Hasil untuk "Biochemistry"

Bhushan S. Pattni, V. Chupin, V. Torchilin

B. Hoffman, D. Lukoyanov, Zhi-Yong Yang et al.

Brian M. Hoffman,* Dmitriy Lukoyanov, Zhi-Yong Yang,† Dennis R. Dean,*,‡ and Lance C. Seefeldt*,† †Department of Chemistry and Biochemistry, Utah State University, 0300 Old Main Hill, Logan, Utah 84322, United States ‡Department of Biochemistry, Virginia Tech, 900 West Campus Drive, Blacksburg, Virginia 24061, United States Departments of Chemistry and Molecular Biosciences, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States

Élodie Boisselier, D. Astruc

C. Pickart, M. Eddins

M. R. Kumar, R. Muzzarelli, C. Muzzarelli et al.

H. Zollinger

Magnus H. Strømme, Alex G. C. de Sá, David B. Ascher

Accurate drug response prediction is a critical bottleneck in computational biochemistry, limited by the challenge of modelling the interplay between molecular structure and cellular context. In cancer research, this is acute due to tumour heterogeneity and genomic variability, which hinder the identification of effective therapies. Conventional approaches often fail to capture non-linear relationships between chemical features and biological outcomes across diverse cell lines. To address this, we introduce DPD-Cancer, a deep learning method based on a Graph Attention Transformer (GAT) framework. It is designed for small molecule anti-cancer activity classification and the quantitative prediction of cell-line specific responses, specifically growth inhibition concentration (pGI50). Benchmarked against state-of-the-art methods (pdCSM-cancer, ACLPred, and MLASM), DPD-Cancer demonstrated superior performance, achieving an Area Under ROC Curve (AUC) of up to 0.87 on strictly partitioned NCI60 data and up to 0.98 on ACLPred/MLASM datasets. For pGI50 prediction across 10 cancer types and 73 cell lines, the model achieved Pearson's correlation coefficients of up to 0.72 on independent test sets. These findings confirm that attention-based mechanisms offer significant advantages in extracting meaningful molecular representations, establishing DPD-Cancer as a competitive tool for prioritising drug candidates. Furthermore, DPD-Cancer provides explainability by leveraging the attention mechanism to identify and visualise specific molecular substructures, offering actionable insights for lead optimisation. DPD-Cancer is freely available as a web server at: https://biosig.lab.uq.edu.au/dpd_cancer/.

Sarah Knapp, Verena Weber, Maud Verheirstraeten et al.

Abstract Mono-ADP-ribosylation, a modification of both proteins and nucleic acids, is implicated in innate immunity. Intracellularly, this modification is catalyzed by PARP enzymes, some induced in response to interferons. Mono-ADP-ribosylation is reversed by hydrolases including proteins with macrodomains, which are conserved across all kingdoms of life. Macrodomains encoded by certain positive-sense single-stranded RNA viruses, such as Chikungunya virus and SARS-CoV-2, antagonize host MARylation to enhance viral replication and suppress the immune response. While macrodomain hydrolase activity is essential for CHIKV replication, in SARS-CoV-2 it predominantly contributes to immune evasion, underscoring viral macrodomains as potential antiviral drug targets. Efforts to develop macrodomain inhibitors include computational modeling, crystallography-based methods, and in vitro assays. However, tools to study macrodomain activity directly in cells remain rare. Here, we established a cell-based assay using PARP15 isoform 1, which we found forms nuclear foci dependent on its ADP-ribosyltransferase activity. Enzymatically active macrodomains dissolve these foci, enabling hydrolase activity monitoring in living cells. Using stable cell lines, this system allows the screening of macrodomain inhibitors while simultaneously addressing cell permeability, toxicity, and physiological relevance. Adaptable to various macrodomains, our platform offers a versatile tool to study macrodomain function in living cells, analyzing mutants, and advancing drug discovery efforts.

Liting Li, Yumeng Wang, Yueheng Sun

Graph-level anomaly detection aims to identify anomalous graphs or subgraphs within graph datasets, playing a vital role in various fields such as fraud detection, review classification, and biochemistry. While Graph Neural Networks (GNNs) have made significant progress in this domain, existing methods rely heavily on large amounts of labeled data, which is often unavailable in real-world scenarios. Additionally, few-shot anomaly detection methods based on GNNs are prone to noise interference, resulting in poor embedding quality and reduced model robustness. To address these challenges, we propose a novel meta-learning-based graph-level anomaly detection framework (MA-GAD), incorporating a graph compression module that reduces the graph size, mitigating noise interference while retaining essential node information. We also leverage meta-learning to extract meta-anomaly information from similar networks, enabling the learning of an initialization model that can rapidly adapt to new tasks with limited samples. This improves the anomaly detection performance on target graphs, and a bias network is used to enhance the distinction between anomalous and normal nodes. Our experimental results, based on four real-world biochemical datasets, demonstrate that MA-GAD outperforms existing state-of-the-art methods in graph-level anomaly detection under few-shot conditions. Experiments on both graph anomaly and subgraph anomaly detection tasks validate the framework's effectiveness on real-world datasets.

William F. Martin

Studies by microbiologists from the 1970s provided robust estimates for the energy supply and demand of a prokaryotic cell. The amount of ATP needed to support growth was calculated from the chemical composition of the cell and known enzymatic pathways that synthesize its constituents from known substrates in culture. Starting in 2015, geneticists and evolutionary biologists began investigating the bioenergetic role of mitochondria at eukaryote origin and energy in metazoan evolution using their own, widely trusted but hitherto unvetted model for the costs of growth in terms of ATP per cell. The more recent model contains, however, a severe and previously unrecognized error that systematically overestimates the ATP cost of amino acid synthesis up to 200 fold. The error applies to all organisms studied by such models and leads to conspicuously false inferences, for example that the synthesis of an average amino acid in humans requires 30 ATP, which no biochemistry textbook will confirm. Their ATP cost calculations would require that Escherichia coli obtains roughly 100 ATP per glucose and that mammals obtain roughly 240 ATP per glucose, propositions that invalidate evolutionary inferences so based. By contrast, established methods for estimating the ATP cost of microbial growth show that the first mitochondrial endosymbionts could have easily doubled the hosts available ATP pool, provided that genes for growth on environmental amino acids were transferred from the mitochondrial symbiont to the archaeal host and that the host for mitochondrial origin was an autotroph using the acetyl-CoA pathway.

Lukas Fesser, Melanie Weber

Graph Neural Networks have emerged as the most popular architecture for graph-level learning, including graph classification and regression tasks, which frequently arise in areas such as biochemistry and drug discovery. Achieving good performance in practice requires careful model design. Due to gaps in our understanding of the relationship between model and data characteristics, this often requires manual architecture and hyperparameter tuning. This is particularly pronounced in graph-level tasks, due to much higher variation in the input data than in node-level tasks. To work towards closing these gaps, we begin with a systematic analysis of individual performance in graph-level tasks. Our results establish significant performance heterogeneity in both message-passing and transformer-based architectures. We then investigate the interplay of model and data characteristics as drivers of the observed heterogeneity. Our results suggest that graph topology alone cannot explain heterogeneity. Using the Tree Mover's Distance, which jointly evaluates topological and feature information, we establish a link between class-distance ratios and performance heterogeneity in graph classification. These insights motivate model and data preprocessing choices that account for heterogeneity between graphs. We propose a selective rewiring approach, which only targets graphs whose individual performance benefits from rewiring. We further show that the optimal network depth depends on the graph's spectrum, which motivates a heuristic for choosing the number of GNN layers. Our experiments demonstrate the utility of both design choices in practice.

Ethan A. Older, Mary K. Mitchell, Andrew Campbell et al.

Correlative studies have linked human gut microbes to specific health conditions. Alistipes is one such microbial genus negatively linked to inflammatory bowel disease (IBD). However, the protective role of Alistipes in IBD is understudied, and the underlying molecular mechanisms remain unknown. In this study, colonization of Il10-deficient mice with Alistipes timonensis DSM 27924 delays colitis development. Colonization does not significantly alter the gut microbiome composition, but instead shifts the host plasma lipidome, increasing phosphatidic acids while decreasing triglycerides. Outer membrane vesicles (OMVs) derived from Alistipes are detected in the plasma of colonized mice, carrying potentially immunomodulatory metabolites into the host circulatory system. Fractions of A. timonensis OMVs suppress LPS-induced Il6, Il1b, and Tnfa expression in vitro in murine macrophages. We detect putative bioactive lipids in the OMVs, including immunomodulatory sulfonolipids (SoLs) in the active fraction, which are also increased in the blood of colonized mice. Treating Il10-deficient mice with purified SoL B, a representative SoL, suppresses colitis development, suggesting its contribution to the anti-inflammatory phenotype observed with A. timonensis colonization. Thus, A. timonensis OMVs represent a potential mechanism for Alistipes-mediated delay of colitis in Il10-deficient mice via delivery of immunomodulatory lipids and modulation of the host plasma lipidome.

Mohamed S. Kishta, Aya Khamis, Hafez AM et al.

Head and neck squamous cell carcinoma (HNSCC) remains a challenging malignancy due to its high rates of recurrence, metastasis, and resistance to conventional therapies. microRNA-200c (miRNA-200c) has emerged as a critical tumor suppressor in HNSCC, with the potential to inhibit epithelial-mesenchymal transition (EMT), which is considered as a key process in cancer metastasis and progression. Interestingly, there are also controversial findings in HNSCC characterizing miRNA-200c as oncogenic factor. This review article provides a comprehensive overview of the current understanding of miRNA-200c's general role in cancer, and particularly in HNSCC, highlighting its mechanisms of action, including the regulation of EMT and other oncogenic pathways.Additionally, the review explores the innovative approach of exosome-mediated delivery of miRNA-200c as a therapeutic strategy. Exosomes, as natural nanocarriers, offer a promising vehicle for the targeted delivery of miRNA-200c to tumor cells, potentially overcoming the limitations of traditional delivery methods and enhancing therapeutic efficacy. The review also discusses the challenges and future directions in the clinical application of miRNA-200c, particularly focusing on its potential to improve outcomes for HNSCC patients. This article seeks to provide valuable insights for researchers and clinicians working towards innovative treatments for this aggressive cancer type.

Andrey N. Pravdivtsev, Ben. J. Tickner, Stefan Glöggler et al.

Nuclear spin hyperpolarization utilizing parahydrogen has the potential for broad applications in chemistry, biochemistry, and medicine. This review examines recent chemical and biochemical insights gained using parahydrogen-induced polarization (PHIP). We begin with photo-induced PHIP, which allows the investigation of short-lived and photo-activated catalysis. Next, we review the partially negative line effect, in which distinctive lineshape helps to reveal information about rapid exchange with parahydrogen and the role of short-lived catalytic species. The NMR signal enhancement of a single proton in oneH-PHIP is discussed, challenging the underpinning concept of the necessity of pairwise hydrogenation. Furthermore, we examine metal-free PHIP facilitated by novel molecular tweezers and radicaloids, demonstrating alternative routes to conventional hydrogenation using metal-based catalysts. Although symmetric molecules incorporating parahydrogen are NMR silent, we showcase methods that reveal hyperpolarized states through post-hydrogenation reactions. We discuss chemical exchange processes that mediate polarization transfer between parahydrogen and a molecular target, expanding the reach of PHIP without synthesizing specialized precursors. We conclude this review by highlighting the role of PHIP in uncovering the H2 activation mechanisms of hydrogenases. By providing a detailed review of these diverse phenomena, we aim to familiarize the reader with the versatility of PHIP and its potential applications for mechanistic studies and chemical analysis.

Julien Hurtaud, Cécile Delacour, Carole Mathevon et al.

Historically, amyloid fibers (AF) in research has always been linked to degenerative diseases. However, HET-s AF, by their morphology and function, have only little in common to pathogenic amyloid fibers such as α-synuclein or a\b{eta} and they have appeared as promising candidate for biocoating since few years. Here we have shown than HET-s amyloid fibers hydrogel is an extremely polyvalent coating material for the in vitro culture of primary hippocampal neurons. First, the non-cytotoxicity was demonstrated in vitro using standardized ISO protocols. Then, it is shown that in vitro culture of primary hippocampal neurons on HET-s AF hydrogels could last more than 45 days with clear signatures of spontaneous network activity, with which is a feat that not many other coatings have achieved yet. Finally, interactions between the cells, the dendrites and the hydrogels are highlighted, showing that dendrites might be able to penetrate the hydrogels in depth, therefore allowing recordings even within micrometer-thick hydrogels. In the end, those properties combined with group functionalization using standard biochemistry techniques, makes HET-s hydrogels ideal candidates to be used for the long-term growth of neurons as well as other types of cells. This versatility and easiness to use are definitely still unheard, especially for protein material. Due to its ability to transform from dry films to hydrogel when in contact with the extracellular matrix (ECM), it could also be used for in vivo implants, solving the issue of hydrogel damaging during the implant surgery.

H. A. Sober

Sara Seager, Janusz J. Petkowski, Jingcheng Huang et al.

Waste gas products from technological civilizations may accumulate in an exoplanet atmosphere to detectable levels. We propose nitrogen trifluoride (NF3) and sulfur hexafluoride (SF6) as ideal technosignature gases. Earth life avoids producing or using any N-F or S-F bond-containing molecules and makes no fully fluorinated molecules with any element. NF3 and SF6 may be universal technosignatures owing to their special industrial properties, which unlike biosignature gases, are not species-dependent. Other key relevant qualities of NF3 and SF6 are: their extremely low water solubility, unique spectral features, and long atmospheric lifetimes. NF3 has no non-human sources and was absent from Earth's pre-industrial atmosphere. SF6 is released in only tiny amounts from fluorine-containing minerals, and is likely produced in only trivial amounts by volcanic eruptions. We propose a strategy to rule out SF6's abiotic source by simultaneous observations of SiF4, which is released by volcanoes in an order of magnitude higher abundance than SF6. Other fully fluorinated human-made molecules are of interest, but their chemical and spectral properties are unavailable. We summarize why life on Earth-and perhaps life elsewhere-avoids using F. We caution, however, that we cannot definitively disentangle an alien biochemistry byproduct from a technosignature gas.

Alexander J. Dear, Georg Meisl, Emil Axell et al.

Analyzing kinetic experiments on protein aggregation using integrated rate laws has led to numerous advances in our understanding of the fundamental chemical mechanisms behind amyloidogenic disorders such as Alzheimer's and Parkinson's diseases. However, the description of biologically relevant processes may require rate equations that are too complex to solve using existing methods, hindering mechanistic insights into these processes. An example of significance is co-aggregation in environments containing multiple amyloid-beta (Abeta) peptide alloforms, which may play a crucial role in the biochemistry of Alzheimer's disease but whose mechanism is still poorly understood. Here, we use the mathematics of symmetry to derive a general integrated rate law valid for most plausible linear self-assembly reactions. We use it in conjunction with experimental data to determine the mechanism of co-aggregation of the most physiologically abundant Abeta alloforms: Abeta42, Abeta40, Abeta38 and Abeta37 peptides. We find that Abeta42 fibril surfaces catalyze the formation of co-oligomers, which accelerate new Abeta40, Abeta38 and Abeta37 fibril formation whilst inhibiting secondary nucleation of new Abeta42 fibrils. The simplicity, accuracy and broad applicability of our general integrated rate law will enable kinetic analysis of more complex filamentous self-assembly reactions, both with and without co-aggregation.

Lingyun Xiong, Alan Garfinkel

Despite widespread and striking examples of physiological oscillations, their functional role is often unclear. Even glycolysis, the paradigm example of oscillatory biochemistry, has seen questions about its oscillatory function. Here, we take a systems approach to summarize evidence that oscillations play critical physiological roles. Oscillatory behavior enables systems to avoid desensitization, to avoid chronically high and therefore toxic levels of chemicals, and to become more resistant to noise. Oscillation also enables complex physiological systems to reconcile incompatible conditions such as oxidation and reduction, by cycling between them, and to synchronize the oscillations of many small units into one large effect. In pancreatic beta cells, glycolytic oscillations are in synchrony with calcium and mitochondrial oscillations to drive pulsatile insulin release, which is pivotal for the liver to regulate blood glucose dynamics. In addition, oscillation can keep biological time, essential for embryonic development in promoting cell diversity and pattern formation. The functional importance of oscillatory processes requires a rethinking of the traditional doctrine of homeostasis, holding that physiological quantities are maintained at constant equilibrium values, a view that has largely failed us in the clinic. A more dynamic approach will enable us to view health and disease through a new light and initiate a paradigm shift in treating diseases, including depression and cancer. This modern synthesis also takes a deeper look into the mechanisms that create, sustain and abolish oscillatory processes, which requires the language of nonlinear dynamics, well beyond the linearization techniques of equilibrium control theory.

Yu Meng, Cheryl Ingram-Smith, Oly Ahmed et al.

Short- and medium-chain acyl-CoA synthetases catalyze similar two-step reactions in which acyl substrate and ATP bind to form an enzyme-bound acyl-adenylate, then CoA binds for formation of the acyl-CoA product. We investigated the roles of active site residues in CoA binding in acetyl-CoA synthetase (Acs) and a medium-chain acyl-CoA synthetase (Macs) that uses 2-methylbutyryl-CoA. Three highly conserved residues, Arg¹⁹³, Arg⁵²⁸, and Arg⁵⁸⁶ of <i>Methanothermobacter thermautotrophicus</i> Acs (Acs_Mt), are predicted to form important interactions with the 5′- and 3′-phosphate groups of CoA. Kinetic characterization of Acs_Mt variants altered at each of these positions indicates these Arg residues play a critical role in CoA binding and catalysis. The predicted CoA binding site of <i>Methanosarcina acetivorans</i> Macs (Macs_Ma) is structurally more closely related to that of 4-chlorobenzoate:coenzyme A ligase (CBAL) than Acs. Alteration of Macs_Ma residues Tyr⁴⁶⁰, Arg⁴⁹⁰, Tyr⁵²⁵, and Tyr⁵²⁷, which correspond to CoA binding pocket residues in CBAL, strongly affected CoA binding and catalysis without substantially affecting acyl-adenylate formation. Both enzymes discriminate between 3′-dephospho-CoA and CoA, indicating interaction between the enzyme and the 3′-phosphate group is important. Alteration of Macs_Ma residues Lys⁴⁶¹ and Lys⁵¹⁹, located at positions equivalent to Acs_Mt Arg⁵²⁸ and Arg⁵⁸⁶, respectively, had only a moderate effect on CoA binding and catalysis. Overall, our results indicate the active site architecture in Acs_Mt and Macs_Ma differs even though these enzymes catalyze mechanistically similar reactions. The significance of this study is that we have delineated the active site architecture with respect to CoA binding and catalysis in this important enzyme superfamily.