scholarly journals Using Chou’s Pseudo Amino Acid Composition and Machine Learning Method to Predict the Antiviral Peptides

2015 ◽  
Vol 9 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Maryam Zare ◽  
Hassan Mohabatkar ◽  
Fateme Kazemi Faramarzi ◽  
Majid Mohammad Beigi ◽  
Mandana Behbahani
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Pseudo Amino Acid Composition ◽  
Antiviral Therapies ◽  
Machine Learning Methods ◽  
Virus Life Cycle ◽  
Antiviral Peptides ◽  
Hiv 1

Traditional antiviral therapies are expensive, limitedly available, and cause several side effects. Currently, designing antiviral peptides is very important, because these peptides interfere with the key stage of virus life cycle. Most of the antiviral peptides are derived from viral proteins for example peptide derived from HIV-1 capsid protein. Because of the importance of these peptides, in this study the concept of pseudo-amino acid composition (PseAAC) and machine learning methods are used to classify or identify antiviral peptides.

Predicting Antibacterial Peptides by the Concept of Chou's Pseudo-amino Acid Composition and Machine Learning Methods

2013 ◽  
Vol 20 (2) ◽  
pp. 180-186 ◽  
Author(s):  
Maede Khosravian ◽  
Fateme Kazemi Faramarzi ◽  
Majid Mohammad Beigi ◽  
Mandana Behbahani ◽  
Hassan Mohabatkar
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Antibacterial Peptides ◽  
Pseudo Amino Acid Composition ◽  
Learning Methods ◽  
Machine Learning Methods

Predicting Antibacterial Peptides by the Concept of Chou’s Pseudo-amino Acid Composition and Machine Learning Methods

2012 ◽  
Vol 20 (2) ◽  
pp. 180-186 ◽  
Author(s):  
Maede Khosravian ◽  
Fateme Kazemi Faramarzi ◽  
Majid Mohammad Beigi ◽  
Mandana Behbahani ◽  
Hassan Mohabatkar
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Antibacterial Peptides ◽  
Pseudo Amino Acid Composition ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Plant-mSubP: a computational framework for the prediction of single- and multi-target protein subcellular localization using integrated machine-learning approaches

AoB Plants ◽  
2019 ◽  
Vol 12 (3) ◽  
Author(s):  
Sitanshu S Sahu ◽  
Cristian D Loaiza ◽  
Rakesh Kaundal
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Subcellular Localization ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Learning Approaches ◽  
Pseudo Amino Acid Composition ◽  
Hybrid Features ◽  
Dual Label ◽  
Localization Prediction

Abstract The subcellular localization of proteins is very important for characterizing its function in a cell. Accurate prediction of the subcellular locations in computational paradigm has been an active area of interest. Most of the work has been focused on single localization prediction. Only few studies have discussed the multi-target localization, but have not achieved good accuracy so far; in plant sciences, very limited work has been done. Here we report the development of a novel tool Plant-mSubP, which is based on integrated machine learning approaches to efficiently predict the subcellular localizations in plant proteomes. The proposed approach predicts with high accuracy 11 single localizations and three dual locations of plant cell. Several hybrid features based on composition and physicochemical properties of a protein such as amino acid composition, pseudo amino acid composition, auto-correlation descriptors, quasi-sequence-order descriptors and hybrid features are used to represent the protein. The performance of the proposed method has been assessed through a training set as well as an independent test set. Using the hybrid feature of the pseudo amino acid composition, N-Center-C terminal amino acid composition and the dipeptide composition (PseAAC-NCC-DIPEP), an overall accuracy of 81.97 %, 84.75 % and 87.88 % is achieved on the training data set of proteins containing the single-label, single- and dual-label combined, and dual-label proteins, respectively. When tested on the independent data, an accuracy of 64.36 %, 64.84 % and 81.08 % is achieved on the single-label, single- and dual-label, and dual-label proteins, respectively. The prediction models have been implemented on a web server available at http://bioinfo.usu.edu/Plant-mSubP/. The results indicate that the proposed approach is comparable to the existing methods in single localization prediction and outperforms all other existing tools when compared for dual-label proteins. The prediction tool will be a useful resource for better annotation of various plant proteomes.

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.

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