Hasil untuk "Biochemistry"

E. Bitzek, P. Koskinen, F. Gähler et al.

Yi Zhang, D. Reinberg

Department of Biochemistry and Biophysics, Curriculum in Genetics and Molecular Biology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, North Carolina 27599-7295, USA; Howard Hughes Medical Institute, Division of Nucleic Acids Enzymology, Department of Biochemistry, University of Medicine and Dentistry of New Jersey, Robert Wood Johnson Medical School, Piscataway, New Jersey 08854, USA

J. Urban, S. Roberts

The intervertebral disc is a cartilaginous structure that resembles articular cartilage in its biochemistry, but morphologically it is clearly different. It shows degenerative and ageing changes earlier than does any other connective tissue in the body. It is believed to be important clinically because there is an association of disc degeneration with back pain. Current treatments are predominantly conservative or, less commonly, surgical; in many cases there is no clear diagnosis and therapy is considered inadequate. New developments, such as genetic and biological approaches, may allow better diagnosis and treatments in the future.

A. Lehninger

Kaiyang Li, Shihao Ji, Zhipeng Cai et al.

Approximate subgraph matching (ASM) is a task that determines the approximate presence of a given query graph in a large target graph. Being an NP-hard problem, ASM is critical in graph analysis with a myriad of applications ranging from database systems and network science to biochemistry and privacy. Existing techniques often employ heuristic search strategies, which cannot fully utilize the graph information, leading to sub-optimal solutions. This paper proposes a Reinforcement Learning based Approximate Subgraph Matching (RL-ASM) algorithm that exploits graph transformers to effectively extract graph representations and RL-based policies for ASM. Our model is built upon the branch-and-bound algorithm that selects one pair of nodes from the two input graphs at a time for potential matches. Instead of using heuristics, we exploit a Graph Transformer architecture to extract feature representations that encode the full graph information. To enhance the training of the RL policy, we use supervised signals to guide our agent in an imitation learning stage. Subsequently, the policy is fine-tuned with the Proximal Policy Optimization (PPO) that optimizes the accumulative long-term rewards over episodes. Extensive experiments on both synthetic and real-world datasets demonstrate that our RL-ASM outperforms existing methods in terms of effectiveness and efficiency. Our source code is available at https://github.com/KaiyangLi1992/RL-ASM.

Rajan, Ishaan Gupta

Molecules are commonly represented as SMILES strings, which can be readily converted to fixed-size molecular fingerprints. These fingerprints serve as feature vectors to train ML/DL models for molecular property prediction tasks in the field of computational chemistry, drug discovery, biochemistry, and materials science. Recent research has demonstrated that SMILES can be used to construct molecular graphs where atoms are nodes ($V$) and bonds are edges ($E$). These graphs can subsequently be used to train geometric DL models like GNN. GNN learns the inherent structural relationships within a molecule rather than depending on fixed-size fingerprints. Although GNN are powerful aggregators, their efficacy on smaller datasets and inductive biases across different architectures is less studied. In our present study, we performed a systematic benchmarking of four different GNN architectures across a diverse domain of datasets (physical chemistry, biological, and analytical). Additionally, we have also implemented a hierarchical fusion (GNN+FP) framework for target prediction. We observed that the fusion framework consistently outperforms or matches the performance of standalone GNN (RMSE improvement > $7\%$) and baseline models. Further, we investigated the representational similarity using centered kernel alignment (CKA) between GNN and fingerprint embeddings and found that they occupy highly independent latent spaces (CKA $\le0.46$). The cross-architectural CKA score suggests a high convergence between isotopic models like GCN, GraphSAGE and GIN (CKA $\geq0.88$), with GAT learning moderately independent representation (CKA $0.55-0.80$).

L. Greene, E. Shooter

P. Jocelyn

P. Kolattukudy

Emmy D.G. Fleuren

Oncogenic FGFR4 signalling represents an attractive therapeutic target across multiple cancers, yet treatment resistance almost uniformly occurs. A critical mechanism steering resistance is a rapid and complex reprogramming of kinase signalling networks, called the adaptive bypass response. Capturing this dynamic rewiring to pinpoint, on a molecular level, the right combinatorial drug for the right FGFR4-driven cancer patient at the right time, will be key to achieving sustained tumour responses. But how can one accurately capture this process across different cancer types exhibiting contrasting levels of FGFR4 signalling pathway components and network behaviours? A recent study by Shin et al. delivers a technically elegant and biologically grounded exploration of the adaptive signalling landscape to tackle this, revealing cell context-dependent combinatorial strategies.

Denis Céolin, Tsveta Miteva, Jean-Pascal Rueff et al.

Understanding the interaction between metal ions and their aqueous environment is fundamental in many areas of chemistry, biology, and environmental science. In this study, we investigate the electronic structure of hydrated calcium ions, focusing on how water molecules influence the behavior of the metal ion. We employed advanced X-ray techniques, including X-ray absorption, photoelectron, and Auger spectroscopies, combined with high-level quantum chemical calculations. Our analysis reveals that, alongside normal Auger decay, distinct ultrafast charge transfer processes occur between the calcium ion and surrounding water molecules, underscoring the complex nature of metal-solvent interactions. Two primary mechanisms were identified. The first one involves electron transfer from water to the calcium ion. The second mechanism depends on the photon energy and is tentatively attributed to the decay of photoelectron satellites, the capture of free solvated electrons or electrons from a Cl$^-$ ion in the second solvation shell. Additionally, we observed significant shifts in electron energies due to post-collision interactions and interpreted the Ca 1s-1 photoelectron satellites mainly as originating from inelastic photoelectron scattering (IPES). These findings provide deeper insights into the electronic properties of hydrated metal ions, with potential implications for fields such as catalysis and biochemistry, where metal ions play a crucial role.

Andjela Zivkovic, Ana Jotic, Ivan Dozic et al.

(1) Background: Laryngeal surgery due to carcinoma leads to significant tissue disruption, cellular injury, and inflammation. This leads to increased levels of reactive oxygen species (ROS), causing oxidative damage that can influence quality of life (QOL) and recovery and complicate the postoperative course. The aim of this study was to compare how postoperative quality of life and surgical complication occurrence interacted with the biomarker levels of oxidative stress (malondialdehyde, MDA; superoxide dismutase, SOD; glutathione peroxidase 1, GPX1; and catalase, CAT) and inflammation (interleukin 1, IL-1; interleukin 6, IL-6; C-reactive protein, CRP) in patients treated with conservative and radical laryngeal surgery. (2) Methods: The study included 56 patients who underwent surgical treatment for laryngeal cancer. Blood samples were collected to analyze oxidative stress and inflammation parameters before surgery and on the first and seventh days postoperatively. Serum concentrations of MDA, SOD, GPX, CAT, IL-1, IL-6, and CRP were measured using coated enzyme-linked immunosorbent assay (ELISA) kits. EORTC QLQ-H&H43 questionnaire was used to measure the QOL of patients. (3) Results and Conclusions: T stage, pain intensity, and the extent of the surgical procedure were established as significant predictive factors for QOL in multivariate analysis. There was a significant positive correlation between surgical complication occurrence and preoperative values of GPX and MDA, but significant predictors of surgical complication occurrence on the 7th postoperative day were SOD and MDA values (<i>p</i> < 0.05).

Eric N. Ponnampalam, Eric N. Ponnampalam, Michelle Kearns et al.

Optimising resource use efficiency in animal- agriculture-production systems is important for the economic, environmental, and social sustainability of food systems. Production of foods with increased health enhancing aspects can add value to the health and wellbeing of the population. However, enrichment of foods, especially meat with health enhancing fatty acids (HEFA) increases susceptibility to peroxidation, which adversely influences its shelf life, nutritional value and eating quality. The meat industry has been challenged to find sustainable strategies that enhance the fatty acid profile and antioxidant actions of meat while mitigating oxidative deterioration and spoilage. Currently, by-products or co-products from agricultural industries containing a balance of HEFA and antioxidant sources seem to be a sustainable strategy to overcome this challenge. However, HEFA and antioxidant enrichment processes are influenced by ruminal lipolysis and biohydrogenation, HEFA-antioxidant interactions in rumen ecosystems and muscle biofortification. A deep understanding of the performance of different agro-by-product-based HEFA and antioxidants and their application in current animal production systems is critical in developing HEFA-antioxidant co-supplementation strategies that would benefit modern consumers who desire nutritious, palatable, safe, healthy, affordable, and welfare friendly meat and processed meat products. The current review presents the latest developments regarding discovery and application of novel sources of health beneficial agro-by-product-based HEFA and antioxidants currently used in the production of HEFA-antioxidant enriched ruminant meats and highlights future research perspectives.

William Bains, Janusz J. Petkowski, Sara Seager

Life is a complex, dynamic chemical system that requires a dense fluid solvent in which to take place. A common assumption is that the most likely solvent for life is liquid water, and some researchers argue that water is the only plausible solvent. However, a persistent theme in astrobiological research postulates that other liquids might be cosmically common, and could be solvents for the chemistry of life. In this paper we present a new framework for the analysis of candidate solvents for life, and deploy this framework to review substances that have been suggested as solvent candidates. We categorize each solvent candidate through four criteria: occurrence, solvation, solute stability and solvent chemical functionality. Our semi-quantitative approach addresses all the requirements for a solvent not only from the point of view of its chemical properties but also from the standpoint of their biochemical function. Only the protonating solvents fulfil all the chemical requirements to be a solvent for life, and of those only water and concentrated sulfuric acid are also likely to be abundant in a rocky planetary context. Among the non-protonating solvents liquid CO2 stands out as a planetary solvent, and its potential as a solvent for life should be explored. We conclude with a discussion of whether it is possible for a biochemistry to change solvents, as an adaptation to radical changes in a planet's environment. Our analysis provides the basis for prioritizing future experimental work exploring potential complex chemistry on other planets.

Dan Yin, Xiao Li, Xin Wang et al.

Bacterial infections seriously threaten human safety. Therefore, it is very important to develop a method for bacterial detection and treatment with rapid response, high sensitivity, and simple operation. A peptide CF₄KY^P (C, cysteine; F₄, phenylalanine tetrapeptide; K, lysine; Y^P, phosphorylated tyrosine) functionalized gold nanoparticle (AuNPs-CF₄KY^P) was synthesized for simultaneous detection and treatment of bacteria based on bacterial alkaline phosphatase (ALP). In solution, ALP can induce AuNPs-CF₄KY^P aggregation and produce significant color changes. After encountering bacteria, monodisperse AuNPs-CF₄KY^P can aggregate/assemble in situ on the surface of the bacterial membrane, change the color of the solution from wine red to grey, destroy the bacterial membrane structure, and induce the production of a large number of reactive oxygen species within the bacteria. The absorption change of AuNPs-CF₄KY^P solution has a good linear relationship with the number of bacteria. Furthermore, the aggregation of AuNPs-CF₄KY^P kills approximately 80% of <i>Salmonella typhimurium</i>. By combining enzyme-instructed peptide self-assembly technology and colorimetric analysis technology, we achieve rapid and sensitive colorimetric detection and killing of bacteria.

Ling Ji, Tianle Li, Huimin Chen et al.

Abstract Type I interferon (IFN-I) plays crucial roles in the regulation of inflammation and it is associated with various inflammatory diseases including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and periodontitis, impacting people's health and quality of life. It is well-established that IFN-Is affect immune responses and inflammatory factors by regulating some signaling. However, currently, there is no comprehensive overview of the crucial regulatory role of IFN-I in distinctive pathways as well as associated inflammatory diseases. This review aims to provide a narrative of the involvement of IFN-I in different signaling pathways, mainly mediating the related key factors with specific targets in the pathways and signaling cascades to influence the progression of inflammatory diseases. As such, we suggested that IFN-Is induce inflammatory regulation through the stimulation of certain factors in signaling pathways, which displays possible efficient treatment methods and provides a reference for the precise control of inflammatory diseases.

Duc Q. Nguyen, Thanh Toan Nguyen, Tho quan

Subgraph matching is a challenging problem with a wide range of applications in database systems, biochemistry, and cognitive science. It involves determining whether a given query graph is present within a larger target graph. Traditional graph-matching algorithms provide precise results but face challenges in large graph instances due to the NP-complete problem, limiting their practical applicability. In contrast, recent neural network-based approximations offer more scalable solutions, but often lack interpretable node correspondences. To address these limitations, this article presents xNeuSM: Explainable Neural Subgraph Matching which introduces Graph Learnable Multi-hop Attention Networks (GLeMA) that adaptively learns the parameters governing the attention factor decay for each node across hops rather than relying on fixed hyperparameters. We provide a theoretical analysis establishing error bounds for GLeMA's approximation of multi-hop attention as a function of the number of hops. Additionally, we prove that learning distinct attention decay factors for each node leads to a correct approximation of multi-hop attention. Empirical evaluation on real-world datasets shows that xNeuSM achieves substantial improvements in prediction accuracy of up to 34% compared to approximate baselines and, notably, at least a seven-fold faster query time than exact algorithms. The source code of our implementation is available at https://github.com/martinakaduc/xNeuSM.

J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller et al.

Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the reliability of MLFFs in molecular dynamics (MD) simulations is facing growing scrutiny due to concerns about instability over extended simulation timescales. Our findings suggest a potential connection between robustness to cumulative inaccuracies and the use of equivariant representations in MLFFs, but the computational cost associated with these representations can limit this advantage in practice. To address this, we propose a transformer architecture called SO3krates that combines sparse equivariant representations (Euclidean variables) with a self-attention mechanism that separates invariant and equivariant information, eliminating the need for expensive tensor products. SO3krates achieves a unique combination of accuracy, stability, and speed that enables insightful analysis of quantum properties of matter on extended time and system size scales. To showcase this capability, we generate stable MD trajectories for flexible peptides and supra-molecular structures with hundreds of atoms. Furthermore, we investigate the PES topology for medium-sized chainlike molecules (e.g., small peptides) by exploring thousands of minima. Remarkably, SO3krates demonstrates the ability to strike a balance between the conflicting demands of stability and the emergence of new minimum-energy conformations beyond the training data, which is crucial for realistic exploration tasks in the field of biochemistry.

David M. Bortz, Daniel A. Messenger, Vanja Dukic

We introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of ODEs. Without relying on any numerical differential equation solvers, WENDy computes accurate estimates and is robust to large (biologically relevant) levels of measurement noise. For low dimensional systems with modest amounts of data, WENDy is competitive with conventional forward solver-based nonlinear least squares methods in terms of speed and accuracy. For both higher dimensional systems and stiff systems, WENDy is typically both faster (often by orders of magnitude) and more accurate than forward solver-based approaches. The core mathematical idea involves an efficient conversion of the strong form representation of a model to its weak form, and then solving a regression problem to perform parameter inference. The core statistical idea rests on the Errors-In-Variables framework, which necessitates the use of the iteratively reweighted least squares algorithm. Further improvements are obtained by using orthonormal test functions, created from a set of C-infinity bump functions of varying support sizes. We demonstrate the high robustness and computational efficiency by applying WENDy to estimate parameters in some common models from population biology, neuroscience, and biochemistry, including logistic growth, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, and a Protein Transduction Benchmark model. Software and code for reproducing the examples is available at (https://github.com/MathBioCU/WENDy).

P. Reynolds