Dataset Information

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

ABSTRACT: 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, AFPs have 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.

SUBMITTER: Usman M

PROVIDER: S-EPMC7188683 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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

Usman Muhammad M Khan Shujaat S Lee Jeong-A JA

Scientific reports 20200428 1

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, AFPs have 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- ...[more]

PMID: 32345989

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Project description:BackgroundAs one of the most common protein post-translational modifications, glycosylation is involved in a variety of important biological processes. Computational identification of glycosylation sites in protein sequences becomes increasingly important in the post-genomic era. A new encoding scheme was employed to improve the prediction of mucin-type O-glycosylation sites in mammalian proteins.ResultsA new protein bioinformatics tool, CKSAAP_OGlySite, was developed to predict mucin-type O-glycosylation serine/threonine (S/T) sites in mammalian proteins. Using the composition of k-spaced amino acid pairs (CKSAAP) based encoding scheme, the proposed method was trained and tested in a new and stringent O-glycosylation dataset with the assistance of Support Vector Machine (SVM). When the ratio of O-glycosylation to non-glycosylation sites in training datasets was set as 1:1, 10-fold cross-validation tests showed that the proposed method yielded a high accuracy of 83.1% and 81.4% in predicting O-glycosylated S and T sites, respectively. Based on the same datasets, CKSAAP_OGlySite resulted in a higher accuracy than the conventional binary encoding based method (about +5.0%). When trained and tested in 1:5 datasets, the CKSAAP encoding showed a more significant improvement than the binary encoding. We also merged the training datasets of S and T sites and integrated the prediction of S and T sites into one single predictor (i.e. S+T predictor). Either in 1:1 or 1:5 datasets, the performance of this S+T predictor was always slightly better than those predictors where S and T sites were independently predicted, suggesting that the molecular recognition of O-glycosylated S/T sites seems to be similar and the increase of the S+T predictor's accuracy may be a result of expanded training datasets. Moreover, CKSAAP_OGlySite was also shown to have better performance when benchmarked against two existing predictors.ConclusionBecause of CKSAAP encoding's ability of reflecting characteristics of the sequences surrounding mucin-type O-glycosylation sites, CKSAAP_ OGlySite has been proved more powerful than the conventional binary encoding based method. This suggests that it can be used as a competitive mucin-type O-glycosylation site predictor to the biological community. CKSAAP_OGlySite is now available at http://bioinformatics.cau.edu.cn/zzd_lab/CKSAAP_OGlySite/.

Project description:BackgroundTraditionally, it is believed that the native structure of a protein corresponds to a global minimum of its free energy. However, with the growing number of known tertiary (3D) protein structures, researchers have discovered that some proteins can alter their structures in response to a change in their surroundings or with the help of other proteins or ligands. Such structural shifts play a crucial role with respect to the protein function. To this end, we propose a machine learning method for the prediction of the flexible/rigid regions of proteins (referred to as FlexRP); the method is based on a novel sequence representation and feature selection. Knowledge of the flexible/rigid regions may provide insights into the protein folding process and the 3D structure prediction.ResultsThe flexible/rigid regions were defined based on a dataset, which includes protein sequences that have multiple experimental structures, and which was previously used to study the structural conservation of proteins. Sequences drawn from this dataset were represented based on feature sets that were proposed in prior research, such as PSI-BLAST profiles, composition vector and binary sequence encoding, and a newly proposed representation based on frequencies of k-spaced amino acid pairs. These representations were processed by feature selection to reduce the dimensionality. Several machine learning methods for the prediction of flexible/rigid regions and two recently proposed methods for the prediction of conformational changes and unstructured regions were compared with the proposed method. The FlexRP method, which applies Logistic Regression and collocation-based representation with 95 features, obtained 79.5% accuracy. The two runner-up methods, which apply the same sequence representation and Support Vector Machines (SVM) and Naïve Bayes classifiers, obtained 79.2% and 78.4% accuracy, respectively. The remaining considered methods are characterized by accuracies below 70%. Finally, the Naïve Bayes method is shown to provide the highest sensitivity for the prediction of flexible regions, while FlexRP and SVM give the highest sensitivity for rigid regions.ConclusionA new sequence representation that uses k-spaced amino acid pairs is shown to be the most efficient in the prediction of the flexible/rigid regions of protein sequences. The proposed FlexRP method provides the highest prediction accuracy of about 80%. The experimental tests show that the FlexRP and SVM methods achieved high overall accuracy and the highest sensitivity for rigid regions, while the best quality of the predictions for flexible regions is achieved by the Naïve Bayes method.

Dataset Information

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

Publications

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

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