Virtual Profiling Research Articles

Surface profiling of the underground infrastructure in the technology of it platforms Part I

The article is devoted to one of main elements of the technology of IT PLATFORMS, i.e. VIRTUAL PROFILING of the underground infrastructure. Implementing of this technology to the wide scale can very much change investment and administrative processes. VIRTUAL PROFILING of underground infrastructure is an example of using IT PLATFORMS VR3D in real investment tasks. This section presents examples of how to use this technology in practice. Keywords: IT platform; Virtual profiling; Underground infrastructure

Biased Diversity for Effective Virtual Screening.

Virtual screening is no longer merely a matter of identifying the subset of compounds from a large collection likely to be active against a particular endpoint. This viewpoint shares some distinctive practices at Novartis, where virtual screening combines multiple computational tools that marry the competing goals of biasing the selection of compounds toward multiple desired properties, while diversifying the selection to sample the available chemistry space, identifying quality compounds that inform drug discovery. Topics include the various considerations needed for a successful virtual screening practice: triaging, compound quality, accuracy and test sets, activity prediction including multitask modeling, virtual profiling, automation, multiproperty bias, diversity and property spaces, and biased-diversity designs.

Sustained Participation in Virtual Communities from a Self-Determination Perspective

Customers’ participation and contribution are vital to the sustainability of virtual communities (VCs) platform while people have many options to freely surf on the Internet. Sustained participation, instead of initial participation, is more meaningful to virtual communities’ sustained development. From the perspective of self-determination theory, this paper explores the effect of community artifacts on sustained participations through users’ satisfaction of psychological need and virtual community identification. With empirical studies in two types of virtual community platforms (interest-based and relational-based), our results reveal several important findings. Firstly, this study finds that virtual co-presence and deep profiling can increase users’ satisfaction of inner psychological needs. But the use of persistent labeling does not affect the user’s satisfaction of psychological needs. In addition, self-presentation is positively related to relational-based community, and rather has no impact on interest-based community. Secondly, this study finds that there exists a positive relationship between users’ satisfied psychological needs and virtual community identification. Finally, virtual community identification significantly impacts sustained participation. This paper offers a new perspective on the psychological mechanism of sustained participation and yields important implications for the managerial practice.

Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation.

The kinome-wide virtual profiling of small molecules with high-dimensional structure-activity data is a challenging task in drug discovery. Here, we present a virtual profiling model against a panel of 391 kinases based on large-scale bioactivity data and the multitask deep neural network algorithm. The obtained model yields excellent internal prediction capability with an auROC of 0.90 and consistently outperforms conventional single-task models on external tests, especially for kinases with insufficient activity data. Moreover, more rigorous experimental validations including 1410 kinase-compound pairs showed a high-quality average auROC of 0.75 and confirmed many novel predicted "off-target" activities. Given the verified generalizability, the model was further applied to various scenarios for depicting the kinome-wide selectivity and the association with certain diseases. Overall, the computational model enables us to create a comprehensive kinome interaction network for designing novel chemical modulators or drug repositioning and is of practical value for exploring previously less studied kinases.

Process-based simulation models and plant phenotyping

Plant phenotyping is difficult due to the complexity of the plant system and its sensitivity to developmental stages, environmental conditions and interactions between these and the genotype. Process-based simulation models can combine different biological functional hypotheses on responses of plant processes to environmental fluctuations. They then allow a virtual representation of plant functioning and can be useful tools to phenotype plants in terms of ecophysiological processes. The Virtual Fruit model (VF) developed by adapting and connecting existing models describing fruit growth, transpiration and respiration, sugar and acid accumulation and ethylene production was used to illustrate the approach. It permitted to compare and contrast the performances of a virtual wild-type plant and a virtual mutant, and then analyse the impact of a single mutation on different processes. The tested virtual mutations corresponded to 1) an increase by a factor 5 of the mobile fraction of plant reserves that can be mobilised to sustain carbon supply to the fruit or 2) a decrease by 70% the fruit's requirement for carbon. The mutation on fruit's requirement for carbon had large effects on several physiological processes (growth, respiration, metabolism and maturation) and quality traits (size, sucrose and citric acid concentration). In contrast, the mutation on reserve mobilization had only a weak effect on fruit growth and acidity. Such a virtual profiling approach can open new avenues for exploring in silico the impact of genetic variations.

KinomeX: a web application for predicting kinome-wide polypharmacology effect of small molecules.

The large-scale kinome-wide virtual profiling for small molecules is a daunting task by experimental and traditional in silico drug design approaches. Recent advances in deep learning algorithms have brought about new opportunities in promoting this process. KinomeX is an online platform to predict kinome-wide polypharmacology effect of small molecules based solely on their chemical structures. The prediction is made by a multi-task deep neural network model trained with over 140 000 bioactivity data points for 391 kinases. Extensive computational and experimental validations have been performed. Overall, KinomeX enables users to create a comprehensive kinome interaction network for designing novel chemical modulators, and is of practical value on exploring the previously less studied or untargeted kinases. KinomeX is available at: https://kinome.dddc.ac.cn. Supplementary data are available at Bioinformatics online.

Computational de-orphanization of the olive oil biophenol oleacein: Discovery of new metabolic and epigenetic targets

The health promoting effects of extra virgin olive oil (EVOO) relate to its unique repertoire of phenolic compounds. Here, we used a chemoinformatics approach to computationally identify endogenous ligands and assign putative biomolecular targets to oleacein, one of the most abundant secoiridoids in EVOO. Using a structure-based virtual profiling software tool and reference databases containing more than 9000 binding sites protein cavities, we identified 996 putative oleacein targets involving more than 700 proteins. We subsequently identified the high-level functions of oleacein in terms of biomolecular interactions, signaling pathways, and protein-protein interaction (PPI) networks. Delineation of the oleacein target landscape revealed that the most significant modules affected by oleacein were associated with metabolic processes (e.g., glucose and lipid metabolism) and chromatin-modifying enzymatic activities (i.e., histone post-translational modifications). We experimentally confirmed that, in a low-micromolar physiological range (<20 μmol/l), oleacein was capable of inhibiting the catalytic activities of predicted metabolic and epigenetic targets including nicotinamide N-methyltransferase, ATP-citrate lyase, lysine-specific demethylase 6A, and N-methyltransferase 4. Our computational de-orphanization of oleacein provides new mechanisms through which EVOO biophenols might operate as chemical prototypes capable of modulating the biologic machinery of healthy aging.

How to Computationally Stack the Deck for Hit-to-Lead Generation: In Silico Molecular Interaction Energy Profiling for de Novo siRNA Guide Strand Surrogate Selection.

The Argonaute-2 protein is part of the RNA-induced silencing complex (RISC) and anchors the guide strand of the small interfering RNA (siRNA). The 3'-end of the RNA contains two unpaired nucleotides (3'-overhang) that interact with the PAZ (PIWI/Argonaute/Zwille) domain of the protein. Theoretical and experimental evidence points toward a direct connection between the PAZ/3'-overhang binding affinity and siRNA's potency and specificity. Among the challenges to overcome when deploying siRNA molecules as therapeutics are their ready degradation under physiological conditions and off-target effects. It has been demonstrated that nuclease resistance can be improved via replacement of the dinucleotide overhang by small molecules which retain the interactions of the RNA guide strand with the PAZ domain. Most commonly, nucleotide analogues are used to substitute the siRNA overhang. However, in this study we adopt a de novo approach to its modification. The X-ray structure of human Argonaute-2 PAZ domain served to perform virtual screening and molecular interaction energy profiling (i.e., decomposition of the force field calculated protein-ligand interaction energies) of tailored-to-purpose fragment libraries. The binding of fragments to the PAZ domain was validated experimentally by NMR spectroscopy. The in silico guided protocol led to the efficient discovery of a number of PAZ domain ligands with affinities comparable to that of a reference dinucleotide (UpU, Kd = 33 μM). Originally starting from a generic fragment library, hits progress from 930 μM down to 14 μM within three iterations for the fragments selected via in silico molecular interaction energy profiling from a bespoke library. These dinucleotide siRNA guide strand surrogates represent potential new siRNA-based therapeutics (when attached to siRNA to form bioconjugates) featuring improved efficacy, specificity, stability, and cellular uptake. This project yielded a portfolio of seven patent applications, four of which have been granted to date.

An In Silico Approach for Assessment of the Membrane Transporter Activities of Phenols: A Case Study Based on Computational Models of Transport Activity for the Transporter Bilitranslocase.

Phenols are the most abundant naturally accessible antioxidants present in a human normal diet. Since numerous beneficial applications of phenols as preventive agents in various diseases were revealed, the evaluation of phenols bioavailability is of high interest of researchers, consumers and drug manufacturers. The hydrophilic nature of phenols makes a cell membrane penetration difficult, which imply an alternative way of uptake via membrane transporters. However, the structural and functional data of membrane transporters are limited, thus the in silico modelling is really challenging and urgent tool in elucidation of transporter ligands. Focus of this research was a particular transporter bilitranslocase (BTL). BTL has a broad tissue expression (vascular endothelium, absorptive and excretory epithelia) and can transport wide variety of poly-aromatic compounds. With available BTL data (pKi [mmol/L] for 120 organic compounds) a robust and reliable QSAR models for BTL transport activity were developed and extrapolated on 300 phenolic compounds. For all compounds the transporter profiles were assessed and results show that dietary phenols and some drug candidates are likely to interact with BTL. Moreover, synopsis of predictions from BTL models and hits/predictions of 20 transporters from Metrabase and Chembench platforms were revealed. With such joint transporter analyses a new insights for elucidation of BTL functional role were acquired. Regarding limitation of models for virtual profiling of transporter interactions the computational approach reported in this study could be applied for further development of reliable in silico models for any transporter, if in vitro experimental data are available.

Magma reflection imaging in Krafla, Iceland, using microearthquake sources

AbstractThe details of magma plumbing beneath active volcanoes remain a major challenge in geochemistry, geophysics, and volcanic hazard evaluation. Here we apply a relatively novel variation of seismic interferometry, which we call Virtual Reflection Seismic Profiling, to produce a high‐resolution image of a known crustal magma body. The technique takes advantage of recent advances in both seismic instrumentation (dense arrays) and seismic analysis (seismic interferometry). We have applied this technique to data recently acquired at an iconic volcanic system, Krafla, which lies on the mid‐Atlantic ridge as exposed in northern Iceland. What make this particular site exceptional are encounters with rhyolitic magma in two drill holes, K‐39 and Iceland Deep Drilling Project‐1 (IDDP‐1). These known magma bodies represent a unique calibration opportunity for surface geophysical measurements of magma distribution at depth. In this study, we produced a stacked, seismic reflection section by applying common depth point processing techniques to virtual shot gathers derived from interferometry of P waves from microearthquakes generated by tectonic, magmatic, and/or geothermal activity. We observe a strong, coherent reflection on the seismic section at a travel time corresponding to the depth at which magma was encountered in the IDDP‐1 wellbore. We interpret this reflection to be from magma or magma‐related fluids. Additional coherent reflections may correspond to other components of the magma plumbing beneath Krafla. These results represent a promising new technique for structural imaging with natural sources that can be applied to a wide array of geologic and energy problems that involve natural or induced seismic clusters.

Multi-Wavelength Raman Characterization of Epitaxial Silicon Wafers for In-Line Process Characterization and Monitoring Applications

Epitaxial Si wafers are widely used for high performance advanced memory devices and CMOS image sensor (CIS) device manufacturing. Surface morphology observation and resistivity measurements are frequently done as quick in-line quality monitoring of epitaxial Si layers. High resolution X-ray diffraction (HRXRD) and etch pit characterizations are done for randomly selected wafers. However, these conventional characterization techniques are not sensitive enough to detect small quality variations in epitaxial Si wafers. For Si wafers with multiple epitaxial layers and different dopant concentrations, the conventional characterization techniques are even less useful. Introduction of new epitaxial characterization techniques is strongly desired for effective in-line process monitoring. A non-contact, in-line characterization technique, with virtual depth profiling capability, would be beneficial for proper characterization of Si wafers with multiple epitaxial layers. In this study, we have characterized 200 mm and 300 mm Si wafers with triple epitaxial layers using multiwavelength, high resolution Raman spectroscopy. Ninety three (93)-point wafer mapping measurements were performed on all wafers, Statistics of Raman signal intensity, shift and full-width-at-half-maximum (FWHM) values, under different excitation wavelengths, were extracted and compared with two types of reference Si wafers (prime Si wafers and reclaimed Si wafers). Significant variations in Raman signal intensity, shift and FWHM were measured between reference wafers and epitaxial wafers, indicating potential Si lattice stress and crystallinity. Excitation wavelength dependence of Raman signal intensity, shift and FWHM also indicated variations in Si lattice stress and crystallinity of epitaxial layers in the depth direction. Non-contact multiwavelength Raman measurements are very sensitive to the quality of epitaxial Si layers and are effective as a complimentary in-line process characterization and monitoring technique. Figure 1

Micro-Raman Characterization of Cluster Carbon Implanted Si before and after Rapid Thermal Annealing

To meet various physical property requirements of materials for advanced high performance devices, combinations of Si1-xGex and Si:C have been introduced for strain control in PMOS and NMOS regions of devices. Two types of carbon (C) doping techniques have been explored for tensile stress generation in the NMOS device region. Carbon-doped Si epitaxial growth with C doping and C-implantation into Si are two major approaches. Carbon implanted Si requires annealing for recrystallization, damage recovery and tensile strain control. Strain characterization, monitoring and control are as important as material preparation. Non-contact, non-destructive strain and crystallinity characterization techniques with high spatial resolution and virtual depth profiling capability are strongly desired for in-line monitoring. In this study, we report the micro-Raman characterization results on cluster C-implanted Si for tensile strain generation before and after various rapid thermal annealing (RTA) techniques and conditions. Phosphorous ion clusters (P4) (8 keV, 4x1015 cm-2) were implanted with carbon ion clusters (C7) targeting 1.3% and 1.5% flat C profile (~50 nm from the surface). Various excitation wavelength laser beams were used in Raman characterization for virtual strain depth profiling. Flash anneal (fRTP) at 1200oC and impulse spike anneal (iRTP) at 1000oC and 1050oC were used as annealing conditions. As-implanted Si wafers showed almost no Raman signal due to the crystalline lattice damage from high dose cluster ion beam implantation. After RTA of implanted Si wafers under different techniques and conditions, significant broadening and shift of Raman peaks towards the lower wavenumber side was measured, indicating the presence of tensile strain and crystallinity alterations. X-ray diffraction (XRD), secondary ion mass spectroscopy (SIMS) depth profiling and transmission electron microscopy (TEM) analysis results will be reported along with multiwavelength micro-Raman characterization results. Figure 1 shows 363.8nm excited micro-Raman spectra from reference Si and cluster C implanted Si wafers before and after rapid thermal annealing (RTA). Figure 1

Micro-Raman Characterization of Cluster Carbon Implanted Si before and after Rapid Thermal Annealing

Phosphorous ion clusters (P4 +) (8 keV, 4x1015 cm-2) were implanted with carbon ion clusters (C7) targeting 1.30 at.% and 1.50 at. % flat C profile (~50 nm from the surface) for NMOS device performance enhancement by tensile strain generation. Flash anneal (fRTP) at 1200oC and impulse spike anneal (iRTP) at 1000oC and 1050oC were used as annealing conditions. Micro-Raman characterization of cluster C-implanted Si was done before and after various rapid thermal annealing (RTA) techniques and conditions under various excitation wavelengths for virtual depth profiling of Si stress. As-implanted Si wafers showed almost no Raman signal due to the crystalline lattice damage from high dose cluster ion beam implantation. After RTA of the implanted Si wafers under different techniques and conditions, significant broadening and shift of Raman peaks, towards the lower wavenumber side, was measured, indicating the presence of tensile strain and crystallinity alterations. High resolution X-ray diffraction (HRXRD), secondary ion mass spectroscopy (SIMS) depth profiling and sheet resistance measurement results were compared with multiwavelength micro-Raman characterization results.

Virtual multi-offset reflection profiling with interferometric imaging for borehole radar

Interferometry is one of the most advanced signal-processing and imaging techniques. It is widely used in geophysical detection and remote sensing. Application of interferometric processing significantly improves the resolution in geophysical imaging. This paper reports the application of an interferometric imaging approach to cross-hole multi-offset transmission (CHMOT) borehole radar data to generate a virtual single-hole, multi-offset reflection (SHMOR) profile with the validation of a real SHMOR data set. In subsurface material property imaging, SHMOR is an effective technique for interface and fracture detection. However, borehole radar survey in SHMOR fashion is not practical for most cases. Transforming cross-hole transmission mode radar data to virtual single-hole, multi-offset reflection data using a wave interferometric virtual source (WIVS) approach was proposed previously but not fully validated with real SHMOR data. In this study, we compare WIVS-derived virtual SHMOR to real SHMOR profiles using data sets acquired in two boreholes (SIMA1 and SIMA2) drilled into crystalline igneous bedrocks. As the calibration and validation, the reflection from borehole SIMA2 (as the known object) is clearly imaged by both the real and WIVS-derived virtual SHMOR profiles in SIMA1. The diffractions and amplitude decay from sharp stratigraphic change also registered at both the real and WIVS-derived virtual SHMOR profiles. The results of this study demonstrate the potential of the WIVS approach to improve structural imaging in bedrocks for hydrogeological, geothermal, and petroleum reservoir development applications.

3D feature-based pharmacophore models: efficient tools for profiling natural products

Pharmacophore-based virtual screening and activity profiling has become one of the most popular in silico techniques for supporting medicinal chemists in their hit finding, hit expansion, hit to lead, and lead optimization programs. [1]

Modeling of ribonucleic acid–ligand interactions

Computational methods play a pivotal role in the early stages of small molecule drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the past half century in medicinal chemistry, almost all the attention has been directed to protein–ligand binding and computational tools were created with such targets in mind. However, with growing discoveries of functional RNAs and their possible applications, RNA macromolecules have gained considerable attention as possible drug targets. This flow of discovery was followed by adapting existing computational tools for RNA applications as well as active development of new RNA‐tailored methods. However, due to the different nature of RNA, especially its tendency to use morphological plasticity (conformational change in ligand binding) this remains a challenging task. The evolution of ‘protein‐based’ drug discovery and related computational methods offers some clues on possible future directions and developments in modeling RNA interactions with small molecule ligands. WIREs Comput Mol Sci 2015, 5:425–439. doi: 10.1002/wcms.1226This article is categorized under: Structure and Mechanism &gt; Computational Biochemistry and Biophysics Computer and Information Science &gt; Chemoinformatics Molecular and Statistical Mechanics &gt; Molecular Interactions

Virtual screening, ADMET profiling, molecular docking and dynamics approaches to search for potent selective natural molecules based inhibitors against metallothionein-III to study Alzheimer’s disease

MotivationMetallothionein-III (MT-III) displays neuro-inhibitory activity and is involved in the repair of neuronal damage. An altered expression level of MT-III suggests that it could be a mitigating factor in Alzheimer’s disease (AD) neuronal dysfunction. Currently there are limited marketed drugs available against MT-III. The inhibitors are mostly pseudo-peptide based with limited ADMET. In our present study, available database InterBioScreen (natural compounds) was screened out for MT-III. Pharmacodynamics and pharmacokinetic studies were performed. Molecular docking and simulations of top hit molecules were performed to study complex stability. ResultsStudy reveals potent selective molecules that interact and form hydrogen bonds with amino acids Ser-6 and Lys-22 are common to established melatonin inhibitors for MT-III. These include DMHMIO, MCA B and s27533 derivatives. The ADMET profiling was better with comparable interaction energy values. It includes properties like blood brain barrier, hepatotoxicity, druggability, mutagenicity and carcinogenicity. Molecular dynamics studies were performed to validate our findings.

Imaging pre‐existing natural fractures using microseismic data

ABSTRACTIn hydraulic fracturing treatments, locating not only hydraulic fractures but also any pre‐existing natural fractures and faults in a subsurface reservoir is very important. Hydraulic fractures can be tracked by locating microseismic events, but to identify the locations of natural fractures, an additional technique is required. In this paper, we present a method to image pre‐existing fractures and faults near a borehole with virtual reverse vertical seismic profiling data or virtual single‐well profiling data (limited to seismic reflection data) created from microseismic monitoring using seismic interferometry. The virtual source data contain reflections from natural fractures and faults, and these features can be imaged by applying migration to the virtual source data. However, the imaging zone of fractures in the proposed method is strongly dependent on the geographic extent of the microseismic events and the location and direction of the fracture. To verify our method, we produced virtual reverse vertical seismic profiling and single‐well profiling data from synthetic microseismic data and compared them with data from real sources in the same relative position as the virtual sources. The results show that the reflection travel times from the fractures in the virtual source data agree well with travel times in the real‐source data. By applying pre‐stack depth migration to the virtual source data, images of the natural fractures were obtained with accurate locations. However, the migrated section of the single‐well profiling data with both real and virtual sources contained spurious fracture images on the opposite side of the borehole. In the case of virtual single‐well profiling data, we could produce correct migration images of fractures by adopting directional redatuming for which the occurrence region of microseismic events is divided into several subdivisions, and fractures located only on the opposite side of the borehole are imaged for each subdivision.

Profiling animal toxicants by automatically mining public bioassay data: a big data approach for computational toxicology.

In vitro bioassays have been developed and are currently being evaluated as potential alternatives to traditional animal toxicity models. Already, the progress of high throughput screening techniques has resulted in an enormous amount of publicly available bioassay data having been generated for a large collection of compounds. When a compound is tested using a collection of various bioassays, all the testing results can be considered as providing a unique bio-profile for this compound, which records the responses induced when the compound interacts with different cellular systems or biological targets. Profiling compounds of environmental or pharmaceutical interest using useful toxicity bioassay data is a promising method to study complex animal toxicity. In this study, we developed an automatic virtual profiling tool to evaluate potential animal toxicants. First, we automatically acquired all PubChem bioassay data for a set of 4,841 compounds with publicly available rat acute toxicity results. Next, we developed a scoring system to evaluate the relevance between these extracted bioassays and animal acute toxicity. Finally, the top ranked bioassays were selected to profile the compounds of interest. The resulting response profiles proved to be useful to prioritize untested compounds for their animal toxicity potentials and form a potential in vitro toxicity testing panel. The protocol developed in this study could be combined with structure-activity approaches and used to explore additional publicly available bioassay datasets for modeling a broader range of animal toxicities.

Identification of host interactions for phenotypic antimalarial hits.

Malaria is one of the most epidemic infectious diseases in the world affecting millions of patients and causing more than 500,000 deaths each year. Although there are several established antimalarials in clinical use, there is an urgent need for new drugs due to rapid resistance development. In recent years, more than 20,000 hits phenotypically active against P. falciparum, one of the major malaria causing agents, were disclosed from three independent HTS campaigns [1-3]. In order to make these hit libraries accessible to as many biological laboratories as possible, the MMV compiled and distributed the Open Access Malaria Box, a set of 400 chemically diverse active compounds [4]. One important task is now to elucidate the mode of action of those compounds. However, beside the parasite targets it is also necessary to identify potential host interactions in order to anticipate the risk of undesired side effects of those chemotypes at the earliest possible stage of development. To this end, we applied a ligand-based virtual target profiling approach to predict possible interactions with human targets [5]. Amongst others, kinases and GPCRs were identified as the most important target classes. Subsequently, several hundred predicted interactions were selected for prospective experimental testing. Results showed that a substantial part of the Malaria Box exhibits the potential of interacting with human GPCRs. To this extent, this was unexpected beforehand since the pathogenic agent does not contain any GPCRs. In this respect, particular attention was given to 5-HT2B receptor agonism, an effect associated to cardiac valvulopathy [6].