Biophysics and Physicobiology

Single molecule methods have become prevalent tools in elucidating molecular processes across various life science fields over the past three decades, driving breakthroughs in understanding their underlying molecular mechanisms. In our study, we employed two single-molecule methods, Forster Resonance Energy Transfer (smFRET) and high-resolution optical tweezers, to investigate the transcription of Mycobacterium tuberculosis RNA polymerase (MtbRNAP) from initiation through to termination. We aim to provide a set of comprehensive biophysical tools to deepen our current understanding of MtbRNAP and its transcription factors. These experimental assays represent an important step towards unraveling the molecular dynamics and interactions that support transcription in Mycobacterium tuberculosis.

GPI-anchored proteins are crucial cell surface proteins with diverse, organism-specific functions, in eukaryotes. They are produced when the GPI transamidase (GPIT), a five-subunit membrane-bound enzyme complex, attaches a pre-formed GPI anchor to the C-terminal end of nascent proteins on the lumenal face of the endoplasmic reticulum. This process requires the removal of a C-terminal signal sequence (SS) on the substrate protein by the action of an endopeptidase subunit of the GPIT, Gpi8/ PIG-K. Using an AMC-tagged peptide in a cell free (post-mitochondrial fraction) assay, this manuscript studies the steady state kinetics of enzymatic cleavage of the substrate by GPIT of the human pathogenic fungus, C. albicans. We show that Mn+2 enhances activity by improving substrate binding but plays no direct role in substrate cleavage per se. Molecular dynamics simulations suggest that the divalent cation binds at a site away from the active site but provides compactness and stability to Gpi8. It also enables a conformation in which a flexible loop (219-244 residues) in the vicinity of the catalytic pocket is able to interact with and position the scissile bond for cleavage by Cys202. Steady state kinetics also indicate that peptides of lengths 7-mer to 9-mer are better bound than 4-mer or 15-mer peptide substrates. A bulky residue at the site of cleavage reduces the catalytic activity of the GPIT. This is the first detailed steady state kinetics study on the endopeptidase activity of a GPIT from any organism.

Protein kinases regulate signaling by recognizing short sequence motifs, and how these motifs bind influences both specificity and therapeutic strategies that target kinase pathways. Peptide-based inhibitors that engage substrate-recognition regions are attracting interest, but designing them requires an understanding of how a flexible peptide approaches and settles into the bound pose. Traditional studies have focused on the bound pose and affinities, whereas the steps that link the initial encounter with the bound pose have been explored less thoroughly because the relevant intermediates are too short-lived to capture experimentally and evolve on timescales that standard molecular dynamics cannot readily access. Here, we focused on Abl kinase and Abltide, the experimentally identified optimal substrate peptide for Abl kinase, and examined the sequence of events linking initial encounter to the bound pose using two-dimensional replica exchange (gREST/REUS), which selectively enhances flexibility in the peptide and its binding interface while also sampling progression along a distance coordinate. The resulting simulations yielded a detailed binding landscape, revealing five distinct encounter regions outside the substrate-binding site and six intermediate states that may connect the initial approach to the bound pose. Some encounter regions and intermediate states participate in the dominant binding pathways. During this process, EF/G/{beta}11 hydrophobic patch, together with G helix negative patch, plays a central role in guiding Abltide toward the substrate-binding site. These findings provide mechanistic insight into substrate recognition by protein kinases and offer a foundation for the rational design of peptide-based inhibitors.

Synaptic vesicle glycoproteins 2 (SV2) are integral membrane proteins essential for neurotransmitter release and are implicated in neurological disorders including epilepsy and Parkinsons disease. In the attempt to develop a ligand selective for SV2C, and in collaboration with UCB, UCB-F was identified as a potential candidate. However, the affinity of UCB-F to SV2C was found to be temperature dependent, decreasing by about 10-fold from +4 to 37 degrees. UCB1A was subsequently identified as SV2C ligand displaying in vitro a 100-fold selectivity for SV2C compared with SV2A. In this study we investigated whether the binding of UCB-1A to SV2A and SV2C was affected by the temperature. A combination of experimental binding assay data and molecular dynamics (MD) simulations were used. The binding studies revealed that UCB1A affinity for SV2A decreased significantly at 37 {degrees}C compared with 4 {degrees}C, whereas binding to SV2C remained largely unchanged. MD simulations reproduced these observations, namely that ligand RMSD values at 310 K showed that UCB1A binding fluctuated markedly in the SV2A complex, with many trajectories exceeding the 3.0 [A] stability cutoff, whereas UCB1A remained relatively well-anchored in SV2C under the same conditions. Structural analysis showed that, while UCB1A adopts a conserved binding pose across all isoforms stabilized by {pi}- {pi} stacking and a hydrogen bond with Asp, SV2C possesses a unique stabilizing feature. In SV2C, Tyr298 is less exposed to the solvent and engages in a persistent hydrogen bond with Asparagine, a structural feature that reinforces pocket stability and limits temperature-induced destabilization. This interaction is absent in SV2A, consistent with its greater temperature sensitivity. Together, these findings provide a mechanistic explanation for the experimentally observed temperature independence of UCB1A binding to SV2C. More broadly, the results highlight the importance of incorporating physiologically relevant temperatures into SV2 ligand evaluation and demonstrate how combining experiments with simulations can uncover isoform-specific mechanisms of ligand recognition and stability.

G-quadruplexes (G4s) are critical nucleic acid secondary structures that play pivotal roles in regulating gene expression. In this study, we conducted a proteome-wide in silico analysis across multiple viruses causing hemorrhagic fevers to identify candidate proteins containing a conserved G4-binding motif. Four peptides belonging to Marburg, Ebola, Hantaan and Yellow fever viruses were shown to bind to G4 in vitro. We selected the NS3 protease domain of Yellow Fever virus for further validation. Biochemical assays demonstrated that the NS3 protease domain binds G4 structures with high specificity and affinity, particularly favoring the parallel conformation. Molecular docking and simulations further revealed that the NS3 protease domain interacts with the terminal G-tetrads and loop regions of G4 via key residues, including PHE40, adopting an insertion and stacking composite binding mode. These findings expand our understanding of virus - G4 interactions and offer novel potential targets for G4-based antiviral strategies. Bullet points- We screened viruses causing hemorrhagic fevers for potential G4-binding peptides. - Four peptides belonging to Marburg, Ebola, Hantaan and Yellow fever viruses were shown to bind to G4 in vitro. - Biochemical assays demonstrated that the NS3 protease domain of YFV binds G4 structures with high specificity and affinity.

Ubiquitin-like protein 3 (UBL3) is a post-translational modifier that sorts proteins into small extracellular vesicles and regulates the trafficking of disease-associated proteins such as -synuclein. The structural and dynamic features of the UBL domain that underlie these functions, however, remain poorly understood. Here we performed in silico structural dynamics analysis of the UBL3 UBL domain using an NMR structure ensemble combined with anisotropic network modeling (ANM) and perturbation response scanning (PRS). Principal component analysis and residue-wise fluctuation analysis consistently revealed high flexibility in the C-terminal region of UBL3. Comparative ANM analysis across 20 ubiquitin-like proteins (UBLs) further showed that C-terminal flexibility is a conserved yet variable property within the UBL family. PRS analysis demonstrated that residues forming the central -helix of the {beta}-grasp fold exert greater dynamic control over collective motions than {beta}-sheet residues. Notably, UBL3 displayed the highest helix/sheet PRS effectiveness ratio among all UBLs analyzed, highlighting the prominent dynamic contribution of helix residues in this domain. Together, these results provide a structural basis for understanding UBL3-dependent protein interactions and disease-related mechanisms, and suggest that helix-centered dynamic control in the UBL domain may represent a potential target for modulating UBL3 function.

Molecular dynamics (MD) simulations are a powerful tool for investigating biomolecular dynamics underlying biological functions. However, the accessible spatiotemporal scales of conventional all-atom simulations remain limited by high computational costs. Coarse-graining reduces these costs by decreasing the number of interaction sites and enabling longer timesteps. In extreme cases, proteins are represented as single spherical particles; while such approximations facilitate cellular-scale simulations, they often sacrifice essential structural information, such as molecular shape and interaction anisotropy. Here, we present CGRig, a rigid-body protein model with residue-level interaction sites designed for long-time, large-scale simulations. In CGRig, each protein is treated as a single rigid-body embedding residue-level interaction sites. Its translational and rotational motions are described by the overdamped Langevin equation incorporating a shape-dependent friction matrix. Intermolecular interactions are calculated using G[o]-like native contact potentials, Debye-Huckel electrostatics, and volume exclusion. We validated that CGRig accurately reproduces the translational and rotational diffusion coefficients expected from the friction matrix for an isolated protein. For dimeric systems, the model successfully maintained native complex structures. Furthermore, two initially separated proteins converged into the correct complex with an association rate consistent with all-atom simulations. Notably, CGRig achieved a simulation performance exceeding 17 s/day for a 1,024-molecule system. These results demonstrate that CGRig provides an efficient framework for simulating protein assembly while retaining residue-level interaction specificity, making it a valuable tool for investigating large-scale biomolecular self-assembly.

Our current understanding of carbohydrate-binding module (CBM) function is limited by the fact that most CBM research has focused on single-binding-site modules. CBM family 92 (CBM92) is a recently characterized family of predominantly trivalent proteins that bind {beta}-1,3- and {beta}-1,6-glucans with high specificity. CpCBM92A from Chitinophaga pinensis stands out as the first trivalent member of the family to be structurally determined. Multivalent CBM families are rare, and the way in which the three binding sites cooperate in ligand recognition remains unclear. Here, we use NMR spectroscopy to demonstrate how each of the proteins binding sites plays distinct roles in ligand binding. One binding site, referred to as the {beta} site, can be identified as the primary attachment point because of its higher affinity for all tested ligands, consistent with previous biochemical data suggesting it is the strongest binding site on CpCBM92A. The other two binding sites, referred to as and {gamma}, preferentially bind longer segments of {beta}-1,3- and {beta}-1,6-glucan chains, respectively. We further show that the glycosidic bond position and anomeric configuration of the binding glucosyl unit strongly affects protein affinity due to a preferred ligand pose in the binding sites. Our results provide insight into how the trivalent architecture of CBM92 might enable cross-linking of scleroglucan chains, which may guide the development of new applications for CBMs in biotechnology.

AI-based molecular dynamics simulation brings ab initio calculations to biomolecules in an efficient way, in which the machine learning force field (MLFF) locates at the central position by accurately predicting the molecular energies and forces. Most existing MLFFs assume localized interatomic interactions, limiting their ability to accurately model non-local interactions, which are crucial in biomolecular dynamics. In this study, we introduce ViSNet-PIMA, which efficiently learns non-local interactions by physics-informed multipole aggregator (PIMA) and accurately encodes molecular geometric information. ViSNet-PIMA outperforms all state-of-the-art MLFFs for energy and force predictions of different kinds of biomolecules and various conformations on MD22 and AIMD-Chig datasets, while adapting the PIMA blocks into other MLFFs further achieves 55.1% performance gains, demonstrating the superiority of ViSNet-PIMA and the universality of the model design. Furthermore, we propose AI2BMD-PIMA to incorporate ViSNet-PIMA into AI2BMD simulation program by introducing "Transfer Learning-Pretraining-Finetuning" scheme and replacing molecular mechanics-based non-local calculations among protein fragments with ViSNet-PIMA, which reduces AI2BMDs energy and force calculation errors by more than 50% for different protein conformations and protein folding and unfolding processes. ViSNet-PIMA advances ab initio calculation for the entire biomolecules, amplifying the application values of AI-based molecular dynamics simulations and property calculations in biochemical research.

Prasugrel is a prodrug, widely used in antiplatelet strategy for secondary prevention after acute coronary syndrome. The metabolism of prasugrel leads to the formation of the Prasugrel Active Metabolite (PAM), an irreversible P2Y12 receptor antagonist. Its mode of binding has not yet been fully established, although it is known that it binds covalently to P2Y12 by forming a disulfide bridge with cysteines and its sulfur moiety. PAM is a molecule with two chiral centers, resulting in four stereoisomers which appear to be stereoselective upon binding. A combination of different molecular modeling methods, such as molecular dynamics, ensemble docking, and Density Functional Theory (DFT), were used to rationalize these differences in antagonism observed in vitro and to elucidate the mode of binding of PAM to P2Y12. PAM is found to bind to the closed P2Y12 conformation in a preferential way. Although the four stereoisomers have comparable affinity, the location of the RS stereoisomer makes the formation of a disulfide bond with cysteines more favorable, particularly with cysteine 175. Compared to the RR stereoisomer, the RS stereoisomer interacts less deeply with the P2Y12 receptor, interacting in particular with the second and third extracellular loops, explaining the competition observed with cangrelor and an intermediate metabolite of prasugrel. Furthermore, DFT calculations have shown that the formation of a disulfide bridge is energetically more favorable with the RS stereoisomer than with the RR stereoisomer. The physical interactions and chemical reaction between the RS stereoisomer and the P2Y12 receptor are key factors in explaining the stereoselective binding of PAM to P2Y12.

Protein translation is a highly regulated process influenced by multiple factors at the initiation, elongation, and termination stages. One notable regulatory element of the ribosome is the CAR interaction surface, a three-residue motif in the structure of the ribosome composed of C1274 and A1427 of S. cerevisiae 18S rRNA (corresponding to C1054 and A1196 in E. coli 16S rRNA) and R146 of ribosomal protein Rps3. CAR is highly conserved and positioned adjacent to the amino-acyl (A site) decoding center. It establishes hydrogen bonds with the +1 codon next in line to enter the ribosome A site, acting as an extension of the tRNA anticodon and forming base-stacking interactions with nucleotide 34 of the tRNA. However, despite CARs enzymatically strategic positioning within the ribosome, its functional relationship with the A site remains poorly characterized. Using molecular dynamics (MD) simulations, we examined the interplay between the A site and CAR site, revealing sequence-dependent modulation of H-bonding and {pi}-stacking interactions within and between the two sites. These findings highlight the interplay between the A site and CAR site, suggesting a structural and functional connection between these two regions of the ribosome that may contribute to mRNA sequence-specific tuning of translation elongation. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/714784v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1d783d3org.highwire.dtl.DTLVardef@f9cd8org.highwire.dtl.DTLVardef@102667corg.highwire.dtl.DTLVardef@967c56_HPS_FORMAT_FIGEXP M_FIG C_FIG

Intercalation of small molecules between DNA base pairs affects DNA conformation, disrupting essential cellular processes including replication, transcription, and repair. We investigated conformational changes in 18-mer DNA upon intercalation of doxorubicin, SYBR Gold and YOYO-1 using extensive MD simulations. Two main patterns for the intercalation were identified: RISE-type intercalation occurs between adjacent base pairs and extends the DNA helix with decreased twist angles, while OPEN-type intercalation proceeds through base-pair opening without significant DNA extension. Kinetic analysis revealed that association rates for intercalation followed the order: first YO-moiety (mono-intercalation) > SYBR Gold > doxorubicin > YOYO-1 (bis-intercalation). Free energy landscape showed that forces at DNA termini reached up to 117 pN during stretching. Notably, base pairs adjacent to intercalators were protected from strand separation, accompanied by additional helical unwinding. MM-PBSA/GBSA analysis revealed that the driving force for intercalation is the stacking energy, and the binding affinity was highest for minor groove binding. Persistence length decreased with single molecule binding but recovered with two molecules due to their electrostatic repulsion. Mechanical properties of intercalated DNA showed position-dependence, demonstrating that multiple intercalation modes coexist in solution. The heterogeneous nature of intercalation explains why experimental measurements reflect ensemble averages rather than single binding configurations.

Mass-conserved reaction-diffusion systems are used as mathematical models for various phenomena such as cell polarity. Numerical simulations of this system present transient dynamics in which multiple stripe patterns converge to spatially monotonic patterns. Previous studies indicated that the transient dynamics are driven by a mass conservation law and by variations in the amount of substance contained in each pattern, which we refer to as "pattern flux". However, it is challenging to mathematically investigate these pattern dynamics. In this study, we introduce a reaction-diffusion compartment model to investigate the pattern dynamics in view of the conservation law and the pattern flux. This model is defined on multiple intervals (compartments), and diffusive couplings are imposed on each boundary of the compartments. Corresponding to the transient dynamics in the original system, we consider the dynamics around stripe patterns in the compartment model. We derive ordinary differential equations describing the pattern dynamics of the compartment model and analyze the existence and stability of equilibria for the reduced ODE with respect to the boundary parameters. For a specific parameter setting, we obtained results consistent with previous studies. Moreover, we present that the stripe patterns in the compartment model are potentially stabilized by changing the parameter, which is not observed in the original system. We expect that the methodology developed in this paper is extendable to various directions, such as membrane-induced pattern control.

Staphylococcus aureus (S. aureus) is a clinically relevant pathogen capable of adapting its membrane composition in response to environmental stress. In this adaptive process, bacterial carotenoids play a crucial role. Although staphyloxanthin (STX) is the main carotenoid produced by the bacterium, S. aureus also synthesizes other pigmented intermediates that play an unknown role in regulating membrane biophysical properties. In this study, we purified 4,4-diaponeurosporenoic acid (4,4'-DNPA) from S. aureus carotenoid extracts and evaluated its effect on the thermotropic and biophysical properties of representative membrane models. The highly rigid triterpenoid 4,4'-DNPA is one of the last precursors in the biosynthesis of STX and is found in high concentrations in the stationary phase of S. aureus. Phase transition temperatures were determined using infrared spectroscopy, while interfacial hydration and hydrophobic core dynamics were investigated using fluorescence spectroscopy through Laurdan generalized polarization and DPH anisotropy. The results show that 4,4'-DNPA increases the main phase transition temperature of lipid bilayers in a concentration-dependent manner. This is in contrast to STX that decreases the transition temperature. This difference is consistent with the additional fatty acid present in STX that changes its effect on the phase behavior. Furthermore, 4,4'-DNPA reduced the interfacial hydration levels and restricted hydrophobic-core dynamics at higher concentrations, consistent with increased molecular order and stability. 4,4'-DNPA therefore complements STX in increasing membrane order and lipid packing. These findings support the notion that the production of bacterial carotenoids functions as a biophysical regulatory mechanism of lipid packing in S. aureus membranes.

Microplastic (MP) pollution, which is present in the ecosystem in vast quantities, adversely affects human health and the environment, making it imperative to develop methods for its mitigation. The challenge of detecting or capturing MPs could potentially be addressed using plastic-binding peptides (PBPs). The ideal PBP for MP remediation would not only bind strongly to plastic, but also have other properties such as high solubility in water or great binding specificity to a certain plastic. However, the scarcity or absence of known PBPs for common plastics along with the lack of methods that can discover PBPs with all of the desired properties precludes the development of peptide-based MP remediation strategies. In this study, we discovered short linear PBPs with high predicted water solubility and binding specificity by employing an in-silico discovery pipeline that combines deep learning and biophysical modeling. First, a long short-term memory (LSTM) network was trained on biophysical modeling data to predict peptide affinity to plastic. High affinity peptides were generated by pairing the trained LSTM with a Monte Carlo tree search (MCTS) algorithm. Molecular dynamics (MD) simulations showed that the PBPs discovered for polyethylene, the most common plastic, had 15% lower binding free energy than PBPs obtained using biophysical modeling alone. PBPs with both high affinity and high predicted solubility in water were found by including the CamSol solubility score in the MCTS peptide scoring function, increasing the average solubility score from 0.2 to 0.9, while only minimally decreasing affinity for polyethylene. The framework also discovered peptides with high binding specificity between polystyrene and polyethylene, two major constituents of MP pollution, using a competitive MCTS approach that optimized the difference in affinity between the two plastics. MD simulations showed that competitive MCTS increased the binding specificity of PBPs for polystyrene and identified peptides with relatively great preference for either of the two plastics. The framework can readily be applied to design PBPs for other types of plastic. Overall, the high-affinity PBPs with desirable properties discovered by marrying artificial intelligence and biophysics can be valuable for remediating MP pollution and protecting the health of humans and the environment.

Cancer cells in hypoxic environments often proliferate less but exhibit enhanced migration relative to their normoxic counterparts. Recent in vitro and in silico studies have characterized the role of hypoxic memory - the ability of cancer cells to retain their hypoxic phenotype even when reoxygenated - in tumor invasion. However, the observations have been limited either to exposing cancer cells to hypoxia for a fixed duration or by assuming a fixed-time persistence of the hypoxic state upon reoxygenation independent of the duration of hypoxia exposure. Thus, time-dependent cell-state changes during hypoxia and their impact on hypoxic memory remains unclear. Here, we first analyze transcriptomic data from breast cancer samples to show that the genes upregulated at transcriptional level and hypomethylated at epigenetic level are enriched in cell invasion, indicating hypoxic memory-driven process of tumor invasion. Next, we used a computational model to investigate how the spatial-temporal dynamics of oxygen levels in a tumor drive time-dependent changes in hypoxic memory and influence tumor invasion dynamics. Our simulation results show that such dynamic hypoxic memory can drive enhanced tumor invasion over a fixed hypoxic memory by a) enriching hypoxic cell density at the tumor front, b) reducing sensitivity of hypoxic cell state to fluctuations in oxygen supply, and c) enhancing effective diffusion of hypoxic cells. Our results highlight the crucial role of dynamic hypoxic memory in shaping tumor invasion dynamics, underscoring the need to elucidate its underlying mechanisms in future studies.

Fluorescence fluctuation spectroscopy (FFS) is an ensemble of techniques for quantitative measurement of molecular dynamics and interactions. Recently, the introduction of small-format array detectors has opened up a new range of spatiotemporal information, allowing for more detailed analysis of system kinetics. However, there is currently no open-source software available for analyzing the high-dimensional FFS data sets. We present BrightEyes-FFS, an open-source Python-based environment for FFS analysis with array detectors. The environment includes a Python package for reading raw FFS data, computing auto- and cross-correlations using various algorithms, and fitting the correlations to several models. A graphical user interface (GUI), available as a standalone executable, makes the analysis fast and user-friendly. An automated Jupyter Notebook writing tool enables transition from the GUI to Jupyter Notebook for custom analysis. We believe that BrightEyes-FFS will enable a wider community to study diffusion, flow, and interaction dynamics.

We examined the overlap in the genes associated with daily rhythms and with behavioral plasticity in ants. We first investigated the daily rhythms of gene expression in the harvester ant, Pogonomyrmex barbatus, and how the rhythmic genes overlap with others previously shown to be associated with plasticity of foraging behavior. Then, to consider whether the overlap is conserved across ant species, we compared rhythms of gene expression in the diurnal, desert harvester ants with those previously reported for a distantly related nocturnal, subtropical carpenter ant, Camponotus floridanus. First, daily transcriptomes in P. barbatus showed that most genes were expressed in light-dark (LD) and constantly dark (DD) conditions at about the same levels; only 11 genes showed at least a two-fold change in expression. Network analysis identified eleven modules of P. barbatus genes under LD conditions. Of these 11 clusters, modules C1 and C2 seem to be central nodes of the gene expression network, because they are the most highly connected in LD, and show the strongest preservation in DD vs. LD, and contain core clock gene Period. Only one module, C2, showed significant overlap with P. barbatus genes that have 24h-rhythmic expression in both LD and DD. There was significant overlap between modules C1, C2, C10, C11, and P. barbatus genes found previously to be associated with plasticity in the regulation of foraging activity to manage water loss. A comparison of the daily transcriptome of P. barbatus with that of C. floridanus showed significant overlap of 24h-rhythmic genes in LD. Modules C1 and C2 of P. barbatus also overlap with C. floridanus genes previously shown to differ in expression rhythms in nurses and foragers. In combination, these results indicate that genes linking plasticity of the circadian clock and of behavior may be broadly conserved in ants.

Silver (Ag+) ions are known to be toxic to bacteria, cells, organisms and living systems; yet its impacts on the locomotion of surface-crawling organisms remain poorly quantified. Here we investigated the short-term (0-6 hours) effects of Ag+ ions on the locomotion of Drosophila melanogaster larvae on flat agarose surfaces containing Ag+ ions at different concentrations (0, 1, 10, and 100 mM). By quantifying their locomotion, we found that Drosophila larvae showed shorter accumulated distances and reduced crawling speed. Additionally, we quantified the go/stop dynamics and peristalsis of the larvae and observed that Ag+ ions disrupted the normal, rhythmic, peristaltic contraction of the larvae and "trapped" them in the stop phase. Such toxic effects were dependent on Ag+ concentration and exposure duration.

We present a new formulation of the low-field effect (LFE) in spin-correlated radical pairs based on a zero-field singlet-triplet basis for the isotropic spin Hamiltonian. The aim is to provide a description that is both formally rigorous and mechanistically transparent, especially in the regime of weak magnetic fields such as the geomagnetic field. For the standard model radical pair containing a single spin [Formula] nucleus, we show that the conventional singlet-triplet basis obscures the distinct dynamical roles of the hyperfine and Zeeman interactions. In the zero-field S-T basis, by contrast, the mechanism separates cleanly: isotropic hyperfine coupling mixes singlet-doublet and triplet-doublet states, whereas the weak-field Zeeman interaction mixes triplet-quartet and triplet-doublet states without directly introducing an additional singlet-triplet coupling. The LFE is therefore revealed as a sequential process in which a weak field unlocks access from a triplet-only manifold to a singlet-accessible triplet manifold, from which hyperfine-driven singlet-triplet interconversion can occur. We then generalize this picture to radical pairs with arbitrary isotropic hyperfine structures by identifying maximal, interior, and, when present, minimal triplet-only manifolds in the zero-field spectrum. Finally, we introduce a practical blockwise dark-state recruitment measure for the triplet-only zero-field state space made singlet-accessible by a weak field, and show how this quantity depends on hyperfine symmetry, including the effects of equivalent nuclei. The resulting framework provides both a simple physical picture of the LFE and a general route to estimating its structural upper bound for arbitrary radical pairs.