Recent progress in separation prediction of counter‐current chromatography

As a liquid‐liquid partition chromatography, counter‐current chromatography has advantages in large sample loading capacity without irreversible adsorption, which has been widely applied in separation and purification fields. The main factors, including partition coefficient, two‐phase solvent systems, apparatus, and operating parameters greatly affect the separation process of counter‐current chromatography. To promote the applications of counter‐current chromatography, it is essential to develop theoretical research to master the principles of counter‐current chromatographic separations so as to achieve predictions before laborious trials. In this article, recent progress about separation prediction methods are reviewed from a point of the steady and unsteady state of the mass transfer process of counter‐current chromatography and its mass transfer characteristics, and then it is divided into three aspects: prediction of partition coefficient, modeling the thermodynamic process of counter‐current chromatography, and modeling the dynamic process of counter‐current chromatography.     

Identification of Potential SARS-CoV-2 Main Protease and Spike Protein Inhibitors from the Genus Aloe: An In Silico Study for Drug Development

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) disease is a global rapidly spreading virus showing very high rates of complications and mortality. Till now, there is no effective specific treatment for the disease. Aloe is a rich source of isolated phytoconstituents that have an enormous range of biological activities. Since there are no available experimental techniques to examine these compounds for antiviral activity against SARS-CoV-2, we employed an in silico approach involving molecular docking, dynamics simulation, and binding free energy calculation using SARS-CoV-2 essential proteins as main protease and spike protein to identify lead compounds from Aloe that may help in novel drug discovery. Results retrieved from docking and molecular dynamics simulation suggested a number of promising inhibitors from Aloe. Root mean square deviation (RMSD) and root mean square fluctuation (RMSF) calculations indicated that compounds 132, 134, and 159 were the best scoring compounds against main protease, while compounds 115, 120, and 131 were the best scoring ones against spike glycoprotein. Compounds 120 and 131 were able to achieve significant stability and binding free energies during molecular dynamics simulation. In addition, the highest scoring compounds were investigated for their pharmacokinetic properties and drug-likeness. The Aloe compounds are promising active phytoconstituents for drug development for SARS-CoV-2.     

In Vitro Inhibitory Analysis of Rationally Designed siRNAs against MERS-CoV Replication in Huh7 Cells

MERS-CoV was identified for the first time in Jeddah, Saudi Arabia in 2012 in a hospitalized patient. This virus subsequently spread to 27 countries with a total of 939 deaths and 2586 confirmed cases and now has become a serious concern globally. Camels are well known for the transmission of the virus to the human population. In this report, we have discussed the prediction, designing, and evaluation of potential siRNA targeting the ORF1ab gene for the inhibition of MERS-CoV replication. The online software, siDirect 2.0 was used to predict and design the siRNAs, their secondary structure and their target accessibility. ORF1ab gene folding was performed by RNAxs and RNAfold software. A total of twenty-one siRNAs were selected from 462 siRNAs according to their scoring and specificity. siRNAs were evaluated in vitro for their cytotoxicity and antiviral efficacy in Huh7 cell line. No significant cytotoxicity was observed for all siRNAs in Huh7 cells. The in vitro study showed the inhibition of viral replication by three siRNAs. The data generated in this study provide preliminary and encouraging information to evaluate the siRNAs separately as well as in combination against MERS-CoV replication in other cell lines. The prediction of siRNAs using online software resulted in the filtration and selection of potential siRNAs with high accuracy and strength. This computational approach resulted in three effective siRNAs that can be taken further to in vivo animal studies and can be used to develop safe and effective antiviral therapies for other prevalent disease-causing viruses.     

Biomolecular interactions with nanoparticles: applications for coronavirus disease 2019

Nanoparticles are small particles sized 1–100 nm, which have a large surface-to-volume ratio, allowing efficient adsorption of drugs, proteins, and other chemical compounds. Consequently, functionalized nanoparticles have potential diagnostic and therapeutic applications. A variety of nanoparticles have been studied, including those constructed from inorganic materials, biopolymers, and lipids. In this review, we focus on recent work targeting the severe acute respiratory syndrome coronavirus 2 virus that causes coronavirus disease (COVID-19). Understanding the interactions between coronavirus-specific proteins (such as the spike protein and its host cell receptor angiotensin-converting enzyme 2) with different nanoparticles paves the way to the development of new therapeutics and diagnostics that are urgently needed for the fight against COVID-19, and indeed for related future viral threats that may emerge.     

Trends in biosensing platforms for SARS-CoV-2 detection: A critical appraisal against standard detection tools

In this ongoing theme of coronavirus disease 2019 (COVID-19) pandemic, highly sensitive analytical testing platforms are extremely necessary to detect SARS-CoV-2 RNA and antiviral antibodies. To limit the viral spread, prompt and precise diagnosis is crucial to facilitate treatment and ensure effective isolation. Accurate detection of antibodies (IgG and IgM) is imperative to understand the prevalence of SARS-CoV-2 in public and to inspect the proportion of immune individuals. In this review, we demonstrate and evaluate some tests that have been used commonly to detect SARS-CoV-2. These include nucleic acid and serological tests for the detection of SARS-CoV-2 RNA and specific antibodies in infected people. Moreover, the vitality of biosensing technologies emphasizing on optical and electrochemical biosensors toward the detection of SARS-CoV-2 has also been discussed here. The early diagnosis of COVID-19 based on detection of reactive oxygen species overproduction because of virus-induced dysfunctioning of lung cells has also been highlighted.     

State-of-the-art colloidal particles and unique interfaces-based SARS-CoV-2 detection methods and COVID-19 diagnosis

In March 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-based infections were declared ‘COVID-19 pandemic’ by the World Health Organization. Pandemic raised the necessity to design and develop genuine and sensitive tests for precise specific SARS-CoV-2 infections detection. Nanotechnological methods offer new ways to fight COVID-19. Nanomaterials are ideal for unique sensor platforms because of their chemically versatile properties and they are easy to manufacture. In this context, selected examples for integrating nanomaterials and distinct biosensor platforms are given to detect SARS-CoV-2 biological materials and COVID-19 biomarkers, giving researchers and scientists more goals and a better forecast to design more relevant and novel sensor arrays for COVID-19 diagnosis.     

The viability of SARS-CoV-2 on solid surfaces

The COVID-19 pandemic had a major impact on life in 2020 and 2021. One method of transmission occurs when the causative virus, SARS-CoV-2, contaminates solids. Understanding and controlling the interaction with solids is thus potentially important for limiting the spread of the disease. We review work that describes the prevalence of the virus on common objects, the longevity of the virus on solids, and surface coatings that are designed to inactivate the virus. Engineered coatings have already succeeded in producing a large reduction in viral infectivity from surfaces. We also review work describing inactivation on facemasks and clothing and discuss probable mechanisms of inactivation of the virus at surfaces.     

Blocking Effect of Demethylzeylasteral on the Interaction between Human ACE2 Protein and SARS-CoV-2 RBD Protein Discovered Using SPR Technology

The novel coronavirus disease (2019-nCoV) has been affecting global health since the end of 2019, and there is no sign that the epidemic is abating. Targeting the interaction between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and the human angiotensin-converting enzyme 2 (ACE2) receptor is a promising therapeutic strategy. In this study, surface plasmon resonance (SPR) was used as the primary method to screen a library of 960 compounds. A compound 02B05 (demethylzeylasteral, CAS number: 107316-88-1) that had high affinities for S-RBD and ACE2 was discovered, and binding affinities (K_D, μM) of 02B05-ACE2 and 02B05-S-RBD were 1.736 and 1.039 μM, respectively. The results of a competition experiment showed that 02B05 could effectively block the binding of S-RBD to ACE2 protein. Furthermore, pseudovirus infection assay revealed that 02B05 could inhibit entry of SARS-CoV-2 pseudovirus into 293T cells to a certain extent at nontoxic concentration. The compoundobtained in this study serve as references for the design of drugs which have potential in the treatment of COVID-19 and can thus accelerate the process of developing effective drugs to treat SARS-CoV-2 infections.     

Heterocyclic Substitutions Greatly Improve Affinity and Stability of Folic Acid towards FRα. an In Silico Insight

Folate receptor alpha (FRα) is known as a biological marker for many cancers due to its overexpression in cancerous epithelial tissue. The folic acid (FA) binding affinity to the FRα active site provides a basis for designing more specific targets for FRα. Heterocyclic rings have been shown to interact with many receptors and are important to the metabolism and biological processes within the body. Nineteen FA analogs with substitution with various heterocyclic rings were designed to have higher affinity toward FRα. Molecular docking was used to study the binding affinity of designed analogs compared to FA, methotrexate (MTX), and pemetrexed (PTX). Out of 19 FA analogs, analogs with a tetrazole ring (FOL03) and benzothiophene ring (FOL08) showed the most negative binding energy and were able to interact with ASP81 and SER174 through hydrogen bonds and hydrophobic interactions with amino acids of the active site. Hence, 100 ns molecular dynamics (MD) simulations were carried out for FOL03, FOL08 compared to FA, MTX, and PTX. The root mean square deviation (RMSD) and root mean square fluctuation (RMSF) of FOL03 and FOL08 showed an apparent convergence similar to that of FA, and both of them entered the binding pocket (active site) from the pteridine part, while the glutamic part was stuck at the FRα pocket entrance during the MD simulations. Molecular mechanics Poisson-Boltzmann surface accessible (MM-PBSA) and H-bond analysis revealed that FOL03 and FOL08 created more negative free binding and electrostatic energy compared to FA and PTX, and both formed stronger H-bond interactions with ASP81 than FA with excellent H-bond profiles that led them to become bound tightly in the pocket. In addition, pocket volume calculations showed that the volumes of active site for FOL03 and FOL08 inside the FRα pocket were smaller than the FA–FRα system, indicating strong interactions between the protein active site residues with these new FA analogs compared to FA during the MD simulations.     

基于改进免疫遗传算法的近红外光谱变量选择方法

该文在免疫遗传算法（IGA）的基础上,提出一种改进免疫遗传算法（iIGA）用于近红外光谱波长变量的选择。该算法舍去了原算法中固定抗体相似度阈值的思想,取而代之的是抗体相似度阈值自适应,同时引入精英保留策略和贪心算法思想,使得算法朝着正确的方向进行局部性探优。将该算法在玉米的淀粉和蛋白质含量数据集上进行实验测试,建立偏最小二乘（PLS）分析模型,并与IGA、遗传算法（GA）以及全谱方法进行了对比。结果表明,在玉米淀粉含量的预测上,iIGA相较于原IGA算法,预测集均方根误差（RMSEP）从0.312 0降至0.298 0,预测集预测精度提升4.5%;在玉米蛋白质含量的预测上,RMSEP从0.124 4降至0.110 3,预测集预测精度提升11.3%。分别对预测淀粉和蛋白质模型的RMSEP值进行显著性检验,F值分别为165.22和182.05,P值分别为9.5 × 10－23和4.5 × 10－24,P值均小于0.05,因此,iIGA能显著提升模型预测精度。