Analytical biochemistry最新文献

Oviductus Ranae is the dried oviduct from Rana dybowskii, a forest frog species with medicinal, tonic, and cosmetic properties. Due to the high price and resource shortage, counterfeit varieties of Oviductus Ranae often appear in the market. However, traditional identification methods cannot accurately differentiate between Oviductus Ranae and its adulterants. In this study, a rapid molecular identification method has been established. The method involves extracting genomic DNA in just 30 s using filter paper purification, species-specific rapid polymerase chain reaction (PCR) amplification, and finally, fluorescence detection of the products. It can accurately identify Oviductus Ranae and its three common adulterants in about 30 min, making the process simple, fast, and highly specific.

Interactions between proteins are ubiquitous in a wide variety of biological processes. Accurately identifying the protein-protein interaction (PPI) is of significant importance for understanding the mechanisms of protein functions and facilitating drug discovery. Although the wet-lab technological methods are the best way to identify PPI, their major constraints are their time-consuming nature, high cost, and labor-intensiveness. Hence, lots of efforts have been made towards developing computational methods to improve the performance of PPI prediction. In this study, we propose a novel hybrid computational method (called KSGPPI) that aims at improving the prediction performance of PPI via extracting the discriminative information from protein sequences and interaction networks. The KSGPPI model comprises two feature extraction modules. In the first feature extraction module, a large protein language model, ESM-2, is employed to exploit the global complex patterns concealed within protein sequences. Subsequently, feature representations are further extracted through CKSAAP, and a two-dimensional convolutional neural network (CNN) is utilized to capture local information. In the second feature extraction module, the query protein acquires its similar protein from the STRING database via the sequence alignment tool NW-align and then captures the graph embedding feature for the query protein in the protein interaction network of the similar protein using the algorithm of Node2vec. Finally, the features of these two feature extraction modules are efficiently fused; the fused features are then fed into the multilayer perceptron to predict PPI. The results of five-fold cross-validation on the used benchmarked datasets demonstrate that KSGPPI achieves an average prediction accuracy of 88.96 %. Additionally, the average Matthews correlation coefficient value (0.781) of KSGPPI is significantly higher than that of those state-of-the-art PPI prediction methods. The standalone package of KSGPPI is freely downloaded at https://github.com/rickleezhe/KSGPPI.

Ionic microenvironment of the nasal secretions especially calcium ions play essential role in the olfactory transmission. However, there is a critical need to determine the free calcium levels in healthy people's nasal secretions in contrast to those of patients with olfactory impairment. A selective spectrofluorometric method was created to quantify nasal calcium levels utilizing its quenching ability to the fluorescence of the functionalized carbon quantum dots. The surface of carbon quantum dots was functionalized with calcium ionophore A23187 and ion association complex, calcium phosphotungstate, to improve the selectively to quantify calcium ions. The functionalized carbon quantum dots exhibited a concentration-dependent fluorescence quenching upon interaction with calcium ions. Different factors influencing the quenching process were done to provide efficient analytical process. The new method, demonstrated accurate calcium determination over the concentration range of 200–4000 ng/mL. The suggested technique was used to measure the calcium in the nasal secretions of both healthy people and patients with olfactory impairment. The findings revealed significantly higher calcium levels in the patient with olfactory dysfunction (healthy vs. patient; 735 ± 20 ng/mL vs. 2987 ± 37 ng/mL, p < 0.05).

Diabetes is a chronic disease that is characterized by high blood sugar levels and can have several harmful outcomes. Hyperglycemia, which is defined by persistently elevated blood sugar, is one of the primary concerns. People can improve their overall well-being and get optimal health outcomes by prioritizing diabetes control. Although the use of experimental approaches in diabetes treatment is cost-effective, it necessitates the development of many strategies for evaluating the efficacy of therapies. Researchers can quickly create new strategies for managing diabetes and get vital insights by enabling virtual screening with computational tools and procedures. In this study, we suggest a predictor named STADIP (STacking-based predictor for AntiDiabetic Peptides), a new method to predict antidiabetic peptides (ADPs) utilizing a stacked-based ensemble approach. It uses 12 different feature encodings and seven machine-learning techniques to construct 84 baseline models. The impacts of various baseline models on ADP prediction were then thoroughly examined. A two-step feature selection method, eXtreme Gradient Boosting with Sequential Forward Selection (XGB-SFS), was employed to determine the optimal number, out of 84 PFs to enhance predictive performance. Subsequently, utilizing the meta-predictor approach, 45 selected PFs were integrated into an XGB classifier to formulate the final hybrid model. The proposed method demonstrated superior predictive capabilities compared to constituent baseline models, as evidenced by evaluations on both cross-validation and independent tests. During extensive independent testing, STADIP achieved promising performance with accuracy and mathew's correlation coefficient of 0.954 and 0.877, respectively. It is anticipated that it will be useful tool in helping the scientific community to identify new antidiabetic proteins.

MicroRNAs (miRNAs) can serve as biomarkers for early-diagnosis, therapy, and postoperative care of cervical cancer. Sensitive and reliable quantification of miRNA remains a huge challenge due to its low expressing levels and background interference. Herein, we propose a novel exonuclease-III (Exo–III)–propelled DNAzyme cascade for sensitive and high-efficient miRNA analysis. This method involves the engineering of compact DNAzyme hairpin probes, including the H1 probe and H2 probe. The H1 probe is designed with exposed analyte recognition subunits that can specifically recognize target miRNA. This recognition triggers two processes: Exo-iii-assisted target regeneration and successive substrate cleavage catalyzed by DNAzyme. The unique character of Exo-III that catalyzes removal of mononucleotides from the blunt or recessed 3′-OH termini of dsDNA confers the approach with a minimal background signal. The multiple signal cycles provided an abundant signal amplification and consequently, the method exhibited a low limit of detection of 3.12 fM, and a better specificity over several homologous miRNAs. In summary, this powerful Exo-III driven DNAzyme cascaded system offers broader and more adaptable methods for comprehending the activities of miRNA in various biological occurrences.

Accurately predicting RNA-protein binding sites is essential to gain a deeper comprehension of the protein-RNA interactions and their regulatory mechanisms, which are fundamental in gene expression and regulation. However, conventional biological approaches to detect these sites are often costly and time-consuming. In contrast, computational methods for predicting RNA protein binding sites are both cost-effective and expeditious. This review synthesizes already existing computational methods, summarizing commonly used databases for predicting RNA protein binding sites. In addition, applications and innovations of computational methods using traditional machine learning and deep learning for RNA protein binding site prediction during 2018–2023 are presented. These methods cover a wide range of aspects such as effective database utilization, feature selection and encoding, innovative classification algorithms, and evaluation strategies. Exploring the limitations of existing computational methods, this paper delves into the potential directions for future development. DeepRKE, RDense, and DeepDW all employ convolutional neural networks and long and short-term memory networks to construct prediction models, yet their algorithm design and feature encoding differ, resulting in diverse prediction performances.

For irreversible denaturation transitions such as those exhibited by monoclonal antibodies, differential scanning calorimetry provides the denaturation temperature, T_m, the rate of denaturation at T_m, and the activation energy at T_m. These three quantities are essential but not sufficient for an accurate extrapolation of the rate of denaturation to temperatures of 25 °C and below. We have observed that the activation energy is not constant but temperature dependent due to the existence of an activation heat capacity, C_p,a. It is shown in this paper that a model that incorporates C_p,a is able to account for previous observations like, for example, that increasing the T_m does not always improve the stability at low temperatures; that some antibodies exhibit lower stabilities at 5 °C than at 25 °C; or that low temperature stabilities do not follow the rank order derived from T_m values. Most importantly, the activation heat capacity model is able to reproduce time dependent stabilities measured by size exclusion chromatography at low temperatures.

Cancer development and progression are intimately related with post-translational protein modifications, e.g., highly reactive thiol moiety of cysteines enables structural rearrangements resulting in redox biological switches. In this context, redox proteomics techniques, such as 2D redox DIGE, biotin switch assay and OxIcat are fundamental tools to identify and quantify redox-sensitive proteins and to understand redox mechanisms behind thiol modifications. Given the great variability in redox proteomics protocols, problems including decreased resolution of peptides and low protein amounts even after enrichment steps may occur. Considering the biological importance of thiol's oxidation in melanoma, we adapted the biotin-switch assay technique for melanoma cells in order to overcome the limitations and improve coverage of detected proteins.

The imperative for the point-of-care testing of methamphetamine and cocaine in drug abuse prevention necessitates innovative solutions. To address this need, we have introduced a multi-channel wearable sensor harnessing CRISPR/Cas12a system. A CRISPR/Cas12a based system, integrated with aptamers specific to methamphetamine and cocaine, has been engineered. These aptamers function as signal-mediated intermediaries, converting methamphetamine and cocaine into nucleic acid signals, subsequently generating single-stranded DNA to activate the Cas12 protein. Additionally, we have integrated a microfluidic system and magnetic separation technology into the CRISPR system, enabling rapid and precise detection of cocaine and methamphetamine. The proposed sensing platform demonstrated exceptional sensitivity, achieving a detection limit as low as 0.1 ng/mL. This sensor is expected to be used for on-site drug detection in the future.

Xing 9 Ling tablet candy (X9LTC) effectively treats alcoholic liver disease (ALD), but its potential mechanism and molecular targets remain unstudied. We aimed to address this gap using network pharmacology. Furthermore, high-performance liquid chromatography (HPLC) and database analysis revealed a total of 35 active ingredients and 311 corresponding potential targets of X9LTC. Protein interaction analysis revealed PTGS2, JUN, and FOS as its core targets. Enrichment analysis indicated that chemical carcinogenesis-receptor activation, IL-17 and TNF signaling pathway were enriched by multiple core targets, which might be the main pathway of action. Further molecular docking validation showed that the core targets had good binding activities with the identified compounds. Animal experiments showed that X9LTC could reduce the high expression of ALT, AST and TG in the serum of ALD mice, alleviate the lesions in liver tissues, and reverse the high expression of PTGS2, JUN, and FOS proteins in the liver tissues. In this study, we established a method for the determination of X9LTC content for the first time, and predicted its active ingredient and mechanism of action in treating ALD, providing theoretical basis for further research.