Biology subjects

Hou, D.

Publications and source records attributed to Hou, D..

13 recordsLinked to original sources

bioRxiv2026

TopoFlow: Evolutionarily Conditioned Flow Matching for Protein Conformational Ensemble Generation

Proteins function through dynamic conformational ensembles rather than a single static structure, yet accurately modeling these ensembles remains challenging. Here, we introduce TopoFlow, an evolutionarily conditioned flow-matching framework for protein conformational ensemble generation. TopoFlow uses evolutionary representations from multiple sequence alignments (MSAs) to encode global conformational heterogeneity. It combines this with structural variables from a variational autoencoder (VAE) to capture local flexibility. These complementary representations are then adaptively fused through a conditional modulation module to construct unified conditioning features that guide protein conformational ensemble generation. On the ATLAS molecular dynamics (MD) benchmark, TopoFlow improved the Jensen Shannon metrics for pairwise distances (JS-PwD), radius of gyration (JS-Rg), and time-lagged independent components (JS-TIC) by 10.73%, 5.78% and 7.75%, respectively, relative to BioEmu. Notably, this performance generalizes to experiment-based evaluation, where TopoFlow exhibits strong agreement with experimental observables, including chemical shifts (CS) and small-angle X-ray scattering (SAXS). Ablation analyses further indicated that the evolutionary representations and structural latent variables contributed complementary information to ensemble generation. Our results suggest that TopoFlow, through its integration of evolutionary and structural latent information, offers a promising strategy for generating structurally plausible and conformationally informative protein ensembles.

bioinformatics↗

bioRxiv2026

Metabolic dehalogenation of halomethanes by mercury methylators

Mercury (Hg) methylators play key roles in the global Hg cycle. Nonetheless, the Hg methylators are widely present in Hg-absent environments and the Hg methylation is a cometabolic process without cell growth, which leave the origin and evolution of the Hg methylation as a mystery. Here, we reported the CCl4/trihalomethane-to-dihalomethane dehalogenation by model Hg methylators in a metabolic way. Heterologously-expressed HgcAB catalyzed both the CCl4 dechlorination and Hg methylation. Halomethanes were shown to sustain the Hg-methylation community without adding external carbon source, electron donor and acceptor. Metadata analyses suggested the halomethane dehalogenation potential of Hg methylators at the global scale. These results, together with much higher global flux and kinetic Vmax of the metabolic halomethanes dehalogenation relative to the co-metabolic Hg methylation, suggested halomethanes as the potential HgcAB substrates for the evolution of Hg methylation.

microbiology↗

bioRxiv2026

Exploring protein conformational ensembles using evolutionary conditional diffusion

Protein conformational ensembles encode the dynamic landscapes underlying biological function, regulation, and allostery. Accurately reconstructing such ensembles while balancing conformational distributions accuracy and physical plausibility remains a fundamental challenge in structural biology, particularly when dynamic data is scarce. Here, we propose DiffEnsemble, a diffusion-based framework designed for modeling protein conformational ensembles. DiffEnsemble learns latent dynamical representations from static protein structures in the Protein Data Bank, integrated with the structural profile derived from the AlphaFold Protein Structure Database as conditional guidance during the diffusion process. Benchmarking on 72 protein targets from the ATLAS molecular dynamics simulation dataset demonstrates that DiffEnsemble outperforms existing methods, including BioEmu and AlphaFLOW. Compared with AlphaFLOW, DiffEnsemble achieves improvements of 28.9% and 11.3% in Pearson correlation coefficients for ensemble pairwise root mean square deviation and root mean square fluctuation, respectively. Importantly, DiffEnsemble successfully captures the dominant motions for 42% of the targets. These results demonstrate that latent dynamical information embedded in static structural data can effectively support the modeling of protein conformational ensembles.

bioinformatics↗

bioRxiv2025

High-accuracy structure modeling for antibody-antigen complexes

Accurate modeling of antibody-antigen complexes is crucial for advancing therapeutics and diagnostics, yet predicting their binding interface remains a formidable challenge. To address this, we introduce DeepAAAssembly, a protocol that enhances static structural information from AlphaFold3 by integrating dynamic interaction patterns to guide complex assembly. Our approach leverages predicted inter-chain residue distances to construct a flexibility-aware energy function, which drives a two-stage conformational sampling process for global exploration and local exploitation. On a benchmark set of 67 representative antibody-antigen complexes, by incorporating a built-in confidence selection mechanism, DeepAAAssembly outperforms AlphaFold3, achieving not only a 12.9% higher average DockQ score and more medium- and high-quality models, but also reliably elevating the most challenging cases from incorrect to acceptable accuracy. These results demonstrate that DeepAAAssembly effectively captures conserved interaction motifs and conformational flexibility, offering a robust framework for high-accuracy antibody-antigen modeling.

bioinformatics↗

bioRxiv2025

ProSiteHunter: A unified framework for sequence-based prediction of protein-nucleic acid and protein-protein binding sites

Accurate prediction of protein binding sites is essential for elucidating protein function, understanding molecular interaction mechanisms, and facilitating drug design. However, existing sequence-based approaches are often designed for specific binding-site types and therefore lack generality, whereas structure-based methods typically rely on high-quality structural models, limiting their applicability. Here, we introduce ProSiteHunter, a unified sequence-based framework for protein binding-site prediction, which integrates a fine-tuned protein language model (SiteT5) with a multi-source feature-fusion network that incorporates evolutionary, geometric, and statistical features, while employing bidirectional semantics, local associations, and global dependencies for comprehensive binding-site characterization. The method was systematically evaluated on diverse binding sites prediction tasks, where ProSiteHunter achieved a 39.1% average improvement in PRAUC for protein-DNA/RNA/protein tasks and a 7.4% PRAUC enhancement on the particularly challenging antibody-antigen task over state-of-the-art methods. Moreover, ProSiteHunter is capable of identifying local flexible sites that complement AlphaFold3 predictions and improving the accuracy of antibody-antigen interaction prediction. These results highlight ProSiteHunter as an efficient and unified approach for accurate and robust prediction of diverse protein binding sites.

bioinformatics↗

bioRxiv2025

MultiSAAI: Sequence-informed Antibody-Antigen Interactions Prediction using Multi-scale Deep Learning

MotivationAntibodies possess high specificity toward target antigens, making them critical for therapeutic applications. However, experimental screening of antibody-antigen interactions is labor-intensive and costly. Existing structure-based computational methods often face significant challenges in modeling the dynamic conformational flexibility that governs antibody-antigen interactions, which limits their ability to accurately identify binding. Therefore, there is an urgent need for accurate and efficient sequence-based prediction method to accelerate antibody discovery and reduce experimental burden. ResultsWe propose MultiSAAI, a sequence-informed framework that models antibody-antigen interactions by explicitly accounting for the distinct roles of antibody heavy and light chains in antigen binding. MultiSAAI integrates language model embeddings, physicochemical properties, geometric constraints, and residue substitutability to characterize antibody-antigen interactions across multiple scales, employing a multi-scale network architecture that simultaneously evaluates global residue-pair compatibility and local amino acid fitness at the binding interface. Furthermore, the incorporation of site-specific information and biologically grounded binding principles allows the model to more closely reflect the actual mechanisms of interactions. Benchmark results demonstrate that MultiSAAI achieves AUROC scores of 0.757 on the generic antibody-antigen interactions dataset and 0.946 on the SARS-CoV-2 dataset, outperforming existing methods such as A2binder and AbAgIntPre. Finally, large-scale preliminary antibody screening further validates the potential of MultiSAAI for high-throughput therapeutic antibody discovery.

bioinformatics↗

bioRxiv2025

SIRT2 Mediates Integrated Stress Response by Deacetylating and Stabilizing 4EBP1 to Suppress Translation

The ability to alleviate nutrient stress, such as amino acid limitation, is crucial for cell survival. The mTORC1 complex and integrated stress response (ISR) are mechanisms that sense the availability of amino acids and regulate ribosomal protein synthesis. Here we have discovered a new SIRT2-mediated pathway, downstream of ISR, that senses the limitation of amino acids to regulate translation. Under amino acid deprivation, SIRT2 protein level is upregulated translationally by its upstream open reading frame (uORF). SIRT2 in turn suppresses global protein translation, which helps cells to survive amino acid limitation. Mechanistically, we identified eukaryotic translation initiation factor 4E (eIF4E) binding protein 1 (4EBP1), which negatively regulates translation, as a substrate of SIRT2. SIRT2 deacetylates 4EBP1 at Lys69 and stabilizes 4EBP1 by protecting it from proteasomal degradation. Our study reveals a novel role for SIRT2 in regulating protein translation and a new regulatory mechanism of 4EBP1 in cells. Our study provides a better understanding of the intricate regulation of translation and may explain the known non-oncogene addiction role of SIRT2 in cancer cells.

cell biology↗

bioRxiv2025

High-accuracy protein complex structure modeling based on sequence-derived structure complementarity

In living organisms, proteins perform key functions required for life activities by interacting to form complexes. Determining the protein complex structure is crucial for understanding and mastering biological functions. Although AlphaFold2 has made a revolutionary breakthrough in predicting protein monomeric structures, accurately capturing inter-chain interaction signals and modeling the structures of protein complexes remain a formidable challenge. In this work, we report DeepSCFold, a pipeline for improving protein complex structure modeling. DeepSCFold uses sequence-based deep learning models to predict protein-protein structural similarity and interaction probability, providing a foundation for identifying interaction partners and constructing deep paired multiple-sequence alignments (MSAs) for protein complex structure prediction. Benchmark results show that DeepSCFold significantly increases the accuracy of protein complex structure prediction compared with state-of-the-art methods. For multimer targets from CASP15, DeepSCFold achieves an improvement of 11.6% and 10.3% in TM-score compared to AlphaFold-Multimer and AlphaFold3, respectively. Furthermore, when applied to antibody-antigen complexes from the SAbDab database, DeepSCFold enhances the prediction success rate for antibody-antigen binding interfaces by 24.7% and 12.4% over AlphaFold-Multimer and AlphaFold3, respectively. These results demonstrate that DeepSCFold effectively captures intrinsic and conserved protein-protein interaction patterns through sequence-derived structure-aware information, rather than relying solely on sequence-level co-evolutionary signals.

bioinformatics↗

bioRxiv2025

A Hyperbolic Discrete Diffusion 3D RNA Inverse Folding Model for functional RNA design

Generative design of functional RNAs presents revolutionary opportunities for diverse RNA-based biotechnologies and biomedical applications. To this end, RNA inverse folding is a promising strategy for generatively designing new RNA sequences that can fold into desired topological structures. However, three-dimensional (3D) RNA inverse folding remains highly challenging due to limited availability of experimentally derived 3D structural data and unique characteristics of RNA 3D structures. In this study, we propose RIdiffusion, a hyperbolic denoising diffusion generative RNA inverse folding model, for 3D RNA design tasks. By embedding geometric features of RNA 3D structures and topological properties into hyperbolic space, RIdiffusion efficiently recovers the distribution of nucleotides for targeted RNA 3D structures based on limited training samples using a discrete diffusion model. We perform extensive evaluations on RIdiffusion using different datasets and strict data-splitting strategies and the results demonstrate that RIdiffusion consistently outperforms baseline generative models for RNA inverse folding. This study introduces RIdiffusion as a powerful tool for the generative design of functional RNAs, even in structure-data-scarce scenarios. By leveraging geometric deep learning, RIdiffusion enhances performance and holds promise for diverse downstream applications.

bioinformatics↗

bioRxiv2025

Structural and Functional Insights into GGCX-FIX Interaction: Implications for Vitamin K-Dependent Bleeding Disorders

Gamma-carboxylation, catalyzed by {gamma}-glutamyl carboxylase (GGCX), is a critical post-translational modification essential for the biological activity of vitamin K-dependent proteins (VKDPs). Mutations in GGCX, depending on their specific location, result in vitamin K-dependent coagulation factor deficiency type 1 (VKCFD1), which encompasses a broad spectrum of clinical manifestations ranging from mild to severe, including bleeding disorders, osteoporosis, and vascular calcification. The limited knowledge of GGCXs structure and functional regions hinders our understanding of the consequences of GGCX mutations and the treatment for VKCFD1. This study aimed to identify key functional regions of GGCX and their interactions with VKDPs to better elucidate the molecular mechanisms underlying these diverse clinical symptoms. Using AlphaFold 3 and molecular dynamics simulations, we developed a complex binding model of GGCX, FIX, and reduced vitamin K, which revealed critical regions and residues involved in their interaction. Site-directed mutagenesis and cell-based assays further validated the model, confirming that multisite and regional cooperative binding of FIX to GGCX plays a key role in modulating {gamma}-carboxylation efficiency. Additionally, novel residues (I296, M303, M401, M402) were identified as essential for GGCXs dual enzymatic activities: carboxylation and vitamin K epoxidation. We further demonstrated that the spatial proximity of these active sites supports the hypothesis that GGCXs carboxylation and vitamin K epoxidation centers are interconnected, ensuring the efficient coupling of these processes. Our GGCX-FIX binding and carboxylation model aligns with known pathogenic GGCX mutations, providing valuable insights into the molecular basis of coagulation disorders caused by GGCX mutants.

pathology↗

bioRxiv2024

Recognition and localization of maize leaves in RGB images based on Point-Line Net

Plant phenotype detection plays a crucial role in understanding and studying plant biology, agriculture, and ecology. It involves the quantification and analysis of various physical traits and characteristics of plants, such as plant height, leaf shape, angle, number, and growth trajectory. By accurately detecting and measuring these phenotypic traits, researchers can gain insights into plant growth, development, stress tolerance, and the influence of environmental factors. Among these phenotypic information, the number of leaves and growth trajectory of the plant are more accessible. Nonetheless, obtaining these information is labor-intensive and financially demanding. With the rapid development of computer vision technology and artificial intelligence, using maize field images to fully analyze plant-related information such as growth trajectory and number of leaves can greatly eliminate repetitive labor work and enhance the efficiency of plant breeding. However, the application of deep learning methods still faces challenges due to the serious occlusion problem and complex background of field plant images. In this study, we developed a deep learning method called Point-Line Net, which is based on the Mask R-CNN frame-work, to automatically recognize maize field images and determine the number and growth trajectory of leaves and roots. The experimental results demonstrate that the object detection accuracy (mAP) of our Point-Line Net can reach 81.5%. Moreover, to describe the position and growth of leaves and roots, we introduced a new lightweight "keypoint" detection branch that achieved 33.5 using our custom distance verification index. Overall, these findings provide valuable insights for future field plant phenotype detection, particularly for the datasets with dot and line annotations.

plant biology↗

bioRxiv2023

NLRP3 Cys126 palmitoylation by ZDHHC7 Promotes Inflammasome Activation

NACHT-, leucine-rich-repeat- (LRR), and pyrin domain-containing protein 3 (NLRP3) mediates inflammasome activation in response to multiple pathogen and damage-associated molecular patterns in macrophages. Hyperactivation of NLRP3 inflammasome contributes to many human chronic inflammatory diseases. Understanding how NLRP3 inflammasome is regulated can potentially provide new strategies to treat inflammatory diseases. Here, we demonstrated that NLRP3 is palmitoylated on Cys126 by palmitoyl-acyltransferase ZDHHC7 in macrophages, which is critical for NLRP3-mediated inflammasome activation. Perturbating NLRP3 Cys126 palmitoylation by ZDHHC7 knockout, pharmacological inhibition, or modification site mutation, diminishes NLRP3 activation and the consequential Caspase-1 and Gasdermin D (GSDMD) cleavage, and IL-1{beta} and IL-18 secretion in mouse primary macrophages and human macrophages. Furthermore, NLRP3 Cys126 palmitoylation is vital for inflammasome activation in vivo, as Zdhhc7 knockout, pharmacological inhibition, or NLRP3 C126A mutation protects mice from LPS-induced endotoxic shock and monosodium urate (MSU)-induced peritonitis. Mechanistically, ZDHHC7-mediated NLRP3 Cys126 palmitoylation promotes resting NLRP3 localizing on the trans-Golgi network (TGN) and activated NLRP3 on the dispersed TGN (dTGN), which is indispensable for the recruitment and oligomerization of adaptor protein ASC after inflammasome activation. The activation of NLRP3 by ZDHHC7-mediated Cys126 palmitoylation is different from the previously reported inhibitory effect by ZDHHC12-mediated Cys841 palmitoylation, highlighting the versatile regulatory roles of S-palmitoylation. Therefore, our study identifies a new regulatory mechanism of NLRP3 activation and suggests targeting ZDHHC7 or NLRP3 Cys126 residue as a potential therapeutic strategy to treat NLRP3-related human disorders. HighlightsO_LINLRP3 Cys126 is palmitoylated by ZDHHC7. C_LIO_LIZDHHC7 promotes NLRP3 activation in macrophages, which can be inhibited by ZDHHCs inhibitors, 2-bromopalmitate and MY-D4. C_LIO_LICys126 palmitoylation of NLRP3 is critical for NLRP3 activation. C_LIO_LINLRP3 TGN/dTGN localization depends on ZDHHC7-mediated Cys126 palmitoylation, which is crucial for ASC recruitment and inflammasome assembly. C_LIO_LINLRP3 inflammasome activation by ZDHHC7 differs from ZDHHC12-mediated NLRP3 inhibition. C_LI

immunology↗

bioRxiv2020

Biomechanical Characterization of SARS-CoV-2 Spike RBD and Human ACE2 Protein-Protein Interaction

The current COVID-19 pandemic has led to a devastating impact across the world. SARS-CoV-2 (the virus causing COVID-19) is known to use receptor-binding domain (RBD) at viral surface spike (S) protein to interact with the angiotensin-converting enzyme 2 (ACE2) receptor expressed on many human cell types. The RBD-ACE2 interaction is a crucial step to mediate the host cell entry of SARS-CoV-2. Recent studies indicate that the ACE2 interaction with the SARS-CoV-2 S protein has higher affinity than its binding with the structurally identical S protein of SARS-CoV-1, the virus causing the 2002-2004 SARS outbreak. However, the biophysical mechanism behind such binding affinity difference is unclear. This study utilizes a combined single-molecule force spectroscopy and steered molecular dynamics (SMD) simulation approach to quantify the specific interactions between CoV-2 or CoV-1 RBD and ACE2. Depending on the loading rates, the unbinding forces between CoV-2 RBD and ACE2 range from 70 to 110 pN, and are 30-50% higher than those of CoV-1 RBD and ACE2 under similar loading rates. SMD results indicate that CoV-2 RBD interacts with the N-linked glycan on Asn90 of ACE2. This interaction is mostly absent in the CoV-1 RBD-ACE2 complex. During the SMD simulations, the extra RBD-N-glycan interaction contributes to a greater force and prolonged interaction lifetime. The observation is confirmed by our experimental force spectroscopy study. After the removal of N-linked glycans on ACE2, its mechanical binding strength with CoV-2 RBD decreases to a similar level of the CoV-1 RBD-ACE2 interaction. Together, the study uncovers the mechanism behind the difference in ACE2 binding between SARS-CoV-2 and SARS-CoV-1, and could aid in the development of new strategies to block SARS-CoV-2 entry. STATEMENT OF SIGNIFICANCEThis study utilizes a combined single-molecule force spectroscopy and steered molecular dynamics simulation approach to quantify the specific interactions between SARS-CoV-2 or SARS-CoV-1 receptor-binding domain and human ACE2. The study reveals the mechanism behind the difference in ACE2 binding between SARS-CoV-2 and SARS-CoV-1, and could aid in the development of new strategies to block SARS-CoV-2 entry.

biophysics↗