scholarly journals Improved Prediction of Cell-Penetrating Peptides via Effective Orchestrating Amino Acid Composition Feature Representation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 163547-163555 ◽  
Author(s):  
Xiangzheng Fu ◽  
Lixia Ke ◽  
Lijun Cai ◽  
Xiangtao Chen ◽  
Xuanbai Ren ◽  
...  
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Cell Penetrating Peptides ◽  
Feature Representation ◽  
Cell Penetrating

scholarly journals Cell-penetrating peptides as transporters for morpholino oligomers: effects of amino acid composition on intracellular delivery and cytotoxicity

2007 ◽  
Vol 35 (15) ◽  
pp. 5182-5191 ◽  
Author(s):  
R. P. Wu ◽  
D. S. Youngblood ◽  
J. N. Hassinger ◽  
C. E. Lovejoy ◽  
M. H. Nelson ◽  
...  
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Intracellular Delivery ◽  
Cell Penetrating Peptides ◽  
Cell Penetrating

scholarly journals WRAP-based nanoparticles for siRNA delivery: a SAR study and a comparison with lipid-based transfection reagents

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Karidia Konate ◽  
Emilie Josse ◽  
Milana Tasic ◽  
Karima Redjatti ◽  
Gudrun Aldrian ◽  
...  
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Gene Silencing ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Sirna Delivery ◽  
Cell Penetrating Peptides ◽  
Sirna Transfection ◽  
Arginine Residues ◽  
Sar Study

AbstractRecently, we designed novel amphipathic cell-penetrating peptides, called WRAP, able to transfer efficiently siRNA molecules into cells. In order to gain more information about the relationship between amino acid composition, nanoparticle formation and cellular internalization of these peptides composed of only three amino acids (leucine, arginine and tryptophan), we performed a structure–activity relationship (SAR) study. First, we compared our WRAP1 and WRAP5 peptides with the C6M1 peptide also composed of the same three amino acids and showing similar behaviors in siRNA transfection. Afterwards, to further define the main determinants in the WRAP activity, we synthesized 13 new WRAP analogues harboring different modifications like the number and location of leucine and arginine residues, the relative location of tryptophan residues, as well as the role of the α-helix formation upon proline insertions within the native WRAP sequence. After having compared the ability of these peptides to form peptide-based nanoparticles (PBNs) using different biophysical methods and to induce a targeted gene silencing in cells, we established the main sequential requirements of the amino acid composition of the WRAP peptide. In addition, upon measuring the WRAP-based siRNA transfection ability into cells compared to several non-peptide transfection agents available on the markets, we confirmed that WRAP peptides induced an equivalent level of targeted gene silencing but in most of the cases with lower cell toxicity as clearly shown in clonogenic assays.

scholarly journals WRAP-based nanoparticles for siRNA delivery: A SAR study and a comparison with lipid-based transfection reagents.

2020 ◽  
Author(s):  
Karidia Konate ◽  
Emilie Josse ◽  
Milana Tasic ◽  
Karima Redjatti ◽  
Gudrun Aldrian ◽  
...  
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Gene Silencing ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Cell Penetrating Peptides ◽  
Luciferase Assay ◽  
Sirna Transfection ◽  
Arginine Residues ◽  
Sar Study

Abstract Recently, we designed novel amphipathic cell-penetrating peptides, called WRAP, able to transfer efficiently siRNA molecules into cells. In order to gain more information about the relationship between amino acid composition, nanoparticle formation and cellular internalization of these peptides composed of only three amino acids (leucine, arginine and tryptophan), we perform a structure activity relationship (SAR) study. First, we compared our WRAP1 and WRAP5 peptides with the C6M1 peptide also composed of the same three amino acids and showing similar behaviors in siRNA transfection. Afterwards, to further define the main determinants in the WRAP activity, we synthesized 13 new WRAP analogues harboring different modifications like the number and location of leucine and arginine residues, the relative location of tryptophan residues, as well as the role of the α-helix formation upon proline insertions within the native WRAP sequence. After having compare the ability of these peptides to form peptide-based nanoparticles (PBNs) or not using different biophysical methods (circular dichroism, dynamic light scattering, gel shift assay) and to induce a targeted gene silencing (luciferase assay) in cells, we were able to establish the main sequential requirements of the amino acid composition of the WRAP peptides to maintain a good siRNA transfection efficacy. In addition, upon measuring the WRAP-based siRNA transfection ability into cells compared to several non-peptide transfection agents available on the markets, we confirmed that WRAP peptides induced an equivalent level of targeted gene silencing but in most of the cases with significant lower cell toxicity as clearly shown in clonogenic assays.

scholarly journals UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning

2021 ◽  
Vol 22 (23) ◽  
pp. 13124
Author(s):  
Phasit Charoenkwan ◽  
Chanin Nantasenamat ◽  
Md Mehedi Hasan ◽  
Mohammad Ali Moni ◽  
Balachandran Manavalan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Large Scale ◽  
Representation Learning ◽  
Sensory Properties ◽  
Feature Representation ◽  
Support Vector ◽  
Pseudo Amino Acid Composition

Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly. As a result, it is preferable to develop computational tools for the large-scale identification of available sequences in order to identify novel peptides with umami sensory properties. Although a computational tool has been developed for this purpose, its predictive performance is still insufficient. In this study, we use a feature representation learning approach to create a novel machine-learning meta-predictor called UMPred-FRL for improved umami peptide identification. We combined six well-known machine learning algorithms (extremely randomized trees, k-nearest neighbor, logistic regression, partial least squares, random forest, and support vector machine) with seven different feature encodings (amino acid composition, amphiphilic pseudo-amino acid composition, dipeptide composition, composition-transition-distribution, and pseudo-amino acid composition) to develop the final meta-predictor. Extensive experimental results demonstrated that UMPred-FRL was effective and achieved more accurate performance on the benchmark dataset compared to its baseline models, and consistently outperformed the existing method on the independent test dataset. Finally, to aid in the high-throughput identification of umami peptides, the UMPred-FRL web server was established and made freely available online. It is expected that UMPred-FRL will be a powerful tool for the cost-effective large-scale screening of candidate peptides with potential umami sensory properties.

Effect of maternal diet on the amino acid composition of human uterine fluid

2014 ◽  
Author(s):  
Alexandra Jayne Kermack ◽  
Ying Cheong ◽  
Nick Brook ◽  
Nick Macklon ◽  
Franchesca D Houghton
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Maternal Diet ◽  
Uterine Fluid
Sign in / Sign up

Export Citation Format

Share Document