Biology subjects

Hong, L.

Publications and source records attributed to Hong, L..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Rewiring of 3D chromatin topology orchestrates transcriptional reprogramming in muscle fiber-type specification and transformation

The composition of muscle fibers, characterized by distinct contractile and metabolic properties, significantly influences meat quality and glucose homeostasis. However, the mechanisms by which three-dimensional (3D) genome topology integrates with epigenetic states to regulate muscle fiber specification and transformation remain poorly understood. Here, we present an integrative analysis of the transcriptome, epigenome, and 3D genome architecture in the slow-twitch glycolytic extensor digitorum longus (EDL) and fast-twitch oxidative soleus (SOL) muscles. Global remodeling of enhancer-promoter (E-P) interactions emerged as a central driver of transcriptional reprogramming associated with muscle contraction and glucose metabolism. We identified tissue-specific super-enhancers (SEs) that regulate muscle fiber-type specification through cooperation of chromatin looping and transcription factors such as KLF5. Notably, the SE-driven activation of STARD7 facilitated the transformation of glycolytic fibers into oxidative fibers by mitigating reactive oxygen species levels and suppressing ERK MAPK signaling. This study elucidates the principles of 3D genome organization in the epigenetic regulation of muscle fiber specification and transformation, providing a foundation for novel therapeutic strategies targeting metabolic disorders and enhancing meat quality. O_FIG O_LINKSMALLFIG WIDTH=142 HEIGHT=200 SRC="FIGDIR/small/643571v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@990eecorg.highwire.dtl.DTLVardef@a2b491org.highwire.dtl.DTLVardef@1fcd142org.highwire.dtl.DTLVardef@10e4d47_HPS_FORMAT_FIGEXP M_FIG Schematic overview of the study. C_FIG

developmental biology↗

bioRxiv2025

Unveiling Protein-DNA Interdependency: Harnessing Unified Multimodal Sequence Modeling, Understanding and Generation

The interdependent orchestration of proteins, coding DNA sequences (CDS), and adjacent regulatory DNA elements underpins biological functionality, where evolutionary constraints shape codon usage, transcriptional regulation, and structural stability. We present ProDMM, a multimodal framework that deciphers these systemic relationships through unified sequence modeling. Integrating a context-aware encoder with conditional generation, our architecture captures codependent patterns between protein motifs and cis-regulatory syntax. ProDMMs encoder demonstrates superior predictive capabilities in forecasting pathway-level phenotypes and protein expression profiles, outperforming conventional single-modality approaches. Its decoder generates synthetically optimized regulatory elements by resolving spatial dependencies between coding sequences and adjacent non-coding regions, enabling transcriptional fine-tuning unavailable to sequence-agnostic methods. By resolving how localized CDS-NCDS-protein interdependencies propagate to system-level phenotypes, ProDMM establishes a paradigm for holistic biological sequence engineering. This approach enables coordinated optimization of translation efficiency, enzymatic activity, and pathway flux, offering transformative potential for designing tailored biosynthesis systems and advancing sustainable bioproduction strategies.

bioinformatics↗

bioRxiv2025

Epidermal Cell Dynamics Regulates Rice Lamina Joint Morphogenesis and Leaf Angle Formation through OsZHD1 and OsZHD2 Regulation

The lamina joint is a critical determinant of leaf angle and crop architecture. While epidermal cells play a fundamental role in organ morphogenesis, influencing the overall shape and function of plants, their impact on lamina joint morphology has been largely overlooked. Here, we tracked the cellular dynamics of the rice lamina joint epidermis during leaf angle formation. We found that the asymmetric elongation between the lateral and medial edges, determined by spatial differences in the longitudinal elongation and number of epidermal cells, was a key factor in leaf angle formation. Mutations in the homeobox genes OsZHD1 and OsZHD2 disrupted the growth patterns of epidermal cells in the lamina joint, resulting in a decreased leaf angle. Restoring OsZHD1 expression in the epidermis reverted the reduced leaf angle phenotype of oszhd1 oszhd2, highlighting the importance of epidermis development for lamina joint morphogenesis. Transcriptome data suggested that OsZHD1 and OsZHD2 were involved in auxin synthesis, which modulates leaf angle by inhibiting the growth of lamina joint epidermal cells. This study underscores the significance of epidermal cells in shaping the lamina joint and elucidates the critical role of OsZHD1 and OsZHD2 in regulating epidermal cell behavior and leaf angle formation.

plant biology↗

bioRxiv2025

Discovery of Expression-Governing Residues in Proteins

Understanding how amino acids influence protein expression is crucial for advancements in biotechnology and synthetic biology. In this study, we introduce Venus-TIGER, a deep learning model designed to accurately identify amino acids critical for expression. By constructing a two-dimensional matrix that links model representations to experimental fitness, Venus-TIGER achieves improved predictive accuracy and enhanced extrapolation capability. We validated our approach on both public deep mutational scanning datasets and low-throughput experimental datasets, demonstrating notable performance compared to traditional methods. Venus-TIGER exhibits robust trans-ferability in zero-shot predicting scenarios and enhanced predictive performance in few-shot learning, even with limited experimental data. This capability is particularly valuable for protein design aimed at enhancing expression, where generating large datasets can be costly and time-consuming. Additionally, we conducted a statistical analysis to identify expression-associated features, such as sequence and structural preferences, distinguishing between those linked to high and low expression. Our investigation also revealed a correlation among stability, activity and expression, providing insight into their interconnected roles and underlying mechanisms.

bioengineering↗

bioRxiv2024

Harnessing Protein Language Model for Structure-Based Discovery of Highly Efficient and Robust PET Hydrolases

Plastic waste, particularly polyethylene terephthalate (PET), presents significant environmental challenges, prompting extensive research into enzymatic biodegradation. Existing PET hydrolases are limited to a narrow sequence space and demonstrate insufficient performance for biodegradation. This study introduces a novel discovery pipeline that combines protein language models (PLMs) with a structural representation tree to identify enzymes based on structural similarity. Using the crystal structure of IsPETase as a template, we employed PLMs and a representation tree to efficiently search and cluster target proteins. Screening of candidate proteins was further refined using PLM-based assessments of solubility and thermal stability. Biochemical experiments showed that 14 of 34 candidates exhibited PET degradation activity across a temperature range of 30-60 {degrees}C. Notably, we identified a PET hydrolase -KbPETase, which possesses a melting temperature 32 {degrees}C higher than that of IsPETase and exhibits the highest PET degradation activity within 30-50 {degrees}C compared to other wild-type PETases. KbPETase also shows higher catalytic efficiency than FastPETase. X-ray crystallography and molecular dynamics simulations further revealed that KbPETase has a conserved catalytic domain and enhanced intramolecular interactions. This work develops a novel deep learning approach to discover natural PETases with enhanced functions.

molecular biology↗

bioRxiv2024

Programmable protein degraders enable selective knockdown of pathogenic β-catenin subpopulations in vitro and in vivo

Aberrant activation of Wnt signaling results in unregulated accumulation of cytosolic {beta}-catenin, which subsequently enters the nucleus and promotes transcription of genes that contribute to cellular proliferation and malignancy. Here, we sought to eliminate pathogenic {beta}-catenin from the cytosol using designer ubiquibodies (uAbs), chimeric proteins composed of an E3 ubiquitin ligase and a target-binding domain that redirect intracellular proteins to the proteasome for degradation. To accelerate uAb development, we leveraged a protein language model (pLM)-driven algorithm called SaLT&PepPr to computationally design "guide" peptides with affinity for {beta}-catenin, which were subsequently fused to the catalytic domain of a human E3 called C-terminus of Hsp70-interacting protein (CHIP). Expression of the resulting peptide-guided uAbs in colorectal cancer cells led to the identification of several designs that significantly reduced the abnormally stable pool of free {beta}-catenin in the cytosol and nucleus while preserving the normal membrane-associated subpopulation. This selective knockdown of pathogenic {beta}-catenin suppressed Wnt/{beta}-catenin signaling and impaired tumor cell survival and proliferation. Furthermore, one of the best degraders selectively decreased cytosolic but not membrane-associated {beta}-catenin levels in livers of BALB/c mice following delivery as a lipid nanoparticle (LNP)-encapsulated mRNA. Collectively, these findings reveal the unique ability of uAbs to selectively eradicate abnormal proteins in vitro and in vivo and open the door to peptide-programmable biologic modulators of other disease-causing proteins.

bioengineering↗

bioRxiv2024

A Watershed Algorithm GUI for Personalized fMRI-guided rTMS Target

Personalized repetitive transcranial magnetic stimulation (rTMS) increasingly relies on resting-state functional magnetic resonance imaging (fMRI) to select stimulation sites, yet most pipelines depend on user-defined thresholds and atlas masks, which can shift individualized targets. We propose a watershed-based approach, implemented in a graphical user interface, that performs threshold-independent segmentation of functional images to support rTMS target localization. As a proof-of-concept, we focused on Alzheimers disease-related circuits within the default mode network, designating the posterior cingulate cortex (PCC) as the deep effective region and the inferior parietal lobule (IPL) as the superficial stimulation target. In a cohort of 21 healthy participants, quantitative comparison with a conventional threshold-based, mask-constrained peak strategy revealed high concordance for PCC peaks but a median spatial displacement of 6.0 mm (95% CI: 0.0-12.7 mm) for IPL targets. Qualitative examples further illustrate that watershed segmentation reduces bias from neighboring functional clusters, truncation by atlas boundaries, and ambiguity among multiple local peaks. By decoupling target definition from user-chosen thresholds and packaging the method in an accessible toolbox, this framework offers a generalizable tool for individualized fMRI-guided rTMS.

neuroscience↗

bioRxiv2024

How to select the best zero-shot model for the viral proteins?

Predicting the fitness of viral proteins holds notable implications for understanding viral evolution, advancing fundamental biological research, and informing drug discovery. However, the considerable variability and evolution of viral proteins make predicting mutant fitness a major challenge. This study introduces the ProPEC, a Perplexity-based Ensemble Model, aimed at improving the performance of zero-shot predictions for protein fitness across diverse viral datasets. We selected five representative pretrained language models (PLMs) as base models. ProPEC, which integrates perplexity-weighted scores from these PLMs with GEMME, demonstrates superior performance compared to individual models. Through parameter sensitivity analysis, we highlight the robustness of perplexity-based model selection in ProPEC. Additionally, a case study on T7 RNA polymerase activity dataset underscores ProPECs predictive capabilities. These findings suggest that ProPEC offers an effective approach for advancing viral protein fitness prediction, providing valuable insights for virology research and therapeutic development. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/616860v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@30be8forg.highwire.dtl.DTLVardef@2ebf35org.highwire.dtl.DTLVardef@10b517eorg.highwire.dtl.DTLVardef@134681_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Unlearning Virus Knowledge Toward Safe and Responsible Mutation Effect Predictions

AO_SCPLOWBSTRACTC_SCPLOWPre-trained deep protein models have become essential tools in fields such as biomedical research, enzyme engineering, and therapeutics due to their ability to predict and optimize protein properties effectively. However, the diverse and broad training data used to enhance the generalizability of these models may also inadvertently introduce ethical risks and pose biosafety concerns, such as the enhancement of harmful viral properties like transmissibility or drug resistance. To address this issue, we introduce a novel approach using knowledge unlearning to selectively remove virus-related knowledge while retaining other useful capabilities. We propose a learning scheme, PROEDIT, for editing a pre-trained protein language model toward safe and responsible mutation effect prediction. Extensive validation on open benchmarks demonstrates that PROEDIT significantly reduces the models ability to enhance the properties of virus mutants without compromising its performance on non-virus proteins. As the first thorough exploration of safety issues in deep learning solutions for protein engineering, this study provides a foundational step toward ethical and responsible AI in biology.

bioinformatics↗

bioRxiv2024

PRESSED FLOWER works downstream of ASYMMETRIC LEAVES 2 to affect sepal flatness in Arabidopsis

The development of flattened organs such as leaves and sepals is essential for proper plant function. While much research has focused on leaf flatness, little is known about how sepals achieve flat organ morphology. Previous study has shown that in Arabidopsis an ASYMMETRIC LEAVES 2 (AS2) gene mutation as2-7D causes ectopic AS2 expression on the abaxial sepal epidermis, which leads to growth discoordination between the two sides of sepals, resulting in outgrowth formation on abaxial sepal epidermis and sepal flatness disruption. Here we report that the PRESSED FLOWER (PRS) works downstream of AS2 in affecting sepal flatness. Genetic analysis showed that PRS mutations suppressed the outgrowth formation on the abaxial sepal epidermis in as2-7D mutant. Through tracking the PRS expression dynamics at a cellular resolution throughout the early developmental stages in WT and as2-7D sepals, we found that on the abaxial epidermis of as2-7D sepals, ectopic AS2 expression up-regulated PRS expression, leading to the epidermal outgrowth initiation. AS2 affected PRS activity on multiple levels: AS2 activated PRS expression through direct binding to PRS promoter region; AS2 also physically interacted with PRS. Our study highlights the complex interplay between AS2 and PRS in modulating sepal flatness.

developmental biology↗

bioRxiv2024

Structural Characterization of mRNA Lipid Nanoparticles in the Presence of Intrinsic Drug-free Lipid Nanoparticles

Lipid nanoparticles (LNPs) have emerged as a versatile platform for mRNA delivery across a range of applications, including disease prevention, cancer immunotherapy, and gene editing. Structural models of mRNA-containing lipid nanoparticles (mRNA-LNPs) have also been proposed based on characterization of samples by using various advanced techniques. Among these, small angle neutron scattering (SANS) has proven essential for elucidating the lipid distribution within mRNA-LNPs, a factor crucial to both their preparation and efficacy. However, recent findings suggest that the mRNA-LNP samples prepared via commercial microfluidic techniques may contain a substantial fraction of drug-free LNPs, casting doubt on the validity of earlier structural models. In this study, we employed contrast variation SANS to characterize both drug-free LNPs and our mRNA-LNP sample, and quantified the proportion of drug-free LNPs present to be [~]30% in our mRNA-LNP sample using nano flow cytometry. By removing the contributions of drug-free LNPs from the SANS data of our mRNA-LNP sample, we were able to precisely characterize the structure of mRNA-LNPs. Consequently, we proposed structural models for both drug-free LNPs and mRNA-LNPs. Notably, our analysis revealed similar lipid distributions and shell thicknesses between the two particle types, while the solvent content in mRNA-LNPs was significantly higher, leading to a larger core size. This work not only offers a method for accurately characterizing the structure of mRNA-LNPs, but also establishes criteria for selecting appropriate analytical techniques based on the structural parameters of interest. Therefore, our findings hold significant implications for the mechanistic understanding and quality control of mRNA-based vaccines. SignificancePrecise structural determination of mRNA-containing lipid nanoparticles (mRNA-LNPs) is vital for mechanistic insights into their preparation, delivery, immunogenicity, and storage, which are critical to the development of mRNA-based vaccines. However, most previous studies overlooked the substantial presence of drug-free LNPs within these samples. Here, we identified that approximately 30% of the nanoparticles in our mRNA-LNP sample were drug-free. By integrating contrast variation small angle neutron scattering (SANS) data from both drug-free LNPs and mRNA-LNPs, we developed structural models for both particle types, and provided a guidance for characterization technique selection based on concerned structural features. Beyond mechanistic insight on structure, our approach offers a robust method for quality assessment and process monitoring in mRNA-based vaccine production.

biophysics↗

bioRxiv2024

AI-enabled Alkaline-resistant Evolution of Protein to Apply in Mass Production

Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.

bioinformatics↗

bioRxiv2024

Revealing a Correlation between Structure and in vitro Activity of mRNA Lipid Nanoparticles

mRNA-loaded lipid nanoparticles (mRNA-LNPs) represent a promising platform for disease prevention, cancer immunotherapy, and gene editing, etc. Despite the success of mRNA-LNPs based vaccines, the relationship between their structure and efficacy remains elusive. Here, we generated a series of mRNA-LNPs with varied structural properties and in vitro cellular activities by altering their processing and storage conditions to investigate the structure-activity relationship (SAR). Our findings revealed a moderate anticorrelation between particle size distribution and in vitro activity. Importantly, the intensity of a characteristic peak, as detected by small angle X-ray scattering (SAXS), demonstrated a strong correlation with in vitro activity while the encapsulation efficiency is high. Additionally, the peak width and area were moderately correlated and anticorrelated with activity, respectively. These observations suggest that a more ordered internal structure is likely associated with enhanced in vitro activity of mRNA-LNPs. Further analysis using 31P nuclear magnetic resonance indicated that lyophilization might induce phase separation of mRNA and lipids within the LNPs, leading to a diminished SAXS peak and reduced in vitro activity. Overall, our study establishes an SAR for mRNA-LNPs, highlighting that a more ordered internal structure correlates with higher efficacy, which could be instrumental in high-throughput screening of LNP libraries.

biophysics↗

bioRxiv2024

GRATCR: epitope-specific T cell receptor sequence generation with data-efficient pre-trained models

T cell receptors (TCRs) play a crucial role in numerous immunotherapies targeting tumor cells. However, their acquisition and optimization present significant challenges, involving laborious and time-consuming wet lab experimental resource. Deep generative model has demonstrated remarkable capabilities in functional protein sequence generation, offering a promising solution for enhancing the acquisition of specific TCR sequences. Here, we propose GRATCR, a framework incorporates two pre-trained modules through a novel "grafting" strategy, to de-novo generate TCR sequences targeting specific epitopes. Experimental results demonstrate that TCRs generated by GRATCR exhibit higher specificity toward desired epitopes and are more biologically functional compared with state-of-the-art model, by using significantly fewer training data. Additionally, the generated sequences display novelty compared to natural sequences, and the interpretability evaluation further confirmed that the model is capable of capturing important binding patterns. GRATCR is freely available at https://github.com/zhzhou23/GRATCR.

bioinformatics↗

bioRxiv2024

Temperature-Dependent Fold-Switching Mechanism of the Circadian Clock Protein KaiB

The oscillator of the cyanobacterial circadian clock relies on the ability of the KaiB protein to switch reversibly between a stable ground-state fold (gsKaiB) and an unstable fold-switched fold (fsKaiB). Rare fold-switching events by KaiB provide a critical delay in the negative feedback loop of this post-translational oscillator. In this study, we experimentally and computationally investigate the temperature dependence of fold switching and its mechanism. We demonstrate that the stability of gsKaiB increases with temperature compared to fsKaiB and that the Q10 value for the gsKaiB [->] fsKaiB transition is nearly three times smaller than that for the reverse transition. Simulations and native-state hydrogen-deuterium exchange NMR experiments suggest that fold switching can involve both subglobally and near-globally unfolded intermediates. The simulations predict that the transition state for fold switching coincides with isomerization of conserved prolines in the most rapidly exchanging region, and we confirm experimentally that proline isomerization is a rate-limiting step for fold switching. We explore the implications of our results for temperature compensation, a hallmark of circadian clocks, through a kinetic model.

biochemistry↗

bioRxiv2024

RiboDiffusion: Tertiary Structure-based RNA Inverse Folding with Generative Diffusion Models

RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the non-unique structure-sequence mapping, and the flexibility of RNA conformation. In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of 11% for sequence similarity splits and 16% for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in-silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints.

bioinformatics↗

bioRxiv2024

DeProt: A protein language model with quantizied structure and disentangled attention

Protein language models (PLMs) have shown remarkable capabilities in various protein function prediction tasks. However, while protein function is intricately tied to structure, most existing PLMs do not incorporate protein structure information. To address this issue, we introduce ProSST, a Transformer-based protein language model that seamlessly integrates both protein sequences and structures. ProSST incorporates a structure quantization module and a Transformer architecture with disentangled attention. The structure quantization module translates a 3D protein structure into a sequence of discrete tokens by first serializing the protein structure into residue-level local structures and then embeds them into dense vector space. These vectors are then quantized into discrete structure tokens by a pre-trained clustering model. These tokens serve as an effective protein structure representation. Furthermore, ProSST explicitly learns the relationship between protein residue token sequences and structure token sequences through the sequence-structure disentangled attention. We pre-train ProSST on millions of protein structures using a masked language model objective, enabling it to learn comprehensive contextual representations of proteins. To evaluate the proposed ProSST, we conduct extensive experiments on the zero-shot mutation effect prediction and several supervised downstream tasks, where ProSST achieves the state-of-the-art performance among all baselines. Our code and pretrained models are publicly available 2.

bioinformatics↗

bioRxiv2024

An integrative approach to protein sequence design through multiobjective optimization

With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the multistate design problem of the foldswitching protein RfaH as an in-depth case study, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications. Author summaryProteins are the fundamental building blocks of life, and engineering them has broad applications in medicine and biotechnology. Computational methods that seek to model and predict the protein sequence-structure-function relationship have seen significant advancement from the recent development in deep learning. As more models become available, it remains an open question how to effectively combine them into a coherent computational design approach. One approach is to perform computational design with one model, and filter the design candidates with the others. In this work, we demonstrate how an optimization algorithm inspired by evolution can be adapted to provide an alternative framework that outperforms the post hoc filtering approach, especially for problems with multiple competing design specifications. Such a framework may enable researchers to more effectively integrate the strengths of different modeling approaches to tackle more complex design problems.

biophysics↗