scholarly journals Differences among Unique Nanoparticle Protein Corona Constructs: A Case Study Using Data Analytics and Multi-Variant Visualization to Describe Physicochemical Characteristics

2018 ◽  
Vol 8 (12) ◽  
pp. 2669 ◽  
Author(s):  
Madison Stewart ◽  
Marina Mulenos ◽  
London Steele ◽  
Christie Sayes
Keyword(s):  
Protein Interactions ◽  
Data Analytics ◽  
Data Transfer ◽  
Three Dimensional ◽  
Protein Corona ◽  
Active Pharmaceutical Ingredient ◽  
Physicochemical Characteristics ◽  
Using Data ◽  
Visualization Techniques ◽  
Complex Datasets

Gold nanoparticles (AuNPs) used in pharmaceutical treatments have been shown to effectively deliver a payload, such as an active pharmaceutical ingredient or image contrast agent, to targeted tissues in need of therapy or diagnostics while minimizing exposure, availability, and accumulation to surrounding biological compartments. Data sets collected in this field of study include some toxico- and pharmacodynamic properties (e.g., distribution and metabolism) but many studies lack information about adsorption of biological molecules or absorption into cells. When nanoparticles are suspended in blood serum, a protein corona cloud forms around its surface. The extent of the applications and implications of this formed cloud are unknown. Some researchers have speculated that the successful use of nanoparticles in pharmaceutical treatments relies on a comprehensive understanding of the protein corona composition. The work presented in this paper uses a suite of data analytics and multi-variant visualization techniques to elucidate particle-to-protein interactions at the molecular level. Through mass spectrometry analyses, corona proteins were identified through large and complex datasets. With such high-output analyses, complex datasets pose a challenge when visualizing and communicating nanoparticle-protein interactions. Thus, the creation of a streamlined visualization method is necessary. A series of user-friendly data informatics techniques were used to demonstrate the data flow of protein corona characteristics. Multi-variant heat maps, pie charts, tables, and three-dimensional regression analyses were used to improve results interpretation, facilitate an iterative data transfer process, and emphasize features of the nanoparticle-protein corona system that might be controllable. Data informatics successfully highlights the differences between protein corona compositions and how they relate to nanoparticle surface charge.

scholarly journals Visualizing Protein Interactions and Dynamics: Evolving a Visual Language for Molecular Animation

2012 ◽  
Vol 11 (1) ◽  
pp. 103-110 ◽  
Author(s):  
Jodie Jenkinson ◽  
Gaël McGill
Keyword(s):  
Protein Interactions ◽  
Molecular Motion ◽  
Relative Effectiveness ◽  
Three Dimensional ◽  
Biology Education ◽  
Undergraduate Biology ◽  
University Of Toronto ◽  
Learning Challenges ◽  
Binding Event ◽  
Visualization Techniques

Undergraduate biology education provides students with a number of learning challenges. Subject areas that are particularly difficult to understand include protein conformational change and stability, diffusion and random molecular motion, and molecular crowding. In this study, we examined the relative effectiveness of three-dimensional visualization techniques for learning about protein conformation and molecular motion in association with a ligand–receptor binding event. Increasingly complex versions of the same binding event were depicted in each of four animated treatments. Students (n = 131) were recruited from the undergraduate biology program at University of Toronto, Mississauga. Visualization media were developed in the Center for Molecular and Cellular Dynamics at Harvard Medical School. Stem cell factor ligand and cKit receptor tyrosine kinase were used as a classical example of a ligand-induced receptor dimerization and activation event. Each group completed a pretest, viewed one of four variants of the animation, and completed a posttest and, at 2 wk following the assessment, a delayed posttest. Overall, the most complex animation was the most effective at fostering students' understanding of the events depicted. These results suggest that, in select learning contexts, increasingly complex representations may be more desirable for conveying the dynamic nature of cell binding events.

scholarly journals In Silico Functional Annotation of VP 128 Hypothetical Protein from Vibrio parahaemolyticus

10.4194/afs37 ◽  
2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Moslema Jahan Mou ◽  
Sk Injamamul Islam ◽  
Sarower Mahfuj
Keyword(s):  
Protein Interactions ◽  
In Silico ◽  
Vibrio Parahaemolyticus ◽  
Active Sites ◽  
Three Dimensional ◽  
Hypothetical Protein ◽  
Physicochemical Characteristics ◽  
Dimensional Structure ◽  
Structural Domain ◽  
Protein Protein Interactions

Unknown or hypothetical proteins exist, but they have yet to be identified or correlated to gene sequences. Domains of unknown function are proteins that have been identified experimentally but do not have a known functional or structural domain. Using a variety of computational approaches and tools, this research investigated and characterized the likely functional characteristics of a hypothetical protein from Vibrio parahaemolyticus (Accession no. QOS18375.1). The physicochemical characteristics, subcellular localization, three-dimensional structure, protein-protein interactions, and functional elucidation of the protein are all available from this in silico perspective. Protein-protein interactions are investigated using the STRING software and resulted that VP128 putative protein interacts strongly with the GlpX type protein Fructose-1,6-bisphosphatase. The in-silico investigation documented the protein’s hydrophilic nature with predominantly alpha (α) helices in its secondary structure. Furthermore, the protein, according to the research, features a VP128 domain and is thought to bind ribosomal subunits and the top active sites of the model described also. Therefore, the research findings will facilitate the development of new antibacterial drugs against acute gastroenteritis and other serious diseases by providing a better knowledge of the role of VP128 domain.

Predicting spatial activity patterns in a neural map from a probabilistic atlas of 3-D reconstructed neurons

1995 ◽  
Vol 53 ◽  
pp. 812-813
Author(s):  
G. Jacobs ◽  
F. Theunissen
Keyword(s):  
Activity Patterns ◽  
Three Dimensional ◽  
Sensory System ◽  
Neural System ◽  
Probabilistic Atlas ◽  
Uniform Sample ◽  
Dimensional Reconstruction ◽  
The Time Domain ◽  
And Function ◽  
Visualization Techniques

In order to understand how the algorithms underlying neural computation are implemented within any neural system, it is necessary to understand details of the anatomy, physiology and global organization of the neurons from which the system is constructed. Information is represented in neural systems by patterns of activity that vary in both their spatial extent and in the time domain. One of the great challenges to microscopists is to devise methods for imaging these patterns of activity and to correlate them with the underlying neuroanatomy and physiology. We have addressed this problem by using a combination of three dimensional reconstruction techniques, quantitative analysis and computer visualization techniques to build a probabilistic atlas of a neural map in an insect sensory system. The principal goal of this study was to derive a quantitative representation of the map, based on a uniform sample of afferents that was of sufficient size to allow statistically meaningful analyses of the relationships between structure and function.

Energy Prediction Using Data Analytics in Smart Grid

2018 ◽  
Vol 06 (06) ◽  
pp. 110-115
Author(s):  
Panchami Anil ◽  
Anas P V ◽  
Naseef Kuruvakkottil ◽  
Anusha K V ◽  
Balagopal N
Keyword(s):  
Smart Grid ◽  
Data Analytics ◽  
Energy Prediction ◽  
Using Data

Quality Management Using Data Analytics: An Application to Pharmaceutical Regulation

2015 ◽  
Author(s):  
Vishal Ahuja ◽  
John R. Birge ◽  
Chad Syverson ◽  
Elbert S. Huang ◽  
Min-Woong Sohn
Keyword(s):  
Quality Management ◽  
Data Analytics ◽  
Pharmaceutical Regulation ◽  
Using Data

Organizing and Managing Data Analytics Teams for Procurement

2020 ◽  
Author(s):  
Benjamin Shao ◽  
Robert D. St. Louis
Keyword(s):  
Best Practices ◽  
Business Strategy ◽  
Data Analytics ◽  
Team Members ◽  
Procurement Process ◽  
Using Data ◽  
And Training ◽  
Different Levels ◽  
The Way

Many companies are forming data analytics teams to put data to work. To enhance procurement practices, chief procurement officers (CPOs) must work effectively with data analytics teams, from hiring and training to managing and utilizing team members. This chapter presents the findings of a study on how CPOs use data analytics teams to support the procurement process. Surveys and interviews indicate companies are exhibiting different levels of maturity in using data analytics, but both the goal of CPOs (i.e., improving performance to support the business strategy) and the way to interact with data analytics teams for achieving that goal are common across companies. However, as data become more reliably available and technologies become more intelligently embedded, the best practices of organizing and managing data analytics teams for procurement will need to be constantly updated.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

scholarly journals Structural Characterization of Receptor–Receptor Interactions in the Allosteric Modulation of G Protein-Coupled Receptor (GPCR) Dimers

2021 ◽  
Vol 22 (6) ◽  
pp. 3241
Author(s):  
Raudah Lazim ◽  
Donghyuk Suh ◽  
Jai Woo Lee ◽  
Thi Ngoc Lan Vu ◽  
Sanghee Yoon ◽  
...  
Keyword(s):  
G Protein ◽  
Protein Interactions ◽  
Metabotropic Glutamate Receptor ◽  
Three Dimensional ◽  
Allosteric Modulation ◽  
Experimental Investigations ◽  
G Protein Coupled Receptor ◽  
Metabotropic Glutamate ◽  
Gpcr Activation ◽  
G Protein Coupled

G protein-coupled receptor (GPCR) oligomerization, while contentious, continues to attract the attention of researchers. Numerous experimental investigations have validated the presence of GPCR dimers, and the relevance of dimerization in the effectuation of physiological functions intensifies the attractiveness of this concept as a potential therapeutic target. GPCRs, as a single entity, have been the main source of scrutiny for drug design objectives for multiple diseases such as cancer, inflammation, cardiac, and respiratory diseases. The existence of dimers broadens the research scope of GPCR functions, revealing new signaling pathways that can be targeted for disease pathogenesis that have not previously been reported when GPCRs were only viewed in their monomeric form. This review will highlight several aspects of GPCR dimerization, which include a summary of the structural elucidation of the allosteric modulation of class C GPCR activation offered through recent solutions to the three-dimensional, full-length structures of metabotropic glutamate receptor and γ-aminobutyric acid B receptor as well as the role of dimerization in the modification of GPCR function and allostery. With the growing influence of computational methods in the study of GPCRs, we will also be reviewing recent computational tools that have been utilized to map protein–protein interactions (PPI).

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