Prediction of cell penetrating peptides and their uptake efficiency using random forest-based feature selections

Cell penetrating peptides (CPPs) are short peptides that can carry biomolecules of varying sizes across the cell membrane into the cytoplasm. Correctly identifying CPPs is the basis for studying their functions and mechanisms. Here, we propose a novel CPP predictor that is able to predict CPPs and their uptake efficiency. In our method, five feature descriptors are applied to encode the sequence and compose a hybrid feature vector. Afterward, the wrapper + random forest algorithm is employed, which combines feature selection with the prediction process to find features that are crucial for identifying CPPs. The jackknife cross validation result shows that our predictor is comparable to state-of-the-art CPP predictors, and our method reduces the feature dimension, which improves computational efficiency and avoids overfitting, allowing our predictor to be adopted to identify large-scale CPP data.

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Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy

Journal of Proteome Research ◽

10.1021/acs.jproteome.8b00148 ◽

2018 ◽

Vol 17 (8) ◽

pp. 2715-2726 ◽

Cited By ~ 74

Author(s):

Balachandran Manavalan ◽

Sathiyamoorthy Subramaniyam ◽

Tae Hwan Shin ◽

Myeong Ok Kim ◽

Gwang Lee

Keyword(s):

Machine Learning ◽

Cell Penetrating Peptides ◽

Uptake Efficiency ◽

Cell Penetrating ◽

Improved Accuracy

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CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning

Briefings in Bioinformatics ◽

10.1093/bib/bby091 ◽

2018 ◽

Cited By ~ 17

Author(s):

Xiaoli Qiang ◽

Chen Zhou ◽

Xiucai Ye ◽

Pu-feng Du ◽

Ran Su ◽

...

Keyword(s):

Large Scale ◽

Representation Learning ◽

Cell Penetrating Peptides ◽

Feature Representation ◽

Cell Penetrating

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Effect of poly‐glutamate on uptake efficiency and cytotoxicity of cell penetrating peptides

IET Nanobiotechnology ◽

10.1049/iet-nbt.2015.0030 ◽

2016 ◽

Vol 10 (2) ◽

pp. 87-95 ◽

Cited By ~ 6

Author(s):

Samad Mussa Farkhani ◽

Ali Shirani ◽

Samaneh Mohammadi ◽

Parvin Zakeri‐Milani ◽

Javid Shahbazi Mojarrad ◽

...

Keyword(s):

Cell Penetrating Peptides ◽

Uptake Efficiency ◽

Cell Penetrating

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Cell penetrating peptides in ocular drug delivery: State of the art

Journal of Controlled Release ◽

10.1016/j.jconrel.2018.06.023 ◽

2018 ◽

Vol 284 ◽

pp. 84-102 ◽

Cited By ~ 29

Author(s):

S. Pescina ◽

C. Ostacolo ◽

I.M. Gomez-Monterrey ◽

M. Sala ◽

A. Bertamino ◽

...

Keyword(s):

Drug Delivery ◽

State Of The Art ◽

Cell Penetrating Peptides ◽

Ocular Drug Delivery ◽

Cell Penetrating

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Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification

Remote Sensing ◽

10.3390/rs11141665 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1665 ◽

Cited By ~ 11

Author(s):

Tianle He ◽

Chuanjie Xie ◽

Qingsheng Liu ◽

Shiying Guan ◽

Gaohuan Liu

Keyword(s):

Time Series ◽

Random Forest ◽

Winter Wheat ◽

Large Scale ◽

Short Term Memory ◽

State Of The Art ◽

Training Sample ◽

Central China ◽

Surface Reflectance ◽

The Impact

Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods.

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Comparison of Penetratin and Other Homeodomain-Derived Cell-Penetrating Peptides: Interaction in a Membrane-Mimicking Environment and Cellular Uptake Efficiency†

Biochemistry ◽

10.1021/bi0518390 ◽

2006 ◽

Vol 45 (5) ◽

pp. 1408-1420 ◽

Cited By ~ 54

Author(s):

Stéphane Balayssac ◽

Fabienne Burlina ◽

Odile Convert ◽

Gérard Bolbach ◽

Gérard Chassaing ◽

...

Keyword(s):

Cellular Uptake ◽

Cell Penetrating Peptides ◽

Uptake Efficiency ◽

Cell Penetrating ◽

Cellular Uptake Efficiency

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Effects of Tryptophan Content and Backbone Spacing on Uptake Efficiency of Cell-Penetrating Peptides

Biophysical Journal ◽

10.1016/j.bpj.2011.11.2671 ◽

2012 ◽

Vol 102 (3) ◽

pp. 487a

Author(s):

Hanna A. Rydberg ◽

Maria Matson ◽

Helene L. Åmand ◽

Elin K. Esbjörner ◽

Bengt Nordén

Keyword(s):

Cell Penetrating Peptides ◽

Uptake Efficiency ◽

Cell Penetrating ◽

Tryptophan Content

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Prediction of Enzyme Mutant Activity Using Computational Mutagenesis and Incremental Transduction

Advances in Bioinformatics ◽

10.1155/2011/958129 ◽

2011 ◽

Vol 2011 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Nada Basit ◽

Harry Wechsler

Keyword(s):

Enzyme Activity ◽

Random Forest ◽

Incremental Learning ◽

Cross Validation ◽

State Of The Art ◽

Delaunay Tessellation ◽

Computational Mutagenesis ◽

Hiv 1 ◽

Over Time ◽

Better Than

Wet laboratory mutagenesis to determine enzyme activity changes is expensive and time consuming. This paper expands on standard one-shot learning by proposing an incremental transductive method (T2bRF) for the prediction of enzyme mutant activity during mutagenesis using Delaunay tessellation and 4-body statistical potentials for representation. Incremental learning is in tune with both eScience and actual experimentation, as it accounts for cumulative annotation effects of enzyme mutant activity over time. The experimental results reported, using cross-validation, show that overall the incremental transductive method proposed, using random forest as base classifier, yields better results compared to one-shot learning methods. T2bRF is shown to yield 90% on T4 and LAC (and 86% on HIV-1). This is significantly better than state-of-the-art competing methods, whose performance yield is at 80% or less using the same datasets.

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