The influence of model building schemes and molecular dynamics sampling on QM-cluster models: the chorismate mutase case study.

Most QM-cluster models of enzymes are constructed based on X-ray crystal structures, which limits comparison to in vivo structure and mechanism. The active site of chorismate mutase from Bacillus subtilis and the enzymatic transformation of chorismate to prephenate is used as a case study to guide construction of QM-cluster models built first from the X-ray crystal structure, then from molecular dynamics (MD) simulation snapshots. The Residue Interaction Network ResidUe Selector (RINRUS) software toolkit, developed by our group to simplify and automate the construction of QM-cluster models, is expanded to handle MD to QM-cluster model workflows. Several options, some employing novel topological clustering from residue interaction network (RIN) information, are evaluated for generating conformational clustering from MD simulation. RINRUS then generates a statistical thermodynamic framework for QM-cluster modeling of the chorismate mutase mechanism via refining 250 MD frames with density functional theory (DFT). The 250 QM-cluster models sampled provide a mean ΔG‡ of 10.3 ± 2.6 kcal mol-1 compared to the experimental value of 15.4 kcal mol-1 at 25 °C. While the difference between theory and experiment is consequential, the level of theory used is modest and therefore "chemical" accuracy is unexpected. More important are the comparisons made between QM-cluster models designed from the X-ray crystal structure versus those from MD frames. The large variations in kinetic and thermodynamic properties arise from geometric changes in the ensemble of QM-cluster models, rather from the composition of the QM-cluster models or from the active site-solvent interface. The findings open the way for further quantitative and reproducible calibration in the field of computational enzymology using the model construction framework afforded with the RINRUS software toolkit.

ZNF384-Related Fusion Genes in Acute Lymphoblastic Leukemia.

Zinc finger protein 384 (ZNF384) encodes a C2H2-type zinc finger protein that can function as a transcription factor. ZNF384 rearrangement in acute lymphoblastic leukemia (ALL) was first reported in 2002. More than 19 different ZNF384 fusion partners have been detected in ALL. These include E1A-binding protein P300 (EP300), CREB-binding protein (CREBBP), transcription factor 3 (TCF3), TATA-box binding protein associated factor 15 (TAF15), Ewing sarcoma breakpoint region 1 gene (EWSR1), AT-rich interactive domain-containing protein 1B (ARID1B), SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily A, member 4 (SMARCA4), SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily A, member 2 (SMARCA2), synergin gamma (SYNRG), clathrin heavy chain (CLTC), bone morphogenic protein 2-inducible kinase (BMP2K), Nipped-B-like protein (NIPBL), A Kinase Anchoring Protein 8 (AKAP8), Chromosome 11 Open Reading Frame 74 (C11orf74), DEAD-Box Helicase 42 (DDX42), ATP Synthase F1 Subunit Gamma (ATP2C1), Euchromatic Histone Lysine Methyltransferase 1 (EHMT1), Testic Expressed 41 (TEX41), etc. Patients diagnosed with ALL harboring ZNF384 rearrangements commonly had a good prognosis. The mechanisms, performance, and features of different ZNF384 rearrangements in acute lymphoblastic leukemia have been well evaluated.

Evaluating the active site-substrate interplay between x-ray crystal structure and molecular dynamics in chorismate mutase.

Designing realistic quantum mechanical (QM) models of enzymes is dependent on reliably discerning and modeling residues, solvents, and cofactors important in crafting the active site microenvironment. Interatomic van der Waals contacts have previously demonstrated usefulness toward designing QM-models, but their measured values (and subsequent residue importance rankings) are expected to be influenceable by subtle changes in protein structure. Using chorismate mutase as a case study, this work examines the differences in ligand-residue interatomic contacts between an x-ray crystal structure and structures from a molecular dynamics simulation. Select structures are further analyzed using symmetry adapted perturbation theory to compute ab initio ligand-residue interaction energies. The findings of this study show that ligand-residue interatomic contacts measured for an x-ray crystal structure are not predictive of active site contacts from a sampling of molecular dynamics frames. In addition, the variability in interatomic contacts among structures is not correlated with variability in interaction energies. However, the results spotlight using interaction energies to characterize and rank residue importance in future computational enzymology workflows.

Theoretical Rovibrational Spectroscopy of Magnesium Tricarbide-Multireference Character Thwarts a Full Analysis of All Isomers.

Magnesium tricarbide isomers are studied herein with coupled cluster theory and multireference configuration interaction to support their possible detection in astrochemical environments such as the circumstellar envelope surrounding the star IRC +10216 or in terrestrial laboratories. Magnesium-bearing species may abound in the interstellar medium (ISM), but only eight (MgNC, MgCN, HMgNC, MgC2H, MgC3N, MgC4H, MgC5N, and MgC6H) have been directly identified thus far. Several possible isomers for the related MgC3 system are explored in their singlet and triplet spin multiplicities. Overall, this work offers quantum chemical insight of rovibrational spectroscopic data for MgC3 using quartic force fields (QFFs) based on the CCSD(T) and CCSD(T)-F12 levels of theory at the complete basis set (CBS) limit. Additional corrections with small basis set CCSDT(Q) and scalar relativistic effects are also included in the analysis. Salient multireference character is found in the singlet diamond electronic state, which makes a definitive assignment of the ground state challenging. Nevertheless, coupled cluster-based composite energies and multireference configuration interaction both predict that the 1A1 diamond isomer is 1.6-2.2 kcal mol-1 lower in energy than the 3A1 diamond isomer. Furthermore, highly accurate binding energies of various isomers MgC3 are provided for comparison to photodetachment experiments. Dipole moments along with harmonic infrared intensities will guide efforts for astronomical and spectroscopic characterization.

Modulating Activity Evaluation of Gut Microbiota with Versatile Toluquinol.

Gut microbiota have important implications for health by affecting the metabolism of diet and drugs. However, the specific microbial mediators and their mechanisms in modulating specific key intermediate metabolites from fungal origins still remain largely unclear. Toluquinol, as a key versatile precursor metabolite, is commonly distributed in many fungi, including Penicillium species and their strains for food production. The common 17 gut microbes were cultivated and fed with and without toluquinol. Metabolic analysis revealed that four strains, including the predominant Enterococcus species, could metabolize toluquinol and produce different metabolites. Chemical investigation on large-scale cultures led to isolation of four targeted metabolites and their structures were characterized with NMR, MS, and X-ray diffraction analysis, as four toluquinol derivatives (1-4) through O1/O4-acetyl and C5/C6-methylsulfonyl substitutions, respectively. The four metabolites were first synthesized in living organisms. Further experiments suggested that the rare methylsulfonyl groups in 3-4 were donated from solvent DMSO through Fenton's reaction. Metabolite 1 displayed the strongest inhibitory effect on cancer cells A549, A2780, and G401 with IC50 values at 0.224, 0.204, and 0.597 µM, respectively, while metabolite 3 displayed no effect. Our results suggest that the dominant Enterococcus species could modulate potential precursors of fungal origin and change their biological activity.

Cheminformatic quantum mechanical enzyme model design: A catechol-O-methyltransferase case study.

To accurately simulate the inner workings of an enzyme active site with quantum mechanics (QM), not only must the reactive species be included in the model but also important surrounding residues, solvent, or coenzymes involved in crafting the microenvironment. Our lab has been developing the Residue Interaction Network Residue Selector (RINRUS) toolkit to utilize interatomic contact network information for automated, rational residue selection and QM-cluster model generation. Starting from an x-ray crystal structure of catechol-O-methyltransferase, RINRUS was used to construct a series of QM-cluster models. The reactant, product, and transition state of the methyl transfer reaction were computed for a total of 550 models, and the resulting free energies of activation and reaction were used to evaluate model convergence. RINRUS-designed models with only 200-300 atoms are shown to converge. RINRUS will serve as a cornerstone for improved and automated cheminformatics-based enzyme model design.

Acylation and deacylation mechanism and kinetics of penicillin G reaction with Streptomyces R61 DD-peptidase.

Two quantum mechanical (QM)-cluster models are built for studying the acylation and deacylation mechanism and kinetics of Streptomyces R61 DD-peptidase with the penicillin G at atomic level detail. DD-peptidases are bacterial enzymes involved in the cross-linking of peptidoglycan to form the cell wall, necessary for bacterial survival. The cross-linking can be inhibited by antibiotic beta-lactam derivatives through acylation, preventing the acyl-enzyme complex from undergoing further deacylation. The deacylation step was predicted to be rate-limiting. Transition state and intermediate structures are found using density functional theory in this study, and thermodynamic and kinetic properties of the proposed mechanism are evaluated. The acyl-enzyme complex is found lying in a deep thermodynamic sink, and deacylation is indeed the severely rate-limiting step, leading to suicide inhibition of the peptidoglycan cross-linking. The usage of QM-cluster models is a promising technique to understand, improve, and design antibiotics to disrupt function of the Streptomyces R61 DD-peptidase.

Quantifying Inter-Residue Contacts through Interaction Energies.

The validity and accuracy of protein modeling is dependent on constructing models that account for the inter-residue interactions crucial for protein structure and function. Residue interaction networks derived from interatomic van der Waals contacts have previously demonstrated usefulness toward designing protein models, but there has not yet been evidence of a connection between network-predicted interaction strength and quantitative interaction energies. This work evaluates the intraprotein contact networks of five proteins against ab initio interaction energies computed using symmetry-adapted perturbation theory. To more appropriately capture the local chemistry of the protein, we deviate from traditional protein network analysis to redefine the interacting nodes in terms of main chain and side chain functional groups rather than complete amino acids. While there is no simple correspondence between the features of the contact network and actual interaction strength, random forest models constructed from minimal structural, network, and chemical descriptors are capable of accurately predicting interaction energy. The results of this work serve as a foundation for the development and improvement of functional group-based contact networks.

Theoretical study of the low-lying electronic states of iron hydride cation.

Both FeH and FeH+ are predicted to be abundant in cool stellar atmospheres and proposed to be molecular components of the gas phase interstellar medium (ISM). However, experimental and simulated data for both species are lacking, which have hindered astronomical detection. There are no published laboratory data for the spectroscopy of FeH+ in any frequency regime. It is also not established if FeH+ possesses salient multireference character, which would pose significant challenges for ab initio modeling of geometric and spectroscopic properties. With a set of high-level coupled cluster and multireference configuration interaction computations, a study of the electronic structure of the ground state and seven excited states of FeH+ was carried out. An X5Δi electronic ground state of FeH+ is found, in agreement with previous theoretical studies. Including corrections for spin-orbit coupling and anharmonic vibrational effects, the Ω = 3, ν = 0 spin ladder of the A5Πi electronic state lies 872 cm-1 higher in energy than the Ω = 4, ν = 0 spin ladder of the ground state. Combined with previous work in our laboratory, the ionization energy of FeH is computed to be 7.4851 eV. With modern multireference configuration interaction and coupled cluster methods, spectroscopic constants (re, Be, ωe, ωexe, αe, and D¯e) for several bound excited states (A5Πi, B 5Σi +, a 3Σr -, b3Φi, c3Πi, d3Δr, and 7Σ+) were characterized. This study will lead efforts to identify FeH+ in the ISM and help solve important remaining questions in quantifying metal-hydride bonding.

Protein structure modeling and refinement by global optimization in CASP12.

For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain re-modeling. For better template selection and model selection, we updated our model quality assessment (QA) method with the newly developed SVMQA (support vector machine for quality assessment). For 3D chain building, we updated our energy function by including restraints generated from predicted residue-residue contacts. New energy terms for the predicted secondary structure and predicted solvent accessible surface area were also introduced. For difficult targets, we proposed a new method, LEEab, where the template term played a less significant role than it did in LEE, complemented by increased contributions from other terms such as the predicted contact term. For TBM (template-based modeling) targets, LEE performed better than LEEab, but for FM targets, LEEab was better. For model refinement, we modified our CASP11 molecular dynamics (MD) based protocol by using explicit solvents and tuning down restraint weights. Refinement results from MD simulations that used a new augmented statistical energy term in the force field were quite promising. Finally, when using inaccurate information (such as the predicted contacts), it was important to use the Lorentzian function for which the maximal penalty arising from wrong information is always bounded.

A transition state "trapped"? QM-cluster models of engineered threonyl-tRNA synthetase.

In a recent study [Science, 2015, 347, 6224], protein engineering was used to design a core within the enzyme threonyl-tRNA synthetase (ThrRS) capable of stabilizing the coplanar transition state conformation of an inserted noncanonical p-biphenylalanine (BiPhe) residue. Using the X-ray crystal structures of the preliminary (Protein Data Bank entries 4S02, 4S0J, 4S0L, 4S0I, and 4S0K) and final (PDB entry 4S03) ThrRS proteins, fully quantum mechanical (QM) cluster models were constructed and analyzed. Density functional theory and molecular dynamics computations were performed to investigate the energetic profiles of BiPhe dihedral rotation within the ThrRS models. For the 4S03 model, results indicate that steric and hydrophobic forces of the residues surrounding BiPhe eliminate the coplanar transition state entirely. Molecular dynamics simulations were carried out that confirmed the extent of BiPhe rotational flexibility, and provided additional information on barrier heights of full BiPhe rotation. Transition states of near-coplanar biphenyl rings of BiPhe were found for the 4S0I and 4S0K models, but are not likely persistent on any observable timescale. The dihedral angle of the biphenyl moiety is thermally allowed to fluctuate within the ThrRS protein core models by a range of 17°-26°. BiPhe-residue interaction counts (RICs) were used to compare the interaction differences among the different ThrRS cores. The RICs demonstrate how BiPhe is compacted within the 4S03 core, resulting in the experimentally observed "trapped" coplanar transition state analogue. This work presents a unique application of QM-cluster models towards studying the inner workings of proteins, and suggests avenues that computational chemistry can be used to further guide bioengineering.

Towards a quantum chemical protocol for the prediction of rovibrational spectroscopic data for transition metal molecules: Exploration of CuCN, CuOH, and CuCCH.

High accuracy electronic structure computations for small transition metal-containing molecules have been a long term challenge. Due to coupling between electronic and nuclear wave functions, even experimental/theoretical identification of the ground electronic state requires tremendous efforts. Quartic force fields (QFFs) are effective ab initio tools for obtaining reliable anharmonic spectroscopic properties. However, the method that employs complete basis set limit extrapolation ("C"), consideration of core electron correlation ("cC"), and inclusion of scalar relativity ("R") to produce the energy points on the QFF, the composite CcCR methodology, has not yet been utilized to study inorganic spectroscopy. This work takes the CcCR methodology and adapts it to test whether such an approach is conducive for the closed-shell, copper-containing molecules CuCN, CuOH, and CuCCH. Gas phase rovibrational data are provided for all three species in their ground electronic states. Equilibrium geometries and many higher-order rovibrational properties show good agreement with earlier studies. However, there are notable differences, especially in computation of fundamental vibrational frequencies. Even with further additive corrections for the inner core electron correlation and coupled cluster with full single, double, and triple substitutions (CCSDT), the differences are still larger than expected indicating that more work should follow for predicting rovibrational properties of transition metal molecules.

Contact-assisted protein structure modeling by global optimization in CASP11.

We have applied the conformational space annealing method to the contact-assisted protein structure modeling in CASP11. For Tp targets, where predicted residue-residue contact information was provided, the contact energy term in the form of the Lorentzian function was implemented together with the physical energy terms used in our template-free modeling of proteins. Although we observed some structural improvement of Tp models over the models predicted without the Tp information, the improvement was not substantial on average. This is partly due to the inaccuracy of the provided contact information, where only about 18% of it was correct. For Ts targets, where the information of ambiguous NOE (Nuclear Overhauser Effect) restraints was provided, we formulated the modeling in terms of the two-tier optimization problem, which covers: (1) the assignment of NOE peaks and (2) the three-dimensional (3D) model generation based on the assigned NOEs. Although solving the problem in a direct manner appears to be intractable at first glance, we demonstrate through CASP11 that remarkably accurate protein 3D modeling is possible by brute force optimization of a relevant energy function. For 19 Ts targets of the average size of 224 residues, generated protein models were of about 3.6 Å Cα atom accuracy. Even greater structural improvement was observed when additional Tc contact information was provided. For 20 out of the total 24 Tc targets, we were able to generate protein structures which were better than the best model from the rest of the CASP11 groups in terms of GDT-TS. Proteins 2016; 84(Suppl 1):189-199. © 2015 Wiley Periodicals, Inc.

Template-free modeling by LEE and LEER in CASP11.

For the template-free modeling of human targets of CASP11, we utilized two of our modeling protocols, LEE and LEER. The LEE protocol took CASP11-released server models as the input and used some of them as templates for 3D (three-dimensional) modeling. The template selection procedure was based on the clustering of the server models aided by a community detection method of a server-model network. Restraining energy terms generated from the selected templates together with physical and statistical energy terms were used to build 3D models. Side-chains of the 3D models were rebuilt using target-specific consensus side-chain library along with the SCWRL4 rotamer library, which completed the LEE protocol. The first success factor of the LEE protocol was due to efficient server model screening. The average backbone accuracy of selected server models was similar to that of top 30% server models. The second factor was that a proper energy function along with our optimization method guided us, so that we successfully generated better quality models than the input template models. In 10 out of 24 cases, better backbone structures than the best of input template structures were generated. LEE models were further refined by performing restrained molecular dynamics simulations to generate LEER models. CASP11 results indicate that LEE models were better than the average template models in terms of both backbone structures and side-chain orientations. LEER models were of improved physical realism and stereo-chemistry compared to LEE models, and they were comparable to LEE models in the backbone accuracy. Proteins 2016; 84(Suppl 1):118-130. © 2015 Wiley Periodicals, Inc.

Template based protein structure modeling by global optimization in CASP11.

For the template-based modeling (TBM) of CASP11 targets, we have developed three new protein modeling protocols (nns for server prediction and LEE and LEER for human prediction) by improving upon our previous CASP protocols (CASP7 through CASP10). We applied the powerful global optimization method of conformational space annealing to three stages of optimization, including multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain remodeling. For more successful fold recognition, a new alignment method called CRFalign was developed. It can incorporate sensitive positional and environmental dependence in alignment scores as well as strong nonlinear correlations among various features. Modifications and adjustments were made to the form of the energy function and weight parameters pertaining to the chain building procedure. For the side-chain remodeling step, residue-type dependence was introduced to the cutoff value that determines the entry of a rotamer to the side-chain modeling library. The improved performance of the nns server method is attributed to successful fold recognition achieved by combining several methods including CRFalign and to the current modeling formulation that can incorporate native-like structural aspects present in multiple templates. The LEE protocol is identical to the nns one except that CASP11-released server models are used as templates. The success of LEE in utilizing CASP11 server models indicates that proper template screening and template clustering assisted by appropriate cluster ranking promises a new direction to enhance protein 3D modeling. Proteins 2016; 84(Suppl 1):221-232. © 2015 Wiley Periodicals, Inc.

An Integrated Experimental and Computational Approach for Characterizing the Kinetics and Mechanism of Triadimefon Racemization.

Enantiomers of chiral molecules commonly exhibit differing pharmacokinetics and toxicities, which can introduce significant uncertainty when evaluating biological and environmental fates and potential risks to humans and the environment. However, racemization (the irreversible transformation of one enantiomer into the racemic mixture) and enantiomerization (the reversible conversion of one enantiomer into the other) are poorly understood. To better understand these processes, we investigated the chiral fungicide, triadimefon, which undergoes racemization in soils, water, and organic solvents. Nuclear magnetic resonance (NMR) and gas chromatography / mass spectrometry (GC/MS) techniques were used to measure the rates of enantiomerization and racemization, deuterium isotope effects, and activation energies for triadimefon in H2 O and D2 O. From these results we were able to determine that: 1) the alpha-carbonyl carbon of triadimefon is the reaction site; 2) cleavage of the C-H (C-D) bond is the rate-determining step; 3) the reaction is base-catalyzed; and 4) the reaction likely involves a symmetrical intermediate. The B3LYP/6-311 + G** level of theory was used to compute optimized geometries, harmonic vibrational frequencies, nature population analysis, and intrinsic reaction coordinates for triadimefon in water and three racemization pathways were hypothesized. This work provides an initial step in developing predictive, structure-based models that are needed to identify compounds of concern that may undergo racemization. Chirality 28:633-641, 2016. © 2016 Wiley Periodicals, Inc.

Tetraarsenic tetrasulfide triggers ROS-induced apoptosis and ferroptosis in B-cell acute lymphoblastic leukaemia by targeting HK2.

PURPOSE: Acute lymphoblastic leukemia (ALL) is the most common type of cancer diagnosed in children. Despite cure rates of higher than 85 %, refractory or relapsed ALL still exhibits a bleak prognosis indicative of the dearth of treatment modalities specific for relapsed or refractory ALL. Prior research has implicated metabolic alterations in leukemia pathogenesis, and literature on the therapeutic efficacy of arsenic compounds targeting metabolic pathways in B-cell acute lymphoblastic leukemia (B-ALL) cells is scarce. METHODS: A compound extracted from realgar, tetraarsenic tetrasulfide (As4S4), and its antitumor effects on B-ALL were experimentally examined in vitro and in vivo. RESULTS: As4S4 apparently targets B-ALL cells by inducing specific cellular responses, including apoptosis, G2/M arrest, and ferroptosis. Interestingly, these effects are attributed to reactive oxygen species (ROS) accumulation, and increased ROS levels have been linked to both the mitochondria-dependent caspase cascade and the activation of p53 signaling. The ROS scavenger N-acetylcysteine (NAC) can counteract the effects of As4S4 treatment on Nalm-6 and RS4;11 cells. Specifically, by targeting Hexokinase-2 (HK2), As4S4 induces alterations in mitochondrial membrane potential and disrupts glucose metabolism, leading to ROS accumulation, and was shown to inhibit B-ALL cell proliferation in vitro and in vivo. Intriguingly, overexpression of HK2 can partially desensitize B-ALL cells to As4S4 treatment. CONCLUSION: Tetraarsenic tetrasulfide can regulate the Warburg effect by controlling HK2 expression, a finding that provides both new mechanistic insight into metabolic alterations and pharmacological evidence for the clinical treatment of B-ALL.

A Machine Learning-Based Investigation of Integrin Expression Patterns in Cancer and Metastasis.

Background: Integrins, a family of transmembrane receptor proteins, play complex roles in cancer development and metastasis. These roles could be better delineated through machine learning of transcriptomic data to reveal relationships between integrin expression patterns and cancer. Methods: We collected publicly available RNA-Seq integrin expression from 8 healthy tissues and their corresponding tumors, along with data from metastatic breast cancer. We then used machine learning methods, including t-SNE visualization and Random Forest classification, to investigate changes in integrin expression patterns. Results: Integrin expression varied across tissues and cancers, and between healthy and cancer samples from the same tissue, enabling the creation of models that classify samples by tissue or disease status. The integrins whose expression was important to these classifiers were identified. For example, ITGA7 was key to classification of breast samples by disease status. Analysis in breast tissue revealed that cancer rewires co-expression for most integrins, but the co-expression relationships of some integrins remain unchanged in healthy and cancer samples. Integrin expression in primary breast tumors differed from their metastases, with liver metastasis notably having reduced expression. Conclusions: Integrin expression patterns vary widely across tissues and are greatly impacted by cancer. Machine learning of these patterns can effectively distinguish samples by tissue or disease status.

Supramolecular hydrogel based on graphene oxides for controlled release system.

Supramolecular hydrogel drug delivery system that composed of F127 modified small-sized graphene oxide (F127-SGO) and alpha-CD was prepared and the controlled release behavior was studied using doxorubicin hydrochloride (DOX) as a model drug. As compared with native hydrogel formed from F127 and alpha-CD, the SGO-containing hybrid hydrogel system shows multiple binding sites to load drug molecules and a more controllable release process that facilitates to tune the drug delivery system. These properties make the supramolecular hydrogel a potential candidate for controlled drug delivery system.

The Glycine N-Methyltransferase Case Study: Another Challenge for QM-Cluster Models?

The methyl transfer reaction between SAM and glycine catalyzed by glycine N-methyltransferase (GNMT) was examined using QM-cluster models generated by Residue Interaction Network ResidUe Selector (RINRUS). RINRUS is a Python-based tool that can build QM-cluster models with rules-based processing of the active site residue interaction network. This way of enzyme model-building allows quantitative analysis of residue and fragment contributions to kinetic and thermodynamic properties of the enzyme. Many residue fragments are important for the GNMT catalytic reaction, such as Gly137, Asn138, and Arg175, which interact with the glycine substrate, and Trp30, Asp85, and Tyr242, which interact with the SAM cofactor. Our study shows that active site fragments that interact with the glycine substrate and the SAM cofactor must both be included in the QM-cluster models. Even though the proposed mechanism is a simple one-step reaction, GNMT may be a rather challenging case study for QM-cluster models because convergence in energetics requires models with >350 atoms. "Maximal" QM-cluster models built with either qualitative contact count ranking or quantitative interaction energies from functional group symmetry adapted perturbation theory provide acceptable results. Hence, important residue fragments that contribute to the energetics of the methyl-transfer reaction in GNMT are correctly identified in the RIN. Observations from this work suggest new directions to better establish an effective approach for constructing atomic-level enzyme models.