Predicting protein lysine phosphoglycerylation sites by hybridizing many sequence based features

Chen, Qing‐Yun; Tang, Jijun; Du, Pu-Feng

doi:10.1039/c6mb00875e

Cited by 20 publications

(11 citation statements)

References 34 publications

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“…The modes cover most of the representation modes that can be generated by PseAAC-General, PseKNC-General, and Pse-In-One. Moreover, UltraPse can generate even more modes, for example, the commonly used one-hot encoding mode [ 53 , 54 , 55 ]. The sequence representation modes of UltraPse can be extended by using BSOs and TDFs.…”

Section: Resultsmentioning

confidence: 99%

UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences

Zhao

Miao

et al. 2017

IJMS

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With the avalanche of biological sequences in public databases, one of the most challenging problems in computational biology is to predict their biological functions and cellular attributes. Most of the existing prediction algorithms can only handle fixed-length numerical vectors. Therefore, it is important to be able to represent biological sequences with various lengths using fixed-length numerical vectors. Although several algorithms, as well as software implementations, have been developed to address this problem, these existing programs can only provide a fixed number of representation modes. Every time a new sequence representation mode is developed, a new program will be needed. In this paper, we propose the UltraPse as a universal software platform for this problem. The function of the UltraPse is not only to generate various existing sequence representation modes, but also to simplify all future programming works in developing novel representation modes. The extensibility of UltraPse is particularly enhanced. It allows the users to define their own representation mode, their own physicochemical properties, or even their own types of biological sequences. Moreover, UltraPse is also the fastest software of its kind. The source code package, as well as the executables for both Linux and Windows platforms, can be downloaded from the GitHub repository.

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Section: Resultsmentioning

confidence: 99%

UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences

Zhao

Miao

et al. 2017

IJMS

Self Cite

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“…The best performance of the classifier was obtained by maintaining the gap value k = 8 36 . It is also evident from the references that an attribute vector obtained from a very large value of k will include redundant features and may not contribute toward prediction 33,47 . Owing to the significance of maintaining this value of k, in this study, we perform all the performance analyses by maintaining the constant gap value of k = 8.…”

Section: Methods Evaluation Parametersmentioning

confidence: 99%

AFP-LSE: Antifreeze Proteins Prediction Using Latent Space Encoding of Composition of k-Spaced Amino Acid Pairs

Usman

Khan

Lee

2020

Sci Rep

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Species living in extremely cold environments resist the freezing conditions through antifreeze proteins (Afps). Apart from being essential proteins for various organisms living in sub-zero temperatures, Afpshave numerous applications in different industries. They possess very small resemblance to each other and cannot be easily identified using simple search algorithms such as BLAST and PSI-BLAST. Diverse AFPs found in fishes (Type I, II, III, IV and antifreeze glycoproteins (AFGPs)), are sub-types and show low sequence and structural similarity, making their accurate prediction challenging. Although several machine-learning methods have been proposed for the classification of AFPs, prediction methods that have greater reliability are required. In this paper, we propose a novel machine-learning-based approach for the prediction of AFP sequences using latent space learning through a deep auto-encoder method. For latent space pruning, we use the output of the auto-encoder with a deep neural network classifier to learn the non-linear mapping of the protein sequence descriptor and class label. The proposed method outperformed the existing methods, yielding excellent results in comparison. A comprehensive ablation study is performed, and the proposed method is evaluated in terms of widely used performance measures. In particular, the proposed method demonstrated a high Matthews correlation coefficient of 0.52, F-score of 0.49, and Youden's index of 0.81 on an independent test dataset, thereby outperforming the existing methods for Afp prediction.In Antarctic fish, a survival mechanism that prevented them from freezing in seawater at sub-zero temperatures was observed, which led to the discovery of antifreeze proteins (AFP) 1 . AFPs have been identified as a crucial substance for resisting a freezing environment in various species including plants, bacteria, fungi, insects, and animals 2 . Ice exists in different geometric shapes due to the varying arrangements of oxygen atoms; therefore, the structural and sequential arrangements of AFPs largely vary to accommodate this heterogeneity of ice molecules 3 . Ice also exhibits the property of recrystallization, by which small ice crystals bind to the water molecules, thus becoming a large ice lattice, causing severe damage to the cell membrane, which, in some cases, may be lethal 4 . AFPs are commonly categorized into glycoproteins (AFGPs) and non-glycoproteins (AFPs) 5 . They protect the organisms using two mechanisms: (i) thermal hysteresis (TH), by which the freezing point of water is depressed to a few degrees by the adsorption-inhibition effect without altering the melting point 6 ; (ii) ice crystal inhibition, by which the AFP sites bind to the surfaces of ice and inhibit their growth to become a larger ice lattice, developing either small harmless ice crystals or forming a needle-shaped lattice, thus diminishing the recrystallization property of ice 2 .AFPs are indispensable in organisms such as fish 7 , fungi 8 , bacteria 9 , plants 10 , and insects 11 . Furthermo...

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“…However, data size is a very crucial part of model training, more than total 2,000-dimensional features was obtained by the CKSAAP encoding scheme which may cause overfitting with small sample size [8]. PhoglyPred is another predictor which focused on selecting the important sequence-based features using the F-score, and evaluated using SVM and jackknife test to predict the phosphoglycerylation sites; moreover, to improve the classification for the imbalanced dataset, the authors set the different parameters for positive and negative datasets [9]. Except for the sequence-based features, EvolStruct-Phogly has incorporated local structure conformations, accessible surface area (ASA) and position-specific scoring matrix (PSSM) to predict phosphoglycerylated lysine residues [10].…”

Section: Introductionmentioning

confidence: 99%

iDPGK: Characterization and Identification of Lysine Phosphoglycerylation Sites Based on Sequence-Based Features

Hung

Kao

Lau

et al. 2020

Preprint

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Background:Protein phosphoglycerylation, the addition of a 1,3-bisphosphoglyceric acid (1,3-BPG) to a lysine residue of a protein and thus to form a 3-phosphoglyceryl-lysine (pgK), is a reversible and non-enzymatic post-translational modification (PTM) and plays a regulatory role in glucose metabolism and glycolytic process. As the number of experimentally verified phosphoglycerylated sites has increased significantly, statistical or machine learning methods are imperative for investigating the characteristics of phosphoglycerylation sites. Currently, research into phosphoglycerylation is very limited, and only a few resources are available for the computational identification of phosphoglycerylation sites. Result: We present a bioinformatics investigation of phosphoglycerylation sites based on sequence-based features. The TwoSampleLogo analysis reveals that the regions surrounding the phosphoglycerylation sites contain a high relatively of positively charged amino acids, especially in the upstream flanking region. Additionally, the non-polar and aliphatic amino acids are more abundant surrounding phosphoglycerylated lysine following the results of PTM-Logo, which may play a functional role in discriminating between phosphoglycerylation and non- phosphoglycerylation sites. Many types of features were adopted to build the prediction model on the training dataset, including amino acid composition, amino acid pair composition, positional weighted matrix and position-specific scoring matrix. Further, to improve the predictive power, numerous top features ranked by F-score were considered as the final combination for classification, and thus the predictive models were trained using DT, RF and SVM classifiers. Evaluation by five-fold cross-validation showed that the selected features was most effective in discriminating between phosphoglycerylated and non- phosphoglycerylated sites.Conclusion: The SVM model trained with the selected sequence-based features performed well, with a sensitivity of 77.5%, a specificity of 73.6%, an accuracy of 74.9%, and a Matthews Correlation Coefficient value of 0.49. Furthermore, the model also consistently provides the effective performance in independent testing set, yielding sensitivity of 75.7% and specificity of 64.9%. Finally, the model has been implemented as a web-based system, namely iDPGK, which is now freely available at http://mer.hc.mmh.org.tw/iDPGK/.

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Predicting protein lysine phosphoglycerylation sites by hybridizing many sequence based features

Cited by 20 publications

References 34 publications

UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences

UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences

AFP-LSE: Antifreeze Proteins Prediction Using Latent Space Encoding of Composition of k-Spaced Amino Acid Pairs

iDPGK: Characterization and Identification of Lysine Phosphoglycerylation Sites Based on Sequence-Based Features

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