Effects of N-terminal and C-terminal modification on cytotoxicity and cellular uptake of amphiphilic cell penetrating peptides

Soleymani-Goloujeh, Mehdi; Nokhodchi, Ali; Niazi, Mehri; Najafi-Hajivar, Saeedeh; Shahbazi-Mojarrad, Javid; Zarghami, Nosratollah; Zakeri–Milani, Parvin; Mohammadi, Ali; Karimi, Mohammad; Valizadeh, Hadi

doi:10.1080/21691401.2017.1414823

Cited by 19 publications

(7 citation statements)

References 38 publications

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“…This means that the CNN model found a higher correlation between the terminal regions of the ACPs and observed activities. This observation is in agreement to our current understanding about the functionality of ACPs. , First, for peptides that target cell membranes, the terminal region with certain structure (such as structural amphiphilicity) or certain physicochemical properties (especially polar, charged, or hydrophobic residues) can help to achieve a higher affinity with the membrane, thereby helping them to penetrate the target cell. ,,− Second, there are hundreds of proteases or peptidases that cause proteolysis of peptides; previous studies revealed that sequences terminated with residues such as M, S, A, T, V and G are more resistant to plasma degradation, thus certain compositions in the terminal regions of peptides can help them to improve in vivo stability and maintain bioactivity. − In addition, studies that artificially modified ACPs for improved activity often mutated residues in the two terminals, ,,, which also reflects the functional importance of the terminal regions. Therefore, the contribution factor analysis shows that the breast model successfully learned some sequential patterns of the breast cancer ACPs and was able to relate these patterns to their functional activity.…”

Section: Resultssupporting

confidence: 87%

xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning

Chen

Cheong

Siu

2021

J. Chem. Inf. Model.

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Cancer is one of the leading causes of death worldwide. Conventional cancer treatment relies on radiotherapy and chemotherapy, but both methods bring severe side effects to patients, as these therapies not only attack cancer cells but also damage normal cells. Anticancer peptides (ACPs) are a promising alternative as therapeutic agents that are efficient and selective against tumor cells. Here, we propose a deep learning method based on convolutional neural networks to predict biological activity (EC50, LC50, IC50, and LD50) against six tumor cells, including breast, colon, cervix, lung, skin, and prostate. We show that models derived with multitask learning achieve better performance than conventional single-task models. In repeated 5-fold cross validation using the CancerPPD data set, the best models with the applicability domain defined obtain an average mean squared error of 0.1758, Pearson's correlation coefficient of 0.8086, and Kendall's correlation coefficient of 0.6156. As a step toward model interpretability, we infer the contribution of each residue in the sequence to the predicted activity by means of feature importance weights derived from the convolutional layers of the model. The present method, referred to as xDeep-AcPEP, will help to identify effective ACPs in rational peptide design for therapeutic purposes. The data, script files for reproducing the experiments, and the final prediction models can be downloaded from http://github.com/chen709847237/xDeep-AcPEP. The web server to directly access this prediction method is at https://app.cbbio.online/acpep/home.

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Section: Resultssupporting

confidence: 87%

xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning

Chen

Cheong

Siu

2021

J. Chem. Inf. Model.

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“…In addition to many bioactive peptides, the C-terminus of the heavy chain of many monoclonal antibody-based drugs is amidated, although its significance is not yet known (Tsubaki et al 2013). Since cells contain exopeptidases, the C-terminus of the cellpenetrating peptides is often amidated, which increases their half-life (Soleymani-Goloujeh et al 2017).…”

Section: C-termini Backgroundmentioning

confidence: 99%

The carboxy-terminus, a key regulator of protein function

Sharma

Schiller

2019

Critical Reviews in Biochemistry and Molecular Biology

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All proteins end with a carboxyl terminus that has unique biophysical properties and is often disordered. Although there are examples of important C-termini functions, a more global role for the C-terminus is not yet established. In this review, we summarize research on C-termini, a unique region in proteins that cells exploit. Alternative splicing and proteolysis increase the diversity of proteins and peptides in cells with unique C-termini. The C-termini of proteins contain minimotifs, short peptides with an encoded function generally characterized as binding, posttranslational modifications, and trafficking. Many of these activities are specific to minimotifs on the C-terminus. Approximately 13% of C-termini in the human proteome have a known minimotif, and the majority, if not all of the remaining termini have conserved motifs inferring a function that remains to be discovered. C-termini, their predictions, and their functions are collated in the C-terminome, Proteus, and Terminus Oriented Protein Function INferred Database (TopFIND) database/web systems. Many C-termini are well conserved, and some have a known role in health and disease. We envision that this summary of C-termini will guide future investigation of their biochemical and physiological significance.

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“…The release exponent “ n ” was 0.185, which agreed with the porous nature of MSN, with a release diffusion behavior that is quasi-Fickian in characteristics (Figure D(III)). The sustained release behavior may be attributed to hydrogen bonding and electrostatic interaction between the functional groups existing in TAT and the carboxylic group of the MTX. , In addition, release of the drug inside the channel of MSNs can be hindered by TAT acting as caps. Because the cap can be removed in acidic conditions, the release became faster (Figure D(I),D(II)).…”

Section: Results and Discussionmentioning

confidence: 99%

Enhancing Methotrexate Delivery in the Brain by Mesoporous Silica Nanoparticles Functionalized with Cell-Penetrating Peptide using in Vivo and ex Vivo Monitoring

et al. 2023

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The blood–brain barrier (BBB) acts as a physical/biochemical barrier that protects brain parenchyma from potential hazards exerted by different xenobiotics found in the systemic circulation. This barrier is created by “a lipophilic gate” as well as a series of highly organized influx/efflux mechanisms. The BBB bottleneck adversely affects the efficacy of chemotherapeutic agents in treating different CNS malignancies such as glioblastoma, an aggressive type of cancer affecting the brain. In the present study, mesoporous silica nanoparticles (MSNs) were conjugated with the transactivator of transcription (TAT) peptide, a cell-penetrating peptide, to produce MSN-NH-TAT with the aim of improving methotrexate (MTX) penetration into the brain. The TAT-modified nanosystem was characterized by Fourier transform infrared spectrometry (FTIR), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), dynamic light scattering (DLS), and N2 adsorption–desorption analysis. In vitro hemolysis and cell viability studies confirmed the biocompatibility of the MSN-based nanocarriers. In addition, in vivo studies showed that the MTX-loaded MSN-NH-TAT improved brain-to-plasma concentration ratio, brain uptake clearance, and the drug’s blood terminal half-life, compared with the use of free MTX. Taken together, the results of the present study indicate that MSN functionalization with TAT is crucial for delivery of MTX into the brain. The present nanosystem represents a promising alternative drug carrier to deliver MTX into the brain via overcoming the BBB.

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Effects of N-terminal and C-terminal modification on cytotoxicity and cellular uptake of amphiphilic cell penetrating peptides

Cited by 19 publications

References 38 publications

xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning

xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning

The carboxy-terminus, a key regulator of protein function

Enhancing Methotrexate Delivery in the Brain by Mesoporous Silica Nanoparticles Functionalized with Cell-Penetrating Peptide using in Vivo and ex Vivo Monitoring

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