Dan Thomas Major

Molecular clusters in the gas phase often exhibit “magic numbers,” corresponding to enhanced abundances at specific cluster sizes, typically attributed to particularly stable geometric arrangements. Here, we combine electrospray ionization mass spectrometry (ESI-MS) with quantum chemical calculations to investigate the structure and stability of protonated glycine clusters. The experimental mass spectrum reveals pronounced magic numbers at cluster sizes n = 3 and n = 6. To elucidate their origin, we performed a systematic computational investigation of glycine clusters up to n = 7, including global structure searches, high-level electronic structure calculations, and thermal stability simulations. Our calculations show that clusters with n = 2, 4, and 5 are predominantly composed of canonical glycine molecules; the n = 7 clusters are composed of both neutral and zwitterionic glycine molecules, whereas the hexamer is the first cluster size for which fully zwitterionic structures become thermodynamically preferred. This neutral-to-zwitterionic transition gives rise to a highly cooperative hydrogen-bonding network and is accompanied by a pronounced increase in thermal stability, providing a natural explanation for the exceptional abundance of the hexamer. In contrast, the enhanced abundance of the trimer is consistent with kinetic stabilization under electrospray ion-source conditions rather than with the thermodynamic stability of its global free-energy minimum. These findings provide molecular-level insight into the emergence of magic numbers in glycine clusters and the onset of zwitterionic stabilization in finite molecular assemblies.

Developing high-performance cathode materials is crucial for advancing grid-scale sodium-ion batteries (SIBs). Here, we introduce a P2-type layered oxide cathode material, Na0·67Ni0·33Mn0·51Ru0·16O2, which demonstrates exceptional electrochemical performance through a robust dual cationic–anionic redox mechanism. This material delivers a high discharge capacity of 175.6 mAh g−1 at an average voltage of 3.6 V and maintains remarkable cycling stability with ∼93% capacity retention after 1000 cycles. A key to this performance is the strategic Ru doping, which plays a pivotal role in electronically stabilizing the oxidized oxygen states. By mediating the reversible formation of oxygen holes and preventing oxygen undercoordination, the Ru dopant stabilizes the anionic redox process. Unlike Na-rich layered oxides, this ligand-to-metal charge-transfer mechanism effectively suppresses detrimental oxygen evolution and preserves the structural integrity of the layered Na-deficient P2-type framework. This design strategy enables a synergistic redox landscape, confirmed by ex situ and operando advanced characterizations and theoretical calculations such as Fukui functions, offering critical insights into the rational design of high-capacity cathode for next-generation SIBs.

The evolutionary expansion of specialized metabolism has shaped the ability of plants to adapt to combined pest, disease, and environmental pressures. In maize (Zea mays), the duplication and divergence of ancestral gibberellin pathway genes have given rise to specialized kauralexin and dolabralexin diterpenoids that serve core functions in stress resilience. Here we identify rosalexins as a previously unrecognized branch of the maize diterpenoid defense network and show how duplication of hormone-metabolic genes generated a new biosynthetic pathway with distinct chemical and biological functions. By integrating genomics-enabled gene discovery, combinatorial enzyme assays, metabolomics, and AI-assisted enzyme mechanistic studies we show that rosalexin biosynthesis proceeds via an unusual 5-rosanol scaffold formed from ent-copalyl diphosphate through the sequential activity of ZmTPS38/CPS2/AN2 and ZmTPS42/KSL1. Further oxygenation by the promiscuous P450 enzyme, ZmCYP71Z18, yields epoxyrosanol. Unexpectedly, only epoxyrosanol strongly inhibited fungal pathogen growth, whereas neither its immediate precursor nor its downstream hydrolysis product showed comparable activity, identifying epoxidation as a key determinant of antibiotic efficacy. Large variation in rosalexin abundance exists across maize genotypes due to ZmTPS42/KSL1 gene sequence variation and pseudogenization. Although no dominant pathogen resistance phenotype associated with rosalexin abundance was observed in maize plants, transcriptomics and metabolomics studies demonstrated the pathogen-elicited accumulation of rosalexins in maize lines featuring functional ZmTPS42/KSL1 genes. Together, these findings reveal how duplication and functional divergence of hormone-metabolic genes generated modular diterpenoid defense layers, illustrating how plants expand chemical immunity through reuse of existing metabolic scaffolds.

Terpene synthases (TPSs) generate complex hydrocarbon scaffolds through carbocationic cyclization cascades that demand precise active‐site control to stabilize reactive intermediates. While π–cation and electrostatic interactions are established stabilizing factors, the role of methionine has remained unclear. Here, we identify a methionine‐rich active site in hydropyrene synthase (HpS), a bacterial Class I TPS involved in pseudopterosin biosynthesis. Crystallography, mutagenesis, and multiscale QM/MM simulations reveal that methionine residues provide steric guidance and direct sulfur–carbocation stabilization during catalysis. Mutations alter product distributions, confirming functional relevance. Quantum chemical calculations indicate that sulfur–carbocation interactions are energetically comparable to π–carbocation interactions. These results uncover a previously unrecognized mechanism of carbocation stabilization in terpene biosynthesis.

Understanding complex chemical reaction cascades remains a major challenge in chemistry. Scalable investigation of their thermodynamic and kinetic properties requires the use of automated reaction prediction tools, which is a rapidly growing area in the study of chemical reactivity. However, the systematic exploration of intricate reaction networks involving highly reactive intermediates continues to pose significant difficulties. Here, we introduce RxnNet, a novel artificial intelligence-assisted platform for the automated prediction of chemical reaction mechanisms. RxnNet integrates heuristic rules with domain-specific chemical knowledge including stereochemistry, regiochemistry, conformational preferences, and isotope labeling, to construct mechanistically informed reaction networks. These networks are represented as graphs and are coupled with on-the-fly quantum chemical evaluations to identify all feasible intermediates and transition states. In this work, we apply RxnNet to carbocation chemistry, a notoriously complex and computationally demanding type of reaction. We demonstrate the method’s capabilities by analyzing three multistep reactions with known mechanisms, each of which poses significant challenges even for expert computational and synthetic chemists. RxnNet provides a robust approach for uncovering reaction mechanisms, which can accelerate the understanding and design of transformations in complex chemical systems.

Understanding protein-peptide interactions is essential for uncovering cellular signaling mechanisms and advancing therapeutic development, as these interactions play central roles in numerous biological processes. Gaining structural insight into such complexes is crucial, yet traditional methods like nuclear magnetic resonance (NMR) and X-ray crystallography are often time-consuming and experimentally demanding. Computational approaches─including physics-based docking and deep-learning (DL) structure predictors such as AlphaFold3, Boltz-2, and Chai-1─offer powerful alternatives. Accurately modeling flexible peptides that bind to shallow, surface-exposed regions remains difficult for physics-based methods, and although multiple sequence alignment-driven DL models can achieve excellent performance in well-behaved systems, they too can struggle when the peptide adopts noncanonical conformations or when sequence identity is low. In such cases, distance restraints are often required to guide the docking toward accurate and biologically meaningful solutions, yet acquiring multiple high-quality restraints is often difficult. To address the limitation of physics and DL approaches, we developed a restraint scoring function that integrates evolutionary conservation, spatial proximity, and geometric distribution to assess the informativeness of restraint sets. This enables a more accurate evaluation of docking inputs and overcomes the shortcomings of relying solely on restraint count. Building on this framework, we introduce a minimal-restraint docking strategy, capable of identifying optimized subsets of restraints that lead to high-quality structural models. We evaluate a comprehensive set of protein-peptide systems, including 43 SH3 domain complexes, 8 WW domain complexes, and 19 medium-difficulty cases from the PepPCBench benchmark. Our approach shows that model quality improves as the restraint score increases, supporting restraint score as a simple, interpretable indicator of docking success. We further identify clear, domain-specific restraint-score thresholds for the SH3 and WW systems that enable accurate model selection. Together, these results offer a scalable and efficient strategy for structure prediction in data-limited contexts and lay the groundwork for restraint-informed modeling with quantifiable confidence, as well as a powerful foundation for data-efficient machine learning-based peptide-protein docking.

Understanding complex chemical reaction cascades remains a major challenge in chemistry. Scalable investigation of their thermodynamic and kinetic properties requires the use of automated reaction prediction tools, which is a rapidly growing area in the study of chemical reactivity. However, the systematic exploration of intricate reaction networks involving highly reactive intermediates continues to pose significant difficulties. Here, we introduce RxnNet, a novel artificial intelligence-assisted platform for the automated prediction of chemical reaction mechanisms. RxnNet integrates heuristic rules with domain-specific chemical knowledge including stereochemistry, regiochemistry, conformational preferences, and isotope labeling, to construct mechanistically informed reaction networks. These networks are represented as graphs and are coupled with on-the-fly quantum chemical evaluations to identify all feasible intermediates and transition states. In this work, we apply RxnNet to carbocation chemistry, a notoriously complex and computationally demanding type of reaction. We demonstrate the method’s capabilities by analyzing three multistep reactions with known mechanisms, each of which poses significant challenges even for expert computational and synthetic chemists. RxnNet provides a robust approach for uncovering reaction mechanisms, which can accelerate the understanding and design of transformations in complex chemical systems.

Rechargeable magnesium batteries (RMBs) are a promising alternative to lithium‐ion batteries because of the high capacity and crustal abundance of magnesium. However, RMBs are primarily plagued by low‐energy‐density of traditional cathode materials, which utilize transition‐metal cationic redox chemistry. Herein, a new family of layered Ti _{1‐ x} Fe _x S ₂ (0 < x < 1) materials is proposed to assess anionic redox and boost the energy density of RMBs. The structural stability and redox activity are systematically investigated using first‐principles calculations and operando experimental techniques. It is found that layered Mg _n Ti _{1‐ x} Fe _x S ₂ materials are stable below x = 0.5, forming the solid‐solution sulfides. The Mg _n Ti _0.75 Fe _0.25 S ₂ sample is experimentally shown to offer a high operating voltage of 1.8 V, a competitive energy density of 103 mAh g ⁻¹ , and better cycling stability compared to prototype TiS ₂ material. Both cations (Ti and Fe) and anion (S) are revealed to take part in the redox reaction. The new family of layered Ti _{1‐ x} Fe _x S ₂ cathodes with joint cationic and anionic redox chemistry proposed in this work are promising high‐energy cathode candidates for RMBs.

Although the enzymatic mechanisms of terpene synthases have been extensively characterized through experimental and computational studies, the atomistic details underlying the product release process have remained elusive. In this study, we present the first atomistic simulations of the initial stages of product release in a terpene synthase, using the bacterial diterpene cyclase CotB2 as a model system. CotB2 catalyzes the complex cyclization of geranylgeranyl diphosphate (GGDP) to the tricyclic diterpene cyclooctat‐9‐en‐7‐ol in a single active site through an 11‐step reaction cascade. Our MD simulations focus on three model systems representing CotB2 with bound GGDP and cyclooctat‐9‐en‐7‐ol, the latter in two states—with the diphosphate fully deprotonated (P₂O₇⁴⁻) and protonated diphosphate (HP₂O₇³⁻). Analysis of the MD trajectories clearly shows that product release is initiated by the dislocation of the diphosphate group, which in turn triggers active site opening via coordinated C‐terminal motions. Notably, protonation of the diphosphate moiety appears to be the key event that weakens its interactions with the active site and enables product release. These findings provide crucial mechanistic insight into the final phase of terpene biosynthesis and open new avenues for rational enzyme engineering targeting product release.