Transition State Conformations Research Articles

Characterization of Cwc2, U6 snRNA, and Prp8 interactions destabilized by Prp16 ATPase at the transition between the first and second steps of splicing.

The spliceosome performs two consecutive transesterification reactions using one catalytic center, thus requiring its rearrangement between the two catalytic steps of splicing. The Prp16 ATPase facilitates exit from the first-step conformation of the catalytic center by destabilizing some interactions important for catalysis. To better understand rearrangements within the Saccharomyces cerevisiae catalytic center, we characterize factors that modulate the function of Prp16: Cwc2, N-terminal domain of Prp8, and U6-41AACAAU46 region. Alleles of these factors were identified through genetic screens for mutants that correct cs defects of prp16-302 alleles. Several of the identified U6, cwc2, and prp8 alleles are located in close proximity of each other in cryo-EM structures of the spliceosomal catalytic conformations. Cwc2 and U6 interact with the intron sequences in the first step, but they do not seem to contribute to the stability of the second-step catalytic center. On the other hand, the N-terminal segment of Prp8 not only affects intron positioning for the first step, but it also makes important contacts in the proximity of the active site for both the first and second steps of splicing. By identifying interactions important for the stability of catalytic conformations, our genetic analyses indirectly inform us about features of the transition-state conformation of the spliceosome.

Generating High-Precision Force Fields for Molecular Dynamics Simulations to Study Chemical Reaction Mechanisms Using Molecular Configuration Transformer.

Theoretical studies on chemical reaction mechanisms have been crucial in organic chemistry. Traditionally, calculating the manually constructed molecular conformations of transition states for chemical reactions using quantum chemical calculations is the most commonly used method. However, this way is heavily dependent on individual experience and chemical intuition. In our previous study, we proposed a research paradigm that used enhanced sampling in molecular dynamics simulations to study chemical reactions. This approach can directly simulate the entire process of a chemical reaction. However, the computational speed limited the use of high-precision potential energy functions for simulations. To address this issue, we presented a scheme for training high-precision force fields for molecular modeling using a previously developed graph-neural-network-based molecular model, molecular configuration transformer. This potential energy function allowed for highly accurate simulations at a low computational cost, leading to more precise calculations of the mechanism of chemical reactions. We applied this approach to study a Claisen rearrangement reaction and a carbonyl insertion reaction catalyzed by manganese.

Formaldehyde-Mediated Hydride Liberation of Alkylamines for Intermolecular Reactions in Hexafluoroisopropanol.

The ability of alkylamines to spontaneously liberate hydride ions is typically restrained, except under specific intramolecular reaction settings. Herein, we demonstrate that this reactivity can be unlocked through simple treatment with formaldehyde in hexafluoroisopropanol (HFIP) solvent, thereby enabling various intermolecular hydride transfer reactions of alkylamines under mild conditions. Besides transformations of small molecules, these reactions enable unique late-stage modification of complex peptides. Mechanistic investigations uncover that the key to these intermolecular hydride transfer processes lies in the accommodating conformation of solvent-mediated macrocyclic transition states, where the aggregates of HFIP molecules act as dexterous proton shuttles. Importantly, negative hyperconjugation between the lone electron pair of nitrogen and the antibonding orbital of amine's α C-H bond plays a critical role in the C-H activation, promoting its hydride liberation.

Diffusion-based generative AI for exploring transition states from 2D molecular graphs

The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input, which demands substantial efforts and computational cost. Here, we propose a generative approach based on the stochastic diffusion method, namely TSDiff, for prediction of TS geometries just from 2D molecular graphs. TSDiff outperforms the existing ML models with 3D geometries in terms of both accuracy and efficiency. Moreover, it enables to sample various TS conformations, because it learns the distribution of TS geometries for diverse reactions in training. Thus, TSDiff finds more favorable reaction pathways with lower barrier heights than those in the reference database. These results demonstrate that TSDiff shows promising potential for an efficient and reliable TS exploration.

Electrostatic-Dominated Conformational Fluctuations and Transition States of Phase Separation in Charge-Balanced Protein Polymer.

Hydration of the protein/polymer is the most important aspect of stability. It is well-known that salts alter the charged polymer's electrostatic forces, ultimately impacting its conformations in solution. The solvent effects lead to certain conformational fluctuations. Previous studies have shown the screening of electrostatic repulsion within the charge-imbalanced protein following charge inversion owing to counterion condensation and phase separation. This article studies conformation stability and phase separation of charge-balanced gelatin (a protein polymer at the isoelectric point) with the addition of different salts. A phenomenon has been reported where the electrostatic effect of salts results in conformational fluctuations in gelatin due to its insufficient hydrations (termed as starvation), which scales with salt concentration. This article also presents different transition states for charge-balanced proteins prior to phase separation. It is concluded that phase separation of a charge-balanced protein passes through a stable state followed by an unstable transition state, where certain unique interactions with salts occur.

Improving Theaflavin-3,3'-digallate Production Efficiency Optimization by Transition State Conformation of Polyphenol Oxidase.

Theaflavins (TFs) are good for health because of their bioactivities. Enzymatic synthesis of TFs has garnered much attention; however, the source and activity of the enzymes needed limit their wide application. In this study, a microbial polyphenol oxidase from Bacillus megaterium was screened for the synthesis of theaflavin-3,3'-digallate (TFDG). Based on structural and mechanistic analyses of the enzyme, the O-O bond dissociation was identified as the rate-determining step. To address this issue, a transition state (TS) conformation optimization strategy was adopted to stabilize the spatial conformation of the O-O bond dissociation, which improved the catalytic efficiency of tyrosinase. Under the optimum transformation conditions of pH 4.0, temperature 25 °C, (-)-epigallocatechin gallate/epicatechin gallate molar ratio of 2:1, and time of 30 min, Mu4 (BmTyrV218A/R209S) produced 960.36 mg/L TFDG with a 44.22% conversion rate, which was 6.35-fold higher than that of the wild type. Thus, the method established has great potential in the synthesis of TFDG and other TFs.

Toward in silico Catalyst Optimization.

In this minireview, we overview a computational pipeline developed within the framework of NCCRCatalysis that can be used to successfully reproduce the enantiomeric ratios of homogeneous catalytic reactions.At the core of this pipeline is the SCINE Molassembler module, a graph-based software that provides algorithmsfor molecular construction of all periodic table elements. With this pipeline, we are able to simultaneously functionalizenand generate ensembles of transition state conformers, which permits facile exploration of the influencenof various substituents on the overall enantiomeric ratio. This allows preconceived back-of-the-envelope designnmodels to be tested and subsequently refined by providing quick and reliable access to energetically low-lyingntransition states, which represents a key step in undertaking in silico catalyst optimization.

Machine learning guided sampling of protein transition pathways.

Capturing a protein conformational transition pathway is an important yet difficult task in biophysics. For example, X-ray-crystallography/cryo-EM techniques have captured open/closed states or inward/outward-facing conformations of many ion-channels/transporters; yet, it remains a challenge to experimentally capture transitioning pathways between these ground states due to their rather short-spanned nature. Multiple advanced techniques based on molecular dynamics (MD) simulations have provided great insights to fill such gaps by predicting hypothetical transition states and pathways. Such MD-based techniques, including both long equilibrium simulations and enhanced sampling ones, have provided insights to better understand how putative transition state conformations and pathways would look like. However, MD-based techniques generally take a long time; therefore, it would be of great utility to sample putative transition states along putative transition pathways more efficiently. We present a machine learning model, developed using variational autoencoder (VAE), with the goal of sampling the transition pathway between different protein's conformational states. Using VAE, which takes in protein raw trajectory coordinates generated by equilibrium MD simulations as input, the model is able to reconstruct a structurally close output. Such a VAE can therefore reduce the high-dimensional protein description in the full space into a meaningful low-dimensional representation. In this low-dimensional space, we can interpolate between the two states of interest. The interpolated pathway represents a putative transition-pathway in the higher-dimensional space, learned by VAE. After reconstruction of the sampled transition pathway into the full protein space, secondary structures, RMSDs, and other quality-control properties are used and compared with the functional mechanism of the protein. The reconstructed putative transition pathway can be used in further MD simulations. This machine learning guided MD approach will accelerate studying the full transition pathways of protein conformations by seeding good starting points generated by VAE for iterative MD simulations.

Development and application of computational methods for the study of protein dynamics with Pm-HMGR as a model system.

Continuous atomistic descriptions of enzyme mechanisms and atomistic dynamics are crucial to the characterization of protein function and dysfunction—a common cause of disease—but unattainable with current techniques, necessitating novel methods which leverage computational-experimental synergy. Computational studies can provide a wealth of information, but carry little weight without experimental validation. Thus, coupling time-resolved crystallographic studies of enzyme mechanisms with computational mechanistic investigation through molecular dynamics (MD) simulation and predictive model generation provides the opportunity to combine approaches. We demonstrate and refine a novel Markov State-informed Multilinear Singular Value Decomposition (MSiMSVD) method with the Pseudomonas mevalonii HMG CoA Reductase (PmHMGR) pathway as a model system. MSiMSVD couples time-resolved crystallographic studies of enzyme mechanisms with computational mechanistic investigation through MD simulation and predictive model generation. Application of the MSiMSVD method to slow dynamical events-such as the PmHMGR 2nd hydride transfer-is limited by the ability of force fields to reproduce atomistic behavior at a transition state. This need can be met via the generation of a Transition State Force Field (TSFF) with a program such as Quantum-guided Molecular Mechanics (Q2MM). However, a frequently encountered problem in the optimization of TSFFs for biomolecules is the high dimensionality of the optimization surface producing local rather than global minima. To address this, we implement a constrained particle swarm algorithm hybridized with a basic genetic algorithm to automate the optimization of TSFFs with Q2MM. The Q2MM and the MSiMSVD methods will become accessible to non-experts for a wide range of systems, such as large biomolecules. Thus far, initial Markov State Models generated from MD simulation of the PmHMGR 2nd hydride transfer indicate that more sampling is necessary to cover the transition state conformation, i.e. the structure of the activated complex.

Unraveling coupled folding and binding dynamics of ribonuclease S with transient infrared spectroscopy

Molecular interactions of proteins consisting of coupled disorder-to-order transitions and spontaneous binding have a crucial role in understanding their biological function and working mechanism. Here we investigate a well-known globular binding system, ribonuclease S (RNase S), to study the dynamics of protein-peptide interaction. Various biophysical approaches have investigated the binding mechanism of two proteolytic fragments, S-peptide and S-protein, which non-covalently bind to form an active enzyme RNase S. Due to challenges in dissecting the coupled nature of protein binding and folding, molecular level details remain elusive. Here, we use amide-I vibrations as a label-free probe of protein secondary structure and temperature as a control variable. By temperature-jump two-dimensional infrared spectroscopy (t-2DIR), we track the thermally-induced conformational changes associated with relaxation time that span from nanoseconds to many minute timescales. Additionally, we compare the results of an unlabeled complex with one containing a 13C=18O isotope-label of a residue buried within the helical segment of S-peptide. t‑2DIR measurements of labeled- and unlabeled- RNase S and isolated S-protein reveal two kinetic components on millisecond (fast) and second (slow) time scales. Both slow and fast phases are dominated by β-sheet disordering signatures that indicate S-protein unfolding. The isotope-label exhibits significant amplitude in the slow phase, suggesting this time window corresponds to both unbinding and unfolding of the S-peptide. Together, these results indicate that thermally-induced partial unfolding of the S-protein destabilizes the hydrophobic binding pocket containing S-peptide that, in turn, promotes unbinding and unfolding of the S-peptide. Our direct observations from transient spectroscopic data demonstrate the role of a conformational transition state during assembly of the RNase S complex.

A Combined DFT, Energy Decomposition, and Data Analysis Approach to Investigate the Relationship Between Noncovalent Interactions and Selectivity in a Flexible DABCOnium/Chiral Anion Catalyst System.

Developing strategies to study reactivity and selectivity in flexible catalyst systems has become an important topic of research. Herein, we report a combined experimental and computational study aimed at understanding the mechanistic role of an achiral DABCOnium cofactor in a regio- and enantiodivergent bromocyclization reaction. It was found that electron-deficient aryl substituents enable rigidified transition states via an anion-π interaction with the catalyst, which drives the selectivity of the reaction. In contrast, electron-rich aryl groups on the DABCOnium result in significantly more flexible transition states, where interactions between the catalyst and substrate are more important. An analysis of not only the lowest-energy transition state structures but also an ensemble of low-energy transition state conformers via energy decomposition analysis and machine learning was crucial to revealing the dominant noncovalent interactions responsible for observed changes in selectivity in this flexible system.

Gas-phase OH-oxidation of 2-butanethiol: Multiconformer transition state theory rate constant with constrained transition state randomization

The prediction of transition state (TS) geometries is crucial for the discovery of reaction mechanisms and calculation of accurate rate constants via TS theory (TST). The existence of a multitude of TS conformers requires the use of adaptations to conventional TST such as multiconformer TST (MC-TST), where considerable computational effort is spent performing the TS sampling. Here, we calculate the MC-TST high-pressure limit rate constant for the 2-butanethiol + OH reaction by including a constrained TS randomization procedure, which yields a rate constant in good agreement with experimental work and is easily adaptable to other OH-oxidation reactions.

Systematic Functional and Computational Analysis of Glucose-Binding Residues in Glycoside Hydrolase Family GH116

Glycoside hydrolases (GH) bind tightly to the sugar moiety at the glycosidic bond being hydrolyzed to stabilize its transition state conformation. We endeavored to assess the importance of glucose-binding residues in GH family 116 (GH116) β-glucosidases, which include human β-glucosylceramidase 2 (GBA2), by mutagenesis followed by kinetic characterization, X-ray crystallography, and ONIOM calculations on Thermoanaerobacterium xylanolyticum TxGH116, the structural model for GH116 enzymes. Mutations of residues that bind at the glucose C3OH and C4OH caused 27–196-fold increases in KM for p-nitrophenyl-β-D-glucoside, and significant decreases in the kcat, up to 5000-fold. At the C6OH binding residues, mutations of E777 decreased the kcat/KM by over 60,000-fold, while R786 mutants increased both the KM (40-fold) and kcat (2–4-fold). The crystal structures of R786A and R786K suggested a larger entrance to the active site could facilitate their faster rates. ONIOM binding energy calculations identified D452, H507, E777, and R786, along with the catalytic residues E441 and D593, as strong electrostatic contributors to glucose binding with predicted interaction energies > 15 kcal mol−1, consistent with the effects of the D452, H507, E777 and R786 mutations on enzyme kinetics. The relative importance of GH116 active site residues in substrate binding and catalysis identified in this work improves the prospects for the design of inhibitors for GBA2 and the engineering of GH116 enzymes for hydrolytic and synthetic applications.

Gibbs energies of activation for reacting systems with multiple reactant‐state and transition‐state conformations

AbstractThree distinct procedures for obtaining the effective Gibbs energies of activation from computed energies for a reaction involving multiple reactant‐state (RS) and transition‐state (TS) conformers are shown to be equivalent. If Boltzmann‐weighted average Gibbs energies for RS and TS are evaluated, then the Gibbs energy contribution from entropy of mixing of both RS and TS conformers must also be included in order to obtain the correct value for the effective Gibbs energy of activation. Application to a solvolytic reaction of 4,4′‐dimethoxybenzhydrylpyridinium cation, which is shown to satisfy the Curtin‐Hammett principle with rapid interconversion of RS conformers, demonstrates the use of each procedure for evaluation of the effective Gibbs energy of activation.

MOF-enabled confinement and related effects for chemical catalyst presentation and utilization.

A defining characteristic of nearly all catalytically functional MOFs is uniform, molecular-scale porosity. MOF pores, linkers and nodes that define them, help regulate reactant and product transport, catalyst siting, catalyst accessibility, catalyst stability, catalyst activity, co-catalyst proximity, composition of the chemical environment at and beyond the catalytic active site, chemical intermediate and transition-state conformations, thermodynamic affinity of molecular guests for MOF interior sites, framework charge and density of charge-compensating ions, pore hydrophobicity/hydrophilicity, pore and channel rigidity vs. flexibility, and other features and properties. Collectively and individually, these properties help define overall catalyst functional behaviour. This review focuses on how porous, catalyst-containing MOFs capitalize on molecular-scale confinement, containment, isolation, environment modulation, energy delivery, and mobility to accomplish desired chemical transformations with potentially superior selectivity or other efficacy, especially in comparison to catalysts in homogeneous solution environments.

Mechanistic Investigation of Palladium-Catalyzed meta-C-H Bond Activation of Arenes with a Carboxyl Directing Group.

The mechanism of Pd(II)-catalyzed meta-C-H bond olefination of arenes with a carboxyl directing group (DG)-containing template has been investigated with density functional theory. The reaction includes three major steps: C-H bond activation, alkene insertion, and β-hydride elimination. The C-H activation step, which proceeds via a concerted metalation-deprotonation pathway, is found to be the rate- and regioselectivity-determining step. We proposed a mono-N-protected amino acid (MPAA)/DG-assisted C-H activation model, in which the carboxyl DG coordinates with the Pd center and delivers it to the meta-position of arene, and the bidentate dianionic MPAA acts as a base for deprotonation. There is a hydrogen bonding interaction between the carboxyl DG and the carboxylate group of MPAA. An alternative Pd(OAc)2-catalyzed mechanism without involvement of MPAA is also operative. The template is conformationally flexible, and multiple low-energy transition-state conformations contribute to the regioselectivity.

Computer Simulations of the Dissociation Mechanism of Gleevec from Abl Kinase with Milestoning.

Gleevec (a.k.a., imatinib) is an important anticancer (e.g., chronic myeloid leukemia) chemotherapeutic drug due to its inhibitory interaction with the Abl kinase. Here, we use atomically detailed simulations within the Milestoning framework to study the molecular dissociation mechanism of Gleevec from Abl kinase. We compute the dissociation free energy profile, the mean first passage time for unbinding, and explore the transition state ensemble of conformations. The milestones form a multidimensional network with average connectivity of about 2.93, which is significantly higher than the connectivity for a one-dimensional reaction coordinate. The free energy barrier for Gleevec dissociation is estimated to be ∼10 kcal/mol, and the exit time is ∼55 ms. We examined the transition state conformations using both, the committor and transition function. We show that near the transition state the highly conserved salt bridge K217 and E286 is transiently broken. Together with the calculated free energy profile, these calculations can advance the understanding of the molecular interaction mechanisms between Gleevec and Abl kinase and play a role in future drug design and optimization studies.

Two distinct catalytic pathways for GH43 xylanolytic enzymes unveiled by X-ray and QM/MM simulations

Xylanolytic enzymes from glycoside hydrolase family 43 (GH43) are involved in the breakdown of hemicellulose, the second most abundant carbohydrate in plants. Here, we kinetically and mechanistically describe the non-reducing-end xylose-releasing exo-oligoxylanase activity and report the crystal structure of a native GH43 Michaelis complex with its substrate prior to hydrolysis. Two distinct calcium-stabilized conformations of the active site xylosyl unit are found, suggesting two alternative catalytic routes. These results are confirmed by QM/MM simulations that unveil the complete hydrolysis mechanism and identify two possible reaction pathways, involving different transition state conformations for the cleavage of xylooligosaccharides. Such catalytic conformational promiscuity in glycosidases is related to the open architecture of the active site and thus might be extended to other exo-acting enzymes. These findings expand the current general model of catalytic mechanism of glycosidases, a main reaction in nature, and impact on our understanding about their interaction with substrates and inhibitors.

Efficient approach for exploring the multiple-channel bimolecular interactions of conformationally flexible reagents. Epoxide ring opening reaction

Algorithm for generation and assessment of probability of possible reaction pathways for multiple-channel bimolecular interactions is presented. The proposed algorithm comprises a combination of few steps. They include conformational search for reaction intermediate using the molecular mechanics (MMX) approach, based on the obtained conformation construction of structures of transition states and pre-reaction complexes, and calculation activation energies to further determine the probable reaction pathways. The proposed algorithm could be adopted for investigation of chemical and biochemical reactions of different types. Here, we have considered the reaction of bicyclo[2.2.1]hept-5-en-endo-2-ylmethylamine (1) with glycidyl ether (2) in a neutral environment that proceeds through SN2-like mechanism forming bipolar ion (3) which is a good starting point for identification of the reaction channels. Conformational properties of intermediate (3) have been investigated using stochastic conformational search. From the 95 localized conformations within 10 kcal/mol of global minimum that have been obtained, 63 unique transition state conformations were generated and optimized by using the PM7 and M062X/6-31G(d) methods for accurate estimation of overall rate constant of reaction. The most energetically favorable pathways have been investigated at the M062X/6-31G(d) level of theory taking into account the influence of solvent.

Ab Initio Quantitative Prediction of Tacticity in Radical Polymerization of Poly(methyl methacrylate) by a Molecular Simulation Technique with the Conformation Indexing for Multiple Transition States

Free-radical polymerization is a critical route for generating vinyl polymers owing to its versatility and productivity, while the realization of stereospecific propagation has been considered very difficult and demanding. Therefore, a concrete investigation on the mechanism of tacticity obtained in radical polymerization is urgent. Experimentally, tacticity of poly(methyl methacrylate) produced by radical polymerization in bulk was reported to be syndiotactic and temperature-dependent. In this study, we intended to rationalize those behaviors on the microscopic scale via density functional theory calculations and a hybrid Monte Carlo/molecular dynamics simulation [Red Moon methodology]. We devised a unique conformation indexing based on the multidimensional conformational analysis and obtained as many as 576 transition state conformations for the propagation reaction. Boltzmann factors of the 576 conformations implied the overall higher stability of pro-racemo conformations than pro-meso ones and, in addition, the more syndiotactic tendency at low temperature. By the Red Moon simulations utilizing the energy barriers of those conformations, we successfully simulated the propagation reaction process and achieved quantitative agreement of the product tacticity. These results exhibit the validity of our ab initio molecular simulation procedure with the conformation indexing to realize quantitative analysis of the selectivity of reactions.