scholarly journals Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties

2018 ◽  
Vol 5 (1) ◽  
pp. 64-71 ◽  
Author(s):  
Matthew R. Findlay ◽  
Daniel N. Freitas ◽  
Maryam Mobed-Miremadi ◽  
Korin E. Wheeler
Keyword(s):  
Machine Learning ◽  
Surface Chemistry ◽  
Physicochemical Properties ◽  
Silver Nanoparticle ◽  
Protein Corona ◽  
Engineered Nanomaterials ◽  
Predictive Analysis ◽  
Environmental Systems ◽  
Corona Formation ◽  
P Proteins

Proteins encountered in biological and environmental systems bind to engineered nanomaterials (ENMs) to form a protein corona (PC) that alters the surface chemistry, reactivity, and fate of the ENMs.

scholarly journals Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles

2020 ◽  
Vol 117 (19) ◽  
pp. 10492-10499 ◽  
Author(s):  
Zhan Ban ◽  
Peng Yuan ◽  
Fubo Yu ◽  
Ting Peng ◽  
Qixing Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Protein Corona ◽  
Functional Composition ◽  
Functional Protein ◽  
Machine Learning Model ◽  
Wide Range ◽  
Corona Formation ◽  
Cellular Recognition ◽  
Organ Targeting

Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by R2 (less than 0.40). Here, the performance with R2 over 0.75 in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the functional protein compositions (e.g., immune proteins, complement proteins, and apolipoproteins) in complex coronas were precisely predicted with good R2 (most over 0.80). Moreover, the method successfully predicted the cellular recognition (e.g., cellular uptake by macrophages and cytokine release) mediated by functional corona proteins. This workflow provides a method to accurately and quantitatively predict the functional composition of the protein corona that determines cellular recognition and nanotoxicity to guide the synthesis and applications of a wide range of NPs by overcoming limitations and uncertainty.

scholarly journals Impact of silica nanoparticle surface chemistry on protein corona formation and consequential interactions with biological cells

2017 ◽  
Vol 75 ◽  
pp. 16-24 ◽  
Author(s):  
Andréa Kurtz-Chalot ◽  
Christian Villiers ◽  
Jérémie Pourchez ◽  
Delphine Boudard ◽  
Matteo Martini ◽  
...  
Keyword(s):  
Surface Chemistry ◽  
Silica Nanoparticle ◽  
Protein Corona ◽  
Biological Cells ◽  
Nanoparticle Surface ◽  
Corona Formation

Toxicity of engineered nanomaterials with different physicochemical properties and the role of protein corona on cellular uptake and intrinsic ROS production

Toxicology ◽  
2020 ◽  
Vol 442 ◽  
pp. 152545
Author(s):  
Alejandro Déciga-Alcaraz ◽  
Estefany I. Medina-Reyes ◽  
Norma L. Delgado-Buenrostro ◽  
Carolina Rodríguez-Ibarra ◽  
Adriana Ganem-Rondero ◽  
...  
Keyword(s):  
Physicochemical Properties ◽  
Cellular Uptake ◽  
Protein Corona ◽  
Engineered Nanomaterials ◽  
Ros Production

The role of morphology, shell composition and protein corona formation in Au/Fe3O4 composite nanoparticle mediated macrophage responses

2021 ◽  
Author(s):  
Lihua He ◽  
Kang Ma ◽  
Xiaonan Liu ◽  
Huixia Li ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Immune System ◽  
Physicochemical Properties ◽  
Biomedical Applications ◽  
Protein Corona ◽  
System P ◽  
Corona Formation ◽  
Composite Nanoparticle ◽  
Shell Composition

The great interest in using nanoparticles (NPs) for biomedical applications is transversal to various materials despite the poorly understood correlation between their physicochemical properties and effects on the immune system....

Insight into the interactions between nanoparticles and cells

2017 ◽  
Vol 5 (2) ◽  
pp. 173-189 ◽  
Author(s):  
Jingchao Li ◽  
Hongli Mao ◽  
Naoki Kawazoe ◽  
Guoping Chen
Keyword(s):  
Cell Proliferation ◽  
Surface Chemistry ◽  
Cellular Uptake ◽  
Shell Structure ◽  
Protein Corona ◽  
Cellular Responses ◽  
Cellular Mechanics ◽  
Corona Formation ◽  
Structure Surface ◽  
Insight Into

This review summarizes the latest advances in nanoparticle (NP)–cell interactions. The influence of NP size, shape, shell structure, surface chemistry and protein corona formation on cellular uptake and cytotoxicity is highlighted in detail. Their impact on other cellular responses such as cell proliferation, differentiation and cellular mechanics is also discussed.

scholarly journals The Combination of Morphology and Surface Chemistry Defines the Biological Identity of Nanocarriers in Human Blood

2020 ◽  
Author(s):  
Nicholas B. Karabin ◽  
Michael P. Vincent ◽  
Sean D. Allen ◽  
Sharan Bobbala ◽  
Molly A. Frey ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Surface Chemistry ◽  
Human Blood ◽  
Rational Design ◽  
Immune Cell ◽  
Protein Corona ◽  
Blood Proteins ◽  
Drug Delivery Vehicles ◽  
Corona Formation ◽  
Delivery Vehicles

AbstractFollowing intravenous administration, an adsorbed corona of blood proteins immediately forms on the surfaces of nanocarriers to confer a distinct biological identity that dictates interactions with the immune system. While the nanocarrier surface chemistry has long been the focus of protein corona formation, the influence of the nanocarrier structure has remained unclear despite well-documented influences on biodistribution, clearance and inflammation. Here, we present design rules for the combined engineering of both nanocarrier structure and surface chemistry derived from a comprehensive proteomic analysis of protein corona formation in human blood. A library of nine soft PEGylated nanocarriers that differ in their combination of morphology (spheres, vesicles, and cylinders) and surface chemistry (methoxy, hydroxyl, and phosphate) were synthesized to represent properties of commonly employed drug delivery vehicles. Using label-free proteomics and high-throughput techniques, we examined the relationship between physicochemical properties and the resulting nanocarrier biological identity, including dynamic changes in protein corona composition, differential immunostimulation and uptake by relevant immune cell populations. In human blood, non-polar spherical micelles developed a similar biological identity to polar vesicles, whereas the identities of polar spheres and cylinders resembled that of non-polar vesicles. The formed protein coronas were compositionally dynamic and morphology-dependent, and these time-dependent fingerprints altered nanocarrier complement activation as well as their uptake by human monocytes, macrophages, and dendritic cells. This comprehensive analysis provides mechanistic insights into rational design choices that impact nanocarrier fate in human blood.One Sentence SummaryWe demonstrate that not only the surface chemistry, but the combined chemical and structural properties of soft drug delivery vehicles impact the composition of blood proteins that adsorb to their surfaces, and these differences specify their interactions with and modulation of human immune cells.

Surface Chemistry of Photoluminescent F8BT Conjugated Polymer Nanoparticles Determines Protein Corona Formation and Internalization by Phagocytic Cells

2015 ◽  
Vol 16 (3) ◽  
pp. 733-742 ◽  
Author(s):  
Raha Ahmad Khanbeigi ◽  
Thais Fedatto Abelha ◽  
Arcadia Woods ◽  
Olivia Rastoin ◽  
Richard D. Harvey ◽  
...  
Keyword(s):  
Surface Chemistry ◽  
Conjugated Polymer ◽  
Protein Corona ◽  
Polymer Nanoparticles ◽  
Phagocytic Cells ◽  
Corona Formation ◽  
Conjugated Polymer Nanoparticles

A Study on Host Tropism Determinants of Influenza Virus Using Machine Learning

2020 ◽  
Vol 15 (2) ◽  
pp. 121-134 ◽  
Author(s):  
Eunmi Kwon ◽  
Myeongji Cho ◽  
Hayeon Kim ◽  
Hyeon S. Son
Keyword(s):  
Machine Learning ◽  
Amino Acids ◽  
Influenza Virus ◽  
Random Forest ◽  
Physicochemical Properties ◽  
Protein Sequences ◽  
Influenza Viruses ◽  
Host Tropism ◽  
Post Hoc ◽  
Ha Protein

Background: The host tropism determinants of influenza virus, which cause changes in the host range and increase the likelihood of interaction with specific hosts, are critical for understanding the infection and propagation of the virus in diverse host species. Methods: Six types of protein sequences of influenza viral strains isolated from three classes of hosts (avian, human, and swine) were obtained. Random forest, naïve Bayes classification, and knearest neighbor algorithms were used for host classification. The Java language was used for sequence analysis programming and identifying host-specific position markers. Results: A machine learning technique was explored to derive the physicochemical properties of amino acids used in host classification and prediction. HA protein was found to play the most important role in determining host tropism of the influenza virus, and the random forest method yielded the highest accuracy in host prediction. Conserved amino acids that exhibited host-specific differences were also selected and verified, and they were found to be useful position markers for host classification. Finally, ANOVA analysis and post-hoc testing revealed that the physicochemical properties of amino acids, comprising protein sequences combined with position markers, differed significantly among hosts. Conclusion: The host tropism determinants and position markers described in this study can be used in related research to classify, identify, and predict the hosts of influenza viruses that are currently susceptible or likely to be infected in the future.

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