Predicting permeation of compounds across the outer membrane of P. aeruginosa using molecular descriptors.

The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds' intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.

Human carnosinases: A brief history, medicinal relevance, and in silico analyses.

Carnosine, an endogenous dipeptide, has been found to have a plethora of medicinal properties, such as antioxidant, antiageing, and chelating effects, but with one downside: a short half-life. Carnosinases and two hydrolytic enzymes, which remain enigmatic, are responsible for these features. Hence, here we emphasize why research is valuable for better understanding crucial concepts like ageing, neurodegradation, and cancerogenesis, given that inhibition of carnosinases might significantly prolong carnosine bioavailability and allow its further use in medicine. Herein, we explore the literature regarding carnosinases and present a short in silico analysis aimed at elucidating the possible recognition pattern between CN1 and its ligands.

Structure of human TRPM8 channel.

TRPM8 is a non-selective cation channel permeable to both monovalent and divalent cations that is activated by multiple factors, such as temperature, voltage, pressure, and changes in osmolality. It is a therapeutic target for anticancer drug development, and its modulators can be utilized for several pathological conditions. Here, we present a cryo-electron microscopy structure of a human TRPM8 channel in the closed state that was solved at 2.7 Å resolution. Our structure comprises the most complete model of the N-terminal pre-melastatin homology region. We also visualized several lipids that are bound by the protein and modeled how the human channel interacts with icilin. Analyses of pore helices in available TRPM structures showed that all these structures can be grouped into different closed, desensitized and open state conformations based on the register of the pore helix S6 which positions particular amino acid residues at the channel constriction.

Molecular determinants of avoidance and inhibition of Pseudomonas aeruginosa MexB efflux pump.

Transporters of the resistance-nodulation-cell division (RND) superfamily of proteins are the dominant multidrug efflux power of Gram-negative bacteria. The major RND efflux pump of Pseudomonas aeruginosa is MexAB-OprM, in which the inner membrane transporter MexB is responsible for the recognition and binding of compounds. The high importance of this pump in clinical antibiotic resistance made it a subject of intense investigations and a promising target for the discovery of efflux pump inhibitors. This study is focused on a series of peptidomimetic compounds developed as effective inhibitors of MexAB-OprM. We performed multi-copy molecular dynamics simulations, machine-learning (ML) analyses, and site-directed mutagenesis of MexB to investigate interactions of MexB with representatives of efflux avoiders, substrates, and inhibitors. The analysis of both direct and water-mediated protein-ligand interactions revealed characteristic patterns for each class, highlighting significant differences between them. We found that efflux avoiders poorly interact with the access binding site of MexB, and inhibition engages amino acid residues that are not directly involved in binding and transport of substrates. In agreement, machine-learning models selected different residues predictive of MexB substrates and inhibitors. The differences in interactions were further validated by site-directed mutagenesis. We conclude that the substrate translocation and inhibition pathways of MexB split at the interface (between the main putative binding sites) and at the deep binding pocket and that interactions outside of the hydrophobic patch contribute to the inhibition of MexB. This molecular-level information could help in the rational design of new inhibitors and antibiotics less susceptible to the efflux mechanism. IMPORTANCE Multidrug transporters recognize and expel from cells a broad range of ligands including their own inhibitors. The difference between the substrate translocation and inhibition routes remains unclear. In this study, machine learning and computational and experimental approaches were used to understand dynamics of MexB interactions with its ligands. Our results show that some ligands engage a certain combination of polar and charged residues in MexB binding sites to be effectively expelled into the exit funnel, whereas others engage aromatic and hydrophobic residues that slow down or hinder the next step in the transporter cycle. These findings suggest that all MexB ligands fit into this substrate-inhibitor spectrum depending on their physico-chemical structures and properties.

Interaction of Radiopharmaceuticals with Somatostatin Receptor 2 Revealed by Molecular Dynamics Simulations.

The development of drugs targeting somatostatin receptor 2 (SSTR2), generally overexpressed in neuroendocrine tumors, is focus of intense research. A few molecules in conjugation with radionuclides are in clinical use for both diagnostic and therapeutic purposes. These radiopharmaceuticals are composed of a somatostatin analogue biovector conjugated to a chelator moiety bearing the radionuclide. To date, despite valuable efforts, a detailed molecular-level description of the interaction of radiopharmaceuticals in complex with SSTR2 has not yet been accomplished. Therefore, in this work, we carefully analyzed the key dynamical features and detailed molecular interactions of SSTR2 in complex with six radiopharmaceutical compounds selected among the few already in use (64Cu/68Ga-DOTATATE, 68Ga-DOTATOC, 64Cu-SARTATE) and some in clinical development (68Ga-DOTANOC, 64Cu-TETATATE). Through molecular dynamics simulations and exploiting recently available structures of SSTR2, we explored the influence of the different portions of the compounds (peptide, radionuclide, and chelator) in the interaction with the receptor. We identified the most stable binding modes and found distinct interaction patterns characterizing the six compounds. We thus unveiled detailed molecular interactions crucial for the recognition of this class of radiopharmaceuticals. The microscopically well-founded analysis presented in this study provides guidelines for the design of new potent ligands targeting SSTR2.

Targeting the multifaceted neurotoxicity of Alzheimer's disease by tailored functionalisation of the curcumin scaffold.

Simultaneous modulation of multifaceted toxicity arising from neuroinflammation, oxidative stress, and mitochondrial dysfunction represents a valuable therapeutic strategy to tackle Alzheimer's disease. Among the significant hallmarks of the disorder, Aß protein and its aggregation products are well-recognised triggers of the neurotoxic cascade. In this study, by tailored modification of the curcumin-based lead compound 1, we aimed at developing a small library of hybrid compounds targeting Aß protein oligomerisation and the consequent neurotoxic events. Interestingly, from in vitro studies, analogues 3 and 4, bearing a substituted triazole moiety, emerged as multifunctional agents able to counteract Aß aggregation, neuroinflammation and oxidative stress. In vivo proof-of-concept evaluations, performed in a Drosophila oxidative stress model, allowed us to identify compound 4 as a promising lead candidate.

Tripartite efflux pumps of the RND superfamily: what did we learn from computational studies?

Bacterial resistance to antibiotics has been long recognized as a priority to address for human health. Among all micro-organisms, the so-called multi-drug resistant (MDR) bacteria, which are resistant to most, if not all drugs in our current arsenal, are particularly worrisome. The World Health Organization has prioritized the ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter species) pathogens, which include four Gram-negative bacterial species. In these bacteria, active extrusion of antimicrobial compounds out of the cell by means of 'molecular guns' known as efflux pumps is a main determinant of MDR phenotypes. The resistance-nodulation-cell division (RND) superfamily of efflux pumps connecting the inner and outer membrane in Gram-negative bacteria is crucial to the onset of MDR and virulence, as well as biofilm formation. Thus, understanding the molecular basis of the interaction of antibiotics and inhibitors with these pumps is key to the design of more effective therapeutics. With the aim to contribute to this challenge, and complement and inspire experimental research, in silico studies on RND efflux pumps have flourished in recent decades. Here, we review a selection of such investigations addressing the main determinants behind the polyspecificity of these pumps, the mechanisms of substrate recognition, transport and inhibition, as well as the relevance of their assembly for proper functioning, and the role of protein-lipid interactions. The journey will end with a perspective on the role of computer simulations in addressing the challenges posed by these beautifully complex machineries and in supporting the fight against the spread of MDR bacteria.

Molecular simulations of SSTR2 dynamics and interaction with ligands.

The cyclic peptide hormone somatostatin regulates physiological processes involved in growth and metabolism, through its binding to G-protein coupled somatostatin receptors. The isoform 2 (SSTR2) is of particular relevance for the therapy of neuroendocrine tumours for which different analogues to somatostatin are currently in clinical use. We present an extensive and systematic computational study on the dynamics of SSTR2 in three different states: active agonist-bound, inactive antagonist-bound and apo inactive. We exploited the recent burst of SSTR2 experimental structures to perform µs-long multi-copy molecular dynamics simulations to sample conformational changes of the receptor and rationalize its binding to different ligands (the agonists somatostatin and octreotide, and the antagonist CYN154806). Our findings suggest that the apo form is more flexible compared to the holo ones, and confirm that the extracellular loop 2 closes upon the agonist octreotide but not upon the antagonist CYN154806. Based on interaction fingerprint analyses and free energy calculations, we found that all peptides similarly interact with residues buried into the binding pocket. Conversely, specific patterns of interactions are found with residues located in the external portion of the pocket, at the basis of the extracellular loops, particularly distinguishing the agonists from the antagonist. This study will help in the design of new somatostatin-based compounds for theranostics of neuroendocrine tumours.

Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative.

Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.

Discovery of a Potent and Highly Selective Dipeptidyl Peptidase IV and Carbonic Anhydrase Inhibitor as "Antidiabesity" Agents Based on Repurposing and Morphing of WB-4101.

The management of patients with type 2 diabetes mellitus (T2DM) is shifting from cardio-centric to weight-centric or, even better, adipose-centric treatments. Considering the downsides of multidrug therapies and the relevance of dipeptidyl peptidase IV (DPP IV) and carbonic anhydrases (CAs II and V) in T2DM and in the weight loss, we report a new class of multitarget ligands targeting the mentioned enzymes. We started from the known α1-AR inhibitor WB-4101, which was progressively modified through a tailored morphing strategy to optimize the potency of DPP IV and CAs while losing the adrenergic activity. The obtained compound 12 shows a satisfactory DPP IV inhibition with a good selectivity CA profile (DPP IV IC50: 0.0490 µM; CA II Ki 0.2615 µM; CA VA Ki 0.0941 µM; CA VB Ki 0.0428 µM). Furthermore, its DPP IV inhibitory activity in Caco-2 and its acceptable pre-ADME/Tox profile indicate it as a lead compound in this novel class of multitarget ligands.

Spinitectus asperus and Klossinemella iheringi , intestinal nematodes of Prochilodus lineatus (Pisces, Prochilodontidae) from the alluvial plain of the Middle Paraná River, Argentina.

Prochilodus lineatus has been considered an ecosystem engineer in the Neotropics given its influence on important ecosystem processes, and it is therefore relevant to understand their inter-specific relationships. The association of P. lineatus with parasitic helminths was studied in two isolated shallow lakes of the alluvial plain of the Middle Paraná River. Only two species of gastrointestinal nematodes were found: Spinitectus asperus (Cystidicolidae) and Klossinemella iheringi (Atractidae), with S. asperus having higher prevalence and mean intensity values. This is the first record of K. iheringi in the Middle Paraná River, Argentina, as well as the southernmost citation of S. asperus.

Computational Insights into the Sequence-Activity Relationships of the NGF(1-14) Peptide by Molecular Dynamics Simulations.

The Nerve Growth Factor (NGF) belongs to the neurothrophins protein family involved in the survival of neurons in the nervous system. The interaction of NGF with its high-affinity receptor TrkA mediates different cellular pathways related to Alzheimer's disease, pain, ocular dysfunction, and cancer. Therefore, targeting NGF-TrkA interaction represents a valuable strategy for the development of new therapeutic agents. In recent years, experimental studies have revealed that peptides belonging to the N-terminal domain of NGF are able to partly mimic the biological activity of the whole protein paving the way towards the development of small peptides that can selectively target specific signaling pathways. Hence, understanding the molecular basis of the interaction between the N-terminal segment of NGF and TrkA is fundamental for the rational design of new peptides mimicking the NGF N-terminal domain. In this study, molecular dynamics simulation, binding free energy calculations and per-residue energy decomposition analysis were combined in order to explore the molecular recognition pattern between the experimentally active NGF(1-14) peptide and TrkA. The results highlighted the importance of His4, Arg9 and Glu11 as crucial residues for the stabilization of NGF(1-14)-TrkA interaction, thus suggesting useful insights for the structure-based design of new therapeutic peptides able to modulate NGF-TrkA interaction.

Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel.

(1) Background: Virtual screening campaigns require target structures in which the pockets are properly arranged for binding. Without these, MD simulations can be used to relax the available target structures, optimizing the fine architecture of their binding sites. Among the generated frames, the best structures can be selected based on available experimental data. Without experimental templates, the MD trajectories can be filtered by energy-based criteria or sampled by systematic analyses. (2) Methods: A blind and methodical analysis was performed on the already reported MD run of the hTRPM8 tetrameric structures; a total of 50 frames underwent docking simulations by using a set of 1000 ligands including 20 known hTRPM8 modulators. Docking runs were performed by LiGen program and involved the frames as they are and after optimization by SCRWL4.0. For each frame, all four monomers were considered. Predictive models were developed by the EFO algorithm based on the sole primary LiGen scores. (3) Results: On average, the MD simulation progressively enhances the performance of the extracted frames, and the optimized structures perform better than the non-optimized frames (EF1% mean: 21.38 vs. 23.29). There is an overall correlation between performances and volumes of the explored pockets and the combination of the best performing frames allows to develop highly performing consensus models (EF1% = 49.83). (4) Conclusions: The systematic sampling of the entire MD run provides performances roughly comparable with those previously reached by using rationally selected frames. The proposed strategy appears to be helpful when the lack of experimental data does not allow an easy selection of the optimal structures for docking simulations. Overall, the reported docking results confirm the relevance of simulating all the monomers of an oligomer structure and emphasize the efficacy of the SCRWL4.0 method to optimize the protein structures for docking calculations.

Recognition of quinolone antibiotics by the multidrug efflux transporter MexB of Pseudomonas aeruginosa.

The drug/proton antiporter MexB is the engine of the major efflux pump MexAB-OprM in Pseudomonas aeruginosa. This protein is known to transport a large variety of compounds, including antibiotics, thus conferring a multi-drug resistance phenotype. Due to the difficulty of producing co-crystals, only two X-ray structures of MexB in a complex with ligands are available to date, and mechanistic aspects are largely hypothesized based on the body of data collected for the homologous protein AcrB of Escherichia coli. In particular, a recent study (Ornik-Cha, Wilhelm, Kobylka et al., Nat. Commun., 2021, 12, 6919) reported a co-crystal structure of AcrB in a complex with levofloxacin, an antibiotic belonging to the important class of (fluoro)-quinolones. In this work, we performed a systematic ensemble docking campaign coupled to the cluster analysis and molecular-mechanics optimization of docking poses to study the interaction between 36 quinolone antibiotics and MexB. We additionally investigated surface complementarity between each molecule and the transporter and thoroughly assessed the computational protocol adopted against the known experimental data. Our study reveals different binding preferences of the investigated compounds towards the sub-sites of the large deep binding pocket of MexB, supporting the hypothesis that MexB substrates oscillate between different binding modes with similar affinity. Interestingly, small changes in the molecular structure translate into significant differences in MexB-quinolone interactions. All the predicted binding modes are available for download and visualization at the following link: https://www.dsf.unica.it/dock/mexb/quinolones.

Cyclo(His-Pro) Exerts Protective Carbonyl Quenching Effects through Its Open Histidine Containing Dipeptides.

Cyclo(His-Pro) (CHP) is a cyclic dipeptide which is endowed with favorable pharmacokinetic properties combined with a variety of biological activities. CHP is found in a number of protein-rich foods and dietary supplements. While being stable at physiological pH, CHP can open yielding two symmetric dipeptides (His-Pro, Pro-His), the formation of which might be particularly relevant from dietary CHP due to the gastric acidic environment. The antioxidant and protective CHP properties were repeatedly reported although the non-enzymatic mechanisms were scantly investigated. The CHP detoxifying activity towards α,ß unsaturated carbonyls was never investigated in detail, although its open dipeptides might be effective as already observed for histidine containing dipeptides. Hence, this study investigated the scavenging properties of TRH, CHP and its open derivatives towards 4-hydroxy-2-nonenal. The obtained results revealed that Pro-His possesses a marked activity and is more reactive than l-carnosine. As investigated by DFT calculations, the enhanced reactivity can be ascribed to the greater electrophilicity of the involved iminium intermediate. These findings emphasize that the primary amine (as seen in l-carnosine) can be replaced by secondary amines with beneficial effects on the quenching mechanisms. Serum stability of the tested peptides was also evaluated, showing that Pro-His is characterized by a greater stability than l-carnosine. Docking simulations suggested that its hydrolysis can be catalyzed by serum carnosinase. Altogether, the reported results evidence that the antioxidant CHP properties can be also due to the detoxifying activity of its open dipeptides, which might be thus responsible for the beneficial effects induced by CHP containing food.

AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials.

Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as ß-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from µs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials.

Anosmin-1-Like Effect of UMODL1/Olfactorin on the Chemomigration of Mouse GnRH Neurons and Zebrafish Olfactory Axons Development.

The impairment of development/migration of hypothalamic gonadotropin-releasing hormone (GnRH) neurons is the main cause of Kallmann's syndrome (KS), an inherited disorder characterized by hypogonadism, anosmia, and other developmental defects. Olfactorin is an extracellular matrix protein encoded by the UMODL1 (uromodulin-like 1) gene expressed in the mouse olfactory region along the migratory route of GnRH neurons. It shares a combination of WAP and FNIII repeats, expressed in complementary domains, with anosmin-1, the product of the ANOS1 gene, identified as the causative of KS. In the present study, we have investigated the effects of olfactorin in vitro and in vivo models. The results show that olfactorin exerts an anosmin-1-like strong chemoattractant effect on mouse-immortalized GnRH neurons (GN11 cells) through the activation of the FGFR and MAPK pathways. In silico analysis of olfactorin and anosmin-1 reveals a satisfactory similarity at the N-terminal region for the overall arrangement of corresponding WAP and FNIII domains and marked similarities between WAP domains' binding modes of interaction with the resolved FGFR1-FGF2 complex. Finally, in vivo experiments show that the down-modulation of the zebrafish z-umodl1 gene (orthologous of UMODL1) in both GnRH3:GFP and omp 2k :gap-CFP rw034 transgenic zebrafish strains leads to a clear disorganization and altered fasciculation of the neurites of GnRH3:GFP neurons crossing at the anterior commissure and a significant increase in olfactory CFP + fibers with altered trajectory. Thus, our study shows olfactorin as an additional factor involved in the development of olfactory and GnRH systems and proposes UMODL1 as a gene worthy of diagnostic investigation in KS.

A multiscale approach to predict the binding mode of metallo beta-lactamase inhibitors.

Antibiotic resistance is a major threat to global public health. ß-lactamases, which catalyze breakdown of ß-lactam antibiotics, are a principal cause. Metallo ß-lactamases (MBLs) represent a particular challenge because they hydrolyze almost all ß-lactams and to date no MBL inhibitor has been approved for clinical use. Molecular simulations can aid drug discovery, for example, predicting inhibitor complexes, but empirical molecular mechanics (MM) methods often perform poorly for metalloproteins. Here we present a multiscale approach to model thiol inhibitor binding to IMP-1, a clinically important MBL containing two catalytic zinc ions, and predict the binding mode of a 2-mercaptomethyl thiazolidine (MMTZ) inhibitor. Inhibitors were first docked into the IMP-1 active site, testing different docking programs and scoring functions on multiple crystal structures. Complexes were then subjected to molecular dynamics (MD) simulations and subsequently refined through QM/MM optimization with a density functional theory (DFT) method, B3LYP/6-31G(d), increasing the accuracy of the method with successive steps. This workflow was tested on two IMP-1:MMTZ complexes, for which it reproduced crystallographically observed binding, and applied to predict the binding mode of a third MMTZ inhibitor for which a complex structure was crystallographically intractable. We also tested a 12-6-4 nonbonded interaction model in MD simulations and optimization with a SCC-DFTB QM/MM approach. The results show the limitations of empirical models for treating these systems and indicate the need for higher level calculations, for example, DFT/MM, for reliable structural predictions. This study demonstrates a reliable computational pipeline that can be applied to inhibitor design for MBLs and other zinc-metalloenzyme systems.

Antitumor activity of novel POLA1-HDAC11 dual inhibitors.

Hybrid molecules targeting simultaneously DNA polymerase α (POLA1) and histone deacetylases (HDACs) were designed and synthesized to exploit a potential synergy of action. Among a library of screened molecules, MIR002 and GEM144 showed antiproliferative activity at nanomolar concentrations on a panel of human solid and haematological cancer cell lines. In vitro functional assays confirmed that these molecules inhibited POLA1 primer extension activity, as well as HDAC11. Molecular docking studies also supported these findings. Mechanistically, MIR002 and GEM144 induced acetylation of p53, activation of p21, G1/S cell cycle arrest, and apoptosis. Oral administration of these inhibitors confirmed their antitumor activity in in vivo models. In human non-small cancer cell (H460) xenografted in nude mice MIR002 at 50 mg/kg, Bid (qd × 5 × 3w) inhibited tumor growth (TGI = 61%). More interestingly, in POLA1 inhibitor resistant cells (H460-R9A), the in vivo combination of MIR002 with cisplatin showed an additive antitumor effect with complete disappearance of tumor masses in two animals at the end of the treatment. Moreover, in two human orthotopic malignant pleural mesothelioma xenografts (MM473 and MM487), oral treatments with MIR002 and GEM144 confirmed their significant antitumor activity (TGI = 72-77%). Consistently with recent results that have shown an inverse correlation between POLA1 expression and type I interferon levels, MIR002 significantly upregulated interferon-α in immunocompetent mice.

Multiscale Workflow for Modeling Ligand Complexes of Zinc Metalloproteins.

Zinc metalloproteins are ubiquitous, with protein zinc centers of structural and functional importance, involved in interactions with ligands and substrates and often of pharmacological interest. Biomolecular simulations are increasingly prominent in investigations of protein structure, dynamics, ligand interactions, and catalysis, but zinc poses a particular challenge, in part because of its versatile, flexible coordination. A computational workflow generating reliable models of ligand complexes of biological zinc centers would find broad application. Here, we evaluate the ability of alternative treatments, using (nonbonded) molecular mechanics (MM) and quantum mechanics/molecular mechanics (QM/MM) at semiempirical (DFTB3) and density functional theory (DFT) levels of theory, to describe the zinc centers of ligand complexes of six metalloenzyme systems differing in coordination geometries, zinc stoichiometries (mono- and dinuclear), and the nature of interacting groups (specifically the presence of zinc-sulfur interactions). MM molecular dynamics (MD) simulations can overfavor octahedral geometries, introducing additional water molecules to the zinc coordination shell, but this can be rectified by subsequent semiempirical (DFTB3) QM/MM MD simulations. B3LYP/MM geometry optimization further improved the accuracy of the description of coordination distances, with the overall effectiveness of the approach depending upon factors, including the presence of zinc-sulfur interactions that are less well described by semiempirical methods. We describe a workflow comprising QM/MM MD using DFTB3 followed by QM/MM geometry optimization using DFT (e.g., B3LYP) that well describes our set of zinc metalloenzyme complexes and is likely to be suitable for creating accurate models of zinc protein complexes when structural information is more limited.