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A long-standing assumption in molecular biology posits that the conservation of protein and nucleic acid sequences emphasizes the functional significance of biomolecules. These conserved sequences fold into distinct secondary and tertiary structures, enable highly specific molecular interactions, and regulate complex yet organized molecular processes within living cells. However, recent evidence suggests that biomolecules can also function through primary sequence regions that lack conservation across species or gene families. These regions typically do not form rigid structures, and their inherent flexibility is critical for their functional roles. This review examines the emerging roles and molecular mechanisms of "nondomain biomolecules," whose functions are not easily predicted due to the absence of conserved functional domains. We propose the hypothesis that both domain- and nondomain-type molecules work together to enable flexible and efficient molecular processes within the highly crowded intracellular environment.
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http://dx.doi.org/10.1111/gtc.13050 | DOI Listing |
Autophagy
September 2025
Department of Biochemistry and Molecular Biology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Macroautophagy/autophagy is an evolutionarily conserved process through which cells degrade cytoplasmic substances via autophagosomes. During the initiation of autophagosome formation, the ULK/Atg1 complex serves as a scaffold that recruits and regulates downstream ATG/Atg proteins and ATG9/Atg9-containing vesicles. Despite the essential role of the ULK/Atg1 complex, its components have changed during evolution; the ULK complex in mammals consists of ULK1 (or ULK2), RB1CC1, ATG13, and ATG101, whereas the Atg1 complex in the yeast lacks Atg101 but instead has Atg29 and Atg31 along with Atg17.
View Article and Find Full Text PDFJ Cell Sci
September 2025
i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.
The microtubule motor dynein-2 is responsible for retrograde intraflagellar transport (IFT), a process critical for cilia assembly and cilium-dependent signaling. Mutations in genes encoding dynein-2 subunits interfere with ciliogenesis and are among the most frequent causes of skeletal ciliopathies. Despite its importance, little is known regarding dynein-2 assembly and regulation.
View Article and Find Full Text PDFPhys Chem Chem Phys
September 2025
Department of Chemistry, Graduate School of Science and Technology, Kwansei Gakuin University, 1 Gakuen Uegahara, Sanda, Hyogo 669-1330, Japan.
Hybrid systems (HSs) of quantum dots (QDs) and molecular photoswitches exhibit luminescence switching of QDs based on energy transfer and have garnered attention for their potential applications in sensors and optical memories. In HSs, the chemical composition, such as the number of attached ligands, is inherently distributed, posing challenges for extracting the energy transfer process from the QDs to a single acceptor molecule. The stochastic model, assuming a Poisson distribution for the number of acceptors, proves to be an effective approach for extracting the process.
View Article and Find Full Text PDFAnal Methods
September 2025
Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection (Guangxi Normal University), Ministry of Education, Guilin 541004, China.
A novel magnetic nanostructured molecularly imprinted polymer probe (FeO@MIP) was developed for the continuous detection of Ti/Fe. The synthesis employed 50 nm FeO nanoparticles as the core matrix, with Ti and Fe serving as template molecules. Functional monomers α-methylacrylic acid (MAA) and acrylamide (AM) were used, along with ethylene glycol dimethacrylate (EGDMA) as the crosslinking agent and 2,2'-azobisisobutyronitrile (AIBN) as the polymerization initiator, utilizing a microwave-assisted procedure.
View Article and Find Full Text PDFJ Chem Theory Comput
September 2025
Dipartimento di Chimica, Università di Pavia, Via Taramelli 12, Pavia 27100, Italy.
Machine learning (ML) and deep learning (DL) methodologies have significantly advanced drug discovery and design in several aspects. Additionally, the integration of structure-based data has proven to successfully support and improve the models' predictions. Indeed, we previously demonstrated that combining molecular dynamics (MD)-derived descriptors with ML models allows to effectively classify kinase ligands as allosteric or orthosteric.
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