POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles

Wang, Jiawei; Yang, Bingjiao; Revote, Jerico; Leier, André; Marquez‐Lago, Tatiana T.; Webb, Geoffrey I.; Song, Jiangning; Chou, Kuo‐Chen; Lithgow, Trevor

doi:10.1093/bioinformatics/btx302

Cited by 157 publications

(92 citation statements)

References 25 publications

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“…In other words, this tool compares PSSM profiles to discover related, though sometimes remote, homologous proteins or DNA. Descriptors based on PSSM have been shown to improve the prediction performance of both the structural and functional properties of proteins across a range of bioinformatics problems [27], including the prediction of protein structural classes [28], protein fold recognition [29], protein-protein interactions [30], protein subcellular localization [31], RNA-binding sites [32] and, relevant here, protein functions [33][34][35][36]. In [35], for instance, 1D descriptors taken from PSSM were classified using probabilistic neural networks (PNN), kNN, decision tree, multi-layer perceptron, and SVM.…”

Section: Journal Of Artificial Intelligence and Systemsmentioning

confidence: 99%

Set of Approaches Based on Position Specific Scoring Matrix and Amino Acid Sequence for Primary Category Enzyme Classification

Nanni¹,

Brahnam²

2020

AIS

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The last decade has witnessed an unprecedented accumulation of proteins in large online databases which has led to the need for automatic prediction of protein function essential for massive and timely annotations of the proteins in these datasets. Protein databases, combined with functional annotations and machine learning (ML) techniques, offer many potential benefits, including significantly facilitating rapid pharmacological target identification. The main objective of this study is to identify, for the problem of enzyme classification, the most powerful combinations of descriptors taken from different protein representations. To achieve this objective, four approaches for representing the Position-Specific Scoring Matrix (PSSM) combined with three methods for representing the Amino Acid Sequence (AAS) are evaluated with the aim of experimentally producing a powerful ensemble of descriptors for enzyme function prediction. Each protein descriptor is classified by a Support Vector Machine (SVM), with the set of SVMs finally combined by sum rule. Cross-validation experiments using these descriptors on single-functional enzymes (n=44,661) extracted from the PDB database demonstrate that the ensemble proposed here achieves superior classification rates compared to state-of-the-art ML techniques reported in the literature on the same dataset. Although the proposed ensemble strongly outperforms these other techniques, it is computationally much heavier, mainly because the PSSM extraction process is time consuming. However, there is a growing repository of proteins where PSSM has already been extracted, making the proposed method more practical and attractive. The MATLAB code and the dataset used in the experiments reported here are available at https://github.com/LorisNanni.

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Section: Journal Of Artificial Intelligence and Systemsmentioning

confidence: 99%

Set of Approaches Based on Position Specific Scoring Matrix and Amino Acid Sequence for Primary Category Enzyme Classification

Nanni¹,

Brahnam²

2020

AIS

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show abstract

“…Actually, user-friendly web-servers as shown in a series of recent publications [40,46,100,[107][108][109][110][111][112][114][115][116][117][118][119][120][121][122] will significantly enhance the impacts of theoretical work because they can attract the broad experimental scientists [52]. In view of this, the web-server for the new predictor pLoc-mGpos has been established at http://www.jci-bioinfo.cn/pLoc-mGpos/.…”

Section: Web Server and User Guidementioning

confidence: 99%

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

Xiao¹,

Cheng²,

Su³

et al. 2017

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The basic unit in life is cell. It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these proteins. But proteins in different organelles or subcellular locations have different functions. Facing the avalanche of protein sequences generated in the postgenomic age, we are challenged to develop high throughput tools for identifying the subcellular localization of proteins based on their sequence information alone. Although considerable efforts have been made in this regard, the problem is far apart from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for drug targets. Using the ML-GKR (Multi-Label Gaussian Kernel Regression) method, we developed a new predictor called "pLoc-mGpos" by in-depth extracting the key information from GO (Gene Ontology) into the Chou's general PseAAC (Pseudo Amino Acid Composition) for predicting the subcellular localization of Gram-positive bacterial proteins with both single and multiple location sites. Rigorous cross-validation on a same stringent benchmark dataset indicated that the proposed pLoc-mGpos predictor is remarkably superior to "iLoc-Gpos", the state-of-the-art predictor for the same purpose. To maximize the convenience of most experimental scientists, a user-friendly web-server for the new powerful predictor has been established at Natural Science http://www.jci-bioinfo.cn/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.

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“…Numerous researches have proved that evolutionary information encoded in the PSSM profile is more informative than protein sequence alone [30]. The PSSM profiles have been widely used in bioinformatics, such as protein remote homology detection [31], protein fold recognition [32], and prediction of protein structural class [33].…”

Section: Introductionmentioning

confidence: 99%

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

Sang

Xiao

Zheng

et al. 2020

Computational and Mathematical Methods in Medicine

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Prediction of DNA-binding proteins (DBPs) has become a popular research topic in protein science due to its crucial role in all aspects of biological activities. Even though considerable efforts have been devoted to developing powerful computational methods to solve this problem, it is still a challenging task in the field of bioinformatics. A hidden Markov model (HMM) profile has been proved to provide important clues for improving the prediction performance of DBPs. In this paper, we propose a method, called HMMPred, which extracts the features of amino acid composition and auto-and cross-covariance transformation from the HMM profiles, to help train a machine learning model for identification of DBPs. Then, a feature selection technique is performed based on the extreme gradient boosting (XGBoost) algorithm. Finally, the selected optimal features are fed into a support vector machine (SVM) classifier to predict DBPs. The experimental results tested on two benchmark datasets show that the proposed method is superior to most of the existing methods and could serve as an alternative tool to identify DBPs.

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POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 157 publications

References 25 publications

Set of Approaches Based on Position Specific Scoring Matrix and Amino Acid Sequence for Primary Category Enzyme Classification

Set of Approaches Based on Position Specific Scoring Matrix and Amino Acid Sequence for Primary Category Enzyme Classification

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

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