RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou’s five-step rule

Zheng, Lei; Huang, Shenghui; Mu, Nengjiang; Zhang, Haoyue; Zhang, Jiayu; Chang, Yu; Yang, Lei; Zuo, Yongchun

doi:10.1093/database/baz131

Cited by 55 publications

(37 citation statements)

References 165 publications

(75 reference statements)

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“…Compared with the classical amino acid composition, the RAACs can reduce protein complexity and eliminate part of the redundant signals without losing sequence information intact ( Wang and Wang, 1999 ; Liu et al, 2018 ). In order to obtain the RAAC from the sequences, Zuo et al (2017) established the online webserver and database ( Zheng et al, 2019 ) that can be used to calculate RAAC.…”

Section: Methodsmentioning

confidence: 99%

Identifying Antioxidant Proteins by Combining Multiple Methods

Tang

et al. 2020

Front. Bioeng. Biotechnol.

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Antioxidant proteins play important roles in preventing free radical oxidation from damaging cells and DNA. They have become ideal candidates of disease prevention and treatment. Therefore, it is urgent to identify antioxidants from natural compounds. Since experimental methods are still cost ineffective, a series of computational methods have been proposed to identify antioxidant proteins. However, the performance of the current methods are still not satisfactory. In this study, a support vector machine based method, called Vote9, was proposed to identify antioxidants, in which the sequences were encoded by using the features generated from 9 optimal individual models. Results from jackknife test demonstrated that Vote9 is comparable with the best one of the existing predictors for this task. We hope that Vote9 will become a useful tool or at least can play a complementary role to the existing methods for identifying antioxidants.

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Section: Methodsmentioning

confidence: 99%

Identifying Antioxidant Proteins by Combining Multiple Methods

Tang

et al. 2020

Front. Bioeng. Biotechnol.

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“…Thus, the 20 amino acids were classified into 19 types in the above scheme (Susko and Roger, 2007), in which Ile (I) and Val (V) were viewed as a single type, while every one of other categories had only one amino acid. Under this reduced scheme, we use the webserver of Zuo (Zheng et al, 2019) to calculate the RAAC of the thermophilic and non-thermophilic proteins.…”

Section: Reduced Amino Acid Composition (Raac)mentioning

confidence: 99%

A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features

Feng

Dan

et al. 2020

Front. Bioeng. Biotechnol.

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The thermostability of proteins is a key factor considered during enzyme engineering, and finding a method that can identify thermophilic and non-thermophilic proteins will be helpful for enzyme design. In this study, we established a novel method combining mixed features and machine learning to achieve this recognition task. In this method, an amino acid reduction scheme was adopted to recode the amino acid sequence. Then, the physicochemical characteristics, auto-cross covariance (ACC), and reduced dipeptides were calculated and integrated to form a mixed feature set, which was processed using correlation analysis, feature selection, and principal component analysis (PCA) to remove redundant information. Finally, four machine learning methods and a dataset containing 500 random observations out of 915 thermophilic proteins and 500 random samples out of 793 non-thermophilic proteins were used to train and predict the data. The experimental results showed that 98.2% of thermophilic and non-thermophilic proteins were correctly identified using 10-fold cross-validation. Moreover, our analysis of the final reserved features and removed features yielded information about the crucial, unimportant and insensitive elements, it also provided essential information for enzyme design.

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“…But the traditional experimental method not merely costs a long time and a lot of money, but also has a relatively lower rate of success. [9][10][11][12][13]. Therefore, a quick way is in urgent demand to identify sequences.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cyclin Protein Using Two-Step Feature Selection Technique

et al. 2020

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Cyclins are a family of proteins that regulate the cell cycle by activating cyclin-dependent kinases or a group of enzymes required in the cell cycle. Constructing a model to classify Cyclins is of importance to understand their function. It is urgent to construct a machine learning based model to identify Cyclins because of low similarity between the sequence of Cyclins. In this study, a method based on support vector machine (SVM) is developed to recognize Cyclins only using amino acid sequence information. 18 feature descriptors with a total of 13151-dimension features were extracted, and the feature dimension were reduced to 8 through feature selection technique. The reserved features show some of feature descriptors such as Autocorrelation, AAC and CTDC are important in the identification of Cyclins. Jackknife cross-validation results indicate our model would classify Cyclins with an accuracy of 91.9%, which is superior to a recent study using the same data set. Our work provides an important tool for discriminating Cyclins.

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RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou’s five-step rule

Cited by 55 publications

References 165 publications

Identifying Antioxidant Proteins by Combining Multiple Methods

Identifying Antioxidant Proteins by Combining Multiple Methods

A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features

Prediction of Cyclin Protein Using Two-Step Feature Selection Technique

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