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- W4205423748 abstract "Engineered nanoparticles are advantageous for biotechnology applications including biomolecular sensing and delivery. However, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable protein corona formation prevents their effective implementation. We develop a random forest classifier trained with mass spectrometry data to identify proteins that adsorb to nanoparticles based solely on the protein sequence (78% accuracy, 70% precision). We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)–based nanosensor and study the relationship between the protein’s amino acid–based properties and binding capacity. Protein features associated with increased likelihood of SWCNT binding include high content of solvent-exposed glycines and nonsecondary structure–associated amino acids. To evaluate its predictive power, we apply the classifier to identify proteins with high binding affinity to SWCNTs, with experimental validation. The developed classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions." @default.
- W4205423748 created "2022-01-26" @default.
- W4205423748 creator A5006376415 @default.
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- W4205423748 date "2022-01-07" @default.
- W4205423748 modified "2023-10-06" @default.
- W4205423748 title "Supervised learning model predicts protein adsorption to carbon nanotubes" @default.
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- W4205423748 cites W1974327005 @default.
- W4205423748 cites W1975304761 @default.
- W4205423748 cites W1996258924 @default.
- W4205423748 cites W1996844579 @default.
- W4205423748 cites W2004935845 @default.
- W4205423748 cites W2019985255 @default.
- W4205423748 cites W2020060039 @default.
- W4205423748 cites W2032324656 @default.
- W4205423748 cites W2033192435 @default.
- W4205423748 cites W2039855332 @default.
- W4205423748 cites W2049202154 @default.
- W4205423748 cites W2049780205 @default.
- W4205423748 cites W2077358620 @default.
- W4205423748 cites W2077535362 @default.
- W4205423748 cites W2110634891 @default.
- W4205423748 cites W2114850508 @default.
- W4205423748 cites W2133059825 @default.
- W4205423748 cites W2148143831 @default.
- W4205423748 cites W2149653886 @default.
- W4205423748 cites W2154531313 @default.
- W4205423748 cites W2158558808 @default.
- W4205423748 cites W2215522337 @default.
- W4205423748 cites W2316648814 @default.
- W4205423748 cites W2319263686 @default.
- W4205423748 cites W2323147899 @default.
- W4205423748 cites W2330407759 @default.
- W4205423748 cites W2342575713 @default.
- W4205423748 cites W2520379539 @default.
- W4205423748 cites W2565599824 @default.
- W4205423748 cites W2595820484 @default.
- W4205423748 cites W2602890096 @default.
- W4205423748 cites W2606668319 @default.
- W4205423748 cites W2748142694 @default.
- W4205423748 cites W2757272801 @default.
- W4205423748 cites W2766309765 @default.
- W4205423748 cites W2773580742 @default.
- W4205423748 cites W2789702774 @default.
- W4205423748 cites W2792416781 @default.
- W4205423748 cites W2800488651 @default.
- W4205423748 cites W2886422707 @default.
- W4205423748 cites W2896033908 @default.
- W4205423748 cites W2905206419 @default.
- W4205423748 cites W2911964244 @default.
- W4205423748 cites W2913660937 @default.
- W4205423748 cites W2917529168 @default.
- W4205423748 cites W2947658335 @default.
- W4205423748 cites W2950374603 @default.
- W4205423748 cites W2950849557 @default.
- W4205423748 cites W2953089157 @default.
- W4205423748 cites W2953681722 @default.
- W4205423748 cites W2958413971 @default.
- W4205423748 cites W2967104394 @default.
- W4205423748 cites W2969438115 @default.
- W4205423748 cites W2969797985 @default.
- W4205423748 cites W2969908486 @default.
- W4205423748 cites W2973494895 @default.
- W4205423748 cites W2995991076 @default.
- W4205423748 cites W2997407559 @default.
- W4205423748 cites W2998969143 @default.
- W4205423748 cites W3012834763 @default.
- W4205423748 cites W3020128160 @default.
- W4205423748 cites W3033262445 @default.
- W4205423748 cites W3035017583 @default.
- W4205423748 cites W3037252060 @default.
- W4205423748 cites W3037369258 @default.
- W4205423748 cites W3083698728 @default.
- W4205423748 cites W3086477311 @default.
- W4205423748 cites W3087034102 @default.
- W4205423748 cites W3092649539 @default.
- W4205423748 cites W3102476541 @default.
- W4205423748 cites W3108870445 @default.
- W4205423748 cites W3109746568 @default.
- W4205423748 cites W3112376646 @default.
- W4205423748 cites W3115593138 @default.
- W4205423748 cites W3131954160 @default.
- W4205423748 cites W3132115846 @default.
- W4205423748 cites W3133400804 @default.
- W4205423748 cites W3146944767 @default.
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- W4205423748 doi "https://doi.org/10.1126/sciadv.abm0898" @default.
- W4205423748 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34995109" @default.
- W4205423748 hasPublicationYear "2022" @default.
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