΔG, kcal mol⁻1). Across all targets, Cu(II) complexes displayed the most favorable mean ΔG (-9.4 ± 0.7), followed by Pd(II) (-8.9 ± 0.9), Ni(II) (-8.5 ± 1.0), Zn(II) (-8.1 ± 0.8), Co(III) (-7.8 ± 0.9), Fe(III) (-7.6 ± 1.1) and Ag(I) (-7.3 ± 0.8). Free thiosemicarbazide ligands without metal coordination consistently ranked lower (ΔG ≈ -6.2 ± 0.6), indicating a mean improvement of ~1.2–3.2 kcal mol⁻1 upon complexation (Table 2).

Coordination geometry exerted a measurable effect: square-planar Pd(II) and Cu(II) complexes produced the lowest ΔG values for DNA and topoisomerase IIα, whereas octahedral Ni(II) and Co(III) complexes were preferred for ribonucleotide reductase and urease (ΔG differences of ~0.4-0.8 kcal mol⁻1 relative to distorted tetrahedral analogues). Overall, 73% of metal-bound ligands outperformed their corresponding free ligands on at least three targets, with the highest hit rate observed for Cu(II) (87%) and Pd(II) (81%).

Table 2.

Docking scores (ΔG, kcal·mol⁻¹) of thiosemicarbazide metal complexes

3.2. Key Protein-Ligand Interactions

Across the target panel, hydrogen bonding contributed strongly to specificity, while hydrophobic contacts provided the dominant stabilizing surface complementarity. Consistent with large-scale analyses of protein–ligand complexes, proteins frequently act as hydrogen-bond donors (e.g., Lys/Arg/Asn/Gln side chains), favoring ligands presenting multiple acceptors [12; 33; 35]. Electrostatic contacts further oriented charged fragments via complementary Asp/Glu and Lys/Arg interactions [12; 33]. Metal–π (cation–π) interactions with aromatic residues (Phe/Tyr/Trp/His) were observed for a subset of complexes and may contribute to selectivity [12; 35]. Hydrophobic contacts were the most frequent noncovalent interaction class and contributed substantially to pose stabilization [12; 33] (Table 3).

Table 3.

Dominant protein–ligand interactions identified from docking

Together, these interaction types form complementary networks that govern binding mode and selectivity.

4. Structure–Binding Relationships and Methodological Considerations

4.1. Structure–Binding Relationships Across the Metal Series

Across the series, Cu(II) and Pd(II) complexes yielded the most favorable ΔG values, consistent with stronger interactions between soft/borderline metal centers and the soft thio-donor under HSAB considerations [5; 7]. Geometry modulated these trends: square-planar Cu(II)/Pd(II) complexes frequently aligned favorably with planar DNA and topoisomerase binding regions, whereas octahedral Ni(II)/Co(III) complexes were more compatible with deeper catalytic pockets [37; 38]. Tetrahedral Zn(II) complexes displayed intermediate scores, consistent with first-row stability trends and metalloprotein selectivity [36]. Data-driven surveys of metal-binding sites further support the roles of donor spacing and coordination number in dictating preferred geometries and interaction modes [5; 7] (Table 4).

Table 4.

Structure–binding correlations across the metal series

4.2. Correlation with Experimental Biological Activity

Docking scores are useful for prioritization but show variable quantitative agreement with experimental potency. Modest correlations (typical R² ≈ 0.4-0.6) have been reported for congeneric antimicrobial/anticancer series under consistent preparation protocols, whereas correlations often deteriorate for chemically diverse sets or for nucleic-acid binding where electrostatics and conformational dynamics are challenging to model [40]. Rescoring (e.g., MM/GBSA) may improve agreement for rigid binding pockets but increases computational cost. Accordingly, ΔG values are interpreted as qualitative indicators of relative binding propensity and are contextualized with experimental considerations.

4.3. Limitations of Docking in Metal-Based Drug Design

Docking of metal-containing ligands is limited by (1) largely rigid receptor representations that neglect induced fit and coordination-sphere adaptation, (2) incomplete transition-metal parameterization in common scoring functions, especially for soft-donor interactions [40], and (3) the high computational cost of explicit-solvent molecular dynamics and QM/MM refinement needed for more realistic metal–protein interaction modeling. Therefore, docking is best used as a front-end filter coupled to careful parameterization and post-docking refinement for prioritized candidates.

5. Conclusions and Future Perspectives

The analysis indicates that metal identity, donor-atom composition and enforced coordination geometry jointly govern predicted binding propensities across DNA- and enzyme-related targets. Soft and borderline-soft metal centers coordinated by sulfur donors frequently delivered the most favorable docking scores, consistent with HSAB-driven metal–ligand interactions [36,37]. Future workflows should integrate ensemble docking from short molecular dynamics trajectories, rescoring (MM/GBSA and/or QM/MM where justified), and machine-learning models encoding metal-specific electronic descriptors to improve predictive power and accelerate development of thiosemicarbazide-based metallopharmaceutical candidates.

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