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Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms. CRYSTALS 2021. [DOI: 10.3390/cryst11040324] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
In the postgenomic age, rapid growth in the number of sequence-known proteins has been accompanied by much slower growth in the number of structure-known proteins (as a result of experimental limitations), and a widening gap between the two is evident. Because protein function is linked to protein structure, successful prediction of protein structure is of significant importance in protein function identification. Foreknowledge of protein structural class can help improve protein structure prediction with significant medical and pharmaceutical implications. Thus, a fast, suitable, reliable, and reasonable computational method for protein structural class prediction has become pivotal in bioinformatics. Here, we review recent efforts in protein structural class prediction from protein sequence, with particular attention paid to new feature descriptors, which extract information from protein sequence, and the use of machine learning algorithms in both feature selection and the construction of new classification models. These new feature descriptors include amino acid composition, sequence order, physicochemical properties, multiprofile Bayes, and secondary structure-based features. Machine learning methods, such as artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), random forest, deep learning, and examples of their application are discussed in detail. We also present our view on possible future directions, challenges, and opportunities for the applications of machine learning algorithms for prediction of protein structural classes.
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Abstract
During the last three decades or so, many efforts have been made to study the protein cleavage
sites by some disease-causing enzyme, such as HIV (Human Immunodeficiency Virus) protease
and SARS (Severe Acute Respiratory Syndrome) coronavirus main proteinase. It has become increasingly
clear <i>via</i> this mini-review that the motivation driving the aforementioned studies is quite wise,
and that the results acquired through these studies are very rewarding, particularly for developing peptide
drugs.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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3
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Chou KC. An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule. Curr Pharm Des 2020; 25:4223-4234. [PMID: 31782354 DOI: 10.2174/1381612825666191129164042] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 11/25/2019] [Indexed: 11/22/2022]
Abstract
OBJECTIVE One of the most challenging and also the most difficult problems is how to formulate a biological sequence with a vector but considerably keep its sequence order information. METHODS To address such a problem, the approach of Pseudo Amino Acid Components or PseAAC has been developed. RESULTS AND CONCLUSION It has become increasingly clear via the 10-year recollection that the aforementioned proposal has been indeed very powerful.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, Massachusetts 02478, United States.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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Shao YT, Liu XX, Lu Z, Chou KC. pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.127042] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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5
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Marrero-Ponce Y, Teran JE, Contreras-Torres E, García-Jacas CR, Perez-Castillo Y, Cubillan N, Peréz-Giménez F, Valdés-Martini JR. LEGO-based generalized set of two linear algebraic 3D bio-macro-molecular descriptors: Theory and validation by QSARs. J Theor Biol 2020; 485:110039. [DOI: 10.1016/j.jtbi.2019.110039] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Revised: 09/11/2019] [Accepted: 10/02/2019] [Indexed: 11/28/2022]
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6
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Wang S, Wang X. Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion. BMC Bioinformatics 2019; 20:701. [PMID: 31874617 PMCID: PMC6929547 DOI: 10.1186/s12859-019-3276-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Protein structural class predicting is a heavily researched subject in bioinformatics that plays a vital role in protein functional analysis, protein folding recognition, rational drug design and other related fields. However, when traditional feature expression methods are adopted, the features usually contain considerable redundant information, which leads to a very low recognition rate of protein structural classes. RESULTS We constructed a prediction model based on wavelet denoising using different feature expression methods. A new fusion idea, first fuse and then denoise, is proposed in this article. Two types of pseudo amino acid compositions are utilized to distill feature vectors. Then, a two-dimensional (2-D) wavelet denoising algorithm is used to remove the redundant information from two extracted feature vectors. The two feature vectors based on parallel 2-D wavelet denoising are fused, which is known as PWD-FU-PseAAC. The related source codes are available at https://github.com/Xiaoheng-Wang12/Wang-xiaoheng/tree/master. CONCLUSIONS Experimental verification of three low-similarity datasets suggests that the proposed model achieves notably good results as regarding the prediction of protein structural classes.
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Affiliation(s)
- Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, People's Republic of China.
| | - Xiaoheng Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, People's Republic of China
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Chou KC. Impacts of Pseudo Amino Acid Components and 5-steps Rule to Proteomics and Proteome Analysis. Curr Top Med Chem 2019; 19:2283-2300. [DOI: 10.2174/1568026619666191018100141] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 08/18/2019] [Accepted: 08/26/2019] [Indexed: 01/27/2023]
Abstract
Stimulated by the 5-steps rule during the last decade or so, computational proteomics has achieved remarkable progresses in the following three areas: (1) protein structural class prediction; (2) protein subcellular location prediction; (3) post-translational modification (PTM) site prediction. The results obtained by these predictions are very useful not only for an in-depth study of the functions of proteins and their biological processes in a cell, but also for developing novel drugs against major diseases such as cancers, Alzheimer’s, and Parkinson’s. Moreover, since the targets to be predicted may have the multi-label feature, two sets of metrics are introduced: one is for inspecting the global prediction quality, while the other for the local prediction quality. All the predictors covered in this review have a userfriendly web-server, through which the majority of experimental scientists can easily obtain their desired data without the need to go through the complicated mathematics.
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Affiliation(s)
- Kuo-Chen Chou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
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8
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Chou KC. Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09910-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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9
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Terán JE, Marrero-Ponce Y, Contreras-Torres E, García-Jacas CR, Vivas-Reyes R, Terán E, Torres FJ. Tensor Algebra-based Geometrical (3D) Biomacro-Molecular Descriptors for Protein Research: Theory, Applications and Comparison with other Methods. Sci Rep 2019; 9:11391. [PMID: 31388082 PMCID: PMC6684663 DOI: 10.1038/s41598-019-47858-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 07/22/2019] [Indexed: 11/16/2022] Open
Abstract
In this report, a new type of tridimensional (3D) biomacro-molecular descriptors for proteins are proposed. These descriptors make use of multi-linear algebra concepts based on the application of 3-linear forms (i.e., Canonical Trilinear (Tr), Trilinear Cubic (TrC), Trilinear-Quadratic-Bilinear (TrQB) and so on) as a specific case of the N-linear algebraic forms. The definition of the kth 3-tuple similarity-dissimilarity spatial matrices (Tensor's Form) are used for the transformation and for the representation of the existing chemical information available in the relationships between three amino acids of a protein. Several metrics (Minkowski-type, wave-edge, etc) and multi-metrics (Triangle area, Bond-angle, etc) are proposed for the interaction information extraction, as well as probabilistic transformations (e.g., simple stochastic and mutual probability) to achieve matrix normalization. A generalized procedure considering amino acid level-based indices that can be fused together by using aggregator operators for descriptors calculations is proposed. The obtained results demonstrated that the new proposed 3D biomacro-molecular indices perform better than other approaches in the SCOP-based discrimination and the prediction of folding rate of proteins by using simple linear parametrical models. It can be concluded that the proposed method allows the definition of 3D biomacro-molecular descriptors that contain orthogonal information capable of providing better models for applications in protein science.
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Affiliation(s)
- Julio E Terán
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Translacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Quito, Pichincha, Ecuador
- Universidad San Francisco de Quito (USFQ), Grupo de Química Computacional y Teórica (QCT-USFQ), Departamento de Ingeniería Química, and Instituto de Simulación Computacional (ISC-USFQ), Quito, Pichincha, Ecuador
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Translacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Quito, Pichincha, Ecuador.
- Universidad de San Buenaventura - Cartagena - Facultad de Ciencias de la Salud - Grupo de Investigación Microbiología & Ambiente (GIMA) - Calle Real de Ternera, Diagonal 32, No. 30-966, Cartagena, Código postal: 1300 10, Colombia.
| | - Ernesto Contreras-Torres
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Translacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Quito, Pichincha, Ecuador
| | - César R García-Jacas
- Cátedras CONACYT - Departamento de Ciencia de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California, Mexico
| | - Ricardo Vivas-Reyes
- Grupo de Química Cuántica y Teórica de la Universidad de Cartagena-Facultad de Ciencias Exactas y Naturales. Programa de Química. Campus de San Pablo and Grupo GINUMED Corporacion Universitaria Rafal Nuñez. Facultad de Salud. Programa de Medicina., Cartagena, Colombia
- Grupo CipTec, Facultad de Ingenierias. Fundacion Universitaria Tecnologico Comfenalco - Cartagena, Cartagena, Bolívar, Colombia
| | - Enrique Terán
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Translacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Quito, Pichincha, Ecuador
| | - F Javier Torres
- Universidad San Francisco de Quito (USFQ), Grupo de Química Computacional y Teórica (QCT-USFQ), Departamento de Ingeniería Química, and Instituto de Simulación Computacional (ISC-USFQ), Quito, Pichincha, Ecuador
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Contreras-Torres E. Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC. J Theor Biol 2018; 454:139-145. [DOI: 10.1016/j.jtbi.2018.05.033] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 05/23/2018] [Accepted: 05/28/2018] [Indexed: 11/24/2022]
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11
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Using a Classifier Fusion Strategy to Identify Anti-angiogenic Peptides. Sci Rep 2018; 8:14062. [PMID: 30218091 PMCID: PMC6138733 DOI: 10.1038/s41598-018-32443-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 09/07/2018] [Indexed: 12/27/2022] Open
Abstract
Anti-angiogenic peptides perform distinct physiological functions and potential therapies for angiogenesis-related diseases. Accurate identification of anti-angiogenic peptides may provide significant clues to understand the essential angiogenic homeostasis within tissues and develop antineoplastic therapies. In this study, an ensemble predictor is proposed for anti-angiogenic peptide prediction by fusing an individual classifier with the best sensitivity and another individual one with the best specificity. We investigate predictive capabilities of various feature spaces with respect to the corresponding optimal individual classifiers and ensemble classifiers. The accuracy and Matthew’s Correlation Coefficient (MCC) of the ensemble classifier trained by Bi-profile Bayes (BpB) features are 0.822 and 0.649, respectively, which represents the highest prediction results among the investigated prediction models. Discriminative features are obtained from BpB using the Relief algorithm followed by the Incremental Feature Selection (IFS) method. The sensitivity, specificity, accuracy, and MCC of the ensemble classifier trained by the discriminative features reach up to 0.776, 0.888, 0.832, and 0.668, respectively. Experimental results indicate that the proposed method is far superior to the previous study for anti-angiogenic peptide prediction.
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12
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Prediction of HIV-1 and HIV-2 proteins by using Chou's pseudo amino acid compositions and different classifiers. Sci Rep 2018; 8:2359. [PMID: 29402983 PMCID: PMC5799304 DOI: 10.1038/s41598-018-20819-x] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 01/24/2018] [Indexed: 01/02/2023] Open
Abstract
Human immunodeficiency virus (HIV) is the retroviral agent that causes acquired immune deficiency syndrome (AIDS). The number of HIV caused deaths was about 4 million in 2016 alone; it was estimated that about 33 million to 46 million people worldwide living with HIV. The HIV disease is especially harmful because the progressive destruction of the immune system prevents the ability of forming specific antibodies and to maintain an efficacious killer T cell activity. Successful prediction of HIV protein has important significance for the biological and pharmacological functions. In this study, based on the concept of Chou’s pseudo amino acid (PseAA) composition and increment of diversity (ID), support vector machine (SVM), logisitic regression (LR), and multilayer perceptron (MP) were presented to predict HIV-1 proteins and HIV-2 proteins. The results of the jackknife test indicated that the highest prediction accuracy and CC values were obtained by the SVM and MP were 0.9909 and 0.9763, respectively, indicating that the classifiers presented in this study were suitable for predicting two groups of HIV proteins.
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Liu B, Wu H, Chou KC. Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences. ACTA ACUST UNITED AC 2017. [DOI: 10.4236/ns.2017.94007] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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14
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Kavianpour H, Vasighi M. Structural classification of proteins using texture descriptors extracted from the cellular automata image. Amino Acids 2016; 49:261-271. [DOI: 10.1007/s00726-016-2354-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 10/18/2016] [Indexed: 12/12/2022]
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15
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A Gram-Negative Bacterial Secreted Protein Types Prediction Method Based on PSI-BLAST Profile. BIOMED RESEARCH INTERNATIONAL 2016; 2016:3206741. [PMID: 27563663 PMCID: PMC4985605 DOI: 10.1155/2016/3206741] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Revised: 07/04/2016] [Accepted: 07/05/2016] [Indexed: 11/29/2022]
Abstract
Prediction of secreted protein types based solely on sequence data remains to be a challenging problem. In this study, we extract the long-range correlation information and linear correlation information from position-specific score matrix (PSSM). A total of 6800 features are extracted at 17 different gaps; then, 309 features are selected by a filter feature selection method based on the training set. To verify the performance of our method, jackknife and independent dataset tests are performed on the test set and the reported overall accuracies are 93.60% and 100%, respectively. Comparison of our results with the existing method shows that our method provides the favorable performance for secreted protein type prediction.
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16
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Liu T, Qin Y, Wang Y, Wang C. Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach. Int J Mol Sci 2015; 17:ijms17010015. [PMID: 26712737 PMCID: PMC4730262 DOI: 10.3390/ijms17010015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Revised: 12/15/2015] [Accepted: 12/18/2015] [Indexed: 11/18/2022] Open
Abstract
The prior knowledge of protein structural class may offer useful clues on understanding its functionality as well as its tertiary structure. Though various significant efforts have been made to find a fast and effective computational approach to address this problem, it is still a challenging topic in the field of bioinformatics. The position-specific score matrix (PSSM) profile has been shown to provide a useful source of information for improving the prediction performance of protein structural class. However, this information has not been adequately explored. To this end, in this study, we present a feature extraction technique which is based on gapped-dipeptides composition computed directly from PSSM. Then, a careful feature selection technique is performed based on support vector machine-recursive feature elimination (SVM-RFE). These optimal features are selected to construct a final predictor. The results of jackknife tests on four working datasets show that our method obtains satisfactory prediction accuracies by extracting features solely based on PSSM and could serve as a very promising tool to predict protein structural class.
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Affiliation(s)
- Taigang Liu
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.
| | - Yufang Qin
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.
| | - Yongjie Wang
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China.
| | - Chunhua Wang
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.
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Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:370756. [PMID: 26788119 PMCID: PMC4693000 DOI: 10.1155/2015/370756] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Revised: 11/19/2015] [Accepted: 12/01/2015] [Indexed: 11/17/2022]
Abstract
Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.
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18
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Gu Q, Ding YS, Zhang TL. An ensemble classifier based prediction of G-protein-coupled receptor classes in low homology. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.12.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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19
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Qin Y, Zheng X, Wang J, Chen M, Zhou C. Prediction of protein structural class based on Linear Predictive Coding of PSI-BLAST profiles. Open Life Sci 2015. [DOI: 10.1515/biol-2015-0055] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
AbstractKnowledge of protein structure plays a key role in the analysis of protein functions, protein binding, rational drug design, and many other related fields and applications. In this study, a novel feature extraction model based on linear predictive coding (LPC) and position-specific score matrices (PSSM) was proposed to predict structural class from protein sequences. First, the PSI-BLAST tool was employed to transform the original protein sequences into PSSMs. Then, the LPC, a signal processing tool, was applied to extract the features from PSSMs. The selected features were finally fed to a support vector machine to perform the prediction. Cross-validation tests on the four benchmark datasets Z277, Z498, 1189 and 25PDB, showed a significant leap in overall accuracy using the proposed method. Compared to existing methods, our method achieved better performance in prediction of protein structural class.
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Chen W, Lin H, Chou KC. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. MOLECULAR BIOSYSTEMS 2015; 11:2620-34. [DOI: 10.1039/c5mb00155b] [Citation(s) in RCA: 262] [Impact Index Per Article: 29.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
With the avalanche of DNA/RNA sequences generated in the post-genomic age, it is urgent to develop automated methods for analyzing the relationship between the sequences and their functions.
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Affiliation(s)
- Wei Chen
- Department of Physics
- School of Sciences
- and Center for Genomics and Computational Biology
- Hebei United University
- Tangshan 063000
| | - Hao Lin
- Gordon Life Science Institute
- Boston
- USA
- Key Laboratory for Neuro-Information of Ministry of Education
- Center of Bioinformatics
| | - Kuo-Chen Chou
- Department of Physics
- School of Sciences
- and Center for Genomics and Computational Biology
- Hebei United University
- Tangshan 063000
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21
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Hayat M, Iqbal N. Discriminating protein structure classes by incorporating Pseudo Average Chemical Shift to Chou's general PseAAC and Support Vector Machine. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 116:184-192. [PMID: 24997484 DOI: 10.1016/j.cmpb.2014.06.007] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Revised: 06/09/2014] [Accepted: 06/13/2014] [Indexed: 06/03/2023]
Abstract
Proteins control all biological functions in living species. Protein structure is comprised of four major classes including all-α class, all-β class, α+β, and α/β. Each class performs different function according to their nature. Owing to the large exploration of protein sequences in the databanks, the identification of protein structure classes is difficult through conventional methods with respect to cost and time. Looking at the importance of protein structure classes, it is thus highly desirable to develop a computational model for discriminating protein structure classes with high accuracy. For this purpose, we propose a silco method by incorporating Pseudo Average Chemical Shift and Support Vector Machine. Two feature extraction schemes namely Pseudo Amino Acid Composition and Pseudo Average Chemical Shift are used to explore valuable information from protein sequences. The performance of the proposed model is assessed using four benchmark datasets 25PDB, 1189, 640 and 399 employing jackknife test. The success rates of the proposed model are 84.2%, 85.0%, 86.4%, and 89.2%, respectively on the four datasets. The empirical results reveal that the performance of our proposed model compared to existing models is promising in the literature so far and might be useful for future research.
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Affiliation(s)
- Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
| | - Nadeem Iqbal
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
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22
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Ding S, Yan S, Qi S, Li Y, Yao Y. A protein structural classes prediction method based on PSI-BLAST profile. J Theor Biol 2014; 353:19-23. [DOI: 10.1016/j.jtbi.2014.02.034] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Revised: 01/27/2014] [Accepted: 02/24/2014] [Indexed: 11/27/2022]
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23
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Zhang L, Zhao X, Kong L. Predict protein structural class for low-similarity sequences by evolutionary difference information into the general form of Chou's pseudo amino acid composition. J Theor Biol 2014; 355:105-10. [PMID: 24735902 DOI: 10.1016/j.jtbi.2014.04.008] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Revised: 02/26/2014] [Accepted: 04/04/2014] [Indexed: 10/25/2022]
Abstract
Knowledge of protein structural class plays an important role in characterizing the overall folding type of a given protein. At present, it is still a challenge to extract sequence information solely using protein sequence for protein structural class prediction with low similarity sequence in the current computational biology. In this study, a novel sequence representation method is proposed based on position specific scoring matrix for protein structural class prediction. By defined evolutionary difference formula, varying length proteins are expressed as uniform dimensional vectors, which can represent evolutionary difference information between the adjacent residues of a given protein. To perform and evaluate the proposed method, support vector machine and jackknife tests are employed on three widely used datasets, 25PDB, 1189 and 640 datasets with sequence similarity lower than 25%, 40% and 25%, respectively. Comparison of our results with the previous methods shows that our method may provide a promising method to predict protein structural class especially for low-similarity sequences.
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Affiliation(s)
- Lichao Zhang
- College of Marine Life Science, Ocean University of China, Yushan Road, Qingdao 266003, PR China
| | - Xiqiang Zhao
- College of Mathematical Science, Ocean University of China, Songling Road, Qingdao 266100, PR China.
| | - Liang Kong
- College of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, PR China
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Protein subcellular localization in human and hamster cell lines: Employing local ternary patterns of fluorescence microscopy images. J Theor Biol 2014; 340:85-95. [DOI: 10.1016/j.jtbi.2013.08.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Revised: 07/09/2013] [Accepted: 08/15/2013] [Indexed: 11/21/2022]
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25
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Zhang S, Liang Y, Yuan X. Improving the prediction accuracy of protein structural class: Approached with alternating word frequency and normalized Lempel–Ziv complexity. J Theor Biol 2014; 341:71-7. [DOI: 10.1016/j.jtbi.2013.10.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2013] [Revised: 09/08/2013] [Accepted: 10/08/2013] [Indexed: 10/26/2022]
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26
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Hayat M, Tahir M, Khan SA. Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces. J Theor Biol 2013; 346:8-15. [PMID: 24384128 DOI: 10.1016/j.jtbi.2013.12.015] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2013] [Revised: 10/30/2013] [Accepted: 12/12/2013] [Indexed: 11/28/2022]
Abstract
Proteins are the executants of biological functions in living organisms. Comprehension of protein structure is a challenging problem in the era of proteomics, computational biology, and bioinformatics because of its pivotal role in protein folding patterns. Owing to the large exploration of protein sequences in protein databanks and intricacy of protein structures, experimental and theoretical methods are insufficient for prediction of protein structure classes. Therefore, it is highly desirable to develop an accurate, reliable, and high throughput computational model to predict protein structure classes correctly from polygenetic sequences. In this regard, we propose a promising model employing hybrid descriptor space in conjunction with optimized evidence-theoretic K-nearest neighbor algorithm. Hybrid space is the composition of two descriptor spaces including Multi-profile Bayes and bi-gram probability. In order to enhance the generalization power of the classifier, we have selected high discriminative descriptors from the hybrid space using particle swarm optimization, a well-known evolutionary feature selection technique. Performance evaluation of the proposed model is performed using the jackknife test on three low similarity benchmark datasets including 25PDB, 1189, and 640. The success rates of the proposed model are 87.0%, 86.6%, and 88.4%, respectively on the three benchmark datasets. The comparative analysis exhibits that our proposed model has yielded promising results compared to the existing methods in the literature. In addition, our proposed prediction system might be helpful in future research particularly in cases where the major focus of research is on low similarity datasets.
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Affiliation(s)
- Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
| | - Muhammad Tahir
- Department of Computer Science, National University of Computer and Emerging Science, Peshawar, Pakistan
| | - Sher Afzal Khan
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
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27
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Chen L, Li BQ, Zheng MY, Zhang J, Feng KY, Cai YD. Prediction of effective drug combinations by chemical interaction, protein interaction and target enrichment of KEGG pathways. BIOMED RESEARCH INTERNATIONAL 2013; 2013:723780. [PMID: 24083237 PMCID: PMC3780555 DOI: 10.1155/2013/723780] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2013] [Accepted: 07/24/2013] [Indexed: 12/11/2022]
Abstract
Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs' targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.
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Affiliation(s)
- Lei Chen
- Institute of Systems Biology, Shanghai University, Shanghai 200444, China
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Bi-Qing Li
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ming-Yue Zheng
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Shanghai 201203, China
| | - Jian Zhang
- Department of Ophthalmology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200080, China
| | - Kai-Yan Feng
- Beijing Genomics Institute, Shenzhen Beishan Industrial Zone, Shenzhen 518083, China
| | - Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, Shanghai 200444, China
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28
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Using protein granularity to extract the protein sequence features. J Theor Biol 2013; 331:48-53. [DOI: 10.1016/j.jtbi.2013.04.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2012] [Revised: 04/16/2013] [Accepted: 04/18/2013] [Indexed: 11/21/2022]
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29
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Niu B, Zhang Y, Ding J, Lu Y, Wang M, Lu W, Yuan X, Yin J. Predicting network of drug-enzyme interaction based on machine learning method. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2013; 1844:214-23. [PMID: 23907006 DOI: 10.1016/j.bbapap.2013.07.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2012] [Revised: 07/16/2013] [Accepted: 07/18/2013] [Indexed: 12/11/2022]
Abstract
It is important to correctly and efficiently map drugs and enzymes to their possible interaction network in modern drug research. In this work, a novel approach was introduced to encode drug and enzyme molecules with physicochemical molecular descriptors and pseudo amino acid composition, respectively. Based on this encoding method, Random Forest was adopted to build the drug-enzyme interaction network. After selecting the optimal features that are able to represent the main factors of drug-enzyme interaction in our prediction, a total of 129 features were attained which can be clustered into nine categories: Elemental Analysis, Geometry, Chemistry, Amino Acid Composition, Secondary Structure, Polarity, Molecular Volume, Codon Diversity and Electrostatic Charge. It is further found that Geometry features were the most important of all the features. As a result, our predicting model achieved an MCC of 0.915 and a sensitivity of 87.9% at the specificity level of 99.8% for 10-fold cross-validation test, and achieved an MCC of 0.895 and a sensitivity of 95.7% at the specificity level of 95.4% for independent set test. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.
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Affiliation(s)
- Bing Niu
- College of Life Science, Shanghai University, 99 Shang-Da Road, Shanghai 200072, China
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30
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Learning protein multi-view features in complex space. Amino Acids 2013; 44:1365-79. [DOI: 10.1007/s00726-013-1472-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2012] [Accepted: 02/13/2013] [Indexed: 12/11/2022]
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31
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32
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Zhang S, Ye F, Yuan X. Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM. J Biomol Struct Dyn 2012; 29:634-42. [PMID: 22545994 DOI: 10.1080/07391102.2011.672627] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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33
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Ding S, Zhang S, Li Y, Wang T. A novel protein structural classes prediction method based on predicted secondary structure. Biochimie 2012; 94:1166-71. [PMID: 22353242 DOI: 10.1016/j.biochi.2012.01.022] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2011] [Accepted: 01/31/2012] [Indexed: 10/14/2022]
Abstract
Knowledge of structural classes plays an important role in understanding protein folding patterns. In this paper, features based on the predicted secondary structure sequence and the corresponding E-H sequence are extracted. Then, an 11-dimensional feature vector is selected based on a wrapper feature selection algorithm and a support vector machine (SVM). Among the 11 selected features, 4 novel features are newly designed to model the differences between α/β class and α + β class, and other 7 rational features are proposed by previous researchers. To examine the performance of our method, a total of 5 datasets are used to design and test the proposed method. The results show that competitive prediction accuracies can be achieved by the proposed method compared to existing methods (SCPRED, RKS-PPSC and MODAS), and 4 new features are demonstrated essential to differentiate α/β and α + β classes. Standalone version of the proposed method is written in JAVA language and it can be downloaded from http://web.xidian.edu.cn/slzhang/paper.html.
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Affiliation(s)
- Shuyan Ding
- School of Mathematical Sciences, Dalian University of Technology, Linggong Road, Dalian, 116024, PR China.
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34
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Dai Q, Wu L, Li L. Improving protein structural class prediction using novel combined sequence information and predicted secondary structural features. J Comput Chem 2011; 32:3393-8. [DOI: 10.1002/jcc.21918] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2011] [Revised: 06/29/2011] [Accepted: 07/25/2011] [Indexed: 11/07/2022]
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35
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Liu T, Geng X, Zheng X, Li R, Wang J. Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles. Amino Acids 2011; 42:2243-9. [PMID: 21698456 DOI: 10.1007/s00726-011-0964-5] [Citation(s) in RCA: 77] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2011] [Accepted: 06/11/2011] [Indexed: 02/07/2023]
Abstract
Computational prediction of protein structural class based solely on sequence data remains a challenging problem in protein science. Existing methods differ in the protein sequence representation models and prediction engines adopted. In this study, a powerful feature extraction method, which combines position-specific score matrix (PSSM) with auto covariance (AC) transformation, is introduced. Thus, a sample protein is represented by a series of discrete components, which could partially incorporate the long-range sequence order information and evolutionary information reflected from the PSI-BLAST profile. To verify the performance of our method, jackknife cross-validation tests are performed on four widely used benchmark datasets. Comparison of our results with existing methods shows that our method provides the state-of-the-art performance for structural class prediction. A Web server that implements the proposed method is freely available at http://202.194.133.5/xinxi/AAC_PSSM_AC/index.htm.
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Affiliation(s)
- Taigang Liu
- College of Information Sciences and Engineering, Shandong Agricultural University, Taian, 271018, China
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36
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Naveed M, Khan AU. GPCR-MPredictor: multi-level prediction of G protein-coupled receptors using genetic ensemble. Amino Acids 2011; 42:1809-23. [DOI: 10.1007/s00726-011-0902-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2010] [Accepted: 03/26/2011] [Indexed: 11/27/2022]
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37
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Zhang S, Ding S, Wang T. High-accuracy prediction of protein structural class for low-similarity sequences based on predicted secondary structure. Biochimie 2011; 93:710-4. [PMID: 21237245 DOI: 10.1016/j.biochi.2011.01.001] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2010] [Accepted: 01/04/2011] [Indexed: 11/30/2022]
Abstract
Information on the structural classes of proteins has been proven to be important in many fields of bioinformatics. Prediction of protein structural class for low-similarity sequences is a challenge problem. In this study, 11 features (including 8 re-used features and 3 newly-designed features) are rationally utilized to reflect the general contents and spatial arrangements of the secondary structural elements of a given protein sequence. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark datasets, 1189 and 25PDB with sequence similarity lower than 40% and 25%, respectively. Comparison of our results with other methods shows that our proposed method is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity datasets.
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Affiliation(s)
- Shengli Zhang
- School of Mathematical Sciences, Dalian University of Technology, Ganjingzi District, Dalian, Liaoning, PR China.
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38
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Chou KC. Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 2010; 273:236-47. [PMID: 21168420 PMCID: PMC7125570 DOI: 10.1016/j.jtbi.2010.12.024] [Citation(s) in RCA: 966] [Impact Index Per Article: 69.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2010] [Revised: 12/08/2010] [Accepted: 12/13/2010] [Indexed: 11/29/2022]
Abstract
With the accomplishment of human genome sequencing, the number of sequence-known proteins has increased explosively. In contrast, the pace is much slower in determining their biological attributes. As a consequence, the gap between sequence-known proteins and attribute-known proteins has become increasingly large. The unbalanced situation, which has critically limited our ability to timely utilize the newly discovered proteins for basic research and drug development, has called for developing computational methods or high-throughput automated tools for fast and reliably identifying various attributes of uncharacterized proteins based on their sequence information alone. Actually, during the last two decades or so, many methods in this regard have been established in hope to bridge such a gap. In the course of developing these methods, the following things were often needed to consider: (1) benchmark dataset construction, (2) protein sample formulation, (3) operating algorithm (or engine), (4) anticipated accuracy, and (5) web-server establishment. In this review, we are to discuss each of the five procedures, with a special focus on the introduction of pseudo amino acid composition (PseAAC), its different modes and applications as well as its recent development, particularly in how to use the general formulation of PseAAC to reflect the core and essential features that are deeply hidden in complicated protein sequences.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, 13784 Torrey Del Mar Drive, San Diego, CA 92130, USA.
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39
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Sahu SS, Panda G. A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction. Comput Biol Chem 2010; 34:320-7. [DOI: 10.1016/j.compbiolchem.2010.09.002] [Citation(s) in RCA: 147] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2010] [Revised: 09/28/2010] [Accepted: 09/28/2010] [Indexed: 10/19/2022]
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40
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Liu T, Zheng X, Wang J. Prediction of protein structural class for low-similarity sequences using support vector machine and PSI-BLAST profile. Biochimie 2010; 92:1330-4. [DOI: 10.1016/j.biochi.2010.06.013] [Citation(s) in RCA: 98] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2010] [Accepted: 06/16/2010] [Indexed: 11/25/2022]
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41
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Wu J, Li YZ, Li ML, Yu LZ. Two multi-classification strategies used on SVM to predict protein structural classes by using auto covariance. Interdiscip Sci 2010; 1:315-9. [PMID: 20640811 DOI: 10.1007/s12539-009-0066-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2009] [Revised: 09/01/2009] [Accepted: 09/04/2009] [Indexed: 11/24/2022]
Abstract
Machine learning methods play the very important role in protein secondary structure prediction and other related works. On condition of a certain approach, the prediction qualities mostly depend on the ways of representing protein sequences into numeric features. In this paper, two Support Vector Machine (SVM) multi-classification strategies, "one-against-one" (1-a-1) and "one-against-all" (1-a-a), were used in protein structural classes identification. Auto covariance (AC), which transforms the physicochemical properties of the amino acids of the proteins into a data matrix, focuses on the neighboring effects and the interactions between residues in protein sequences. "1-a-1" approach was used on SVM to predict protein structural classes and obtained very promising overall accuracy 90.69% by Jackknife test. It was more than 10% higher than the accuracy obtained by using "1-a-a". Experimental results led to the finding that the SVM predictor constructed by "1-a-1" can avoid the appearance of biased prediction accuracy. This current method, using the protein primary sequence information described by auto covariance (AC) and "1-a-1" approach on SVM, should play an important complementary role in other related applications.
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Affiliation(s)
- Jiang Wu
- College of Chemistry, Sichuan University, Chengdu, China
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42
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Zheng X, Li C, Wang J. An information-theoretic approach to the prediction of protein structural class. J Comput Chem 2010; 31:1201-6. [PMID: 19777491 DOI: 10.1002/jcc.21406] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
An information-theoretical approach, which combines a sequence decomposition technique and a fuzzy clustering algorithm, is proposed for prediction of protein structural class. This approach could bypass the process of selecting and comparing sequence features as done previously. First, distances between each pair of protein sequences are estimated using a conditional decomposition technique in information theory. Then, the fuzzy k-nearest neighbor algorithm is used to identify the structural class of a protein given as set of sample sequences. To verify the strength of our method, we choose three widely used datasets constructed by Chou and Zhou. It is shown by the Jackknife test that our approach represents an improvement in the prediction of accuracy over existing methods.
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Affiliation(s)
- Xiaoqi Zheng
- Department of Mathematics, Shanghai Normal University, Shanghai 200234, China
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43
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Zhang N, Duan G, Gao S, Ruan J, Zhang T. Prediction of the parallel/antiparallel orientation of beta-strands using amino acid pairing preferences and support vector machines. J Theor Biol 2010; 263:360-8. [PMID: 20035768 DOI: 10.1016/j.jtbi.2009.12.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2009] [Revised: 11/05/2009] [Accepted: 12/17/2009] [Indexed: 10/20/2022]
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44
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Wu J, Li ML, Yu LZ, Wang C. An ensemble classifier of support vector machines used to predict protein structural classes by fusing auto covariance and pseudo-amino acid composition. Protein J 2010; 29:62-7. [PMID: 20049515 DOI: 10.1007/s10930-009-9222-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The purpose of this article is to identify protein structural classes by using support vector machine (SVM) ensemble classifier, which is very efficient in enhancing prediction performance. Firstly, auto covariance (AC) and pseudo-amino acid composition (PseAAC) were used in protein representation. AC focuses on adjacent effects and PseAA composition takes sequence order patterns into account. Secondly, SVMs were trained on the datasets represented by different descriptors. The last, ensemble classifier, which constructed on the individual classifiers through a voting strategy, gave the final prediction results. Meanwhile, very promising prediction accuracy 93.14% was obtained by Jackknife test. The experimental results showed that the ensemble system can improve the prediction performance greatly and generate more stable and safer predictors. The current method featured by fusing the protein primary sequence information transferred by AC and described by protein PseAA composition may play an important complementary role in other related applications.
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Affiliation(s)
- Jiang Wu
- College of Chemistry, Sichuan University, 610064, Chengdu, China
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45
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Mizianty MJ, Kurgan L. Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences. BMC Bioinformatics 2009; 10:414. [PMID: 20003388 PMCID: PMC2805645 DOI: 10.1186/1471-2105-10-414] [Citation(s) in RCA: 79] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2009] [Accepted: 12/13/2009] [Indexed: 11/13/2022] Open
Abstract
Background Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. Results The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. Conclusions The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/.
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Affiliation(s)
- Marcin J Mizianty
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
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46
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Liu T, Zheng X, Wang J. Prediction of protein structural class using a complexity-based distance measure. Amino Acids 2009; 38:721-8. [PMID: 19330425 DOI: 10.1007/s00726-009-0276-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2008] [Accepted: 03/11/2009] [Indexed: 11/30/2022]
Abstract
Knowledge of structural class plays an important role in understanding protein folding patterns. So it is necessary to develop effective and reliable computational methods for prediction of protein structural class. To this end, we present a new method called NN-CDM, a nearest neighbor classifier with a complexity-based distance measure. Instead of extracting features from protein sequences as done previously, distance between each pair of protein sequences is directly evaluated by a complexity measure of symbol sequences. Then the nearest neighbor classifier is adopted as the predictive engine. To verify the performance of this method, jackknife cross-validation tests are performed on several benchmark datasets. Results show that our approach achieves a high prediction accuracy over some classical methods.
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Affiliation(s)
- Taigang Liu
- Department of Applied Mathematics, Dalian University of Technology, 116024 Dalian, China.
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47
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Xiao X, Lin WZ, Chou KC. Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes. J Comput Chem 2008; 29:2018-24. [PMID: 18381630 DOI: 10.1002/jcc.20955] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Using the pseudo amino acid (PseAA) composition to represent the sample of a protein can incorporate a considerable amount of sequence pattern information so as to improve the prediction quality for its structural or functional classification. However, how to optimally formulate the PseAA composition is an important problem yet to be solved. In this article the grey modeling approach is introduced that is particularly efficient in coping with complicated systems such as the one consisting of many proteins with different sequence orders and lengths. On the basis of the grey model, four coefficients derived from each of the protein sequences concerned are adopted for its PseAA components. The PseAA composition thus formulated is called the "grey-PseAA" composition that can catch the essence of a protein sequence and better reflect its overall pattern. In our study we have demonstrated that introduction of the grey-PseAA composition can remarkably enhance the success rates in predicting the protein structural class. It is anticipated that the concept of grey-PseAA composition can be also used to predict many other protein attributes, such as subcellular localization, membrane protein type, enzyme functional class, GPCR type, protease type, among many others.
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Affiliation(s)
- Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333000, China.
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48
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Chen K, Kurgan LA, Ruan J. Prediction of protein structural class using novel evolutionary collocation-based sequence representation. J Comput Chem 2008; 29:1596-604. [PMID: 18293306 DOI: 10.1002/jcc.20918] [Citation(s) in RCA: 131] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Knowledge of structural classes is useful in understanding of folding patterns in proteins. Although existing structural class prediction methods applied virtually all state-of-the-art classifiers, many of them use a relatively simple protein sequence representation that often includes amino acid (AA) composition. To this end, we propose a novel sequence representation that incorporates evolutionary information encoded using PSI-BLAST profile-based collocation of AA pairs. We used six benchmark datasets and five representative classifiers to quantify and compare the quality of the structural class prediction with the proposed representation. The best, classifier support vector machine achieved 61-96% accuracy on the six datasets. These predictions were comprehensively compared with a wide range of recently proposed methods for prediction of structural classes. Our comprehensive comparison shows superiority of the proposed representation, which results in error rate reductions that range between 14% and 26% when compared with predictions of the best-performing, previously published classifiers on the considered datasets. The study also shows that, for the benchmark dataset that includes sequences characterized by low identity (i.e., 25%, 30%, and 40%), the prediction accuracies are 20-35% lower than for the other three datasets that include sequences with a higher degree of similarity. In conclusion, the proposed representation is shown to substantially improve the accuracy of the structural class prediction. A web server that implements the presented prediction method is freely available at http://biomine.ece.ualberta.ca/Structural_Class/SCEC.html.
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Affiliation(s)
- Ke Chen
- Department of Electrical and Computer Engineering, ECERF, University of Alberta, Edmonton, Alberta, Canada
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Xiao X, Wang P, Chou KC. Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image. J Theor Biol 2008; 254:691-6. [PMID: 18634802 DOI: 10.1016/j.jtbi.2008.06.016] [Citation(s) in RCA: 89] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2008] [Revised: 06/18/2008] [Accepted: 06/18/2008] [Indexed: 11/28/2022]
Abstract
A novel approach was developed for predicting the structural classes of proteins based on their sequences. It was assumed that proteins belonging to the same structural class must bear some sort of similar texture on the images generated by the cellular automaton evolving rule [Wolfram, S., 1984. Cellular automation as models of complexity. Nature 311, 419-424]. Based on this, two geometric invariant moment factors derived from the image functions were used as the pseudo amino acid components [Chou, K.C., 2001. Prediction of protein cellular attributes using pseudo amino acid composition. Proteins: Struct., Funct., Genet. (Erratum: ibid., 2001, vol. 44, 60) 43, 246-255] to formulate the protein samples for statistical prediction. The success rates thus obtained on a previously constructed benchmark dataset are quite promising, implying that the cellular automaton image can help to reveal some inherent and subtle features deeply hidden in a pile of long and complicated amino acid sequences.
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Affiliation(s)
- Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 33300, China.
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Feng Y, Luo L. Use of tetrapeptide signals for protein secondary-structure prediction. Amino Acids 2008; 35:607-14. [PMID: 18431531 DOI: 10.1007/s00726-008-0089-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2007] [Accepted: 03/04/2008] [Indexed: 10/22/2022]
Abstract
This paper develops a novel sequence-based method, tetra-peptide-based increment of diversity with quadratic discriminant analysis (TPIDQD for short), for protein secondary-structure prediction. The proposed TPIDQD method is based on tetra-peptide signals and is used to predict the structure of the central residue of a sequence fragment. The three-state overall per-residue accuracy (Q (3)) is about 80% in the threefold cross-validated test for 21-residue fragments in the CB513 dataset. The accuracy can be further improved by taking long-range sequence information (fragments of more than 21 residues) into account in prediction. The results show the tetra-peptide signals can indeed reflect some relationship between an amino acid's sequence and its secondary structure, indicating the importance of tetra-peptide signals as the protein folding code in the protein structure prediction.
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Affiliation(s)
- Yonge Feng
- Laboratory of Theoretical Biophysics, Faculty of Science and Technology, Inner Mongolia University, Hohhot, 010021, China.
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