A computational analysis of molecular evolution for virulence genes of zoonotic novel coronavirus (COVID-19)

Zoonotic Novel coronavirus disease 2019 (COVID-19) is highly pathogenic and transmissible considered as emerging pandemic disease. The virus belongs from a large virus Coronaviridae family affect respiratory tract of animal and human likely originated from bat and homology to SARA-CoV and MERS-CoV. The virus consists of single-stranded positive genomic RNA coated by nucleocapsid protein. The rate of mutation in any virulence gene may influence the phenomenon of host radiation. We have studied the molecular evolution of selected virulence genes (HA, N, RdRP and S) of novel COVID-19. We used a site-specific comparison of synonymous (silent) and non-synonymous (amino acid altering) nucleotide substitutions. Maximum Likelihood genealogies based on differential gamma distribution rates were used for the analysis of null and alternate hypothesis. The null hypothesis was found more suitable for the analysis using Likelihood Ratio Test (LRT) method, confirming higher rate of substitution. The analysis revealed that RdRP gene had the fastest rate evolution followed by HA gene. We have also reported the new motifs for different virulence genes, which are further useful to design new detection and diagnosis kit for COVID -19.     

Efficient machine learning model for predicting drug-target interactions with case study for Covid-19

BackgroundDiscover possible Drug Target Interactions (DTIs) is a decisive step in the detection of the effects of drugs as well as drug repositioning. There is a strong incentive to develop effective computational methods that can effectively predict potential DTIs, as traditional DTI laboratory experiments are expensive, time-consuming, and labor-intensive. Some technologies have been developed for this purpose, however large numbers of interactions have not yet been detected, the accuracy of their prediction still low, and protein sequences and structured data are rarely used together in the prediction process.MethodsThis paper presents DTIs prediction model that takes advantage of the special capacity of the structured form of proteins and drugs. Our model obtains features from protein amino-acid sequences using physical and chemical properties, and from drugs smiles (Simplified Molecular Input Line Entry System) strings using encoding techniques. Comparing the proposed model with different existing methods under K-fold cross validation, empirical results show that our model based on ensemble learning algorithms for DTI prediction provide more accurate results from both structures and features data.ResultsThe proposed model is applied on two datasets:Benchmark (feature only) datasets and DrugBank (Structure data) datasets. Experimental results obtained by Light-Boost and ExtraTree using structures and feature data results in 98 % accuracy and 0.97 f-score comparing to 94 % and 0.92 achieved by the existing methods. Moreover, our model can successfully predict more yet undiscovered interactions, and hence can be used as a practical tool to drug repositioning.A case study of applying our prediction model on the proteins that are known to be affected by Corona viruses in order to predict the possible interactions among these proteins and existing drugs is performed. Also, our model is applied on Covid-19 related drugs announced on DrugBank. The results show that some drugs like DB00691 and DB05203 are predicted with 100 % accuracy to interact with ACE2 protein. This protein is a self-membrane protein that enables Covid-19 infection. Hence, our model can be used as an effective tool in drug reposition to predict possible drug treatments for Covid-19.     

Establishing an Analogue Based In Silico Pipeline in the Pursuit of Novel Inhibitory Scaffolds against the SARS Coronavirus 2 Papain-Like Protease

The ongoing coronavirus pandemic has been a burden on the worldwide population, with mass fatalities and devastating socioeconomic consequences. It has particularly drawn attention to the lack of approved small-molecule drugs to inhibit SARS coronaviruses. Importantly, lessons learned from the SARS outbreak of 2002–2004, caused by severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), can be applied to current drug discovery ventures. SARS-CoV-1 and SARS-CoV-2 both possess two cysteine proteases, the main protease (M^pro) and the papain-like protease (PL^pro), which play a significant role in facilitating viral replication, and are important drug targets. The non-covalent inhibitor, GRL-0617, which was found to inhibit replication of SARS-CoV-1, and more recently SARS-CoV-2, is the only PL^pro inhibitor co-crystallised with the recently solved SARS-CoV-2 PL^pro crystal structure. Therefore, the GRL-0617 structural template and pharmacophore features are instrumental in the design and development of more potent PL^pro inhibitors. In this work, we conducted scaffold hopping using GRL-0617 as a reference to screen over 339,000 ligands in the chemical space using the ChemDiv, MayBridge, and Enamine screening libraries. Twenty-four distinct scaffolds with structural and electrostatic similarity to GRL-0617 were obtained. These proceeded to molecular docking against PL^pro using the AutoDock tools. Of two compounds that showed the most favourable predicted binding affinities to the target site, as well as comparable protein-ligand interactions to GRL-0617, one was chosen for further analogue-based work. Twenty-seven analogues of this compound were further docked against the PL^pro, which resulted in two additional hits with promising docking profiles. Our in silico pipeline consisted of an integrative four-step approach: (1) ligand-based virtual screening (scaffold-hopping), (2) molecular docking, (3) an analogue search, and, (4) evaluation of scaffold drug-likeness, to identify promising scaffolds and eliminate those with undesirable properties. Overall, we present four novel, and lipophilic, scaffolds obtained from an exhaustive search of diverse and uncharted regions of chemical space, which may be further explored in vitro through structure-activity relationship (SAR) studies in the search for more potent inhibitors. Furthermore, these scaffolds were predicted to have fewer off-target interactions than GRL-0617. Lastly, to our knowledge, this work contains the largest ligand-based virtual screen performed against GRL-0617.     

Molecular Dynamic Simulation Search for Possible Amphiphilic Drug Discovery for Covid-19

To determine whether quaternary ammonium (k21) binds to Severe Acute Respiratory Syndrome–Coronavirus 2 (SARS-CoV-2) spike protein via computational molecular docking simulations, the crystal structure of the SARS-CoV-2 spike receptor-binding domain complexed with ACE-2 (PDB ID: 6LZG) was downloaded from RCSB PD and prepared using Schrodinger 2019-4. The entry of SARS-CoV-2 inside humans is through lung tissues with a pH of 7.38–7.42. A two-dimensional structure of k-21 was drawn using the 2D-sketcher of Maestro 12.2 and trimmed of C18 alkyl chains from all four arms with the assumption that the core moiety k-21 was without C18. The immunogenic potential of k21/QA was conducted using the C-ImmSim server for a position-specific scoring matrix analyzing the human host immune system response. Therapeutic probability was shown using prediction models with negative and positive control drugs. Negative scores show that the binding of a quaternary ammonium compound with the spike protein’s binding site is favorable. The drug molecule has a large Root Mean Square Deviation fluctuation due to the less complex geometry of the drug molecule, which is suggestive of a profound impact on the regular geometry of a viral protein. There is high concentration of Immunoglobulin M/Immunoglobulin G, which is concomitant of virus reduction. The proposed drug formulation based on quaternary ammonium to characterize affinity to the SARS-CoV-2 spike protein using simulation and computational immunological methods has shown promising findings.     

实时荧光聚合酶链式反应偶联高特异性核酸侵入反应检测单核苷酸多态性

建立了实时荧光聚合酶链式反应( PCR)偶联高特性核酸侵入反应检测单核苷酸多态性( SNP)的方法。优化了体系中flap核酸内切酶1(FEN1酶)和野生型检测探针等用量,确定了最佳反应条件,即FEN1酶用量为1.5 U,野生型检测探针用量为0.125μmol/L,0.5μmol/L Invader突变型检测探针,各0.25μmol/L通用野生型( VIC)和突变型( FAM)荧光共振转移发卡探针,显著降低了野生型样本和突变型样本背景信号,避免了背景信号对检测结果分型的干扰。采用本方法对编码乙醛脱氢酶2( ALDH2)基因ALDH2*2位点21例样本、细胞色素P4502C19基因CYP2C19*2和CYP2C19*3位点各19例样本进行分型检测,结果表明, AL-DH2*2位点GG纯合10例,GA杂合8例,AA纯合3例;CYP2C19*2位点GG纯合9例,GA杂合8例,AA纯合2例;CYP2C19*3位点GG纯合18例,GA杂合1例。使用焦磷酸测序进行验证,两种方法检测结果一致。本方法特异性好、操作简便、耗时短、成本低,可实现对SNP单管闭管无污染的分型检测。     

Una nota sui gruppi policiclici che ammettono un automorfismo con numero di Reidemeister finito

LetG be a group and ϕ an automorphism ofG. Two elementsx, y ∈ G are called ϕ-conjugate if there existsg ∈ G such thatx=g ⁻¹ yg ^θ. It is easily verified that the ϕ-conjugation is an equivalence relation; the numberR(ϕ) of ϕ-classes ofG is called the Reidemeister number of the automorphism ϕ. In this paper we prove that if a polycyclic groupsG admits an automorphism ϕ of ordern such thatR(ϕ)<∞, thenG contains a subgroup of finite index with derived length at most 2 ⁿ⁻¹ .

     

Generalized spherical and simplicial coordinates

Elementary trigonometric quantities are defined in l2,p analogously to that in l2,2, the sine and cosine functions are generalized for each p>0 as functions sinp and cosp such that they satisfy the basic equation p|cosp(φ)|+p|sinp(φ)|=1. The p-generalized radius coordinate of a point ξ∈Rn is defined for each p>0 as . On combining these quantities, ln,p-spherical coordinates are defined. It is shown that these coordinates are nearly related to ln,p-simplicial coordinates. The Jacobians of these generalized coordinate transformations are derived. Applications and interpretations from analysis deal especially with the definition of a generalized surface content on ln,p-spheres which is nearly related to a modified co-area formula and an extension of Cavalieri's and Torricelli's indivisibeln method, and with differential equations. Applications from probability theory deal especially with a geometric interpretation of the uniform probability distribution on the ln,p-sphere and with the derivation of certain generalized statistical distributions.     

Co:LaMgAl11O19 saturable absorber Q-switch for a 1.319 μm Nd:YAG laser

Passively Q-switched output of a flashlamp-pumped 1.319 μm Nd:YAG laser is realized by using Co²⁺:LaMgAl₁₁O₁₉ (Co:LMA) as saturable absorber. When initial transmission of the saturable absorber T₀ is 78%, a Q-switched output pulse with pulse width (FWHM) 44.8 ns and pulse energy 17.4 mJ is obtained, corresponding to 19.3% of the free-running energy under the equal pumping energy of 45.4 J. The experimental results show that the higher T₀ will result in a lower pumping threshold of the laser, but lower T₀ can make the laser generate pulses with higher single-pulse energy, narrower pulse width, and accordingly higher peak power.     

Paths for $${\mathcal{Z}}_k$$ Parafermionic Models

We present a simple bijection between restricted Bressoud lattice paths and paths in regime II of the Andrews–Baxter–Forrester restricted solid–on–solid model. Both types of paths describe states in parafermionic irreducible modules. The bijection implies a direct correspondence between a RSOS path and a parafermionic state in a quasi-particle basis.