Journal of Molecular Biology最新文献

WD repeat-containing protein 5 (WDR5) is a core scaffolding component of multiple chromatin-modifying complexes that engages diverse partner proteins through a conserved arginine-binding cavity known as the WDR5-interacting (WIN) site. Dysregulation of WDR5 has been implicated in oncogenesis, making the WIN site a promising therapeutic target. Current inhibitor development has primarily focused on mimicking canonical WIN motif interactions, thereby limiting exploration of alternative recognition modes. Here, we present high-resolution crystal structures of two arginine-containing peptide probes that reveal previously unrecognized binding geometries at the WIN pocket. One peptide adopts an extended linear conformation that bridges both the WIN pocket and the adjacent S7 site. The other binds in a reversed, or "trans-WIN," orientation, in which a C-terminal arginine anchors the WIN site while an upstream proline residue occupies the S7 pocket. Isothermal titration calorimetry confirmed moderate and specific affinities for both peptides. These findings reveal unexpected conformational adaptability of the WIN site and demonstrate that its recognition capacity extends beyond the canonical mode defined by histone H3 and other partner proteins. Collectively, our results expand the structural repertoire of WIN-site recognition and establish a framework for rational design of next-generation WDR5 inhibitors that exploit multi-site engagement and alternative binding topologies.

Numerous web-based tools have been developed to support large-scale genomics research, whereas challenges remain due to their limited functionality. Therefore, we developed VarXOmics, an end-to-end, versatile web server for querying variants and genes, streamlining germline variant analysis, prioritizing variants with multi-omics insights, and providing interactive visualizations. The utility of VarXOmics was demonstrated by analyzing multiple small variants of the whole-genome sequencing data from a breast cancer patient. It prioritized BRCA2 c.3751dup as the most likely pathogenic variant, and highlighted disease associations with cell cycle regulation, DNA repair pathways, and type 2 diabetes through multi-omics evidence, gene set enrichment, and network analysis. Overall, VarXOmics serves as a practical genomics platform for researchers and clinicians. It shows potential in identifying pathogenic variants and causal genes, uncovering the molecular mechanisms of disease pathogenesis, providing valuable references for clinical decision-making and therapeutic strategies, thus advancing precision medicine. VarXOmics is publicly available at https://www.phenomeportal.org/varxomics.

Dr. Yulong Li received his undergraduate education in biophysics and physiology at Peking University, and subsequently completed his Ph.D. training under the mentorship of Dr. George J. Augustine at Duke University, where he investigated fundamental mechanisms of synaptic transmission. He then pursued postdoctoral research in the laboratory of Dr. Richard W. Tsien at Stanford University, where he began developing genetically encoded indicators for applications in neuroscience. Since 2012, Dr. Yulong Li established his lab at Peking University. His research has been at the forefront of developing the genetically encoded fluorescent sensors for neurotransmitters and neuromodulators, which have emerged as powerful tools for real-time monitoring of the dynamic changes of these molecules with high sensitivity, selectivity, spatiotemporal resolution, and minimal invasiveness in vivo. This article provides a comprehensive overview of the design strategies and key progress in this rapid evolving field, emphasizing how these tools have transformed the study of neuromodulation.

Molecular modeling and simulation play a crucial role in advancing our understanding of protein function at the molecular level, offering insights that complement experimental approaches. In particular, molecular dynamics (MD) simulations with explicit lipid bilayers have become essential for a molecular level understanding of protein-lipid interactions that regulate the structure, dynamics, and function of membrane proteins. CHARMM-GUI (http://www.charmm-gui.org) is a web-based graphical user interface designed to generate MD simulation systems and input files for various simulation engines. Here, we introduce Quick Bilayer, a new CHARMM-GUI module, which provides a streamlined and efficient one-stop platform for assembling protein structures with a diverse set of biologically relevant membrane environments. It features advanced search capabilities that allow users to identify specific lipid types and design bilayers with customized lipid compositions to meet specific research needs. To further enhance usability and scalability, Quick Bilayer now supports a REST-like API that enables seamless integration with backend services. This newly implemented command-line interface allows users to programmatically access the module, facilitating automated workflows and large-scale system generation.

I got my PhD degree under the supervision of Prof. Kegang Shang in 2003. And I did my postdoc research in Azim Surani's lab. Then I set up my own lab in Biomedical Pioneering Innovation Center at Peking University in 2010. My research has focused on developing single-cell omics sequencing technologies and employing these powerful tools to dissect the gene regulation networks in human germline cell development under both physiological and pathological conditions. My lab systematically developed a serial of single-cell omics sequencing technologies, including the first single-cell DNA methylome sequencing technology in 2013, which was considered to pioneer the single-cell epigenome field. In recent years, my lab has focused on developing single-cell omics long-read sequencing technologies based on single-molecule sequencing platforms, which can reveal critical features of the repetitive elements. The repetitive elements are considered as 'dark matter', which account for over half of our genome and play important roles for both normal development and numerous diseases. The research in my lab revealed critical features of the epigenetic reprogramming of human germline cells, deepening our understanding of these cells which are fundamental to the transgenerational immortality of the human species.

The rise of protein Language Models (pLMs) is reshaping the landscape of protein prediction. Embeddings are powerful protein representations provided by pLMs, but they come at a cost: their generation requires expensive hardware, and leveraging models often requires expert knowledge. To some extent, these hurdles limit the ease of use and benefits of those methods both for experimental and computational biologists. Biocentral aims at providing a free and open embedding-based service which addresses these challenges. We support standardized access to most pLMs currently in use, enabling researchers to generate embeddings, get embedding-based protein feature predictions, and train embedding-based models. Here, we showcase biocentral in a large-scale analysis of the BFVD virus database through biocentral's predict module. We also show how readily biocentral's training module reproduces an existing embedding-based prediction method. The server is accessible through a graphical user interface and a programmatic Application Programming Interface (API) at: https://biocentral.rostlab.org.

Symbioses between plants and microbes such as mycorrhizal fungi and rhizobia, provide critical advantages in plant nutrient acquisition and stress resilience, and thereby underpin agricultural sustainability. However, plants coexist with a myriad of soil microbes, including mutualists, pathogens and commensals, and so must accurately differentiate between beneficial, detrimental, and neutral partners to optimize tradeoffs between growth and defense. Since 2013, our research group has been dedicated to addressing fundamental questions in plant-microbe symbioses. Our work encompasses the exchange of nutrients and signals between symbionts, and the differentiation between mutualistic and pathogenic microbes within the rhizosphere microbiome. We first discovered fatty acids as the main carbon source supplied by plants to arbuscular mycorrhizal (AM) fungi and later revealed the phosphate starvation response-centered regulatory network that controls the root and AM fungi phosphorus uptake pathways. In addition, we identified the receptors that recognize Myc factors and have made inroads on revealing the mechanisms underlying how plants distinguish symbiotic and immune signals. The legume-rhizobium symbiosis is understood to have evolved from arbuscular mycorrhizal symbiosis. Related to this, our group identified the Nod factor co-receptor, MtLICK1/2, and revealed that a SHR-SCR module specifies legume cortical cell fate to enable root nodulation. Collectively, our work has provided fundamental insights into the two most agriculturally important plant-microbe symbioses, thereby paving the way for innovative strategies that harness these interactions to advance sustainable agriculture.

The survival of plants depends on sensitive and efficient systems that perceive and integrate internal hormonal signals with external environmental cues. Deciphering how plants sense and adapt to changing conditions is a fundamental biological question with direct relevance to crop improvement and sustainable agriculture. Hongwei Guo received training in plant molecular genetics and light signaling during his doctoral studies, then turned to how diverse signal pathways converge to coordinate plant development. In his postdoctoral work, he identified EBF1/2-mediated ubiquitin-proteasome turnover of EIN3 as a core mechanism of ethylene signaling. Building on this foundation, his independent research uncovered additional post-transcriptional strategies: proteolytic cleavage and translational repression that fine-tune ethylene responses. He also established an EIN3-centered regulatory network that integrates hormonal and environmental cues to coordinate diverse physiological processes. A forward genetic screen of ethylene-activated plants unexpectedly extended Dr. Guo's research to siRNA-based regulation. His group discovered a cytoplasmic "dual-safeguard" mechanism in which impairment of mRNA decay triggers the production of coding-transcript-derived siRNAs (ct-siRNAs) that silence endogenous genes. They further showed that stress-induced 22-nt ct-siRNAs amplify silencing to modulate nitrate assimilation and energy balance under abiotic stress. More recently, Dr. Guo's laboratory has focused on how plant cells sense physical and chemical changes in their surroundings. They identified two extracellular peptide-receptor complexes as apoplastic pH sensors, and demonstrated that cytoplasmic protein DCP5 senses osmotic stress through phase separation to form new stress granules and rapidly reprogram gene expression. Collectively, Dr. Guo's research connects hormone signaling, gene regulation, and environmental adaptation.

The B. cereus family comprises members highly pathogenic for mammals or insects, with B. anthracis and B. thuringiensis respectively as notable examples. The biofilm operon of these bacteria encodes two TasA-like proteins, the 60% identical Camelysins CalY1 and CalY2. In this study, we observed that at neutral pH CalY2 alone polymerizes readily into filaments, whereas CalY1 forms a polydispersed mixture of oligomers without filament formation. However, at basic or acidic pH CalY1 also modestly polymerizes. CalY2 polymerization into filaments involves ß-sheet remodeling via donor strand complementation, as demonstrated here by a combination of NMR and AlphaFold studies. In contrast to TasA of B. subtilis, this process is spontaneous and does not require initiation by a TapA homolog. NMR studies show that the functionally relevant region (β1-β2-β3) of the CalY2 monomer structure closely resembles that of B. subtilis TasA, and differs from AlphaFold models. A survey of AlphaFold 2 predictions on 12 homologous B. cereus group Camelysins yielded only four correctly predicted β1-β2-β3 segments, which decreased to one when using AlphaFold 3. Since crucial residues in the protomer contact region are conserved among TasA-like proteins, we investigated whether family members of different species could form mixed filaments. NMR revealed features in CalY2 filaments that are structurally conserved with TasA filaments but sequentially different, promoting specificity. These interactions and differences, respectively, involve the C-terminus and the beginning of β3, which most likely hinder joint TasA and CalY1 copolymerization. A protease activity could not be observed for the heterologously expressed B. cereus Camelysins. SIGNIFICANCE: The B. cereus group includes extremely harmful and surprisingly benign bacterial strains. The Anthrax-toxin-producing B. anthracis is one of the most toxic bacterial threats to man, whereas B. thuringiensis toxin is used as a biological insecticide. Other B. cereus strains pose problems in food production and medical implant usage. These bacteria can exist as biofilms allowing them to survive and proliferate, an essential feature of which are protein filaments. Here we characterize the B. cereus Camelysins CalY1 and CalY2 and compare their structure and filament formation with B. subtilis filaments to understand principles determining patterns of conservation and specificity. This investigation provides the basis for developing novel means to suppress or enhance biofilms with potential benefits for plant protection.

The precise localization of proteins within prokaryotic cells is fundamental to understanding their function. However, existing models still struggle with challenging localization classes, such as cell wall or outer membrane proteins. We introduce LocPred-Prok, a novel deep learning framework that redefines performance standards for prokaryotic subcellular localization. LocPred-Prok employs a purpose-built dual-branch architecture that synergistically integrates global and local sequence features extracted from pLM embeddings. On a stringent, homology-partitioned benchmark, LocPred-Prok achieves a state-of-the-art accuracy of 91.2 % and a Matthews Correlation Coefficient (MCC) of 0.889. Critically, it resolves long-standing prediction challenges, demonstrating exceptional performance on notoriously difficult classes like Gram-positive cell wall and Gram-negative outer membrane proteins. It substantially outperforms recent and classic methods across all organismal subgroups, representing a significant leap forward in the field. The LocPred-Prok web server is freely accessible athttps://huggingface.co/spaces/isyslab/LocPred-Prok.