Screening and Stability Evaluation of Angiotensin Converting Enzyme Inhibitory Peptides from Bangia fusco-purpurea

In this study, peptide fractions (F1-F4) with different molecular masses were obtained from Bangia fusco-purpurea through enzymatic hydrolysis and ultrafiltration. F2, with molecular masses of 800–2 000 Da, exhibited the highest in vitro angiotensin-converting enzyme (ACE) inhibitory activity as determined by high performance liquid chromatography (HPLC). The amino acid sequence of F2 was identified through liquid chromatography-tandem mass spectrometry (LC-MS/MS) and de novo sequencing using PEAKS Studio software. Six ACE inhibitory peptides that stably bind to ACE were selected through molecular docking. The predicted peptides were synthesized by solid-phase synthesis and their in vitro ACE inhibitory activity was verified. Among them, L1 (LVLLFLFGE) showed the highest ACE inhibitory activity with a half maximal inhibitory concentration (IC50) value of 14.22 μg/mL. Molecular docking results indicated that the inhibition of ACE by L1 was mainly attributed to its ability to form hydrogen bond interactions with the active site of ACE. Finally, the effects of temperature, pH, metal ions, light exposure, and simulated gastrointestinal digestion on the stability of L1 were investigated. The results revealed that L1 was highly stable to heat and ionic strength. However, its activity gradually decreased at pH > 2, and was affected by ultraviolet treatment. The ACE inhibitory activity of L1 decreased after simulated gastric and intestinal digestion, but was still significant

An Overview of the Prediction of Protein DNA-Binding Sites

Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications

Fixed Point Iteration Based Algorithm for Asynchronous TOA-Based Source Localization

This paper investigates the problem of source localization using signal time-of-arrival (TOA) measurements in the presence of unknown start transmission time. Most state-of-art methods are based on convex relaxation technologies, which possess global solution for the relaxed optimization problem. However, computational complexity of the convex optimization–based algorithm is usually large, and need CVX toolbox to solve it. Although the two stage weighted least squares (2SWLS) algorithm has very low computational complexity, its estimate performance is susceptible to sensor geometry and threshold phenomenon. A new algorithm that is directly derived from maximum likelihood estimator (MLE) is developed. The newly proposed algorithm is named as fixed point iteration (FPI); it only involves simple calculations, such as addition, multiplication, division, and square-root. Unlike state-of-the-art methods, there is no matrix inversion operation and can avoid the unstable performance incurred by singular matrix. The FPI algorithm can be easily extended to the scenario with sensor position errors. Finally, simulation results demonstrate that the proposed algorithm reaches a good balance between computational complexity and localization accuracy

Computational Prediction of RNA-Binding Proteins and Binding Sites

Proteins and RNA interaction have vital roles in many cellular processes such as protein synthesis, sequence encoding, RNA transfer, and gene regulation at the transcriptional and post-transcriptional levels. Approximately 6%–8% of all proteins are RNA-binding proteins (RBPs). Distinguishing these RBPs or their binding residues is a major aim of structural biology. Previously, a number of experimental methods were developed for the determination of protein–RNA interactions. However, these experimental methods are expensive, time-consuming, and labor-intensive. Alternatively, researchers have developed many computational approaches to predict RBPs and protein–RNA binding sites, by combining various machine learning methods and abundant sequence and/or structural features. There are three kinds of computational approaches, which are prediction from protein sequence, prediction from protein structure, and protein-RNA docking. In this paper, we review all existing studies of predictions of RNA-binding sites and RBPs and complexes, including data sets used in different approaches, sequence and structural features used in several predictors, prediction method classifications, performance comparisons, evaluation methods, and future directions

Optimization of Markov Queuing Model in Hospital Bed Resource Allocation

Bed resources are the platform in which most medical and health resources in the hospital play a role and carry the core functions of the health service system. How to improve the efficiency of the use of bed resources through scientific management measures and methods and ultimately achieve the optimization of overall health resources is the focus of hospital management teams. This paper analyzes the previous research models of knowledge related to queuing theory in medical services. From the perspective of the hospital and the patient, several indicators such as the average total number of people, the utilization rate of bed resources, the patient stop rate, and the patient average waiting time are defined to measure the performance of the triage queue calling model, which makes the patient queue more reasonable. According to the actual task requirements of a hospital, a Markov queuing strategy based on Markov service is proposed. A mathematical queuing model is constructed, and the process of solving steady-state probability based on Markov theory is analyzed. Through the comparative analysis of simulation experiments, the advantages and disadvantages of the service Markov queuing model and the applicable scope are obtained. Based on the theory of the queuing method, a queuing network model of bed resource allocation is established in principle. Experimental results show that the queuing strategy of bed resource allocation based on Markov optimization effectively improves resource utilization and patient satisfaction and can well meet the individual needs of different patients. It does not only provide specific optimization measures for the object of empirical research but also provides a reference for the development of hospital bed resource allocation in theory

WTM: The Site-Wise Empirical Wuhan University Tropospheric Model

The tropospheric model is the key model in space geodetic techniques such as Global Navigation Satellite Systems (GNSS) and Very Long Baseline Interferometry (VLBI). In this paper, we established the site-wise empirical Wuhan University Tropospheric Model (WTM) by using 10-year (2011–2020) monthly mean and 5-year (2016–2020) hourly ERA5 reanalysis data, where the Zenith Path Delay (ZPD), mapping function, and horizontal gradient as well as meteorological parameters are provided at 1583 specific space geodetic stations with additionally considering the diurnal and semi-diurnal variations. The mapping function and horizontal gradient from the WTM model were evaluated at 524 globally distributed GNSS stations during the year 2020 and compared with the latest grid-wise (1° × 1°) Global Pressure and Temperature 3 (GPT3) model. The significant improvements of the WTM model to the GPT3 model were found at the stations with terrain relief, and the maximal mapping function and horizontal gradient accuracy improvements reached 12.8 and 14.71 mm. The ZPD and mapping functions from the two models were also validated at 31 Multi-GNSS Experiment (MGEX) stations spanning the year 2020 by BeiDou Navigation Satellite System (BDS) Precise Point Positioning (PPP). The significant vertical coordinate and ZTD difference biases between the PPP schemes adopted by the two models were also found, and the largest biases reached −1.78 and 0.87 mm