Biology subjects

Qian, K.

Publications and source records attributed to Qian, K..

18 recordsLinked to original sources

bioRxiv2026

Systematic evaluation of 24 extraction and library preparation combinations for metagenomic sequencing of SARS-CoV-2 in saliva

Sequencing the respiratory tract transcriptome has the potential to provide insights into infectious pathogens and the hosts immune response. While DNA-based sequencing is more standard in clinical laboratories due to its stability, RNA assays offer unique advantages. RNA reflects dynamic physiological changes, and for RNA viruses, viral RNA particles directly represent copies of the viral genome, enabling greater diagnostic sensitivity. However, RNAs susceptibility to degradation remains a significant challenge, particularly in RNase-rich specimens like saliva. To address this, we conducted a systematic, combinatorial evaluation of 24 distinct mNGS workflows, crossing eight nucleic acid extraction methods with three RNA-Seq library preparation protocols. Remnant saliva samples (n = 6) were pooled and spiked with MS2 phage as a control. The SARS-CoV-2 virus was spiked into half of the samples, which were extracted using the eight different extraction methods (n = 3) and compared using RNA Integrity Number equivalent (RINe) scores and RNA concentration. The extracted RNA was then processed across the three library construction methods and subjected to short-read sequencing to assess all 24 combinations head-to-head. We compared methods based on viral read recovery and found that RINe and concentration did not correlate with viral detection. The Zymo Quick-RNA Magbead kit and the Tecan Revelo RNA-Seq High-Sensitivity RNA library kit were the extraction and library-preparation kits that yielded the most SARS-CoV-2 reads, respectively. Importantly, our combinatorial analysis revealed that any small variability attributable to different nucleic acid extraction methods was heavily overshadowed by differences in quality attributable to the RNA-Seq library preparation methods. These findings challenge the reliance on conventional RNA quality metrics for clinical metagenomics and underscore the need to redefine extraction quality standards for mNGS applications. IMPORTANCEmNGS is a powerful and unbiased approach towards pathogen detection that has mostly been applied to blood and cerebrospinal fluid samples. However mNGS has recently been applied to more areas including the respiratory pathogen detection space, with potential applications in both in-patient diagnostics and public health surveillance. Saliva samples are an ideal sample type for these use cases since they can be collected non-invasively. However, saliva is also a challenging sample type due to its high RNase activity and often yields low-quality nucleic acid. This study explores the feasibility of using saliva specimens in mNGS with contrived SARS-CoV-2 samples to optimize the combination of two factors: nucleic acid extraction and RNA-seq library preparation. Exploration in this area could enhance the sensitivity of saliva-based mNGS assays, with the goal of future expansion of this specimen type in clinical diagnostics and public health surveillance. Key PointsO_LIThe choice of RNA-Seq library preparation kit has a greater impact on pathogen detection than the nucleic acid extraction method. C_LIO_LIThe combination of Zymo Quick-RNA Magbead extraction kit and TECAN Revelo RNA-Seq High Sensitivity RNA library kit recovered the highest percentage of total SARS-CoV-2 reads. C_LIO_LIRNA quantity and RINe score do not correlate with viral read capture, indicating a need for an alternative metric to assess RNA quality for downstream mNGS clinical diagnostics. C_LI

genomics↗

bioRxiv2026

MetaSAG: A Tool for Multi-level Exploration and Taxonomic Analysis of Microbial Single-Amplified Genomes

Microbial single-amplified genome (SAG) sequencing technologies have elevated microbial research resolution to the single-cell level. However, neither upstream data processing nor downstream analysis has been fully developed, greatly limiting the research in strain level. Herein, we developed MetaSAG (Multi-level Exploration and Taxonomic Analysis of microbial Single-Amplified Genomes), which enables accurate and rapid taxonomic classification of microbial SAGs. MetaSAG outperforms existing method in species classification certainty, computational efficiency, and sensitivity of low abundance species identification. In addition, MetaSAG enables species-level functional analysis, as well as strain-level evolutionary analysis. With the help of MetaSAG, we discovered the parasitic relationship between phages and bacteria, identifying multiple susceptible bacteria and a broad spectrum of phages. Furthermore, we developed MetaK-Lytic (k-mer-based meta-learning framework to predict phage lytic ability) to achieve accurate prediction of phage lytic activity based on 31-mer short sequences, which is well adapted to the characteristics of incomplete SAG sequences. Overall, we offer a comprehensive integrated tool that can parse microbial SAG data from raw data to the strain level to decipher the functional ecology of microbial dark matter, with broad implications for microbial ecology and phage therapy (https://github.com/liangcheng-hrbmu/MetaSAG). Significance StatementThis work provides a comprehensive framework for high-resolution SAG data analysis. The developed pipeline improves taxonomic annotation sensitivity and speed. Strain-level tracking enables dynamic evolutionary and functional insights, while single-cell bacterial-virus network reconstruction reveals precise interaction patterns. The novel annotation-free short sequence-based MetaK-Lytic facilitates functional prediction of uncharacterized phage sequences. Integrated into the MetaSAG platform, these tools deliver a streamlined, multi-level solution for interpreting SAG data, advancing studies in microbial ecology, evolution, and virology.

microbiology↗

bioRxiv2026

Small brown planthopper infestation enhances it reproduction and insecticide tolerance by manipulating glucose distribution and levels in rice

The evolutionary arms race between plants and insects involves not only direct defense and counter-defense but also sophisticated resource manipulation. However, how herbivorous insects respond to host nutritional signals to modulate their fitness traits remains unclear. This study investigates how the small brown planthopper (SBPH, Laodelphax striatellus) manipulates host plant carbohydrate allocation, and to elucidate the molecular mechanisms by which the acquired glucose enhances SBPH fecundity and insecticide tolerance. Using molecular, pharmacological, and biochemical approaches, we found that SBPH infestation induced systemic carbohydrate reallocation in rice, elevating whole-plant glucose levels by promoting aerial accumulation while depleting root reserves. Host-derived glucose enhanced SBPH fecundity by activating the target of rapamycin (TOR) pathway, upregulating juvenile hormone (JH) signaling, and increasing vitellogenin production. For imidacloprid tolerance, glucose boosted glutathione S-transferase (GST) activity via two synergistic mechanisms: by upregulating glutamate cysteine ligase (GCL) to increase glutathione synthesis, and transcriptionally via the glucose-TOR-JH axis to induce LsGSTe1 (SBPH epsilon class GST) and LsGSTo1 (SBPH omega class GST) expression. Our findings establish host-derived glucose as a central signaling molecule that SBPH utilizes to modulate conserved pathways for simultaneous optimization of reproduction and insecticide resistance. This reveals a multifaceted nutrient-responsive mechanism in insect pests and identifies the glucose-TOR-JH axis as critical molecular targets for developing nutrient-based pest control strategies, such as disrupting insect nutrient-sensing pathways or modulating host carbohydrate metabolism. HighlightsO_LISBPH infestation elevates whole-plant glucose levels by promoting aerial accumulation while suppressing root abundance. C_LIO_LISBPH-induced glucose in the rice aerial tissues boosts reproduction of SBPH via TOR-JH-Vg pathway. C_LIO_LISBPH-induced glucose in the rice aerial tissues enhances imidacloprid tolerance of SBPH through GCL-GSH-GST and TOR-JH-GST axis. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/701313v2_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@39e084org.highwire.dtl.DTLVardef@1c310cborg.highwire.dtl.DTLVardef@1838575org.highwire.dtl.DTLVardef@1aedebe_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

Host EV derived lipids promote malaria parasite development via mosquito fatty acid oxidation

Malaria parasites must transition between mammals and mosquitoes, but how they adapt to such distinct environments is unclear. We show that blood from infected mammals buffers this environmental shift by packaging parasite-induced signals into extracellular vesicles (EVs). EVs internalized by mosquito midgut increases expression of VLCAD and raises levels of a 22-carbon fatty acid, driving fatty acid breakdown and acetyl-CoA production. The parasite exploits this metabolic shift to synthesize its own lipids during oocyst growth. Stable isotope tracing confirmed that13C4-labelled behenic acid is degraded and incorporated into parasite fatty acids. Lipidomic profiling further revealed that ceramides enriched in the EVs are key mediators. These findings identify a mechanism by which host bood-derived EVs reprogram mosquito lipid metabolism to promote parasite transmission.

microbiology↗

bioRxiv2025

Single-cell transcriptome sequencing for opening the blood-brain barrier through specific mode electroacupuncture stimulation

BackgroundThe blood-brain barrier (BBB) interferes with the treatment of central nervous system disorders owing to the complexity of its structure and restrictive function. Thus, it is challenging to develop central nervous system drug delivery strategies. Specific mode electroacupuncture (EA) stimulation can effectively open the BBB in rats. MethodsHere, we used single-cell RNA sequencing (scRNA-seq) to comprehensively map the cell population in the Sprague-Dawley rat cerebral cortex. ResultsWe identified 23 cell subsets and eight types of cells in the brain by cell annotation. scRNA-seq revealed transcriptional changes in the cerebral cortex under EA. ConclusionsOur findings offer valuable insights into the molecular and cellular modifications in the brain resulting from EA intervention and serve as a resource for drug delivery across healthy and diseased states. Innovative approaches to enhance BBB opening will lead to more effective therapeutic plans and enhanced drug delivery.

neuroscience↗

bioRxiv2025

Interdisciplinary Study on Drug-Induced-Phospholipidosis of Repurposing Libraries through Machine Learning and Experimental Evaluation in Different Cell Lines

Bigger PicturePhospholipidosis is a cellular condition characterized by the excessive accumulation of phospholipids within cells, that also can be induced by medications--especially those known as cationic amphiphilic drugs. While this phenomenon is not always harmful in itself, it can interfere with how cells respond in laboratory experiments and complicate the interpretation of drug screening results, leading to potential delays or failures in the development of new therapies. As the pharmaceutical industry increasingly relies on high-throughput screening and machine learning to accelerate early-stage drug discovery, there is growing interest in identifying compounds that may cause phospholipidosis before they move too far through the development pipeline. In response to this challenge, we at the EU-Horizon HLTH Remedi4ALL project compiled and analyzed one of the most comprehensive datasets to date--over 5,000 repurposed drugs tested across multiple cell lines--to better understand which compounds are likely to induce phospholipidosis. Using this dataset, we developed a machine learning model capable of predicting the risk of phospholipidosis based on a drugs chemical structure. By validating our approach across diverse experimental settings, our goal is to provide the broader scientific and pharmaceutical communities with a practical tool to flag problematic compounds early, ultimately helping bring safer, more effective treatments to patients faster. Highlights- A comprehensive dataset for a repurposing collection, comprising 5,228 compounds, for induction of phospholipidosis in A549-ACE2 and Vero-E6 cells -A machine learning model developed using in-house experimental phospholipidosis data across multiple cell lines -Exploration of chemical features that induce phospholipidosis and investigation of the relationships underlying different cell line susceptibilities -A case study: correlation of drug-induced phospholipidosis with antiviral compound effects against SARS-CoV-2 using phenotypic screening data Phospholipidosis (PLD), a cellular adverse effect that is, among others, caused by numerous cationic amphiphilic drugs. Interest is raised within pharma discovery to predict this phenomenon, as it can impact the outcome of phenotypic cellular screens and significantly delay drug development processes. The development of accurate and validated machine learning models for predicting drug-induced PLD across different cell lines and research centers could provide a valuable early application tool for the pharmaceutical industry, potentially accelerating drug discovery and reducing the risk of late-stage failures. We report here the assembly, curation, testing and modeling of one of the largest datasets of repurposed drugs (5K+) tested for PLD induction on different cell lines. A machine-learning classification method was developed and validated to predict whether molecules are prone to induce PLD effects when applied in cell-based screens. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/663638v1_ufig1.gif" ALT="Figure 1"> View larger version (67K): org.highwire.dtl.DTLVardef@1c4760org.highwire.dtl.DTLVardef@92084dorg.highwire.dtl.DTLVardef@15effbaorg.highwire.dtl.DTLVardef@1e73f4d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

molecular biology↗

bioRxiv2024

Dietary control of peripheral adipose storage capacity through membrane lipid remodelling

Complex genetic and dietary cues contribute to the development of obesity, but how these are integrated on a molecular level is incompletely understood. Here, we show that PPAR{gamma} supports hypertrophic expansion of adipose tissue via transcriptional control of LPCAT3, a membrane-bound O-acyltransferase that enriches diet-derived omega-6 (n-6) polyunsaturated fatty acids (PUFAs) in the phospholipidome. In high-fat diet-fed mice, lowering membrane n-6 PUFA levels by adipocyte-specific Lpcat3 knockout (Lpcat3AKO) or by dietary lipid manipulation leads to dysfunctional triglyceride (TG) storage, ectopic fat deposition and insulin resistance. Aberrant lipolysis of stored TGs in Lpcat3AKO adipose tissues instigates a non-canonical adaptive response that engages a futile lipid cycle to increase energy expenditure and limit further body weight gain. Mechanistically, we find that adipocyte LPCAT3 activity promotes TG storage by selectively enriching n-6 arachidonoyl-phosphatidylethanolamine at the ER-lipid droplet interface, which in turn favours the budding of large droplets that exhibit greater resistance to ATGL-dependent hydrolysis. Thus, our study highlights the PPAR{gamma}-LPCAT3 pathway as a molecular link between dietary n-6 PUFA intake, adipose expandability and systemic energy balance.

physiology↗

bioRxiv2024

Deep Learning Driven Cell-Type-Specific Embedding for Inference of Single-Cell Co-expression Networks

The inference of gene co-expression module in specific cell types is important for understanding of cell-type-specific biological processes. We have developed DeepCSCN, an unsupervised deep-learning framework, to infer gene co-expression modules from single-cell RNA sequencing (scRNA-seq) data. Utilizing a global-to-local network construction approach, DeepCSCN can infer co-expression at the whole sample level and construct cell-type-specific co-expression networks. Systematic evaluations on eight public scRNA-seq datasets show that DeepCSCN significantly outperforms eight existing methods in co-expression network construction. Furthermore, DeepCSCN effectively identifies cell-type-specific co-expression networks that are more enriched for cell-specific functional pathways compared to current methods. Finally, application of DeepCSCN on public scRNA-seq data revealed 280 cell-specific gene modules across 27 cell types, including epithelial cells, immune cells, and myonuclei, demonstrating its versatility and accuracy in elucidating cell-type-specific gene co-expression regulation. DeepCSCN offers a powerful tool for researchers to dissect the intricate gene co-expression networks within distinct cell types.

bioinformatics↗

bioRxiv2023

Machine assisted annotation in neuroanatomy

One of the important yet labor intensive tasks in neuroanatomy is the identification of select populations of cells. Current high-throughput techniques enable marking cells with histochemical fluorescent molecules as well as through the genetic expression of fluorescent proteins. Modern scanning microscopes allow high resolution multi-channel imaging of the mechanically or optically sectioned brain with thousands of marked cells per square millimeter. Manual identification of all marked cells is prohibitively time consuming. At the same time, simple segmentation algorithms suffer from high error rates and sensitivity to variation in fluorescent intensity and spatial distribution. We present a methodology that combines human judgement and machine learning that serves to significantly reduce the labor of the anatomist while improving the consistency of the annotation. As a demonstration, we analyzed murine brains with marked premotor neurons in the brainstem. We compared the error rate of our method to the disagreement rate among human anatomists. This comparison shows that our method can reduce the time to annotate by as much as ten-fold without significantly increasing the rate of errors. We show that our method achieves significant reduction in labor while achieving an accuracy that is similar to the level of agreement between different anatomists.

neuroscience↗

bioRxiv2023

Development of FERM domain protein-protein interaction inhibitors for MSN and CD44 as a potential therapeutic strategy for Alzheimer's disease.

Recent genome-wide association studies have revealed genetic risk factors for Alzheimers disease (AD) that are exclusively expressed in microglia within the brain. A proteomics approach identified moesin (MSN), a FERM (four-point-one ezrin radixin moesin) domain protein, and the receptor CD44 as hub proteins found within a co-expression module strongly linked to AD clinical and pathological traits as well as microglia. The FERM domain of MSN interacts with the phospholipid PIP2 and the cytoplasmic tails of receptors such as CD44. This study explored the feasibility of developing protein-protein interaction inhibitors that target the MSN-CD44 interaction. Structural and mutational analyses revealed that the FERM domain of MSN binds to CD44 by incorporating a beta strand within the F3 lobe. Phage-display studies identified an allosteric site located close to the PIP2 binding site in the FERM domain that affects CD44 binding within the F3 lobe. These findings support a model in which PIP2 binding to the FERM domain stimulates receptor tail binding through an allosteric mechanism that causes the F3 lobe to adopt an open conformation permissive for binding. High-throughput screening of a chemical library identified two compounds that disrupt the MSN-CD44 interaction, and one compound series was further optimized for biochemical activity, specificity, and solubility. The results suggest that the FERM domain holds potential as a drug development target. The small molecule preliminary leads generated from the study could serve as a foundation for additional medicinal chemistry effort with the goal of controlling microglial activity in AD by modifying the MSN-CD44 interaction.

molecular biology↗

bioRxiv2022

Simulation study of Cell Transmembrane Potential and Electroporation Induced by Time-Varying Magnetic Fields

In this work, an improved simulation model was proposed to assess the transmembrane potential (TMP) evolution on the cellular membrane exposed to time-varying magnetic fields (TMFs). Comparatively, we extended the research on TMP induced by TMF to the electroporation phenomenon by introducing the Smoluchowski function, thereby predicting the occurrence of electroporation. The simulation results based on our numerical model showed that with exposure to the sub-microsecond trapezoidal pulsed magnetic field (PMF), the pore density did not reach the conventional electroporation criterion (1014 m-2) even if the TMP exceeded the electroporation threshold ([~]1V); however, with the same energy import, it was easier for the nanosecond pulse to electroporate the membrane evidenced by higher pore density. Further, the capability of predicting the occurrence of electroporation was verified by extending our simulation model to compare experimental results. The comparative analysis showed that our simulation model has predictive and guiding significance for experimental studies and practical applications.

biophysics↗

bioRxiv2022

Tracking single cell evolution via clock-like chromatin accessibility

Single cell chromatin accessibility sequencing (scATAC) reconstructs developmental trajectory by phenotypic similarity. However, inferring the exact developmental trajectory is challenging. Here, we show a simple, accurate and phenotypic-neutral measure of cell developmental hierarchy - the fraction of accessible clock-like loci. As cells undergo mitosis, the heterogeneity of chromatin accessibility on clock-like loci is reduced, providing a measure of mitotic age. We developed a method, EpiTrace, that counts the fraction of opened clock-like loci from scATAC data to determine cell age and perform lineage tracing. EpiTrace works in various cell lineages and animal species, shows concordance with known developmental hierarchies, correlates well with DNA methylation-based clocks, and is complementary with mutation-based lineage tracing, RNA velocity, and stemness predictions. Applying EpiTrace to scATAC data revealed a multitude of novel biological insights with clinically relevant implications, ranging from hematopoiesis, organ development, tumor biology and immunity to cortical gyrification. Our work discovered a universal epigenomic hallmark during cellular development, which facilitates the study of cellular hierarchies and organismal aging.

bioinformatics↗

bioRxiv2021

Emergence of an adaptive epigenetic cell state in human bladder urothelial carcinoma evolution

Intratumor heterogeneity (ITH) of bladder cancer (BLCA) facilitates therapy resistance and immune evasion to affect clinical prognosis directly. However, the molecular and cellular mechanism generating ITH in BLCA remains elusive. Here we show that a TM4SF1-positive cancer subpopulation (TPCS) drives ITH diversification in BLCA. By extensive profiling of the epigenome and transcriptome of BLCA from 79 donors across all stages, we elucidated the evolution trajectories of luminal and basal BLCA. TPCS emerges from the basal trajectory and shows extensive transcriptional plasticity with a distinct epigenomic landscape. Clinically, TPCS were enriched in advanced stage patients and associated with poor prognosis. Our results showed how cancer adapts to its environment by adopting a stem cell-like epigenomic landscape.

cancer biology↗

bioRxiv2021

scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data

The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Even though different batch effect removal methods have been developed, none of the existing methods is suitable for het-erogeneous single-cell samples coming from multiple biological conditions. To address this challenge, we propose a method named scINSIGHT to learn coordinated gene expression patterns that are common among or specific to different biological conditions, offering a unique chance to identify cellular identities and key biological processes across single-cell samples. We have evaluated scINSIGHT in comparison with state-of-the-art methods using simulated and real data, which consistently demonstrate its improved performance. In addition, our results show the applicability of scINSIGHT in diverse biomedical and clinical problems.

bioinformatics↗

bioRxiv2021

A Chromosome-level Assembly of a Wild Castor Genome Provides New Insights into the Adaptive Evolution in a Tropical Desert

Wild castor grows in the high-altitude tropical desert of the African Plateau, a region known for high ultraviolet radiations, strong lights and extremely dry conditions. To investigate the potential genetic basis of adaptation to both highland and tropical deserts, we generated a chromosome-level genome sequence of the wild castor accession WT05, with genome size of 316 Mb and scaffold and contig N50 sizes of 31.93 Mb and 8.96 Mb, respectively. Compared with cultivated castor and other Euphorbiaceae species, the wild castor exhibits positive selection and gene family expansion for genes involved in DNA repair, photosynthesis and abiotic stress responses. Genetic variations associated with positive selection were identified in several key genes, such as LIG1, DDB2, and RECG1, involved in nucleotide excision repair. Moreover, a study of genomic diversity among wild and cultivated accessions revealed genomic regions containing selection signatures associated with the adaptation to extreme environments. The identification of the genes and alleles with selection signatures provides insights into the genetic mechanisms underlying the adaptation of wild castor to the high-altitude tropical desert and would facilitate direct improvement of modern castor varieties.

genomics↗

bioRxiv2020

CAR Macrophages for SARS-CoV-2 Immunotherapy

Targeted therapeutics for the treatment of coronavirus disease 2019 (COVID-19), especially severe cases, are currently lacking. As macrophages have unique effector functions as a first-line defense against invading pathogens, we genetically armed human macrophages with chimeric antigen receptors (CARs) to reprogram their phagocytic activity against SARS-CoV-2. After investigation of CAR constructs with different intracellular receptor domains, we found that although cytosolic domains from MERTK (CARMERTK) did not trigger antigen-specific cellular phagocytosis or killing effects, unlike those from MEGF10, FcR{gamma} and CD3{zeta} did, these CARs all mediated similar SARS-CoV-2 clearance in vitro. Notably, we showed that CARMERTK macrophages reduced the virion load without upregulation of proinflammatory cytokine expression. These results suggest that CARMERTK drives an immunologically silent scavenger effect in macrophages and pave the way for further investigation of CARs for the treatment of individuals with COVID-19, particularly those with severe cases at a high risk of hyperinflammation.

bioengineering↗

bioRxiv2020

Potent neutralization of 2019 novel coronavirus by recombinant ACE2-Ig

2019-nCoV, which is a novel coronavirus emerged in Wuhan, China, at the end of 2019, has caused at least infected 11,844 as of Feb 1, 2020. However, there is no specific antiviral treatment or vaccine currently. Very recently report had suggested that novel CoV would use the same cell entry receptor, ACE2, as the SARS-CoV. In this report, we generated a novel recombinant protein by connecting the extracellular domain of human ACE2 to the Fc region of the human immunoglobulin IgG1. An ACE2 mutant with low catalytic activity was also used in the study. The fusion proteins were then characterized. Both fusion proteins has high affinity binding to the receptor-binding domain (RBD) of SARS-CoV and 2019-nCoV and exerted desired pharmacological properties. Moreover, fusion proteins potently neutralized SARS-CoV and 2019-nCoV in vitro. As these fusion proteins exhibit cross-reactivity against coronaviruses, they could have potential applications for diagnosis, prophylaxis, and treatment of 2019-nCoV.

bioengineering↗

bioRxiv2018

The Dynamic Conformational Landscapes of the Protein Methyltransferase SETD8

Elucidating conformational heterogeneity of proteins is essential for understanding protein functions and developing exogenous ligands for chemical perturbation. While structural biology methods can provide atomic details of static protein structures, these approaches cannot in general resolve less populated, functionally relevant conformations and uncover conformational kinetics. Here we demonstrate a new paradigm for illuminating dynamic conformational landscapes of target proteins. SETD8 (Pr-SET7/SET8/KMT5A) is a biologically relevant protein lysine methyltransferase for in vivo monomethylation of histone H4 lysine 20 and nonhistone targets. Utilizing covalent chemical inhibitors and depleting native ligands to trap hidden high-energy conformational states, we obtained diverse novel X-ray structures of SETD8. These structures were used to seed massively distributed molecular simulations that generated six milliseconds of trajectory data of SETD8 in the presence or absence of its cofactor. We used an automated machine learning approach to reveal slow conformational motions and thus distinct conformational states of SETD8, and validated the resulting dynamic conformational landscapes with multiple biophysical methods. The resulting models provide unprecedented mechanistic insight into how protein dynamics plays a role in SAM binding and thus catalysis, and how this function can be modulated by diverse cancer-associated mutants. These findings set up the foundation for revealing enzymatic mechanisms and developing inhibitors in the context of conformational landscapes of target proteins.

biophysics↗