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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zervou, Michaela Areti Doutsi, Effrosyni Tsakalides, Panagiotis Institute of Computer Science Foundation for Research and Technology-Hellas Heraklion Greece University of Crete Department of Computer Science Heraklion Greece

ISBN: (纸本)9781728163277

Predicting the structural class of a protein from its amino acid sequence is among the most significant problems in bioinformatics, especially for proteins with a low sequence similarity. While current methods using recurrent neural networks achieve a notable accuracy in this task, their approach relies on extracting a large quantity of features, impacting the efficiency and reliability of the prediction. In this work, we introduce an efficient and accurate classification scheme based on chaos game representation and recurrent neural networks. The proposed scheme achieves comparable results with state-of-the-art methods, while using a significantly lower-dimensional representation of the feature space. © 2023 IEEE.

关键词： Chaos Game Representation Gated Recurrent Unit Multidimensional Time-series protein structural class prediction

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Image-based effective feature generation for protein structural class and ligand binding prediction

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PEERJ COMPUTER SCIENCE 2020年第2期6卷 e253页

作者： Sadique, Nafees Ahmed, Al Amin Neaz Islam, Md Tajul Pervage, Md Nawshad Shatabda, Swakkhar United Int Univ Dept Comp Sci & Engn Dhaka Bangladesh

proteins are the building blocks of all cells in both human and all living creatures of the world. Most of the work in the living organism is performed by proteins. proteins are polymers of amino acid monomers which are biomolecules or macromolecules. The tertiary structure of protein represents the three-dimensional shape of a protein. The functions, classification and binding sites are governed by the protein's tertiary structure. If two protein structures are alike, then the two proteins can be of the same kind implying similar structural class and ligand binding properties. In this paper, we have used the protein tertiary structure to generate effective features for applications in structural similarity to detect structural class and ligand binding. Firstly, we have analyzed the effectiveness of a group of image-based features to predict the structural class of a protein. These features are derived from the image generated by the distance matrix of the tertiary structure of a given protein. They include local binary pattern (LBP) histogram, Gabor filtered LBP histogram, separate row multiplication matrix with uniform LBP histogram, neighbor block subtraction matrix with uniform LBP histogram and atom bond. Separate row multiplication matrix and neighbor block subtraction matrix filters, as well as atom bond, are our novels. The experiments were done on a standard benchmark dataset. We have demonstrated the effectiveness of these features over a large variety of supervised machine learning algorithms. Experiments suggest support vector machines is the best performing classifier on the selected dataset using the set of features. We believe the excellent performance of Hybrid LBP in terms of accuracy would motivate the researchers and practitioners to use it to identify protein structural class. To facilitate that, a classification model using Hybrid LBP is readily available for use at http://***/PL/. protein-ligand binding is accountable for managing the

关键词： protein structural class prediction protein-ligand binding Image-based features Similarity based supervised learning algorithms

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prediction of protein structural class for low-similarity sequences using Chou's pseudo amino acid composition and wavelet denoising

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JOURNAL OF MOLECULAR GRAPHICS & MODELLING 2017年 76卷 260-273页

作者： Yu, Bin Lou, Lifeng Li, Shan Zhang, Yusen Qiu, Wenying Wu, Xue Wang, Minghui Tian, Baoguang Qingdao Univ Sci & Technol Coll Math & Phys Qingdao 266061 Peoples R China Univ Sci & Technol China Dept Geophys & Planetary Sci CAS Key Lab Geospace Environm Hefei 230026 Anhui Peoples R China Qingdao Univ Sci & Technol Bioinformat & Syst Biol Res Ctr Qingdao 266061 Peoples R China Shandong Univ Weihai Sch Math & Stat Weihai 264209 Peoples R China

prediction of protein structural class plays an important role in protein structure and function analysis, drug design and many other biological applications. prediction of protein structural class for low similarity sequences is still a challenging task. Based on the theory of wavelet denoising, this paper presents a novel method of prediction of protein structural class for the first time. Firstly, the features of the protein sequence are extracted by using Chou's pseudo amino acid composition (PseAAC). Then the extracted feature information is denoised by two-dimensional (2D) wavelet. Finally, the optimal feature vectors are input to support vector machine (SVM) classifier to predict protein structural classes. We obtained significant predictive results using jackknife test on three low-similarity protein structural class datasets 25PDB, 1189 and 640, and compared our method with previous methods The results indicate that the method proposed in this paper can effectively improve the prediction accuracy of protein structural class, which will be a reliable tool for prediction of protein structural class, especially for low-similarity (C) 2017 Elsevier Inc. All rights reserved.

关键词： protein structural class prediction Pseudo amino acid composition Two-dimensional wavelet denoising Support vector machine

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A step-by-step classification algorithm of protein secondary structures based on double-layer SVM model

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GENOMICS 2020年第2期112卷 1941-1946页

作者： Ge, Yongzhen Zhao, Shuo Zhao, Xiqiang Ocean Univ China Sch Math Sci Qingdao 266100 Peoples R China Ocean Univ China Coll Informat Sci & Engn Qingdao 266100 Peoples R China

In this paper, a step-by-step classification algorithm based on double-layer SVM model is constructed to predict the secondary structure of proteins. The most important feature of this algorithm is to improve the prediction accuracy of alpha+beta and alpha/beta classes through transforming the prediction of two classes of proteins, alpha+beta and alpha/beta classes, with low accuracy in the past, into the prediction of all-alpha and all-beta classes with high accuracy. A widely-used dataset, 25PDB dataset with sequence similarity lower than 40%, is used to evaluate this method. The results show that this method has good performance, and on the basis of ensuring the accuracy of other three structural classes of proteins, the accuracy of alpha+beta class proteins is improved significantly.

关键词： Step-by-step classification algorithm Double-layer SVM protein structural class prediction Secondary structure

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Improving protein structural class prediction Using Novel Combined Sequence Information and Predicted Secondary structural Features

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2011年第16期32卷 3393-3398页

作者： Dai, Qi Wu, Li Li, Lihua Zhejiang Sci Tech Univ Coll Life Sci Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Inst Biomed Engn & Instrumentat Hangzhou 310018 Peoples R China

protein structural class prediction solely from protein sequences is a challenging problem in bioinformatics. Numerous efficient methods have been proposed for protein structural class prediction, but challenges remain. Using novel combined sequence information coupled with predicted secondary structural features (PSSF), we proposed a novel scheme to improve prediction of protein structural classes. Given an amino acid sequence, we first transformed it into a reduced amino acid sequence and calculated its word frequencies and word position features to combine novel sequence information. Then we added the PSSF to the combine sequence information to predict protein structural classes. The proposed method was tested on four benchmark datasets in low homology and achieved the overall prediction accuracies of 83.1%, 87.0%, 94.5%, and 85.2%, respectively. The comparison with existing methods demonstrates that the overall improvements range from 2.3% to 27.5%, which indicates that the proposed method is more efficient, especially for low-homology amino acid sequences. (C) 2011 Wiley Periodicals, Inc. J Comput Chem 32: 3393-3398, 2011

关键词： protein structural class prediction word frequency information word position information predicted secondary structure support vector machine

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SVM-BASED METHOD FOR protein structural class prediction USING SECONDARY structural CONTENT AND structural INFORMATION OF AMINO ACIDS

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JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2011年第4期9卷 489-502页

作者： Mohammad, Tabrez Anwar Shamim Nagarajaram, Hampapathalu Adimurthy Ctr DNA Fingerprinting & Diagnost CDFD Lab Computat Biol Hyderabad 500001 Andhra Pradesh India

The knowledge collated from the known protein structures has revealed that the proteins are usually folded into the four structural classes: all-alpha, all-beta, alpha/beta and alpha broken vertical bar beta . A number of methods have been proposed to predict the protein's structural class from its primary structure;however, it has been observed that these methods fail or perform poorly in the cases of distantly related sequences. In this paper, we propose a new method for protein structural class prediction using low homology (twilight-zone) protein sequences dataset. Since protein structural class prediction is a typical classification problem, we have developed a Support Vector Machine (SVM)-based method for protein structural class prediction that uses features derived from the predicted secondary structure and predicted burial information of amino acid residues. The examination of different individual as well as feature combinations revealed that the combination of secondary structural content, secondary structural and solvent accessibility state frequencies of amino acids gave rise to the best leave-one-out cross-validation accuracy of similar to 81% which is comparable to the best accuracy reported in the literature so far.

关键词： protein structural class prediction SVM structural information of amino acids

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A high-accuracy protein structural class prediction algorithm using predicted secondary structural information

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JOURNAL OF THEORETICAL BIOLOGY 2010年第3期267卷 272-275页

作者： Liu, Tian Jia, Cangzhi Northeast Univ Dept Math Shenyang 0004 Peoples R China Dalian Univ Technol Dept Biosci & Biotechnol Dalian 116024 Peoples R China

One major problem with the existing algorithm for the prediction of protein structural classes is low accuracies for proteins from alpha/beta and alpha+beta classes. In this study, three novel features were rationally designed to model the differences between proteins from these two classes. In combination with other rational designed features, an 11-dimensional vector prediction method was proposed. By means of this method, the overall prediction accuracy based on 25PDB dataset was 1.5% higher than the previous best-performing method, MODAS. Furthermore, the prediction accuracy for proteins from alpha+beta class based on 25PDB dataset was 5% higher than the previous best-performing method, SCPRED. The prediction accuracies obtained with the D675 and FC699 datasets were also improved. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： protein structural class prediction Secondary structure Alternating frequency Parallel and anti-parallel beta-sheets Support vector machine

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Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou's general PseAAC

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2015年 142卷 28-35页

作者： Zhang, Shengli Xidian Univ Sch Math & Stat Xian 710071 Peoples R China

Determination of protein structural class using a fast and suitable computational method has become a hot issue in protein science. prediction of protein structural class for low-similarity sequences remains a challenge problem. In this study, a 111-dimensional feature vector is constructed to predict protein structural classes. Among the 111 features, 100 features based on pseudo-position specific scoring matrix (PsePSSM) are selected to reflect the evolutionary information and the sequence-order information, and the other 11 rational features based on predicted protein secondary structure sequences (PSSS) are designed in the previous works. To evaluate the performance of the proposed method (named by PSSS-PsePSSM), jackknife cross-validation tests are performed on three widely used benchmark datasets: 1189, 25PDB and 640. Our method achieves competitive performance on prediction accuracies, especially for the overall prediction accuracies for datasets 1189, 25PDB and 640, which reach 86.6%, 89.5% and 81.0%, respectively. The PSSS-PsePSSM algorithm also outperforms other existing methods, indicating that our proposed method is a cost-effective computational tool for protein structural class prediction. (C) 2015 Elsevier B.V. All rights reserved.

关键词： protein structural class prediction Secondary structure sequences Support vector machine Transition probability matrix Pseudo-position specific scoring matrix

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

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GENE 2015年第2期554卷 241-248页

作者： Wang, Junru Wang, Cong Cao, Jiajia Liu, Xiaoqing Yao, Yuhua Dai, Qi Zhejiang Sci Tech Univ Coll Life Sci Hangzhou 310018 Zhejiang Peoples R China Zhejiang Sci Tech Univ Coll Mech Engn & Automat Hangzhou 310018 Zhejiang Peoples R China Hangzhou Dianzi Univ Coll Sci Hangzhou 310018 Zhejiang Peoples R China Univ Texas Dallas Dept Mol & Cell Biol Dallas TX 75080 USA

Many efficient methods have been proposed to advance protein structural class prediction, but there are still some challenges where additional insight or technology is needed for low-similarity sequences. In this work, we schemed out a new prediction method for low-similarity datasets using reduced PSSM and position-based secondary structural features. We evaluated the proposed method with four experiments and compared it with the available competing prediction methods. The results indicate that the proposed method achieved the best performance among the evaluated methods, with overall accuracy 3-5% higher than the existing best-performing method. This paper also found that the reduced alphabets with size 13 simplify PSSM structures efficiently while reserving its maximal information. This understanding can be used to design more powerful prediction methods for protein structural class. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Reduced PSSM Global correlation Secondary structural feature protein structural class prediction

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A novel Multi-Agent Ada-Boost algorithm for predicting protein structural class with the information of protein secondary structure

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JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2015年第5期13卷 1550022-1550022页

作者： Fan, Ming Zheng, Bin Li, Lihua Hangzhou Dianzi Univ Inst Biomed Engn & Instrumentat Hangzhou 310018 Zhejiang Peoples R China Hunan Mech & Elect Polytech Chang Sha 410151 Peoples R China

Knowledge of the structural class of a given protein is important for understanding its folding patterns. Although a lot of efforts have been made, it still remains a challenging problem for prediction of protein structural class solely from protein sequences. The feature extraction and classification of proteins are the main problems in prediction. In this research, we extended our earlier work regarding these two aspects. In protein feature extraction, we proposed a scheme by calculating the word frequency and word position from sequences of amino acid, reduced amino acid, and secondary structure. For an accurate classification of the structural class of protein, we developed a novel Multi-Agent Ada-Boost (MA-Ada) method by integrating the features of Multi-Agent system into Ada-Boost algorithm. Extensive experiments were taken to test and compare the proposed method using four benchmark datasets in low homology. The results showed classification accuracies of 88.5%, 96.0%, 88.4%, and 85.5%, respectively, which are much better compared with the existing methods. The source code and dataset are available on request.

关键词： protein structural class prediction secondary structural sequence multi-classifier fusion multi-agent Ada-Boost

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