Combining PSSM and physicochemical feature for protein structure prediction with support vector machine

Kurniawan, Ilham; Haryanto, Toto; Hasibuan, Lailan Sahrina; Agmalaro, Muhammad Asyhar

doi:10.1088/1742-6596/835/1/012006

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…SVM is one of the most extensively applied ML algorithm in various computational studies involving classification and regression tasks [44,[47][48][49][50]. This algorithm finds an optimal hyperplane in a high-dimensional feature space using a kernel and then categorizes the input vectors into two classes [51].…”

Section: Support Vector Machinementioning

confidence: 99%

Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

et al. 2021

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Background Post-translational modification (PTM) is a biological process that alters proteins and is therefore involved in the regulation of various cellular activities and pathogenesis. Protein phosphorylation is an essential process and one of the most-studied PTMs: it occurs when a phosphate group is added to serine (Ser, S), threonine (Thr, T), or tyrosine (Tyr, Y) residue. Dysregulation of protein phosphorylation can lead to various diseases—most commonly neurological disorders, Alzheimer’s disease, and Parkinson’s disease—thus necessitating the prediction of S/T/Y residues that can be phosphorylated in an uncharacterized amino acid sequence. Despite a surplus of sequencing data, current experimental methods of PTM prediction are time-consuming, costly, and error-prone, so a number of computational methods have been proposed to replace them. However, phosphorylation prediction remains limited, owing to substrate specificity, performance, and the diversity of its features. Methods In the present study we propose machine-learning-based predictors that use the physicochemical, sequence, structural, and functional information of proteins to classify S/T/Y phosphorylation sites. Rigorous feature selection, the minimum redundancy/maximum relevance approach, and the symmetrical uncertainty method were employed to extract the most informative features to train the models. Results The RF and SVM models generated using diverse feature types in the present study were highly accurate as is evident from good values for different statistical measures. Moreover, independent test sets and benchmark validations indicated that the proposed method clearly outperformed the existing methods, demonstrating its ability to accurately predict protein phosphorylation. Conclusions The results obtained in the present work indicate that the proposed computational methodology can be effectively used for predicting putative phosphorylation sites further facilitating discovery of various biological processes mechanisms.

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Section: Support Vector Machinementioning

confidence: 99%

Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

et al. 2021

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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%

“…As one of the most widely used ML algorithms applied to classification problems [11,13,14,28,35,37,39,42,53], SVM [54] maps the input data into a high-dimensional space through the use of kernel functions and finds a hyperplane that maximizes the distance between the hyperplane and two types of samples. By mapping the unseen samples into the same space, SVM can predict the new samples based on which side of the hyperplane they fall in.…”

Section: Support Vector Machinementioning

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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“…Inspired by the great success in the fields of computer vision [1], speech recognition [2], and emotion classification [3], the method based on deep learning has been widely used in many biological research fields [4,5]. Examples include protein contact map [6], drugtarget binding affinity [7,8], chromatin accessibility [9], protein function [10,11], and using Support Vector Machine (SVM) to solve the problem of protein structure prediction [12]. The main advantage of the deep learning method is that it can automatically represent the original sequence and learn hidden patterns through nonlinear transformation [13].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Protein Secondary Structure Based on WS-BiLSTM Model

Gao

Zhao

et al. 2022

Symmetry

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Protein secondary structure prediction is an important topic in bioinformatics. This paper proposed a novel model named WS-BiLSTM, which combined the wavelet scattering convolutional network and the long-short-term memory network for the first time to predict protein secondary structure. This model captures nonlocal interactions between amino acid sequences and remembers long-range interactions between amino acids. In our WS-BiLSTM model, the wavelet scattering convolutional network is used to extract protein features from the PSSM sliding window; the extracted features are combined with the original PSSM data as the input features of the long-short-term memory network to predict protein secondary structure. It is worth noting that the wavelet scattering convolutional network is asymmetric as a member of the continuous wavelet family. The Q3 accuracy on the test set CASP9, CASP10, CASP11, CASP12, CB513, and PDB25 reached 85.26%, 85.84%, 84.91%, 85.13%, 86.10%, and 85.52%, which were higher 2.15%, 2.16%, 3.5%, 3.19%, 4.22%, and 2.75%, respectively, than using the long-short-term memory network alone. Comparing our results with the state-of-art methods shows that our proposed model achieved better results on the CB513 and CASP12 data sets. The experimental results show that the features extracted from the wavelet scattering convolutional network can effectively improve the accuracy of protein secondary structure prediction.

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Combining PSSM and physicochemical feature for protein structure prediction with support vector machine

Cited by 8 publications

References 6 publications

Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

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

Prediction of Protein Secondary Structure Based on WS-BiLSTM Model

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