Virtual Combinatorial Library Screening of Quinadoline B Derivatives against SARS-CoV-2 RNA-Dependent RNA Polymerase

The unprecedented global health threat of SARS-CoV-2 has sparked a continued interest in discovering novel anti-COVID-19 agents. To this end, we present here a computer-based protocol for identifying potential compounds targeting RNA-dependent RNA polymerase (RdRp). Starting from our previous study wherein, using a virtual screening campaign, we identified a fumiquinazolinone alkaloid quinadoline B (Q3), an antiviral fungal metabolite with significant activity against SARS-CoV-2 RdRp, we applied in silico combinatorial methodologies for generating and screening a library of anti-SARS-CoV-2 candidates with strong in silico affinity for RdRp. For this study, the quinadoline pharmacophore was subjected to structural iteration, obtaining a Q3-focused library of over 900,000 unique structures. This chemical library was explored to identify binders of RdRp with greater affinity with respect to the starting compound Q3. Coupling this approach with the evaluation of physchem profile, we found 26 compounds with significant affinities for the RdRp binding site. Moreover, top-ranked compounds were submitted to molecular dynamics to evaluate the stability of the systems during a selected time, and to deeply investigate the binding mode of the most promising derivatives. Among the generated structures, five compounds, obtained by inserting nucleotide-like scaffolds (1, 2, and 5), heterocyclic thiazolyl benzamide moiety (compound 3), and a peptide residue (compound 4), exhibited enhanced binding affinity for SARS-CoV-2 RdRp, deserving further investigation as possible antiviral agents. Remarkably, the presented in silico procedure provides a useful computational procedure for hit-to-lead optimization, having implications in anti-SARS-CoV-2 drug discovery and in general in the drug optimization process.

Effect of Chitosan Deacetylation on Its Affinity to Type III Collagen: A Molecular Dynamics Study

The ability to form strong intermolecular interactions by linear glucosamine polysaccharides with collagen is strictly related to their nonlinear dynamic behavior and hence bio-lubricating features. Type III collagen plays a crucial role in tissue regeneration, and its presence in the articular cartilage affects its bio-technical features. In this study, the molecular dynamics methodology was applied to evaluate the effect of deacetylation degree on the chitosan affinity to type III collagen. The computational procedure employed docking and geometry optimizations of different chitosan structures characterized by randomly distributed deacetylated groups. The eight different degrees of deacetylation from 12.5% to 100% were taken into account. We found an increasing linear trend (R2 = 0.97) between deacetylation degree and the collagen–chitosan interaction energy. This can be explained by replacing weak hydrophobic contacts with more stable hydrogen bonds involving amino groups in N-deacetylated chitosan moieties. In this study, the properties of chitosan were compared with hyaluronic acid, which is a natural component of synovial fluid and cartilage. As we found, when the degree of deacetylation of chitosan was greater than 0.4, it exhibited a higher affinity for collagen than in the case of hyaluronic acid.

Exploring the magnetohydrodynamic stretched flow of Williamson Maxwell nanofluid through porous matrix over a permeated sheet with bioconvection and activation energy

The evolution of compact density heat gadgets demands effective thermal transportation. The notion of nanofluid plays active role for this requirements. A comparative account for Maxwell nanofluids and Williamson nanofluid is analyzed. The bioconvection of self motive microorganisms, non Fourier heat flux and activation energy are new aspects of this study. This article elaborates the effects of viscous dissipation, Cattaneo–Christov diffusion for Maxwell and Williamson nanofluid transportation that occurs due to porous stretching sheet. The higher order non-linear partial differential equations are solved by using similarity transformations and a new set of ordinary differential equations is formed. For numerical purpose, Runge–Kutta method with shooting technique is applied. Matlab plateform is used for computational procedure. The graphs for various profiles .i.e. velocity, temperature, concentration and concentration of motile micro-organisms are revealed for specific non-dimensional parameters. It is observed that enhancing the magnetic parameter M, the velocity of fluid decreases but opposite behavior happens for temperature, concentration and motile density profile. Also the motile density profile decrease down for Pe and Lb. The skin friction coefficient is enhanced for both the Williamson and Maxwell fluid.

Comprehensive simulations of boiling with a resolved microlayer: validation and sensitivity study

The dynamics of the microlayer beneath a growing bubble in nucleate boiling significantly impacts the heat-transfer characteristics of the process. The minute thickness of the microlayer motivates the use of direct numerical simulation (DNS) to model its behaviour if empirical models are to be avoided. In this work, we develop a computational strategy for utilising DNS to model nucleate boiling by resolving explicitly the microlayer, directly coupling, in a stable manner, the mass, momentum and energy conservation equations with the conjugate heat transfer between the solid and fluid domains. To this end, closure models for the treatment of interfacial heat transfer and the dynamic contact angle are introduced and substantiated. The computational procedure is validated against relevant experimental data recently measured at the Massachusetts Institute of Technology; it is shown that the main observed growth features and surface heat-transfer characteristics are well reproduced using our model. We go on to perform a sensitivity study of the dependence of the initial microlayer thickness distribution on the applied superheat and fluid properties. The results indicate that an equation derived from lubrication theory captures the observed trends well. Finally, a first demonstration of DNS of boiling with an explicitly resolved microlayer in three-dimensional Cartesian coordinates is presented in one of the appendices.

Is entrepreneurship an emerging area of research? A computational response

Objective: We aim to answer four questions. First, with the increasing number of publications, is there a concentration in specific subjects, or on the contrary, a dispersion, amplifying the span of themes related to entrepreneurship? Second, is there a hierarchy of subjects, in the sense that some of them constitute the “core” of entrepreneurship? Third, are they connected with other established research areas? Finally, it is possible to identify papers that are influential, acting as hubs in the cluster’s formation? Method: We developed an original version of the computational procedure proposed by Shibata et al (2008), which allows us to understand the diversity of the different sub-areas of the topic investigated, reducing the need for specialist supervision. Originality / Relevance: We developed and applied a method to capture the formation and evolution of research areas in entrepreneurship literature, via direct citation networks, allowing us to understand the iteration between the different research sub-areas. Results: The dispersion is a feature of entrepreneurship as field research, with a hierarchy between research areas, indicating an emergent organization in the expansion processes. We concluded that research on entrepreneurship consists of specialization, that is, by application in niches.

Token frequency as a determinant of morphological change

This paper demonstrates that morphological change tends to involve the replacement of low frequency forms in inflectional paradigms by innovative forms based on high frequency forms, using Greek data involving the diachronic reorganisation of verbal inflection classes. A computational procedure is outlined for generating a possibility space of morphological changes which can be represented as analogical proportions, on the basis of synchronic paradigms in ancient Greek. I then show how supplementing analogical proportions with token frequency information can help to predict whether a hypothetical change actually took place in the language’s subsequent development. Because of the crucial role of inflected surface forms serving as analogical bases in this model, I argue that the results support theories in which inflected forms can be stored whole in the lexicon.

Combinatorial library screening of quinadoline B derivatives against SARS-CoV-2 RNA-dependent RNA polymerase

The unprecedented global health threat of SARS-CoV-2 has sparked a continued interest to discover novel anti-COVID-19 agents. To this end, we present here a computer-based protocol for identifying potential compounds targeting RNA-dependent RNA polymerase (RdRp). Starting from our previous study in which, by a virtual screening campaign, we identified a fumiquinazolinone alkaloid quinadoline B (Q3), an antiviral fungal metabolite with significant activity against SARS-CoV-2 RdRp, we applied an in silico combinatorial methodologies for generating and screening a library of anti-SARS-CoV-2 candidates with strong in silico affinity for RdRp. For this study, the quinadoline pharmacophore was subjected to structural iteration obtaining a Q3-focused library of over 900,000 unique structures. This chemical library was explored to identify binders of RdRp with greater affinity with respect to the starting compound Q3. Coupling this approach with the evaluation of physchem profile, we found 26 compounds with significant affinities for the RdRp binding site. Moreover, top-ranked compounds were submitted to molecular dynamics to evaluate the stability of the systems during a selected time, and for deeply investigating the binding mode of the most promising derivatives. Among the generated structures, five compounds, obtained by inserting nucleotide-like scaffolds (1, 2, and 5), heterocyclic thiazolyl benzamide moiety (compound 3), and a peptide residue (compound 4), exhibited enhanced binding affinity for SARS-CoV-2 RdRp, deserving further investigation as possible antiviral agents. Remarkably, the presented in silico procedure provides a useful computational procedure for hit-to-lead optimization, having implications in anti-SARS-CoV-2 drug discovery and in general in the drug optimization process.

A VIRTUAL PISTON-TYPE WAVE MAKER BASED ON OPENFOAM

OpenFOAM is an open source CFD (Computational Fluid Dynamics) toolbox and recently attracts many researchers to develop codes based on it for their own applications. In order to numerically generate waves based on the wave-maker theory for a piston motion, numerical improvements have been done on the base of OpenFOAM by the author. In gen- eral, the present new tool can be employed to simulate wave generation as long as the piston motion is given. This paper presents the related computational procedure and simulations for generating relatively long finite-amplitude waves ac- cording to Madsen’s second-order wave-maker theory. The sensitivities of the computed incident wave profile to grid density and time step are investigated for the case of generating a wave with permanent form. The simulation accuracy is validated by comparison with the analytical solution and available experimental data.

METHOD OF IDENTIFICATION OF OBJECT STATES ACCORDING TO THE RESULTS OF FUZZY MEASUREMENTS OF CONTROLLED PARAMETERS

The subject of consideration is the task of identifying the states of an object based on the results of fuzzy measurements of a set of controlled parameters. The fuzziness of the initial data of the task further complicates it due to the resulting inequality of the controlled parameters. The aim of the study is to develop a method of identifying the states of a fuzzy object using a fuzzy mechanism of logical output taking into account possible differences in the level of information content of its controlled parameters. The method of obtaining the desired result is based on the modification of the known mathematical apparatus for building an expert system of artificial intelligence by solving two subtasks. The first is the development of a method for assessing the informativity of controlled parameters. The second is the development of a method for constructing a mechanism for logical inference of the relative state of an object based on the results of measuring controlled parameters, which provides identification. In the first problem, a method is proposed for estimating the informativity of parameters, free from the known disadvantages of the traditional Kulbak informativity measure. In implementing the method, it is assumed that the range of possible values for each parameter is divided into subbands in accordance with possible states of the object. For each of these states, the function of belonging to the fuzzy values of the corresponding parameter is defined. At the same time, the correct problem of estimating the informativity of a parameter is solved for cases when this parameter is measured accurately or determined fuzzily by its belonging function. The fundamental difference between the proposed logical output mechanism and the traditional one is the refusal to use the production rule base, which ensures the practical independence of the computational procedure from the dimension of the task. To solve the main problem of identifying states, a non-productive approach is proposed, the computational complexity of which practically does not depend on the dimension of the problem (the product of the number of possible states Results.per the number of controlled parameters). The logic output mechanism generates a probability distribution of the system states. In this case, a set of functions of belonging of each parameter to the range of its possible values for each of the states of the object is used, as well as a set of functions of belonging to fuzzy measurement results of each parameter. Conclusions. Thus, a method of identifying the state of fuzzy objects with a fuzzy non-productive output mechanism is proposed, the complexity of which does not depend on the dimension of the task.

A new approach for displacement and stress monitoring of tunnel based on iFEM methodology

Structural monitoring plays a key role for underground structures such as tunnels. Strain readings are expected to report structural conditions during construction and at the final delivery of the works. Furthermore, it is increasingly requested an extension to long-term monitoring from contractors with possible use of the same system in service during construction. A robust and efficient monitoring methodology from discrete strain measurements is the inverse Finite Element Method (iFEM), which allows to reconstruct the structural response without input data on the load pattern applied to the structure as well as material and inertial properties of the elements and therefore it is interesting for structural configurations affected by uncertain loading conditions, such as the tunnel. The formulation presented in this paper, based on the iFEM theory, is improved from the previous work available in literature for both the shape functions used and the computational procedure. Indeed, the approach allows to overcome inconsistencies related to structural loading conditions and a pseudo-inverse matrix preserve all the rigid body modes without imposing specific constraints which is typical for tunnels. Numerical validation of the iFEM procedure is performed by simulating the input data coming from a tunnel working in a heterogeneous soil under different loading conditions with direct FEM analysis.