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Abbass J, Parisi C. Machine learning-based prediction of proteins' architecture using sequences of amino acids and structural alphabets. J Biomol Struct Dyn 2024:1-16. [PMID: 38505995 DOI: 10.1080/07391102.2024.2328736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/05/2024] [Indexed: 03/21/2024]
Abstract
In addition to the growth of protein structures generated through wet laboratory experiments and deposited in the PDB repository, AlphaFold predictions have significantly contributed to the creation of a much larger database of protein structures. Annotating such a vast number of structures has become an increasingly challenging task. CATH is widely recognized as one the most common platforms for addressing this challenge, as it classifies proteins based on their structural and evolutionary relationships, offering the scientific community an invaluable resource for uncovering various properties, including functional annotations. While CATH annotation involves - to some extent - human intervention, keeping up with the classification of the rapidly expanding repositories of protein structures has become exceedingly difficult. Therefore, there is a pressing need for a fully automated approach. On the other hand, the abundance of protein sequences stemming from next generation sequencing technologies, lacking structural annotations, presents an additional challenge to the scientific community. Consequently, 'pre-annotating' protein sequences with structural features, ensuring a high level of precision, could prove highly advantageous. In this paper, after a thorough investigation, we introduce a novel machine-learning model capable of classifying any protein domain, whether it has a known structure or not, into one of the 40 main CATH Architectures. We achieve an F1 Score of 0.92 using only the amino acid sequence and a score of 0.94 using both the sequence of amino acids and the sequence of structural alphabets.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Jad Abbass
- School of Computer Science and Mathematics, Kingston University, London, UK
| | - Charles Parisi
- School of Computer Science and Mathematics, Kingston University, London, UK
- Telecom Physique Strasbourg, Strasbourg University, Strasbourg, France
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2
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Bankapur S, Patil N. Enhanced Protein Structural Class Prediction Using Effective Feature Modeling and Ensemble of Classifiers. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2409-2419. [PMID: 32149653 DOI: 10.1109/tcbb.2020.2979430] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Protein Secondary Structural Class (PSSC) information is important in investigating further challenges of protein sequences like protein fold recognition, protein tertiary structure prediction, and analysis of protein functions for drug discovery. Identification of PSSC using biological methods is time-consuming and cost-intensive. Several computational models have been developed to predict the structural class; however, they lack in generalization of the model. Hence, predicting PSSC based on protein sequences is still proving to be an uphill task. In this article, we proposed an effective, novel and generalized prediction model consisting of a feature modeling and an ensemble of classifiers. The proposed feature modeling extracts discriminating information (features) by leveraging three techniques: (i) Embedding - features are extracted on the basis of spatial residue arrangements of the sequences using word embedding approaches; (ii) SkipXGram Bi-gram - various sets of skipped bi-gram features are extracted from the sequences; and (iii) General Statistical (GS) based features are extracted which covers the global information of structural sequences. The combined effective sets of features are trained and classified using an ensemble of three classifiers: Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM). The proposed model when assessed on five benchmark datasets (high and low sequence similarity), viz. z277, z498, 25PDB, 1189, and FC699, reported an overall accuracy of 93.55, 97.58, 81.82, 81.11, and 93.93 percent respectively. The proposed model is further validated on a large-scale updated low similarity ( ≤ 25%) dataset, where it achieved an overall accuracy of 81.11 percent. The proposed generalized model is robust and consistently outperformed several state-of-the-art models on all the five benchmark datasets.
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3
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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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4
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Sarkar A, Sen S. A Comparative Analysis of the Molecular Interaction Techniques for In Silico Drug Design. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09830-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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5
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Cui Y, Chen Q, Li Y, Tang L. A new model of flavonoids affinity towards P-glycoprotein: genetic algorithm-support vector machine with features selected by a modified particle swarm optimization algorithm. Arch Pharm Res 2016; 40:214-230. [DOI: 10.1007/s12272-016-0876-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 12/16/2016] [Indexed: 01/04/2023]
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Arana-Daniel N, Gallegos AA, López-Franco C, Alanís AY, Morales J, López-Franco A. Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures. Evol Bioinform Online 2016; 12:285-302. [PMID: 27980384 PMCID: PMC5140013 DOI: 10.4137/ebo.s40912] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 10/19/2016] [Accepted: 10/20/2016] [Indexed: 11/05/2022] Open
Abstract
With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.
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Affiliation(s)
- Nancy Arana-Daniel
- Centro Universitario de Ciencias Exactas e Ingenieras, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - Alberto A Gallegos
- Centro Universitario de Ciencias Exactas e Ingenieras, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - Carlos López-Franco
- Centro Universitario de Ciencias Exactas e Ingenieras, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - Alma Y Alanís
- Centro Universitario de Ciencias Exactas e Ingenieras, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - Jacob Morales
- Centro Universitario de Ciencias Exactas e Ingenieras, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - Adriana López-Franco
- Centro Universitario de Ciencias Exactas e Ingenieras, Universidad de Guadalajara, Guadalajara, Jalisco, México
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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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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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Li X, Liu T, Tao P, Wang C, Chen L. A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination. Comput Biol Chem 2015; 59 Pt A:95-100. [DOI: 10.1016/j.compbiolchem.2015.08.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Revised: 08/30/2015] [Accepted: 08/30/2015] [Indexed: 12/11/2022]
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Abbass J, Nebel JC. Customised fragments libraries for protein structure prediction based on structural class annotations. BMC Bioinformatics 2015; 16:136. [PMID: 25925397 PMCID: PMC4419399 DOI: 10.1186/s12859-015-0576-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2014] [Accepted: 04/17/2015] [Indexed: 12/05/2022] Open
Abstract
Background Since experimental techniques are time and cost consuming, in silico protein structure prediction is essential to produce conformations of protein targets. When homologous structures are not available, fragment-based protein structure prediction has become the approach of choice. However, it still has many issues including poor performance when targets’ lengths are above 100 residues, excessive running times and sub-optimal energy functions. Taking advantage of the reliable performance of structural class prediction software, we propose to address some of the limitations of fragment-based methods by integrating structural constraints in their fragment selection process. Results Using Rosetta, a state-of-the-art fragment-based protein structure prediction package, we evaluated our proposed pipeline on 70 former CASP targets containing up to 150 amino acids. Using either CATH or SCOP-based structural class annotations, enhancement of structure prediction performance is highly significant in terms of both GDT_TS (at least +2.6, p-values < 0.0005) and RMSD (−0.4, p-values < 0.005). Although CATH and SCOP classifications are different, they perform similarly. Moreover, proteins from all structural classes benefit from the proposed methodology. Further analysis also shows that methods relying on class-based fragments produce conformations which are more relevant to user and converge quicker towards the best model as estimated by GDT_TS (up to 10% in average). This substantiates our hypothesis that usage of structurally relevant templates conducts to not only reducing the size of the conformation space to be explored, but also focusing on a more relevant area. Conclusions Since our methodology produces models the quality of which is up to 7% higher in average than those generated by a standard fragment-based predictor, we believe it should be considered before conducting any fragment-based protein structure prediction. Despite such progress, ab initio prediction remains a challenging task, especially for proteins of average and large sizes. Apart from improving search strategies and energy functions, integration of additional constraints seems a promising route, especially if they can be accurately predicted from sequence alone. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0576-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jad Abbass
- Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE, UK.
| | - Jean-Christophe Nebel
- Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE, UK.
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11
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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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12
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Prediction of protein structural class using tri-gram probabilities of position-specific scoring matrix and recursive feature elimination. Amino Acids 2015; 47:461-8. [DOI: 10.1007/s00726-014-1878-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Accepted: 11/17/2014] [Indexed: 10/24/2022]
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13
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PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations. PLoS One 2014; 9:e92863. [PMID: 24675610 PMCID: PMC3968047 DOI: 10.1371/journal.pone.0092863] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2013] [Accepted: 02/27/2014] [Indexed: 02/05/2023] Open
Abstract
Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.
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Das Roy R, Dash D. Selection of relevant features from amino acids enables development of robust classifiers. Amino Acids 2014; 46:1343-51. [PMID: 24604165 DOI: 10.1007/s00726-014-1697-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2013] [Accepted: 02/14/2014] [Indexed: 12/30/2022]
Abstract
Machine learning (ML) has been extensively applied to develop models and to understand high-throughput data of biological processes. However, new ML models, trained with novel experimental results, are required to build regularly for more precise predictions. ML methods can build models from numeric data, whereas biological data are generally textual (DNA, protein sequences) or images and needs feature calculation algorithms to generate quantitative features. Programming skills along with domain knowledge are required to develop these algorithms. Therefore, the process of knowledge discovery through ML is decelerated due to lack of generic tools to construct features and to build models directly from the data. Hence, we developed a schema that calculates about 5,000 features, selects relevant features and develops protein classifiers from the training data. To demonstrate the general applicability and robustness of our method, fungal adhesins and nuclear receptor proteins were used for building classifiers which outperformed existing classifiers when tested on independent data. Next, we built a classifier for mitochondrial proteins of Plasmodium falciparum which causes human malaria because the latest corresponding classifiers are not publically accessible. Our classifier attained 98.18 % accuracy and 0.95 Matthews correlation coefficient by fivefold cross-validation and outperformed existing classifiers on independent test set. We implemented this schema as user-friendly and open source application Pro-Gyan ( http://code.google.com/p/pro-gyan/ ), to build and share executable classifiers without programming knowledge.
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Affiliation(s)
- Rishi Das Roy
- GN Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, Mall Road, Delhi, 110007, India,
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Dehzangi A, Paliwal K, Lyons J, Sharma A, Sattar A. Proposing a highly accurate protein structural class predictor using segmentation-based features. BMC Genomics 2014; 15 Suppl 1:S2. [PMID: 24564476 PMCID: PMC4046757 DOI: 10.1186/1471-2164-15-s1-s2] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Prediction of the structural classes of proteins can provide important information about their functionalities as well as their major tertiary structures. It is also considered as an important step towards protein structure prediction problem. Despite all the efforts have been made so far, finding a fast and accurate computational approach to solve protein structural class prediction problem still remains a challenging problem in bioinformatics and computational biology. RESULTS In this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins. By applying SVM to our extracted features, for the first time we enhance the protein structural class prediction accuracy to over 90% and 85% for two popular low-homology benchmarks that have been widely used in the literature. We report 92.2% and 86.3% prediction accuracies for 25PDB and 1189 benchmarks which are respectively up to 7.9% and 2.8% better than previously reported results for these two benchmarks. CONCLUSION By proposing segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins, we are able to enhance the protein structural class prediction performance significantly.
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Sharma A, Paliwal KK, Dehzangi A, Lyons J, Imoto S, Miyano S. A strategy to select suitable physicochemical attributes of amino acids for protein fold recognition. BMC Bioinformatics 2013; 14:233. [PMID: 23879571 PMCID: PMC3724710 DOI: 10.1186/1471-2105-14-233] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2012] [Accepted: 06/20/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Assigning a protein into one of its folds is a transitional step for discovering three dimensional protein structure, which is a challenging task in bimolecular (biological) science. The present research focuses on: 1) the development of classifiers, and 2) the development of feature extraction techniques based on syntactic and/or physicochemical properties. RESULTS Apart from the above two main categories of research, we have shown that the selection of physicochemical attributes of the amino acids is an important step in protein fold recognition and has not been explored adequately. We have presented a multi-dimensional successive feature selection (MD-SFS) approach to systematically select attributes. The proposed method is applied on protein sequence data and an improvement of around 24% in fold recognition has been noted when selecting attributes appropriately. CONCLUSION The MD-SFS has been applied successfully in selecting physicochemical attributes of the amino acids. The selected attributes show improved protein fold recognition performance.
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Affiliation(s)
- Alok Sharma
- Laboratory of DNA Information Analysis, University of Tokyo, Minato-ku, Tokyo, Japan.
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Dehzangi A, Paliwal K, Sharma A, Dehzangi O, Sattar A. A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013; 10:564-75. [PMID: 24091391 DOI: 10.1109/tcbb.2013.65] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Better understanding of structural class of a given protein reveals important information about its overall folding type and its domain. It can also be directly used to provide critical information on general tertiary structure of a protein which has a profound impact on protein function determination and drug design. Despite tremendous enhancements made by pattern recognition-based approaches to solve this problem, it still remains as an unsolved issue for bioinformatics that demands more attention and exploration. In this study, we propose a novel feature extraction model that incorporates physicochemical and evolutionary-based information simultaneously. We also propose overlapped segmented distribution and autocorrelation-based feature extraction methods to provide more local and global discriminatory information. The proposed feature extraction methods are explored for 15 most promising attributes that are selected from a wide range of physicochemical-based attributes. Finally, by applying an ensemble of different classifiers namely, Adaboost.M1, LogitBoost, naive Bayes, multilayer perceptron (MLP), and support vector machine (SVM) we show enhancement of the protein structural class prediction accuracy for four popular benchmarks.
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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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Li ZC, Lai YH, Chen LL, Chen C, Xie Y, Dai Z, Zou XY. Identifying subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm. MOLECULAR BIOSYSTEMS 2013; 9:658-67. [DOI: 10.1039/c3mb25451h] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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20
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Exploring Potential Discriminatory Information Embedded in PSSM to Enhance Protein Structural Class Prediction Accuracy. ACTA ACUST UNITED AC 2013. [DOI: 10.1007/978-3-642-39159-0_19] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2023]
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21
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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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22
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QSAR study on the interactions between antibiotic compounds and DNA by a hybrid genetic-based support vector machine. MONATSHEFTE FUR CHEMIE 2011. [DOI: 10.1007/s00706-011-0493-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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23
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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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24
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Zhou X, Li Z, Dai Z, Zou X. QSAR modeling of peptide biological activity by coupling support vector machine with particle swarm optimization algorithm and genetic algorithm. J Mol Graph Model 2010; 29:188-96. [DOI: 10.1016/j.jmgm.2010.06.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2010] [Revised: 05/26/2010] [Accepted: 06/13/2010] [Indexed: 01/04/2023]
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25
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Accurate prediction of the burial status of transmembrane residues of α-helix membrane protein by incorporating the structural and physicochemical features. Amino Acids 2010; 40:991-1002. [PMID: 20740371 DOI: 10.1007/s00726-010-0727-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2010] [Accepted: 08/13/2010] [Indexed: 10/19/2022]
Abstract
Predicting the burial status (the residue exposure to the lipid bilayer or buried within the protein core) of transmembrane (TM) residues of α-helix membrane protein (αHMP) is of great importance for genome-wide annotation and for experimental researchers to elucidate diverse physiological processes. In this work, we developed a new computational model that can be used for predicting the burial status of TM residues of αHMP. By incorporating physicochemical scales and conservation index, an efficient prediction model using least squares support vector machine (LS-SVM) was developed. The model was developed from 43 protein chains and its prediction ability was evaluated by an independent test set of other non-redundant ten protein chains. The prediction accuracy of our method was much better than the results of the reported works. Our results demonstrate that the LS-SVM prediction model incorporating structural and physicochemical features derived from sequence information could greatly improve the prediction accuracy.
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iFC²: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content. Amino Acids 2010; 40:963-73. [PMID: 20730460 DOI: 10.1007/s00726-010-0721-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2010] [Accepted: 08/06/2010] [Indexed: 10/19/2022]
Abstract
Several descriptors of protein structure at the sequence and residue levels have been recently proposed. They are widely adopted in the analysis and prediction of structural and functional characteristics of proteins. Numerous in silico methods have been developed for sequence-based prediction of these descriptors. However, many of them do not have a public web-server and only a few integrate multiple descriptors to improve the predictions. We introduce iFC² (integrated prediction of fold, class, and content) server that is the first to integrate three modern predictors of sequence-level descriptors. They concern fold type (PFRES), structural class (SCEC), and secondary structure content (PSSC-core). The server exploits relations between the three descriptors to implement a cross-evaluation procedure that improves over the predictions of the individual methods. The iFC² annotates fold and class predictions as potentially correct/incorrect. When tested on datasets with low-similarity chains, for the fold prediction iFC² labels 82% of the PFRES predictions as correct and the accuracy of these predictions equals 72%. The accuracy of the remaining 28% of the PFRES predictions equals 38%. Similarly, our server assigns correct labels for over 79% of SCEC predictions, which are shown to be 98% accurate, while the remaining SCEC predictions are only 15% accurate. These results are shown to be competitive when contrasted against recent relevant web-servers. Predictions on CASP8 targets show that the content predicted by iFC² is competitive when compared with the content computed from the tertiary structures predicted by three best-performing methods in CASP8. The iFC² server is available at http://biomine.ece.ualberta.ca/1D/1D.html .
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Li Z, Zhou X, Dai Z, Zou X. Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm. BMC Bioinformatics 2010; 11:325. [PMID: 20550715 PMCID: PMC2905366 DOI: 10.1186/1471-2105-11-325] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2009] [Accepted: 06/16/2010] [Indexed: 11/25/2022] Open
Abstract
Background Because a priori knowledge about function of G protein-coupled receptors (GPCRs) can provide useful information to pharmaceutical research, the determination of their function is a quite meaningful topic in protein science. However, with the rapid increase of GPCRs sequences entering into databanks, the gap between the number of known sequence and the number of known function is widening rapidly, and it is both time-consuming and expensive to determine their function based only on experimental techniques. Therefore, it is vitally significant to develop a computational method for quick and accurate classification of GPCRs. Results In this study, a novel three-layer predictor based on support vector machine (SVM) and feature selection is developed for predicting and classifying GPCRs directly from amino acid sequence data. The maximum relevance minimum redundancy (mRMR) is applied to pre-evaluate features with discriminative information while genetic algorithm (GA) is utilized to find the optimized feature subsets. SVM is used for the construction of classification models. The overall accuracy with three-layer predictor at levels of superfamily, family and subfamily are obtained by cross-validation test on two non-redundant dataset. The results are about 0.5% to 16% higher than those of GPCR-CA and GPCRPred. Conclusion The results with high success rates indicate that the proposed predictor is a useful automated tool in predicting GPCRs. GPCR-SVMFS, a corresponding executable program for GPCRs prediction and classification, can be acquired freely on request from the authors.
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Affiliation(s)
- Zhanchao Li
- School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, PR China
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Fernandez M, Caballero J, Fernandez L, Sarai A. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM). Mol Divers 2010; 15:269-89. [PMID: 20306130 DOI: 10.1007/s11030-010-9234-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2009] [Accepted: 01/25/2010] [Indexed: 10/19/2022]
Abstract
Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.
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Affiliation(s)
- Michael Fernandez
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology (KIT), 680-4 Kawazu, Iizuka, 820-8502, Japan.
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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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Sequence physical properties encode the global organization of protein structure space. Proc Natl Acad Sci U S A 2009; 106:14345-8. [PMID: 19706520 DOI: 10.1073/pnas.0903433106] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
It is demonstrated that, properly represented, the amino acid composition of protein sequences contains the information necessary to delineate the global properties of protein structure space. A numerical representation of amino acid sequence in terms of a set of property factors is used, and the values of those property factors are averaged over individual sequences and then over sets of sequences belonging to structurally defined groups. These sequence sets then can be viewed as points in a 10-dimensional space, and the organization of that space, determined only by sequence properties, is similar at both local and global scales to that of the space of protein structures determined previously.
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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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