Ji-Yong Sung

Mitochondrial Complex I generates reactive oxygen species at its flavin cofactor, where spin-dependent reaction pathways have been proposed. Models of this chemistry treat the flavin– oxygen radical pair in isolation, although the flavin is wired to a chain of eight iron–sulfur clusters. Here we model the pair with this chain using the human Complex I structure and open-quantum-system models of spin coupling and electron transfer. In the simulated network, spin coherence decays rapidly along the chain, whereas proximal clusters alter the calculated radical-pair singlet yield. Their influence is kinetically gated: a downstream cluster affects the modelled yield when its electron-transfer rate competes with radical-pair recombination, a threshold robust to driving force and to the transfer model. Modelling does set the sign: one-directional spin hops predict suppression and inflate coherence transport, whereas charge-resolved transfer drives the singlet yield towards its spin-randomized limit unless triplet recombination dominates. Detectable spin-dependent effects in this framework require superoxide spin relaxation to be substantially slower than in free solution, a condition not yet established for Complex I.

Mitochondrial complex I translocates protons through hydrated channels whose connectivity and electrostatics are both defined by structure, but whether they are distinctly constrained is unknown. From a 2.39 Å cryo-EM structure modelling 2,945 ordered waters, we built the proton wire of the seven mtDNA-encoded ND subunits and scored all 40,166 missense substitutions on two near-orthogonal axes (r = 0.17): electrostatic shift at proton-loading sites, and topological disruption of the wire. Across 858 germline variant sites, loading sites (0.51) and cut vertices (0.67) are both depleted. In tumours only loading sites are enriched (1.42 TCGA, 1.67 PCAWG, 1.29 MSK-IMPACT), a 3.06-fold difference tested as a wire class × tissue-type interaction (P = 3.6 × 10LL). It survives adjustment for conservation, burial and coverage, and is concentrated in ND1 within 15 Å of the quinone cavity, not spread along the proton path. Selection separates charge from connectivity, without establishing a fitness benefit. Significance: Mitochondrial complex I is essential for cellular energy production, yet how cancer alters evolutionary constraints on its proton-translocating machinery remains unclear. By reconstructing hydrogen-bonded proton networks from high-resolution structural data, we distinguish two fundamental properties: electrostatic regulation of proton loading and structural connectivity of proton-transfer pathways. Across three independent cancer cohorts, mutations affecting proton-loading electrostatics are enriched, whereas mutations disrupting network connectivity are not. This divergence is concentrated near the quinone-binding cavity rather than distributed along the proton pathways. Our findings reveal that cancer-associated mitochondrial evolution selectively relaxes constraints on charge regulation while preserving constraints on proton-network integrity, providing a biophysical framework for understanding how mitochondrial mutations differ between human populations and tumors.

Mitochondrial electron transfer depends on quantum-mechanical coupling between redox cofactors, but its evolutionary constraints in cancer remain unclear. We combined the human complex III cryo-EM structure, Pathways calculations and Marcus theory to predict how missense substitutions in mitochondrial cytochrome b (MT-CYB), which coordinates the Q-cycle haem centres, affect electron-transfer kinetics. Comparing 524 somatic mutations from TCGA and PCAWG with 597 germline variant sites, we found that charge-changing substitutions near the haems were depleted in the germline (observed/expected, 0.12) but enriched in tumours (1.81). Greater predicted kinetic perturbations corresponded to stronger germline depletion and tumour enrichment, predominantly at low heteroplasmy and independently of tumour purity. Predicted kinetic effects correlated strongly with electrostatic perturbations (r = 0.94), precluding identification of tunnelling-specific selection. These findings support relaxed purifying selection on MT-CYB variants predicted to disrupt mitochondrial electron transfer during the evolution of diverse human cancers, without demonstrating adaptive benefits or selection on tunnelling itself.

One-electron oxidation of DNA generates migrating holes that come to rest at guanine runs, a positional pattern of damage characterized in vitro but not shown to reach somatic mutation in human cancer. In mismatch-repair-deficient gastric, colorectal and endometrial tumours, we find G>T substitutions within guanine runs enriched at the 5′-guanine (0.86–0.87) and depleted at the 3′-guanine (0.05–0.08), specific to G>T and near-identical across all three cancer types. The profile along runs of two to five guanines, including the flattening of the profile as runs lengthen, matches the charge density computed for a delocalized hole without fitting (r = 0.87–0.93); treating the transfer integral as the only free parameter returns 0.073–0.088 eV from four datasets: three tumour cohorts, and mismatch-repair-knockout cells grown without added damage that share no part of the tumour pipeline. A defined one-electron oxidant reproduces the asymmetry in repair-proficient cells, and the strong positional bias persists among high-variant-allele-fraction mutations, arguing against library-preparation oxidation as its principal source. Guanine ionization energy nonetheless does not predict the genomic distribution of C>A substitutions, in any oxidative stratum of non-hypermutated tumours or in the reference signature attributed to reactive oxygen species, delimiting where this chemistry reaches the cancer mutation spectrum.

Iron–sulfur (Fe–S) clusters are among the most ancient biological cofactors, yet the physical properties associated with the recurrent biological utilization of specific Fe–S architectures remain unclear. Here, we analyzed 2,404 experimentally resolved Fe–S clusters using an integrated framework combining structural geometry, geometry-derived effective-coupling network robustness, and coarse-grained open-system quantum transport simulations. We show that 4Fe–4S clusters occupy a compact and partially distinct region of structural-descriptor space characterized by low geometric distortion, elevated geometry-derived network robustness, and broad taxonomic representation within the available structural dataset. Geometry-derived coarse-grained effective Hamiltonian reconstruction and Lindblad simulations further showed architecture-dependent differences in Hamiltonian organization and simulated transport behavior under a common set of model assumptions. Together, these findings establish a hierarchical comparative framework linking Fe–S geometry, effective Hamiltonian organization, simulated open-system transport, and broad taxonomic recurrence, and suggest that geometry-derived transport organization may represent one physical property contributing to the recurrent biological utilization of Fe–S architectures.

Mitochondrial complex I produces reactive oxygen species at its flavin mononucleotide (FMN) cofactor. We combined steric sampling of oxygen positions in human complex I with radical-pair spin simulations to examine how the FMN pocket shapes reaction-yield sensitivity, which provided singlet yields across FMN–O ₂ centroid separations, magnetic fields and spin-dephasing conditions. When candidate oxygen positions overlapping the FMN cofactor itself were excluded, almost no accessible positions remained within 3.3 Å of the isoalloxazine centre (≤0.2%), and a composite score combining geometric accessibility with normalised field and dephasing responses had no near-contact maximum. In the reference structure (PDB 5XTD), the highest-scoring 10% of accessible configurations had a mean separation of 5.8 Å (2.5th–97.5th percentiles, 5.6–6.0 Å); in the independently determined 9TI4 structure, the corresponding mean was 7.9 Å (7.7–8.0 Å), reflecting differences in the modelled pocket. Within each structure, this location changed little when the dephasing model, flavin hyperfine parameters, steric radii and recombination/dephasing rates were varied. Over 2.5–20 Å, the exchange-only score formed a broad plateau without a localized maximum. Small prescribed fluctuations in separation produced less singlet-yield variability in the high-scoring configurations than elsewhere in the accessible ensemble. A near-contact score maximum reported in an earlier version of this analysis arose because positions overlapping FMN had been counted as accessible. The accessibility-weighted spin-response map of the complex I flavin site is therefore set mainly by pocket geometry and should be evaluated over structural ensembles rather than a single model.

Cancer multi-omics faces challenges in handling the scale, complexity, and heterogeneity of multi-omics data, limiting progress in variant interpretation, tumor classification, and modeling cancer evolution. Quantum computing offers a new paradigm using superposition, entanglement, and quantum interference to efficiently explore vast solution spaces. This Perspective highlights how quantum algorithms-such as Quantum Support Vector Machines, Quantum Principal Component Analysis, and quantum generative models-could enhance key tasks in precision oncology, including multi-omics integration, spatial transcriptomics, and neoantigen prediction. Current technical barriers, like qubit noise and limited quantum memory, are discussed alongside strategies to connect quantum computing with biomedical research. Interdisciplinary collaboration will be essential to realizing quantum advantage in cancer multi-omics

Single-cell transcriptomics has transformed the characterization of cellular heterogeneity by enabling systematic analysis of biological gene programs. However, existing computational approaches primarily quantify the activity of individual programs independently and therefore provide limited insight into how multiple simultaneously active programs collectively determine cellular identity. Here we present Gene Program Negotiation (GPN), a graph-based computational framework that models regulatory decision-making among concurrently active biological programs. GPN reconstructs cell-specific program interaction networks from local transcriptional neighborhoods and quantifies regulatory organization using the Gene Program Coherence Index (GPCI) together with measures of local regulatory conflict, program diversity, and dominance. These graph-derived properties enable the classification of individual cells into five regulatory decision states: Consensus, Competition, Negotiation, Dominance, and Low activity. Applying GPN to gastric cancer single-cell transcriptomes revealed that cells sharing the same dominant biological program frequently occupied distinct regulatory decision states, demonstrating that dominant program identity alone does not uniquely define cellular regulatory organization. Competition states consistently exhibited elevated local regulatory conflict and were preferentially enriched among transition-like cells, indicating that regulatory competition is closely associated with transcriptional plasticity. Independent validation using glioblastoma single-cell transcriptomes reproduced these regulatory patterns without modification of the computational framework, supporting the robustness and generalizability of the approach across biologically distinct malignancies. These findings establish regulatory negotiation as an additional layer of cellular organization beyond conventional gene-program activity analysis. By explicitly modeling interactions among simultaneously active biological programs, GPN provides a general computational framework for investigating regulatory coordination, cellular plasticity, and dynamic cell-state organization in single-cell transcriptomic data.

Single-cell technologies have enabled increasingly detailed reconstruction of developmental trajectories, yet a fundamental question remains unresolved: when does future cellular identity become predictable from a cell’s current molecular state? Existing approaches infer lineage relationships, transition probabilities or future transcriptional dynamics, but do not directly quantify the emergence of fate predictability during cellular state transitions. Here we present FateLimit, an information-theoretic framework for measuring the temporal dynamics of cell-fate predictability from single-cell omics data. FateLimit combines probabilistic fate assignment, fate entropy and mutual information to quantify how information about future cellular outcomes is encoded in present molecular states. We introduce two quantitative descriptors: the Fate Information Half-Life (FIHL), which measures the characteristic timescale of fate-information dynamics, and the Prediction Horizon (PH), defined as the earliest developmental stage at which observed fate predictability exceeds the 95th percentile of a permutation-derived null distribution. We applied FateLimit across developmental, lineage-tracing and reprogramming systems, including pancreatic endocrinogenesis, CellTag reprogramming, human hematopoiesis and zebrafish embryogenesis. Across all datasets, FateLimit identified significant fate information and reproducible prediction horizons that were robust to cell-state representation, lineage structure and biological context. Comparative analysis revealed that prediction horizons differ substantially among cellular lineages, indicating that distinct developmental programs acquire predictive information at different rates. FateLimit establishes a general framework for quantifying the predictability of future cellular identity from present molecular states. By transforming developmental trajectories into predictability landscapes, FateLimit enables systematic comparison of commitment dynamics across biological systems and establishes prediction horizons as a quantitative measure of cell-fate determination.

Leigh syndrome is the most common pediatric mitochondrial encephalopathy, yet the physical mechanisms linking diverse pathogenic mutations to respiratory-chain failure remain poorly understood. Here we show that Leigh syndrome mutations are not randomly distributed within human mitochondrial Complex I but are preferentially enriched near the electron-transfer axis connecting flavin mononucleotide and iron–sulfur cofactors. By integrating structural mutation mapping with residue-level free-volume analysis, packing-density measurements, and a protein glass index (PGI), we identify a distinct class of mutation-associated microenvironments characterized by reduced free volume, elevated packing density, and increased structural constraint. These protein-glass-like microenvironments were associated with elevated reorganization-energy proxies and increased transport vulnerability. Structure-informed Marcus analyses further identified a dominant bottleneck that was primarily associated with reduced electronic coupling arising from donor–acceptor separation, while local microenvironmental constraints further amplified transport sensitivity. Structure-informed Marcus analyses identify the emergence of a dominant kinetic bottleneck within the iron–sulfur cluster network, and a Lindblad-based open quantum transport model indicates that local microenvironmental perturbations can propagate into network-level transport efficiency across the Complex I redox chain. Notably, pathogenic mutations preferentially accumulate in structural neighborhoods that are intrinsically sensitive to electron-transfer perturbation, suggesting that disease-associated variants may amplify pre-existing transport vulnerabilities embedded within the protein architecture. Collectively, our findings suggest a structural-energetic link between mutation landscapes, local protein-glass-like organization, and mitochondrial electron transport. We propose that Leigh syndrome can be viewed, in part, through a protein-glass lens in which pathogenic mutations preferentially map to structural-energy landscapes surrounding redox cofactors that are predicted to be vulnerableto electron-transfer perturbations. This framework provides a physical perspective for understanding genotype-to-phenotype convergence in mitochondrial disease and identifies local protein-glass analogous microenvironments as previously unrecognized determinants of respiratory-chain dysfunction.