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Su, Z.

Publications and source records attributed to Su, Z..

At least 19 recordsLinked to original sources

Decoding the Physical Mechanisms of Protein-Small Molecule Association by Multiscale Kinetic Monte-Carlo Simulations

Protein-small molecule association involves multiple recognition stages governed by distinct physical interactions, yet resolving their kinetic contributions remains computationally challenging. Here, we develop a multiscale Monte Carlo framework that combines an atomistic, conformationally flexible ligand with a coarse-grained protein binding pocket and an atomistic steric envelope. Association is decomposed into three stages: molecular encounter, pocket entry, and productive binding. We evaluate the framework using ten protein-ligand complexes representing nine distinct targets, with experimentally derived association rates spanning nearly four orders of magnitude. Simulated pocket-entry rates exhibit the strongest correspondence with experimental kinetics (Pearson r = 0.617; Spearman {rho} = 0.758), whereas productive-binding rates show closer agreement in absolute magnitude. Systematic perturbations of electrostatic interactions, hydrophobic attraction, hydrogen bonding, and ligand torsional flexibility reveal stage-dependent sensitivities. Initial encounter is comparatively insensitive to these perturbations, while hydrophobic attraction and conformational flexibility influence pocket entry. Productive binding exhibits additional sensitivity to local interactions, with excessive hydrogen-bond strengthening potentially hindering native-pose formation. Probability-derived kinetic-stage barriers and normalized mechanism descriptors further reveal heterogeneous recognition behavior across protein targets. Together, this framework provides a computationally efficient and interpretable approach for investigating protein-ligand association pathways and their physical determinants across diverse molecular systems.

biophysics↗

Terminal Modification Reprograms Supramolecular Organization of Integrin Ligand Assemblies to Shape Cell Migration

Precise organization of bioactive ligands at material-cell interfaces is critical for directing integrin-mediated cell behavior, yet tuning supramolecular organization commonly necessitates orchestrated redesign of molecular composition or ligand sequence. Here we establish terminal modification as a minimal, sequence-preserving strategy to reprogram the organization and function of self-assembling integrin ligands. Using FFFIKVAV as a model system, N- and C-terminal modifications generated distinct supramolecular states with markedly different integrin activation and cell migration responses. Notably, stronger intermolecular association did not produce stronger biological activity, as N-terminal acetylation promoted scaffold consolidation while reducing the configurational freedom of the exposed IKVAV ligand and diminishing integrin activation and cell migration. Alongside modulating migration magnitude, terminal chemistry also diversified the cellular programs supporting migration, with the parent peptide and its C-terminally amidated analogue favoring protrusion-dominant and stress-fiber-dominant adhesion-cytoskeletal programs, respectively. These functional states were reproduced when the assemblies were introduced directly to pre-adherent cells, extending their activity beyond preformed material coatings. Our findings identify terminal chemistry as a compact molecular design variable that links supramolecular organization to ligand function while diversifying the biological output of a shared bioactive sequence.

bioengineering↗

Integrative Nanopore and Illumina sequencing reveals age-associated tRNA modification and CCA-tail dynamics in yeast

Aging is characterized by a progressive loss of proteostasis. Transfer RNAs (tRNAs) are essential regulators of translation, yet their dynamics during aging remain poorly understood due to challenges in sequencing highly modified RNAs. Here we present a benchmarked Nanopore direct RNA sequencing (RNA004 chemistry) resource that profiles the Saccharomyces cerevisiae tRNAome during replicative aging at single-molecule resolution. Using in vitro transcribed tRNA controls, we establish modification detection thresholds and validate key findings with orthogonal Illumina sequencing. While overall tRNA abundance remains largely stable, our resource reveals age-associated terminal A cleavage at the 3' CCA tail of mature tRNAs, targeted T-loop and anticodon modification changes, and single-molecule evidence of modification co-occurrence. This dataset provides a resource for exploring tRNA regulation, translation fidelity, and longevity.

genomics↗

Rapid Shift Toward Pulsed Field Ablation and Precision Risk Stratification in High-Impact Atrial Fibrillation Research

Conventional bibliometrics rely on lifetime citations, obscuring immediate shifts in cardiovascular research paradigms. To track emerging trends in atrial fibrillation management, we performed a comparative bibliometric analysis of the 50 highest-cited original research articles per year in OpenAlex topic T10065 across consecutive 2023 (Class of 2025) and 2024 (Class of 2026) publication cohorts. Articles were ranked using a fixed 18-month post-publication citation window, and extracted concepts were normalized into 719 canonical topics and 32 parent themes using large language model curation. Concept frequency tracking demonstrated a swift technological shift, with pulsed field ablation showing the largest topic frequency increase (+0.08, from 0.30 to 0.38) to become the leading canonical topic in 2024, displacing conventional thermal pulmonary vein isolation (-0.18, 0.40 to 0.22). Simultaneously, stroke prevention focus shifted toward refined predictive modeling, with increases in Risk Stratification and Predictive Models (+0.08, 0.30 to 0.38) and CHA2DS2-VASc scoring (+0.08, from 0.10 to 0.18). High-impact atrial fibrillation research is rapidly pivoting toward non-thermal ablation safety profiling and precision risk stratification, highlighting the utility of fixed-window concept mining for capturing real-time scientific evolution. Online explorer of the result is available at https://pri.pepkio.com.

bioinformatics↗

Temozolomide Induces Aberrant RNA Alkylation and Widespread Translational Repression

Temozolomide (TMZ) is a frontline alkylating chemotherapy, yet its direct impact on RNA modification and global translation dynamics remains poorly understood. Here, we demonstrate that TMZ induces pervasive RNA alkylation causing severe translational impairment. TMZ directly deposits aberrant methyl groups onto single-stranded mRNA in vitro, creating physical lesions that lower translational efficiency. In glioblastoma cells, acute TMZ exposure triggers a rapid, widespread accumulation of m7G on cellular RNAs, leading to the significant attenuation of global protein synthesis. Nanopore direct RNA sequencing identified distinct guanine-specific error signatures and sequence context preferences associated with TMZ-induced damage. Using a quantitative yeast spike-in ribosome profiling strategy, we mapped this translational repression at transcript-level, revealing a global downregulation of translational efficiency. This widespread repression disproportionately targets highly interconnected networks essential for cellular proliferation, specifically chromosome organization. We show that the severity of this translational repression is driven by a transcript's coding guanine density, stability and translation initiation speed. Together, our findings suggest that TMZ-induced alkylation targets stable, highly translated, guanine-rich transcripts. This establishes aberrant RNA methylation and subsequent translational arrest as a potential mechanism of temozolomide cytotoxicity.

biochemistry↗

Small-RNA Profiling Links 5'-tRNA Halves to Post-therapeutic Disease Persistence and Poor Patient Survival in Glioblastoma

Glioblastoma (GBM) is a highly lethal brain cancer with limited therapeutic durability, where the majority of patients develop recurrent or persistent disease after standard chemoradiotherapy. Meanwhile, tRNA-derived fragments (tRFs) have become increasingly relevant to cancer biology; however, their clinical relevance in GBM remains undefined. Here, we report that a specific family of tRFs, 5-tRNA halves (tiR5s) dominates the small RNA landscape of GBM patient tumors and associates with worse overall survival, post-therapeutic disease persistence, and pro-invasive proteogenomic pathways across two independent GBM patient cohorts. This association between elevated tiR5 levels and therapeutic resistance re-emerges in radiation-resistant GBM xenograft models. Our findings reveal that tiR5s are an underappreciated molecular feature of highly aggressive GBM tumors, supporting further investigation into their biological roles and prognostic utility in GBM. HighlightsO_LItiR5s are the predominant tRF family in primary GBM patient tumors C_LIO_LIElevated tiR5 expression distinguishes primary GBM tumors that develop persistent disease after first-line therapy C_LIO_LIRadiation-resistant GBM PDX models show elevated tiR5 expression C_LIO_LIElevated tiR5 expression associates with poor overall patient survival and pro-invasive molecular programs in GBM patient tumors C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/738483v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@3219d5org.highwire.dtl.DTLVardef@13e09b8org.highwire.dtl.DTLVardef@1f0328borg.highwire.dtl.DTLVardef@867b09_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

Spatial mitochondrial lineage tracing uncovers a premetastatic niche and microenvironment programmed fate switching in osteosarcoma

Osteosarcoma progression involves complex spatiotemporal dynamics, yet the lineage relationships underlying tumor cell fate decisions remain poorly understood. Here we integrate single cell and spatial transcriptomics and mitochondrial variants-based lineage tracing to map the evolution of a late-stage, non-metastatic osteosarcoma. Single cell pseudotemporal reconstruction of malignant cells reveals a stepwise cascade of transcriptional programs that instructs bifurcation of COL3A1 progenitors into ALPL osteoblastic and THY1 mesenchymal lineages. Critically, we demonstrate that the metastatic THY1 mesenchymal cell fate is not governed by cell-autonomous transcriptional programs alone; this pro-tumorigenic state requires strict spatial licensing via direct co-localization with PLVAP dysfunctional endothelia to form a pro-metastatic niche (THY1_Endo) enriched for PTN/NOTCH signaling. To distinguish clonal ancestry from microenvironmental plasticity, we leveraged spatially resolved somatic mitochondrial variants as endogenous lineage tracers. A spatial autocorrelation filter confirmed significant clonal spatial coherence (Z = 10.07, p {approx} 4x10-{superscript 2}). Phylogenetic reconstruction using a Wright-Fisher drift model coupled with a hidden Markov tree (WF-HMT) revealed a striking directional transition rate from COL3A1 to THY1 tumor cells (724.08) versus the reverse (4.09)--a [~]177-fold asymmetry--whereas the COL3A1 to ALPL transition rate was lower (0.47). Notably, drift-agnostic coarse VAF clustering failed to resolve ALPL versus THY1 bifurcation, underscoring that explicit modeling of stochastic mitochondrial drift is essential for uncovering directional fate locking. We propose a "time-space-lineage" relay model where sequential microenvironmental signals program tumor cell fate and pre-metastatic niche assembly through irreversible clonal commitment. This multi-dimensional framework bridges single-cell states, spatial organization, temporal dynamics, and clonal ancestry --underscoring the power of somatic mitochondrial variants to resolve clonal history and intercept cancer evolution.

bioinformatics↗

A Variant-Resistant Linear Epitope in the SARS-CoV-2 Nucleocapsid Protein for Antigen Detection

We previously generated a mouse monoclonal antibody, N179, against the SARS-CoV-2 nucleocapsid (N) protein and developed a colloidal gold-based immunochromatographic test strip. This assay achieved a detection limit of 2 ng/mL and displayed 98% concordance with RT-qPCR results. However, the precise epitope recognized by mAb N179 had not been defined. Using a panel of GST-fused N protein truncation fragments, we mapped the linear B-cell epitope recognized by mAb N179 to the flexible C-terminal tail of the N protein by Western blotting and ELISA. The minimal binding motif required for mAb N179 recognition was identified as 390QTVTLL395. Multiple sequence alignment of 11 representative SARS-CoV-2 lineages, including Alpha, Beta, Gamma, Delta, and Omicron subvariants BA.1, BA.2, and BA.3.2, revealed that this epitope was completely conserved across all variants analyzed. Stringent local pairwise alignment analysis using EMBOSS WATER further showed that the 390QTVTLL395 motif achieved a perfect 6/6 match exclusively in SARS-CoV-2; no identical sequence was detected in the seven common human coronaviruses, four influenza viruses, or five bat coronaviruses examined. Structural prediction analyses indicated that this region is surface-exposed and possesses a strong linear B-cell epitope propensity. Together, these findings identify 390QTVTLL395 as a specific molecular signature of SARS-CoV-2 among the viruses analyzed. Our results provide an epitope-level explanation for the sustained diagnostic reliability of the mAb N179-based assay against emerging variants, clarify the molecular basis for its lack of cross-reactivity, and may inform the rational design of SARS-CoV-2 diagnostics targeting conserved, mutation-resistant epitopes. ImportanceWe identified the exact nucleocapsid protein epitope recognized by monoclonal antibody N179, a diagnostic antibody used in a colloidal gold rapid assay. The identified 390QTVTLL395 motif at residues 390 to 395 was conserved among the SARS-CoV-2 variants analyzed and was not present as an identical continuous sequence in the related respiratory viruses examined. This work supports precise epitope mapping as a useful strategy for evaluating and revalidating diagnostic antibodies as respiratory viruses evolve.

microbiology↗

Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis

Cancer genomics and diagnostics is a rapidly evolving field in which identifying which topics attract early citation prominence can inform laboratory investment, clinical translation, and research strategy. We developed a bibliometric framework to identify and characterize the most influential recent publications in this domain across two consecutive annual cohorts. Using a mathematically exact threshold-expansion algorithm, we ranked over 10,000 OpenAlex-indexed research articles per cohort by 18-month post-publication citation count. Large language model (LLM)-based topical relevance filtering yielded 50 substantively on-topic papers per cohort (100 total). LLM-based concept extraction and a two-stage, embedding-guided normalization pipeline produced 1,090 canonical concepts organized into 77 parent themes, enabling structured cross-cohort comparison of paper-level concept prevalence. The most cited papers in both cohorts were large-scale genomic infrastructure resources rather than single-disease mechanistic studies. Between consecutive cohorts, normalized frequencies increased most for clonal evolution and intratumoral heterogeneity, single-cell and spatial omics technologies, spatial transcriptomics, immune cell infiltration, and copy number variation, while liquid biopsy and ctDNA-related themes showed the largest declines. These findings indicate that early citation impact in cancer genomics is shifting toward integrative, spatially resolved, and heterogeneity-aware research, and demonstrate that LLM-augmented citation ranking provides a replicable, semantically enriched lens for monitoring thematic evolution in precision oncology. A web interface for exploring the results is available at https://pri.pepkio.com/.

bioinformatics↗

Overexpression of magA in Acidithiobacillus ferrooxidans increases magnetosome production and pyrite bioleaching

Acidithiobacillus ferrooxidans, a chemolithoautotrophic iron- and sulfur-oxidizing acidophile, is a key contributor to industrial-scale copper metal bioleaching. These cells naturally produce magnetosomes, and they may serve as an emerging platform for magnetosome bioproduction, as magnetotactic bacteria (MTB) are difficult to cultivate and to genetically modify. Here we manipulated the expression of the endogenous homologs to the magA and mamB genes in A. ferrooxidans, which are implicated in iron transport required for magnetosome synthesis. Modulation of mamB had no impact on cell behavior. Overexpression of magA increased magnetosome formation and magnetic responsiveness and theses effects were attenuated by CRISPRi knockdown of magA. The augmented magnetosome formation in the magA overexpression cells also led to enhanced bioleaching of pyrite, which is weakly paramagnetic, and this could be further enhanced by addition of an external magnetic field. These results confirm that magA plays a critical role in magnetosome formation in A. ferrooxidans and that magnetosome expression can be enhanced through genetic engineering. In addition, these results demonstrate the potential to improve metal sulfide bioleaching through the manipulation of genes involved in magnetosome formation. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/727969v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1ac1822org.highwire.dtl.DTLVardef@1649140org.highwire.dtl.DTLVardef@17c0eccorg.highwire.dtl.DTLVardef@1afa828_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

TRMT6/61A-mediated m1A methylation facilitates human pre-tRNA maturation and prevents surveillance by XRN2

Transfer RNAs (tRNAs) are dynamically regulated by RNA modifications. The conserved TRMT6/61A catalyzes m1A (N1-methyladenosine) deposition at position 58. While TRMT6/61A dysregulation is linked to human diseases, its downstream processing consequences and molecular surveillance mechanisms remain unclear. Here we demonstrate that TRMT6/61A installs m1 A on precursor tRNAs prior to processing. Utilizing a dTAG rapid depletion system, we show that acute loss of TRMT6/61A swiftly reprograms the human tRNAome. Although elongator tRNA fluctuations are buffered by isodecoder redundancy, hypomethylated tRNAiMet is selectively and rapidly degraded by the exoribonuclease XRN2, reducing global protein synthesis and activating ATF4 expression. Furthermore, m1A58 is a prerequisite for tRNA end processing; its absence leads to the aberrant accumulation of unprocessed pre-tRNAs and disrupted tRNA-derived fragment (tRF) populations. Mechanistically, TRMT6/61A facilitates in vitro RNase Z cleavage, likely by promoting proper pre-tRNA folding. Lastly, XRN2 inhibition rescues tRNAiMet levels and reverses growth defects, identifying the XRN2-mediated surveillance of tRNAiMet as a primary driver of the cellular pathology. Collectively, our results uncover a pivotal role for TRMT6/61A-dependent m1A in human tRNA maturation and define the molecular checkpoints essential for translational homeostasis.

genetics↗

MISSTE: a multiscale integrative spatial simulator for understanding the mechanisms underlying tissue ecosystems

Multiscale tissue ecosystems are governed by coupled intracellular decision-making, cell-cell interactions, and spatially structured microenvironmental signals, yet these scales are often studied separately. Here we present MISSTE, a modular framework that integrates Boolean intracellular state logic, agent-based modeling, and partial differential equation fields within a unified spatial simulation architecture. As a proof of concept, we applied MISSTE to CAR-T therapy in a solid tumor microenvironment. The model recapitulated emergent features of CAR-T behavior, including limited tumor penetration, stromal suppression, localized cytokine remodeling, hypoxia-associated constraint, and progressive functional exhaustion. Comparison of baseline and optimized conditions showed that coordinated enhancement of interaction range, migration, and cytotoxic function improved immune persistence and partial tumor control. Systematic parameter scans further identified effective immune-tumor contact as a stronger determinant of outcome than killing strength alone, highlighting spatial access as the dominant bottleneck. Guided by these results, we designed sequential intervention strategies and found that time-ordered enhancement of infiltration, killing, and late functional protection outperformed a static optimized regime. Together, these results establish MISSTE as a generalizable multiscale methodology for dissecting tissue ecosystems and for generating mechanistically grounded strategies for engineered cellular therapy design.

bioinformatics↗

Topographic structure and function of locus coeruleus norepinephrine neurons

Norepinephrine (NE) is released throughout most of the central nervous system by neurons in the locus coeruleus (LC). We found a relationship between the morphologies, gene expression, and activity of LC-NE neurons in mice. Axonal projections of individual neurons were extensive but largely confined to subsets of brain regions. Axonal projections and graded gene expression correlated with locations of cell bodies in LC. In a behavioral task requiring ongoing learning from actions, neurons in dorsal LC projecting to the cerebral cortex were excited when mice made a different choice from the previous one and by reward prediction errors, a signal driving learning. Background activity of neurons in ventral LC was higher when mice ignored stimuli indicating potential reward availability. These observations reveal a topographically organized structure and function of a neurotransmitter system and show that it contains learning signals for flexible behavior.

neuroscience↗

OPTIMIS: Optimizing Personalized Therapies through Integrated Multiscale Intelligent Simulation

Controlling complex biological systems across multiple scales remains a major challenge in computational medicine, because whole-body disease behavior is closely shaped by noisy cellular events at much smaller scales. Standard deterministic models often miss this molecular variability, while fully stochastic simulations are too slow for the repeated, high-throughput interactions needed to train artificial intelligence. To address this problem, we developed a new AI-based framework that combines a discrete stochastic Gillespie algorithm for microscale receptor dynamics with continuous, nonlinear ordinary differential equations for systemic macroscale behavior. To reach the speed needed for deep reinforcement learning (RL), we compress this hybrid system into a differentiable Neural ODE surrogate that acts as a fast digital twin. As a proof of concept, we applied this framework to engineered cellular therapy and used RL agents to learn dynamic, closed-loop treatment policies inside the surrogate environment. By tracking microscopic, unpredictable cellular activity as an early-warning signal, the AI learned to continuously adjust the drug dose--anticipating and stopping dangerous immune reactions before they could spiral out of control. This computational advance improved successful control rates to more than 70% in highly unstable simulated phenotypes and provides a practical, general framework for adaptive intervention in multiscale biological systems.

bioinformatics↗

Multiscale conformational sampling of multidomain fusion proteins by a physics informed diffusion model

Multidomain fusion proteins, such as bispecific antibodies, rely on highly flexible linker regions for their therapeutic efficacy. Characterizing these vast conformational ensembles is crucial for rational drug design; however, while all-atom molecular dynamics (MD) is the traditional gold standard, its immense computational cost makes simulating large-scale domain motions prohibitive. Recently, deep generative diffusion models have emerged as a rapid alternative for sampling protein dynamics. Yet, being trained primarily on massive databases of structured, static domains, these generic models often lack the biophysical constraints required to thoroughly sample the large-scale dynamics of highly flexible multidomain architectures. To overcome this, we leverage microsecond MD trajectories of a multidomain protein construct with various linkers to train a multiscale diffusion framework utilizing an Equivariant Graph Neural Network (EGNN). To efficiently model the dynamics of the large molecular complexes, we employ a coarse-grained spatial graph that condenses rigid domains into center-of-mass anchors while preserving explicit backbone resolution for the flexible linker. By further integrating foundational rules in biophysics directly into both the training objective and the inference process, our model generates high-fidelity conformational ensembles that reproduce the thermodynamic distributions of long-timescale MD. This physics-informed approach provides a mathematically stable, highly scalable platform for the rapid multiscale characterization of flexible biologics, significantly accelerating the rational design of fusion protein therapeutics.

bioinformatics↗

Molecular Glue-Induced Homodimerization Drives Targeted CRBN Autodegradation

Molecular glue degraders (MGDs) offer a sophisticated, proximity-based approach to protein modulation. In this study, we introduce LJY-3-60, a novel proximity-inducing agent that unexpectedly triggers the potent and selective autodegradation of CRBN. Evidence from CRISPR-Cas9 screening and IP-MS reveals that this degradation process is strictly governed by the intrinsic CRL4CRBN machinery, independent of any extrinsic E3 recruitment. Through a combination of cellular and biophysical characterizations, we demonstrate that LJY-3-60 acts as a molecular bridge to template CRBN homodimerization. This mechanism is unequivocally elucidated by the atomic-resolution co-crystal structure of the CRBNMidi-LJY-3-60 complex. The structure explicitly delineates the homodimerization interface, revealing how the ligand reorganizes the protein surface to stabilize a non-canonical architecture that drives trans-autoubiquitination and subsequent proteasomal degradation. Furthermore, LJY-3-60 serves as a highly effective, controllable off-switch to mitigate PROTAC-induced toxicity. Ultimately, this work delivers a robust chemical tool for modulating CRBN stability. By demonstrating how a small molecule can functionally mimic an endogenous E3 substrates degron to catalyse targeted autodegradation, this study establishes a rational structural framework for designing the next generation of self-destructive modulators in targeted protein degradation (TPD) therapeutics.

biochemistry↗

rRNA Expansion Segments Mediate Ribosome Dimerization as a Conserved Stress Response

Inhibition of mRNA translation is a common feature in proteostatic stress cellular responses. Puromycin, a widely used compound for studying translation, disrupts protein synthesis by mimicking the 3 end of aminoacyl-tRNAs. Despite its extensive use as a research tool to probe the connection between translation activity and various physiological and pathological states, the cellular response associated with puromycin-induced translation stress remains incompletely understood. Utilizing in situ cellular electron tomography and topology analysis, we visualized the translation machinery at high resolution. Our analysis revealed that puromycin-treated neuronal cells exhibit an accumulation of "idle ribosomes" characterized by the binding of eIF5A, indicating a close association of this factor with translationally inactive ribosome states under stress. Additionally, the idle ribosomes formed dimeric complexes mediated by ribosomal RNA expansion segments, suggesting an evolved mechanism involving these regions in translation hibernating and protecting idle ribosomes. We show that the hibernating disome formation is not unique to puromycin administration but represents a conserved mechanism as a response to different cellular stressors including those associated with endoplasmic reticulum (ER) stress and amino acid depletion. Altogether, our findings shed light on previously unexplored aspects regarding unique states of mammalian ribosome hibernation, and collectively offers new avenues for understanding the correlation of cellular stress response and the regulation of protein synthesis.

molecular biology↗

The genetic repertoire of the deep sea: from sequence to structure and function

The deep sea as the largest and maybe most hostile environment on Earth is still underexplored especially regarding its genetic repertoire. Yet, previous work has revealed significant habitat-specific deep-sea biodiversity. Here, we present an integrated deep-sea genetic dataset comprising 502 million nonredundant genes from 2,138 samples and 2.4 million predicted structures, and used it to link specific protein structures with genetic variants associated with life in the deep sea and to assess their biotechnology potential. Combining global sequence analysis with biophysical and biochemical measurements revealed unprecedented sequence diversity, yet substantial structural conservation of proteins. Especially proteins involved in replication, recombination, and repair were identified to be under rapid evolution and with specialized properties. Among these, a structurally divergent helicase exhibited advantages in controlling nanopore sequencing speed. Thus, our work positions the deep sea as a unique evolutionary engine that generates and hosts genetic diversity and bridges genetic knowledge with biotechnology.

bioinformatics↗