The Performance of Different Water Models on the Structure and Function of Cytochrome P450 Enzymes

Molecular Dynamics Simulation Reveal the Structure-Activity Relationships of Kainoid Synthases

Kainoid synthases are key enzymes in the biosynthesis of kainoids. Kainoids, as represented by DA and KA, are a class of naturally occurring non-protein amino acids with strong neurotransmitter activity in the mammalian central nervous system. Marine algae kainoid synthases include PnDabC from diatoms, which synthesizes domoic acid (DA), and DsKabC and GfKabC from red algae, which synthesize kainic acid (KA). Elucidation of the catalytic mechanism of kainoid synthases is of great significance for the rational design of better biocatalysts to promote the industrial production of kainoids for use in new drugs. Through modeling, molecular docking, and molecular dynamics simulations, we investigated the conformational dynamics of kainoid synthases. We found that the kainoid synthase complexes showed different stability in the simulation, and the binding and catalytic processes showed significant conformational transformations of kainoid synthase. The residues involved in specific interactions with the substrate contributed to the binding energy throughout the simulation process. Binding energy, the relaxed active pocket, electrostatic potential energy of the active pocket, the number and rotation of aromatic residues interacting with substrates during catalysis, and the number and frequency of hydrogen bonds between the individual functional groups revealed the structure-activity relationships and affected the degree of promiscuity of kainoid synthases. Our research enriches the understanding of the conformational dynamics of kainoid synthases and has potential guiding significance for their rational design.

Multiscale Study on the Intramolecular C-S Bond Formation Catalyzed by P450 Monooxygenase CxnD Involved in the Biosynthesis of Chuangxinmycin: The Critical Roles of Noncrystal Water Molecule and Conformational Change

Cytochrome P450 monooxygenase CxnD catalyzes intramolecular C-S bond formation in the biosynthesis of chuangxinmycin, which is representative of the synthesis of sulfur-containing natural heterocyclic compounds. The intramolecular cyclization usually requires the activation of two reaction sites and a large conformational change; thus, illuminating its detailed reaction mechanism remains challengeable. Here, the reaction pathway of CxnD-catalyzed C-S bond formation was clarified by a series of calculations, including Gaussian accelerated molecular dynamics simulations and quantum mechanical-molecular mechanical calculations. Our results revealed that the C-S formation follows a diradical coupling mechanism. CxnD first employs Cpd I to abstract the hydrogen atom from the imino group of the indole ring, and then, the resulted Cpd II further extracts another hydrogen atom from the thiol group of the side chain to afford a diradical intermediate, in which a noncrystal water molecule entering into the active site after the formation of Cpd I was proved to play an indispensable role. Moreover, the diradical intermediate cannot directly perform the coupling reaction. It should first undergo a series of conformational changes leading to the proximity of two reaction sites. It is the flexibility of the active site of the enzyme and the side chain of the substrate that makes the diradical coupling to be successful.

Heme Spin Distribution in the Substrate-Free and Inhibited Novel CYP116B5hd: A Multifrequency Hyperfine Sublevel Correlation (HYSCORE) Study

The cytochrome P450 family consists of ubiquitous monooxygenases with the potential to perform a wide variety of catalytic applications. Among the members of this family, CYP116B5hd shows a very prominent resistance to peracid damage, a property that makes it a promising tool for fine chemical synthesis using the peroxide shunt. In this meticulous study, we use hyperfine spectroscopy with a multifrequency approach (X- and Q-band) to characterize in detail the electronic structure of the heme iron of CYP116B5hd in the resting state, which provides structural details about its active site. The hyperfine dipole-dipole interaction between the electron and proton nuclear spins allows for the locating of two different protons from the coordinated water and a beta proton from the cysteine axial ligand of heme iron with respect to the magnetic axes centered on the iron. Additionally, since new anti-cancer therapies target the inhibition of P450s, here we use the CYP116B5hd system-imidazole as a model for studying cytochrome P450 inhibition by an azo compound. The effects of the inhibition of protein by imidazole in the active-site geometry and electron spin distribution are presented. The binding of imidazole to CYP116B5hd results in an imidazole-nitrogen axial coordination and a low-spin heme FeIII. HYSCORE experiments were used to detect the hyperfine interactions. The combined interpretation of the gyromagnetic tensor and the hyperfine and quadrupole tensors of magnetic nuclei coupled to the iron electron spin allowed us to obtain a precise picture of the active-site geometry, including the orientation of the semi-occupied orbitals and magnetic axes, which coincide with the porphyrin N-Fe-N axes. The electronic structure of the iron does not seem to be affected by imidazole binding. Two different possible coordination geometries of the axial imidazole were observed. The angles between gx (coinciding with one of the N-Fe-N axes) and the projection of the imidazole plane on the heme were determined to be -60° and -25° for each of the two possibilities via measurement of the hyperfine structure of the axially coordinated 14N.

FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning

Significant improvements have been made in the past decade to methods that rapidly and accurately predict binding affinity through free energy perturbation (FEP) calculations. This has been driven by recent advances in small-molecule force fields and sampling algorithms combined with the availability of low-cost parallel computing. Predictive accuracies of ∼1 kcal mol-1 have been regularly achieved, which are sufficient to drive potency optimization in modern drug discovery campaigns. Despite the robustness of these FEP approaches across multiple target classes, there are invariably target systems that do not display expected performance with default FEP settings. Traditionally, these systems required labor-intensive manual protocol development to arrive at parameter settings that produce a predictive FEP model. Due to the (a) relatively large parameter space to be explored, (b) significant compute requirements, and (c) limited understanding of how combinations of parameters can affect FEP performance, manual FEP protocol optimization can take weeks to months to complete, and often does not involve rigorous train-test set splits, resulting in potential overfitting. These manual FEP protocol development timelines do not coincide with tight drug discovery project timelines, essentially preventing the use of FEP calculations for these target systems. Here, we describe an automated workflow termed FEP Protocol Builder (FEP-PB) to rapidly generate accurate FEP protocols for systems that do not perform well with default settings. FEP-PB uses an active-learning workflow to iteratively search the protocol parameter space to develop accurate FEP protocols. To validate this approach, we applied it to pharmaceutically relevant systems where default FEP settings could not produce predictive models. We demonstrate that FEP-PB can rapidly generate accurate FEP protocols for the previously challenging MCL1 system with limited human intervention. We also apply FEP-PB in a real-world drug discovery setting to generate an accurate FEP protocol for the p97 system. FEP-PB is able to generate a more accurate protocol than the expert user, rapidly validating p97 as amenable to free energy calculations. Additionally, through the active-learning workflow, we are able to gain insight into which parameters are most important for a given system. These results suggest that FEP-PB is a robust tool that can aid in rapidly developing accurate FEP protocols and increasing the number of targets that are amenable to the technology.

Multiscale QM/MM modelling of catalytic systems with ChemShell

Hybrid quantum mechanical/molecular mechanical (QM/MM) methods are a powerful computational tool for the investigation of all forms of catalysis, as they allow for an accurate description of reactions occurring at catalytic sites in the context of a complicated electrostatic environment. The scriptable computational chemistry environment ChemShell is a leading software package for QM/MM calculations, providing a flexible, high performance framework for modelling both biomolecular and materials catalysis. We present an overview of recent applications of ChemShell to problems in catalysis and review new functionality introduced into the redeveloped Python-based version of ChemShell to support catalytic modelling. These include a fully guided workflow for biomolecular QM/MM modelling, starting from an experimental structure, a periodic QM/MM embedding scheme to support modelling of metallic materials, and a comprehensive set of tutorials for biomolecular and materials modelling.

Molecular Dynamics Simulations Reveal the Conformational Transition of GH33 Sialidases

Sialidases are increasingly used in the production of sialyloligosaccharides, a significant component of human milk oligosaccharides. Elucidating the catalytic mechanism of sialidases is critical for the rational design of better biocatalysts, thereby facilitating the industrial production of sialyloligosaccharides. Through comparative all-atom molecular dynamics simulations, we investigated the structural dynamics of sialidases in Glycoside Hydrolase family 33 (GH33). Interestingly, several sialidases displayed significant conformational transition and formed a new cleft in the simulations. The new cleft was adjacent to the innate active site of the enzyme, which serves to accommodate the glycosyl acceptor. Furthermore, the residues involved in the specific interactions with the substrate were evolutionarily conserved in the whole GH33 family, highlighting their key roles in the catalysis of GH33 sialidases. Our results enriched the catalytic mechanism of GH33 sialidases, with potential implications in the rational design of sialidases.

Theoretical study on hydrogen transfer in the dissociation of dimethyl disulfide radical cations

Hydrogen transfer (HT) is of crucial importance in biochemistry and atmospheric chemistry. Here, HT processes involved in the dissociation reaction of dimethyl disulfide radical cations (DMDS˙⁺, CH₃SSCH₃˙⁺) are investigated using quantum chemical calculations. Four HTs from the C to S atom and one HT from the S to S atom are observed and the most probable paths are proposed in the dissociation channel from DMDS˙⁺ to CH_nS⁺ (n = 2-4). The mechanisms of all these five HTs are described as hydrogen atom transfer (HAT) and four of them are accompanied by electron transfer (ET). Considering the catalytic effect of water molecules existing in organisms and the atmosphere, five HT processes in the dissociation of the [DMDS + H₂O]˙⁺ complex are further explored, which show lower free energy barriers. With the participation of water molecules acting as a base, two HTs from the C to the S atom, which have the largest decrease in energy barriers, are characterized as concerted proton-coupled electron transfer (cPCET). These results can be extended to understand the mechanism of the HT process during the dissociation of disulfide and help provide a strategy to design a rare cPCET mechanism for the activation of the C-H bond.