Prediction of protein structural classes for low-similarity sequences using reduced PSSM and position-based secondary structural features

Wang, Junru; Cao, Junhui; Liu, Xiaoqing; Yao, Yao; Dai, Qi

doi:10.1016/j.gene.2014.10.037

Cited by 29 publications

(8 citation statements)

References 47 publications

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“…The evolutionary data in the form of Position-Specific Scoring Matrix (PSSM) profile are informative and have proved useful in a number of biological classification problems [28,[33][34][35][36][37][38][39][40][41][42][43][44][45]. In this work, the PSSM profile was generated by running PSI-BLAST against the uniref50 database with the parameters j = 3 and h = 0.001.…”

Section: Position-specific Scoring Matrix Based Transformation (Pssm)mentioning

confidence: 99%

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Zhang

Xie

Wang

et al. 2018

Briefings in Bioinformatics

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As a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, computational methods to accurately identify malonylation sites are still lacking and urgently needed. In particular, there is currently no comprehensive analysis and assessment of different features and machine learning (ML) methods that are required for constructing the necessary prediction models. Here, we review, analyze and compare 11 different feature encoding methods, with the goal of extracting key patterns and characteristics from residue sequences of Kmal sites. We identify optimized feature sets, with which four commonly used ML methods (random forest, support vector machines, K-nearest neighbor and logistic regression) and one recently proposed [Light Gradient Boosting Machine (LightGBM)] are trained on data from three species, namely, Escherichia coli, Mus musculus and Homo sapiens, and compared using randomized 10-fold cross-validation tests. We show that integration of the single method-based models through ensemble learning further improves the prediction performance and model robustness on the independent test. When compared to the existing state-of-the-art predictor, MaloPred, the optimal ensemble models were more accurate for all three species (AUC: 0.930, 0.923 and 0.944 for E. coli, M. musculus and H. sapiens, respectively). Using the ensemble models, we developed an accessible online predictor, kmal-sp, available at http://kmalsp.erc.monash.edu/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for PTM site prediction, expedite the discovery of new malonylation and other PTM types and facilitate hypothesis-driven experimental validation of novel malonylated substrates and malonylation sites.

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Section: Position-specific Scoring Matrix Based Transformation (Pssm)mentioning

confidence: 99%

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Zhang

Xie

Wang

et al. 2018

Briefings in Bioinformatics

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“…Some studies have shown that the content and spatial arrangement of secondary structural elements are also important factors affecting the complex function or structure of proteins. Therefore, one of the methods to improve the prediction accuracy is to add secondary structural features to the feature set [ 24 – 31 ]. In this work, PSI-PRED is used to predict the secondary structure sequence [ 39 ], and the 11 widely used predicted secondary structural features are calculated to improve protein structural class prediction [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

“…Nanni et al introduced a prediction method that combines the characteristics of the first-level sequence and the characteristics of the second-level structure [ 30 ]. Wang et al have combined improved simplified PSSM with secondary structural features for protein structural class prediction [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Using Recursive Feature Selection with Random Forest to Improve Protein Structural Class Prediction for Low-Similarity Sequences

Wang

Yang

et al. 2021

Computational and Mathematical Methods in Medicine

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Many combinations of protein features are used to improve protein structural class prediction, but the information redundancy is often ignored. In order to select the important features with strong classification ability, we proposed a recursive feature selection with random forest to improve protein structural class prediction. We evaluated the proposed method with four experiments and compared it with the available competing prediction methods. The results indicate that the proposed feature selection method effectively improves the efficiency of protein structural class prediction. Only less than 5% features are used, but the prediction accuracy is improved by 4.6-13.3%. We further compared different protein features and found that the predicted secondary structural features achieve the best performance. This understanding can be used to design more powerful prediction methods for the protein structural class.

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“…And the regularization parameter C and kernel parameter γ can be tuned to optimize the classification performance in the classification of biological data Hayat et al, 2014;Lin and Chen, 2011). At present, SVM has been successfully applied to many important tasks in bioinformatics (Bhasin and Raghava, 2004;Chou and Cai, 2002;Cai et al, 2004;Lin et al, 2012;Liu et al, 2013Liu et al, , 2014bLiu et al, , 2015aLiu et al, , 2010Wang et al, 2004Wang et al, , 2015Yang and Chou, 2004). In addition, the quadratic discriminant analysis (Q DA) also has been successfully applied in model identification (Feng, 2014;Feng et al, 2014;Feng and Luo, 2008) in recent years.…”

Section: Quadratic Discriminant Analysismentioning

confidence: 99%

Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts

Kou

Feng

2015

Journal of Theoretical Biology

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Prediction of protein structural classes for low-similarity sequences using reduced PSSM and position-based secondary structural features

Cited by 29 publications

References 47 publications

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Using Recursive Feature Selection with Random Forest to Improve Protein Structural Class Prediction for Low-Similarity Sequences

Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts

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