Disruption of an Active Site Network Leads to Activation of C2α-Lactylthiamin Diphosphate on the Antibacterial Target 1-Deoxy-d-xylulose-5-phosphate Synthase.

The bacterial metabolic enzyme 1-deoxy-d-xylulose-5-phosphate synthase (DXPS) catalyzes the thiamin diphosphate (ThDP)-dependent formation of DXP from pyruvate and d-glyceraldehyde-3-phosphate (d-GAP). DXP is an essential bacteria-specific metabolite that feeds into the biosynthesis of isoprenoids, pyridoxal phosphate (PLP), and ThDP. DXPS catalyzes the activation of pyruvate to give the C2α-lactylThDP (LThDP) adduct that is long-lived on DXPS in a closed state in the absence of the cosubstrate. Binding of d-GAP shifts the DXPS-LThDP complex to an open state which coincides with LThDP decarboxylation. This gated mechanism distinguishes DXPS in ThDP enzymology. How LThDP persists on DXPS in the absence of cosubstrate, while other pyruvate decarboxylases readily activate LThDP for decarboxylation, is a long-standing question in the field. We propose that an active site network functions to prevent LThDP activation on DXPS until the cosubstrate binds. Binding of d-GAP coincides with a conformational shift and disrupts the network causing changes in the active site that promote LThDP activation. Here, we show that the substitution of putative network residues, as well as nearby residues believed to contribute to network charge distribution, predictably affects LThDP reactivity. Substitutions predicted to disrupt the network have the effect to activate LThDP for decarboxylation, resulting in CO2 and acetate production. In contrast, a substitution predicted to strengthen the network fails to activate LThDP and has the effect to shift DXPS toward the closed state. Network-disrupting substitutions near the carboxylate of LThDP also have a pronounced effect to shift DXPS to an open state. These results offer initial insights to explain the long-lived LThDP intermediate and its activation through disruption of an active site network, which is unique to DXPS. These findings have important implications for DXPS function in bacteria and its development as an antibacterial target.

Mechanism of proton transfer in class A ß-lactamase catalysis and inhibition by avibactam.

Gram-negative bacteria expressing class A ß-lactamases pose a serious health threat due to their ability to inactivate all ß-lactam antibiotics. The acyl-enzyme intermediate is a central milestone in the hydrolysis reaction catalyzed by these enzymes. However, the protonation states of the catalytic residues in this complex have never been fully analyzed experimentally due to inherent difficulties. To help unravel the ambiguity surrounding class A ß-lactamase catalysis, we have used ultrahigh-resolution X-ray crystallography and the recently approved ß-lactamase inhibitor avibactam to trap the acyl-enzyme complex of class A ß-lactamase CTX-M-14 at varying pHs. A 0.83-Å-resolution CTX-M-14 complex structure at pH 7.9 revealed a neutral state for both Lys73 and Glu166. Furthermore, the avibactam hydroxylamine-O-sulfonate group conformation varied according to pH, and this conformational switch appeared to correspond to a change in the Lys73 protonation state at low pH. In conjunction with computational analyses, our structures suggest that Lys73 has a perturbed acid dissociation constant (pKa) compared with acyl-enzyme complexes with ß-lactams, hindering its function to deprotonate Glu166 and the initiation of the deacylation reaction. Further NMR analysis demonstrated Lys73 pKa to be ∼5.2 to 5.6. Together with previous ultrahigh-resolution crystal structures, these findings enable us to follow the proton transfer process of the entire acylation reaction and reveal the critical role of Lys73. They also shed light on the stability and reversibility of the avibactam carbamoyl acyl-enzyme complex, highlighting the effect of substrate functional groups in influencing the protonation states of catalytic residues and subsequently the progression of the reaction.

Characterization and engineering of a two-enzyme system for plastics depolymerization.

Plastics pollution represents a global environmental crisis. In response, microbes are evolving the capacity to utilize synthetic polymers as carbon and energy sources. Recently, Ideonella sakaiensis was reported to secrete a two-enzyme system to deconstruct polyethylene terephthalate (PET) to its constituent monomers. Specifically, the I. sakaiensis PETase depolymerizes PET, liberating soluble products, including mono(2-hydroxyethyl) terephthalate (MHET), which is cleaved to terephthalic acid and ethylene glycol by MHETase. Here, we report a 1.6 Å resolution MHETase structure, illustrating that the MHETase core domain is similar to PETase, capped by a lid domain. Simulations of the catalytic itinerary predict that MHETase follows the canonical two-step serine hydrolase mechanism. Bioinformatics analysis suggests that MHETase evolved from ferulic acid esterases, and two homologous enzymes are shown to exhibit MHET turnover. Analysis of the two homologous enzymes and the MHETase S131G mutant demonstrates the importance of this residue for accommodation of MHET in the active site. We also demonstrate that the MHETase lid is crucial for hydrolysis of MHET and, furthermore, that MHETase does not turnover mono(2-hydroxyethyl)-furanoate or mono(2-hydroxyethyl)-isophthalate. A highly synergistic relationship between PETase and MHETase was observed for the conversion of amorphous PET film to monomers across all nonzero MHETase concentrations tested. Finally, we compare the performance of MHETase:PETase chimeric proteins of varying linker lengths, which all exhibit improved PET and MHET turnover relative to the free enzymes. Together, these results offer insights into the two-enzyme PET depolymerization system and will inform future efforts in the biological deconstruction and upcycling of mixed plastics.

Exploring Routes to Enhance the Calculation of Free Energy Differences via Non-Equilibrium Work SQM/MM Switching Simulations Using Hybrid Charge Intermediates between MM and SQM Levels of Theory or Non-Linear Switching Schemes.

Non-equilibrium work switching simulations and Jarzynski's equation are a reliable method for computing free energy differences, ΔAlow→high, between two levels of theory, such as a pure molecular mechanical (MM) and a quantum mechanical/molecular mechanical (QM/MM) description of a system of interest. Despite the inherent parallelism, the computational cost of this approach can quickly become very high. This is particularly true for systems where the core region, the part of the system to be described at different levels of theory, is embedded in an environment such as explicit solvent water. We find that even for relatively simple solute-water systems, switching lengths of at least 5 ps are necessary to compute ΔAlow→high reliably. In this study, we investigate two approaches towards an affordable protocol, with an emphasis on keeping the switching length well below 5 ps. Inserting a hybrid charge intermediate state with modified partial charges, which resembles the charge distribution of the desired high level, makes it possible to obtain reliable calculations with 2 ps switches. Attempts using step-wise linear switching paths, on the other hand, did not lead to improvement, i.e., a faster convergence for all systems. To understand these findings, we analyzed the solutes' properties as a function of the partial charges used and the number of water molecules in direct contact with the solute, and studied the time needed for water molecules to reorient themselves upon a change in the solute's charge distribution.

CIFDock: A novel CHARMM-based flexible receptor-flexible ligand docking protocol.

Docking studies play a critical role in the current workflow of drug discovery. However, limitations may often arise through factors including inadequate ligand sampling, a lack of protein flexibility, scoring function inadequacies (e.g., due to metals, co-factors, etc.), and difficulty in retaining explicit water molecules. Herein, we present a novel CHARMM-based induced fit docking (CIFDock) workflow that can circumvent these limitations by employing all-atom force fields coupled to enhanced sampling molecular dynamics procedures. Self-guided Langevin dynamics simulations are used to effectively sample relevant ligand conformations, side chain orientations, crystal water positions, and active site residue motion. Protein flexibility is further enhanced by dynamic sampling of side chain orientations using an expandable rotamer library. Steps in the procedure consisting of fixing individual components (e.g., the ligand) while sampling the other components (e.g., the residues in the active site of the protein) allow for the complex to adapt to conformational changes. Ultimately, all components of the complex-the protein, ligand, and waters-are sampled simultaneously and unrestrained with SGLD to capture any induced fit effects. This modular flexible docking procedure is automated using CHARMM scripting, interfaced with SLURM array processing, and parallelized to use the desired number of processors. We validated the CIFDock procedure by performing cross-docking studies using a data set comprised of 21 pharmaceutically relevant proteins. Five variants of the CHARMM-based SWISSDOCK scoring functions were created to quantify the results of the final generated poses. Results obtained were comparable to, or in some cases improved upon, commercial docking program data.

Obtaining QM/MM binding free energies in the SAMPL8 drugs of abuse challenge: indirect approaches.

Accurately predicting free energy differences is essential in realizing the full potential of rational drug design. Unfortunately, high levels of accuracy often require computationally expensive QM/MM Hamiltonians. Fortuitously, the cost of employing QM/MM approaches in rigorous free energy simulation can be reduced through the use of the so-called "indirect" approach to QM/MM free energies, in which the need for QM/MM simulations is avoided via a QM/MM "correction" at the classical endpoints of interest. Herein, we focus on the computation of QM/MM binding free energies in the context of the SAMPL8 Drugs of Abuse host-guest challenge. Of the 5 QM/MM correction coupled with force-matching submissions, PM6-D3H4/MM ranked submission proved the best overall QM/MM entry, with an RMSE from experimental results of 2.43 kcal/mol (best in ranked submissions), a Pearson's correlation of 0.78 (second-best in ranked submissions), and a Kendall [Formula: see text] correlation of 0.52 (best in ranked submissions).

Tuaimenal A, a Meroterpene from the Irish Deep-Sea Soft Coral Duva florida, Displays Inhibition of the SARS-CoV-2 3CLpro Enzyme.

Cold water benthic environments are a prolific source of structurally diverse molecules with a range of bioactivities against human disease. Specimens of a previously chemically unexplored soft coral, Duva florida, were collected during a deep-sea cruise that sampled marine invertebrates along the Irish continental margin in 2018. Tuaimenal A (1), a cyclized merosesquiterpenoid representing a new carbon scaffold with a highly substituted chromene core, was discovered through exploration of the soft coral secondary metabolome via NMR-guided fractionation. The absolute configuration was determined through vibrational circular dichroism. Functional biochemical assays and in silico docking experiments found tuaimenal A selectively inhibits the viral main protease (3CLpro) of SARS-CoV-2.

Not Drug-like, but Like Drugs: Cnidaria Natural Products.

Phylum Cnidaria has been an excellent source of natural products, with thousands of metabolites identified. Many of these have not been screened in bioassays. The aim of this study was to explore the potential of 5600 Cnidaria natural products (after excluding those known to derive from microbial symbionts), using a systematic approach based on chemical space, drug-likeness, predicted toxicity, and virtual screens. Previous drug-likeness measures: the rule-of-five, quantitative estimate of drug-likeness (QED), and relative drug likelihoods (RDL) are based on a relatively small number of molecular properties. We augmented this approach using reference drug and toxin data sets defined for 51 predicted molecular properties. Cnidaria natural products overlap with drugs and toxins in this chemical space, although a multivariate test suggests that there are some differences between the groups. In terms of the established drug-likeness measures, Cnidaria natural products have generally lower QED and RDL scores than drugs, with a higher prevalence of metabolites that exceed at least one rule-of-five threshold. An index of drug-likeness that includes predicted toxicity (ADMET-score), however, found that Cnidaria natural products were more favourable than drugs. A measure of the distance of individual Cnidaria natural products to the centre of the drug distribution in multivariate chemical space was related to RDL, ADMET-score, and the number of rule-of-five exceptions. This multivariate similarity measure was negatively correlated with the QED score for the same metabolite, suggesting that the different approaches capture different aspects of the drug-likeness of individual metabolites. The contrasting of different drug similarity measures can help summarise the range of drug potential in the Cnidaria natural product data set. The most favourable metabolites were around 210-265 Da, quite often sesquiterpenes, with a moderate degree of complexity. Virtual screening against cancer-relevant targets found wide evidence of affinities, with Glide scores <-7 in 19% of the Cnidaria natural products.

Characterization and engineering of a plastic-degrading aromatic polyesterase.

Poly(ethylene terephthalate) (PET) is one of the most abundantly produced synthetic polymers and is accumulating in the environment at a staggering rate as discarded packaging and textiles. The properties that make PET so useful also endow it with an alarming resistance to biodegradation, likely lasting centuries in the environment. Our collective reliance on PET and other plastics means that this buildup will continue unless solutions are found. Recently, a newly discovered bacterium, Ideonella sakaiensis 201-F6, was shown to exhibit the rare ability to grow on PET as a major carbon and energy source. Central to its PET biodegradation capability is a secreted PETase (PET-digesting enzyme). Here, we present a 0.92 Å resolution X-ray crystal structure of PETase, which reveals features common to both cutinases and lipases. PETase retains the ancestral α/ß-hydrolase fold but exhibits a more open active-site cleft than homologous cutinases. By narrowing the binding cleft via mutation of two active-site residues to conserved amino acids in cutinases, we surprisingly observe improved PET degradation, suggesting that PETase is not fully optimized for crystalline PET degradation, despite presumably evolving in a PET-rich environment. Additionally, we show that PETase degrades another semiaromatic polyester, polyethylene-2,5-furandicarboxylate (PEF), which is an emerging, bioderived PET replacement with improved barrier properties. In contrast, PETase does not degrade aliphatic polyesters, suggesting that it is generally an aromatic polyesterase. These findings suggest that additional protein engineering to increase PETase performance is realistic and highlight the need for further developments of structure/activity relationships for biodegradation of synthetic polyesters.

δ-Azaproline and Its Oxidized Variants.

Peptides featuring backbone N-amino substituents exhibit unique conformational properties owing to additional electrostatic, hydrogen-bonding, and steric interactions. Here, we describe the synthesis and conformational analysis of three δ-azaproline derivatives as potential proline surrogates. Our studies demonstrate stereoelectronic tuning of heterocyclic ring pucker, cis/trans amide propensity, and amide isomerization barriers within a series of oxidation state variants. A combination of NMR, X-ray diffraction, and density functional theory calculations shows that electron density and hybridization at the δ position play a dominant role in the conformational preferences of each analogue. Both δ-azaproline and γ,δ-dehydro-δ-azaproline exhibit strong trans amide rotamer propensities irrespective of ring conformation, while a novel residue, γ-oxo-δ-azaproline, features rapid amide isomerization kinetics and isoenergetic amide bond geometries influenced by torsional strain and H-bonding interactions. The introduction of the δ heteroatom in each residue allows the decoupling of structural effects that are typically linked in proline and its pyrrolidine-substituted analogues. δ-Azaproline derivatives thus represent useful probes of prolyl amide isomerism with potential applications in peptidomimetic drug design and protein folding.

Enantiomerization of Axially Chiral Biphenyls: Polarizable MD Simulations in Water and Butylmethylether.

In this study, we investigate the influence of chiral and achiral cations on the enantiomerization of biphenylic anions in n-butylmethylether and water. In addition to the impact of the cations and solvent molecules on the free energy profile of rotation, we also explore if chirality transfer between a chiral cation and the biphenylic anion is possible, i.e., if pairing with a chiral cation can energetically favour one conformer of the anion via diastereomeric complex formation. The quantum-mechanical calculations are accompanied by polarizable MD simulations using umbrella sampling to study the impact of solvents of different polarity in more detail. We also discuss how accurate polarizable force fields for biphenylic anions can be constructed from quantum-mechanical reference data.

Modeling Boronic Acid Based Fluorescent Saccharide Sensors: Computational Investigation of d-Fructose Binding to Dimethylaminomethylphenylboronic Acid.

Designing organic saccharide sensors for use in aqueous solution is a nontrivial endeavor. Incorporation of hydrogen bonding groups on a sensor's receptor unit to target saccharides is an obvious strategy but not one that is likely to ensure analyte-receptor interactions over analyte-solvent or receptor-solvent interactions. Phenylboronic acids are known to reversibly and covalently bind saccharides (diols in general) with highly selective affinity in aqueous solution. Therefore, recent work has sought to design such sensors and understand their mechanism for allowing fluorescence with bound saccharides. In past work, binding orientations of several saccharides were determined to dimethylaminomethylphenylboronic acid (DMPBA) receptors with an anthracene fluorophore; however, the binding orientation of d-fructose to such a sensor could not be determined. In this work, we investigate the potential binding modes by generating 20 possible bidentate and six possible tridentate modes between fructose and DMPBA, a simplified receptor model. Gas phase and implicit solvent geometry optimizations, with a myriad functional/basis set pairs, were carried out to identify the lowest energy bidentate and tridentate binding modes of d-fructose to DMPBA. An interesting hydrogen transfer was observed during selected bidentate gas phase optimizations; this transfer suggests a strong sharing of the hydrogen atom between the boronate hydroxyl and amine nitrogen.

The Good, the Bad, and the Ugly: "HiPen", a New Dataset for Validating (S)QM/MM Free Energy Simulations.

Indirect (S)QM/MM free energy simulations (FES) are vital to efficiently incorporating sufficient sampling and accurate (QM) energetic evaluations when estimating free energies of practical/experimental interest. Connecting between levels of theory, i.e., calculating Δ A l o w → h i g h , remains to be the most challenging step within an indirect FES protocol. To improve calculations of Δ A l o w → h i g h , we must: (1) compare the performance of all FES methods currently available; and (2) compile and maintain datasets of Δ A l o w → h i g h calculated for a wide-variety of molecules so that future practitioners may replicate or improve upon the current state-of-the-art. Towards these two aims, we introduce a new dataset, "HiPen", which tabulates Δ A g a s M M → 3 o b (the free energy associated with switching from an M M to an S C C - D F T B molecular description using the 3ob parameter set in gas phase), calculated for 22 drug-like small molecules. We compare the calculation of this value using free energy perturbation, Bennett's acceptance ratio, Jarzynski's equation, and Crooks' equation. We also predict the reliability of each calculated Δ A g a s M M → 3 o b by evaluating several convergence criteria including sample size hysteresis, overlap statistics, and bias metric ( Π ). Within the total dataset, three distinct categories of molecules emerge: the "good" molecules, for which we can obtain converged Δ A g a s M M → 3 o b using Jarzynski's equation; "bad" molecules which require Crooks' equation to obtain a converged Δ A g a s M M → 3 o b ; and "ugly" molecules for which we cannot obtain reliably converged Δ A g a s M M → 3 o b with either Jarzynski's or Crooks' equations. We discuss, in depth, results from several example molecules in each of these categories and describe how dihedral discrepancies between levels of theory cause convergence failures even for these gas phase free energy simulations.

Origins of the Mechanochemical Coupling of Peptide Bond Formation to Protein Synthesis.

Mechanical forces acting on the ribosome can alter the speed of protein synthesis, indicating that mechanochemistry can contribute to translation control of gene expression. The naturally occurring sources of these mechanical forces, the mechanism by which they are transmitted 10 nm to the ribosome's catalytic core, and how they influence peptide bond formation rates are largely unknown. Here, we identify a new source of mechanical force acting on the ribosome by using in situ experimental measurements of changes in nascent-chain extension in the exit tunnel in conjunction with all-atom and coarse-grained computer simulations. We demonstrate that when the number of residues composing a nascent chain increases, its unstructured segments outside the ribosome exit tunnel generate piconewtons of force that are fully transmitted to the ribosome's P-site. The route of force transmission is shown to be through the nascent polypetide's backbone, not through the wall of the ribosome's exit tunnel. Utilizing quantum mechanical calculations we find that a consequence of such a pulling force is to decrease the transition state free energy barrier to peptide bond formation, indicating that the elongation of a nascent chain can accelerate translation. Since nascent protein segments can start out as largely unfolded structural ensembles, these results suggest a pulling force is present during protein synthesis that can modulate translation speed. The mechanism of force transmission we have identified and its consequences for peptide bond formation should be relevant regardless of the source of the pulling force.

Force matching as a stepping stone to QM/MM CB[8] host/guest binding free energies: a SAMPL6 cautionary tale.

Use of quantum mechanical/molecular mechanical (QM/MM) methods in binding free energy calculations, particularly in the SAMPL challenge, often fail to achieve improvement over standard additive (MM) force fields. Frequently, the implementation is through use of reference potentials, or the so-called "indirect approach", and inherently relies on sufficient overlap existing between MM and QM/MM configurational spaces. This overlap is generally poor, particularly for the use of free energy perturbation to perform the MM to QM/MM free energy correction at the end states of interest (e.g., bound and unbound states). However, by utilizing MM parameters that best reproduce forces obtained at the desired QM level of theory, it is possible to lessen the configurational disparity between MM and QM/MM. To this end, we sought to use force matching to generate MM parameters for the SAMPL6 CB[8] host-guest binding challenge, classically compute binding free energies, and apply energetic end state corrections to obtain QM/MM binding free energy differences. For the standard set of 11 molecules and the bonus set (including three additional challenge molecules), error statistics, such as the root mean square deviation (RMSE) were moderately poor (5.5 and 5.4 kcal/mol). Correlation statistics, however, were in the top two for both standard and bonus set submissions ([Formula: see text] of 0.42 and 0.26, [Formula: see text] of 0.64 and 0.47 respectively). High RMSE and moderate correlation strongly indicated the presence of systematic error. Identifiable issues were ameliorated for two of the guest molecules, resulting in a reduction of error and pointing to strong prospects for the future use of this methodology.

Synthesis and ß-sheet propensity of constrained N-amino peptides.

The stabilization of ß-sheet secondary structure through peptide backbone modification represents an attractive approach to protein mimicry. Here, we present strategies toward stable ß-hairpin folds based on peptide strand N-amination. Novel pyrazolidinone and tetrahydropyridazinone dipeptide constraints were introduced via on-resin Mitsunobu cyclization between α-hydrazino acid residues and a serine or homoserine side chain. Acyclic and cyclic N-amino peptide building blocks were then evaluated for their effect on ß-hairpin stability in water using a GB1-derived model system. Our results demonstrate the strong ß-sheet stabilizing effect of the peptide N-amino substituent, and provide useful insights into the impact of covalent dipeptide constraint on ß-sheet folding.

Disaggregation is a Mechanism for Emission Turn-On of ortho-Aminomethylphenylboronic Acid-Based Saccharide Sensors.

ortho-Aminomethylphenylboronic acid-based receptors with appended fluorophores are commonly used as molecular sensors for saccharides in aqueous media. The mechanism for fluorescence modulation in these sensors has been attributed to some form of photoinduced electron transfer (PET) quenching, which is diminished in the presence of saccharides. Using a well-known boronic acid-based saccharide sensor (3), this work reveals a new mechanism for fluorescence turn-on in these types of sensors. Compound 3 exhibits an excimer, and the associated ground-state aggregation is responsible for fluorescence modulation under certain conditions. When fructose was titrated into a solution of 3 in 2:1 water/methanol with NaCl, the fluorescence intensity increased. Yet, when the same titration was repeated in pure methanol, a solvent in which the sensor does not aggregate, no fluorescence response to fructose was observed. This reveals that the fluorescence increase is not fully associated with fructose binding, but instead disaggregation of the sensor in the presence of fructose. Further, an analogue of the sensor that does not contain a boronic acid (4) responded nearly identically to 3 in the presence of fructose, despite having no functional group with which to bind the saccharide. This further supports the claim that fluorescence modulation is not primarily a result of binding, but of disaggregation. Using an indicator displacement assay and isothermal titration calorimetry, it was confirmed that fructose does indeed bind to the sensor. Thus, our evidence reveals that while binding occurs with fructose in the aqueous solvent system used, it is not related to the majority of the fluorescence modulation. Instead, disaggregation dominates the signal turn-on, and is thus a mechanism that should be investigated in other ortho-aminomethylphenylboronic acid-based sensors.

Efficiently computing pathway free energies: New approaches based on chain-of-replica and Non-Boltzmann Bennett reweighting schemes.

BACKGROUND: Accurately modeling condensed phase processes is one of computation's most difficult challenges. Include the possibility that conformational dynamics may be coupled to chemical reactions, where multiscale (i.e., QM/MM) methods are needed, and this task becomes even more daunting. METHODS: Free energy simulations (i.e., molecular dynamics), multiscale modeling, and reweighting schemes. RESULTS: Herein, we present two new approaches for mitigating the aforementioned challenges. The first is a new chain-of-replica method (off-path simulations, OPS) for computing potentials of mean force (PMFs) along an easily defined reaction coordinate. This development is coupled with a new distributed, highly-parallel replica framework (REPDstr) within the CHARMM package. Validation of these new schemes is carried out on two processes that undergo conformational changes. First is the simple torsional rotation of butane, while a much more challenging glycosidic rotation (in vacuo and solvated) is the second. Additionally, a new approach that greatly improves (i.e., possibly an order of magnitude) the efficiency of computing QM/MM PMFs is introduced and compared to standard schemes. Our efforts are grounded in the recently developed method for efficiently computing QM-based free energies (i.e., QM-Non-Boltzmann Bennett, QM-NBB). Again, we validate this new technique by computing the QM/MM PMF of butane's torsional rotation. CONCLUSIONS: The OPS-REPDstr method is a promising new approach that overcomes many limitations of standard pathway simulations in CHARMM. The combination of QM-NBB with pathway techniques is very promising as it offers significant advantages over current procedures. GENERAL SIGNIFICANCE: Efficiently computing potentials of mean force is a major, unresolved, area of interest. This article is part of a Special Issue entitled Recent developments of molecular dynamics.

Ligand-Induced Proton Transfer and Low-Barrier Hydrogen Bond Revealed by X-ray Crystallography.

Ligand binding can change the pKa of protein residues and influence enzyme catalysis. Herein, we report three ultrahigh resolution X-ray crystal structures of CTX-M ß-lactamase, directly visualizing protonation state changes along the enzymatic pathway: apo protein at 0.79 Å, precovalent complex with nonelectrophilic ligand at 0.89 Å, and acylation transition state (TS) analogue at 0.84 Å. Binding of the noncovalent ligand induces a proton transfer from the catalytic Ser70 to the negatively charged Glu166, and the formation of a low-barrier hydrogen bond (LBHB) between Ser70 and Lys73, with a length of 2.53 Å and the shared hydrogen equidistant from the heteroatoms. QM/MM reaction path calculations determined the proton transfer barrier to be 1.53 kcal/mol. The LBHB is absent in the other two structures although Glu166 remains neutral in the covalent complex. Our data represents the first X-ray crystallographic example of a hydrogen engaged in an enzymatic LBHB, and demonstrates that desolvation of the active site by ligand binding can provide a protein microenvironment conducive to LBHB formation. It also suggests that LBHBs may contribute to stabilization of the TS in general acid/base catalysis together with other preorganized features of enzyme active sites. These structures reconcile previous experimental results suggesting alternatively Glu166 or Lys73 as the general base for acylation, and underline the importance of considering residue protonation state change when modeling protein-ligand interactions. Additionally, the observation of another LBHB (2.47 Å) between two conserved residues, Asp233 and Asp246, suggests that LBHBs may potentially play a special structural role in proteins.

Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface.

Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a web-based tool for structure activity relationship and quantitative structure activity relationship modeling to add to the services provided by CHARMMing (www.charmming.org). This new module implements some of the most recent advances in modern machine learning algorithms-Random Forest, Support Vector Machine, Stochastic Gradient Descent, Gradient Tree Boosting, so forth. A user can import training data from Pubchem Bioassay data collections directly from our interface or upload his or her own SD files which contain structures and activity information to create new models (either categorical or numerical). A user can then track the model generation process and run models on new data to predict activity.