1
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Qin X, Liu M, Liu G. ResCNNT-fold: Combining residual convolutional neural network and Transformer for protein fold recognition from language model embeddings. Comput Biol Med 2023; 166:107571. [PMID: 37864911 DOI: 10.1016/j.compbiomed.2023.107571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/30/2023] [Accepted: 10/11/2023] [Indexed: 10/23/2023]
Abstract
A comprehensive understanding of protein functions holds significant promise for disease research and drug development, and proteins with analogous tertiary structures tend to exhibit similar functions. Protein fold recognition stands as a classical approach in the realm of protein structure investigation. Despite significant advancements made by researchers in this field, the continuous updating of protein databases presents an ongoing challenge in accurately identifying protein fold types. In this study, we introduce a predictor, ResCNNT-fold, for protein fold recognition and employ the LE dataset for testing purpose. ResCNNT-fold leverages a pre-trained language model to obtain embedding representations for protein sequences, which are then processed by the ResCNNT feature extractor, a combination of residual convolutional neural network and Transformer, to derive fold-specific features. Subsequently, the query protein is paired with each protein whose structure is known in the template dataset. For each pair, the similarity score of their fold-specific features is calculated. Ultimately, the query protein is identified as the fold type of the template protein in the pair with the highest similarity score. To further validate the utility and efficacy of the proposed ResCNNT-fold predictor, we conduct a 2-fold cross-validation experiment on the fold level of the LE dataset. Remarkably, this rigorous evaluation yields an exceptional accuracy of 91.57%, which surpasses the best result among other state-of-the-art protein fold recognition methods by an approximate margin of 10%. The excellent performance unequivocally underscores the compelling advantages inherent to our proposed ResCNNT-fold predictor in the realm of protein fold recognition. The source code and data of ResCNNT-fold can be downloaded from https://github.com/Bioinformatics-Laboratory/ResCNNT-fold.
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Affiliation(s)
- Xinyi Qin
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
| | - Min Liu
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
| | - Guangzhong Liu
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
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2
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Qin X, Zhang L, Liu M, Xu Z, Liu G. ASFold-DNN: Protein Fold Recognition Based on Evolutionary Features With Variable Parameters Using Full Connected Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2712-2722. [PMID: 34133282 DOI: 10.1109/tcbb.2021.3089168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Protein fold recognition contribute to comprehend the function of proteins, which is of great help to the gene therapy of diseases and the development of new drugs. Researchers have been working in this direction and have made considerable achievements, but challenges still exist on low sequence similarity datasets. In this study, we propose the ASFold-DNN framework for protein fold recognition research. Above all, four groups of evolutionary features are extracted from the primary structures of proteins, and a preliminary selection of variable parameter is made for two groups of features including ACC _HMM and SXG _HMM, respectively. Then several feature selection algorithms are selected for comparison and the best feature selection scheme is obtained by changing their internal threshold values. Finally, multiple hyper-parameters of Full Connected Neural Network are fully optimized to construct the best model. DD, EDD and TG datasets with low sequence similarities are chosen to evaluate the performance of the models constructed by the framework, and the final prediction accuracy are 85.28, 95.00 and 88.84 percent, respectively. Furthermore, the ASTRAL186 and LE datasets are introduced to further verify the generalization ability of our proposed framework. Comprehensive experimental results prove that the ASFold-DNN framework is more prominent than the state-of-the-art studies on protein fold recognition. The source code and data of ASFold-DNN can be downloaded from https://github.com/Bioinformatics-Laboratory/project/tree/master/ASFold.
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3
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Roethel A, Biliński P, Ishikawa T. BioS2Net: Holistic Structural and Sequential Analysis of Biomolecules Using a Deep Neural Network. Int J Mol Sci 2022; 23:2966. [PMID: 35328384 PMCID: PMC8954277 DOI: 10.3390/ijms23062966] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/05/2022] [Accepted: 03/08/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND For decades, the rate of solving new biomolecular structures has been exceeding that at which their manual classification and feature characterisation can be carried out efficiently. Therefore, a new comprehensive and holistic tool for their examination is needed. METHODS Here we propose the Biological Sequence and Structure Network (BioS2Net), which is a novel deep neural network architecture that extracts both sequential and structural information of biomolecules. Our architecture consists of four main parts: (i) a sequence convolutional extractor, (ii) a 3D structure extractor, (iii) a 3D structure-aware sequence temporal network, as well as (iv) a fusion and classification network. RESULTS We have evaluated our approach using two protein fold classification datasets. BioS2Net achieved a 95.4% mean class accuracy on the eDD dataset and a 76% mean class accuracy on the F184 dataset. The accuracy of BioS2Net obtained on the eDD dataset was comparable to results achieved by previously published methods, confirming that the algorithm described in this article is a top-class solution for protein fold recognition. CONCLUSIONS BioS2Net is a novel tool for the holistic examination of biomolecules of known structure and sequence. It is a reliable tool for protein analysis and their unified representation as feature vectors.
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Affiliation(s)
- Albert Roethel
- Department of Molecular Biology, Institute of Biochemistry, Faculty of Biology, University of Warsaw, 02-096 Warsaw, Poland;
- College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, 02-097 Warsaw, Poland
| | - Piotr Biliński
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, 02-097 Warsaw, Poland;
| | - Takao Ishikawa
- Department of Molecular Biology, Institute of Biochemistry, Faculty of Biology, University of Warsaw, 02-096 Warsaw, Poland;
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4
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Kumar G, Srinivasan N, Sandhya S. Profiles of Natural and Designed Protein-Like Sequences Effectively Bridge Protein Sequence Gaps: Implications in Distant Homology Detection. Methods Mol Biol 2022; 2449:149-167. [PMID: 35507261 DOI: 10.1007/978-1-0716-2095-3_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Sequence-based approaches are fundamental to guide experimental investigations in obtaining structural and/or functional insights into uncharacterized protein families. Powerful profile-based sequence search methods rely on a sequence space continuum to identify non-trivial relationships through homology detection. The computational design of protein-like sequences that serve as "artificial linkers" is useful in identifying relationships between distant members of a structural fold. Such sequences act as intermediates and guide homology searches between distantly related proteins. Here, we describe an approach that represents natural intermediate sequences and designed protein-like sequences as HMM (Hidden Markov Models) profiles, to improve the sensitivity of existing search methods. Searches made within the "Profile database" were shown to recognize the parent structural fold for 90% of the search queries at query coverage better than 60%. For 1040 protein families with no available structure, fold associations were made through searches in the database of natural and designed sequence profiles. Most of the associations were made with the Alpha-alpha superhelix, Transmembrane beta-barrels, TIM barrel, and Immunoglobulin-like beta-sandwich folds. For 11 domain families of unknown functions, we provide confident fold associations using the profiles of designed sequences and a consensus from other fold recognition methods. For two DUFs (Domain families of Unknown Functions), we performed detailed functional annotation through comparisons with characterized templates of families of known function.
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Affiliation(s)
- Gayatri Kumar
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | | | - Sankaran Sandhya
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India.
- Department of Biotechnology, Faculty of Life and Allied Health Sciences, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India.
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5
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Ao C, Jiao S, Wang Y, Yu L, Zou Q. Biological Sequence Classification: A Review on Data and General Methods. RESEARCH 2022. [DOI: 10.34133/research.0011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
With the rapid development of biotechnology, the number of biological sequences has grown exponentially. The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information. There are many branches of biological sequence classification research. In this review, we mainly focus on the function and modification classification of biological sequences based on machine learning. Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA, RNA, proteins, and peptides. However, there are hundreds of classification models developed for biological sequences, and the quite varied specific methods seem dizzying at first glance. Here, we aim to establish a long-term support website (
http://lab.malab.cn/~acy/BioseqData/home.html
), which provides readers with detailed information on the classification method and download links to relevant datasets. We briefly introduce the steps to build an effective model framework for biological sequence data. In addition, a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included. Finally, we discuss the current challenges and future perspectives of biological sequence classification research.
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Affiliation(s)
- Chunyan Ao
- School of Computer Science and Technology, Xidian University, Xi’an, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Shihu Jiao
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Yansu Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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6
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Shen Z, Liu T, Xu T. Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5770981. [PMID: 34413898 PMCID: PMC8369162 DOI: 10.1155/2021/5770981] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/15/2021] [Accepted: 07/26/2021] [Indexed: 01/19/2023]
Abstract
Antioxidant proteins (AOPs) play important roles in the management and prevention of several human diseases due to their ability to neutralize excess free radicals. However, the identification of AOPs by using wet-lab experimental techniques is often time-consuming and expensive. In this study, we proposed an accurate computational model, called AOP-HMM, to predict AOPs by extracting discriminatory evolutionary features from hidden Markov model (HMM) profiles. First, auto cross-covariance (ACC) variables were applied to transform the HMM profiles into fixed-length feature vectors. Then, we performed the analysis of variance (ANOVA) method to reduce the dimensionality of the raw feature space. Finally, a support vector machine (SVM) classifier was adopted to conduct the prediction of AOPs. To comprehensively evaluate the performance of the proposed AOP-HMM model, the 10-fold cross-validation (CV), the jackknife CV, and the independent test were carried out on two widely used benchmark datasets. The experimental results demonstrated that AOP-HMM outperformed most of the existing methods and could be used to quickly annotate AOPs and guide the experimental process.
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Affiliation(s)
- Zhehan Shen
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Taigang Liu
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Ting Xu
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
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7
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Chen TR, Juan SH, Huang YW, Lin YC, Lo WC. A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction. PLoS One 2021; 16:e0255076. [PMID: 34320027 PMCID: PMC8318245 DOI: 10.1371/journal.pone.0255076] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 07/11/2021] [Indexed: 11/18/2022] Open
Abstract
Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.
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Affiliation(s)
- Teng-Ruei Chen
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Sheng-Hung Juan
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yu-Wei Huang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yen-Cheng Lin
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Wei-Cheng Lo
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- The Center for Bioinformatics Research, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- * E-mail:
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8
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Qin X, Liu M, Zhang L, Liu G. Structural protein fold recognition based on secondary structure and evolutionary information using machine learning algorithms. Comput Biol Chem 2021; 91:107456. [PMID: 33610129 DOI: 10.1016/j.compbiolchem.2021.107456] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 01/04/2021] [Accepted: 02/06/2021] [Indexed: 11/18/2022]
Abstract
Understanding the function of protein is conducive to research in advanced fields such as gene therapy of diseases, the development and design of new drugs, etc. The prerequisite for understanding the function of a protein is to determine its tertiary structure. The realization of protein structure classification is indispensable for this problem and fold recognition is a commonly used method of protein structure classification. Protein sequences of 40% identity in the ASTRAL protein classification database are used for fold recognition research in current work to predict 27 folding types which mostly belong to four protein structural classes: α, β, α/β and α + β. We extract features from primary structure of protein using methods covering DSSP, PSSM and HMM which are based on secondary structure and evolutionary information to convert protein sequences into feature vectors that can be recognized by machine learning algorithm and utilize the combination of LightGBM feature selection algorithm and incremental feature selection method (IFS) to find the optimal classifiers respectively constructed by machine learning algorithms on the basis of tree structure including Random Forest, XGBoost and LightGBM. Bayesian optimization method is used for hyper-parameter adjustment of machine learning algorithms to make the accuracy of fold recognition reach as high as 93.45% at last. The result obtained by the model we propose is outstanding in the study of protein fold recognition.
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Affiliation(s)
- Xinyi Qin
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
| | - Min Liu
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
| | - Lu Zhang
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
| | - Guangzhong Liu
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
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9
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Bankapur S, Patil N. An Enhanced Protein Fold Recognition for Low Similarity Datasets Using Convolutional and Skip-Gram Features With Deep Neural Network. IEEE Trans Nanobioscience 2020; 20:42-49. [PMID: 32894720 DOI: 10.1109/tnb.2020.3022456] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The protein fold recognition is one of the important tasks of structural biology, which helps in addressing further challenges like predicting the protein tertiary structures and its functions. Many machine learning works are published to identify the protein folds effectively. However, very few works have reported the fold recognition accuracy above 80% on benchmark datasets. In this study, an effective set of global and local features are extracted from the proposed Convolutional (Conv) and SkipXGram bi-gram (SXGbg) techniques, and the fold recognition is performed using the proposed deep neural network. The performance of the proposed model reported 91.4% fold accuracy on one of the derived low similarity (< 25%) datasets of latest extended version of SCOPe_2.07. The proposed model is further evaluated on three popular and publicly available benchmark datasets such as DD, EDD, and TG and obtained 85.9%, 95.8%, and 88.8% fold accuracies, respectively. This work is first to report fold recognition accuracy above 85% on all the benchmark datasets. The performance of the proposed model has outperformed the best state-of-the-art models by 5% to 23% on DD, 2% to 19% on EDD, and 3% to 30% on TG dataset.
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10
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Elhefnawy W, Li M, Wang J, Li Y. DeepFrag-k: a fragment-based deep learning approach for protein fold recognition. BMC Bioinformatics 2020; 21:203. [PMID: 33203392 PMCID: PMC7672895 DOI: 10.1186/s12859-020-3504-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 04/16/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment level to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multi-modal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolutional neural network (CNN) to classify the fragment vector into the corresponding fold. RESULTS Our results show that DeepFrag-k yields 92.98% accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition. CONCLUSIONS There is a set of fragments that can serve as structural "keywords" distinguishing between major protein folds. The deep learning architecture in DeepFrag-k is able to accurately identify these fragments as structure features to improve protein fold recognition.
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Affiliation(s)
- Wessam Elhefnawy
- Department of Computer Science, Old Dominion University, Norfolk, U.S.A
| | - Min Li
- Department of Computer Science, Central South University, Changsha, China
| | - Jianxin Wang
- Department of Computer Science, Central South University, Changsha, China
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, U.S.A..
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11
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Sharma R, Kumar S, Tsunoda T, Kumarevel T, Sharma A. Single-stranded and double-stranded DNA-binding protein prediction using HMM profiles. Anal Biochem 2020; 612:113954. [PMID: 32946833 DOI: 10.1016/j.ab.2020.113954] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 08/26/2020] [Accepted: 09/10/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND DNA-binding proteins perform important roles in cellular processes and are involved in many biological activities. These proteins include crucial protein-DNA binding domains and can interact with single-stranded or double-stranded DNA, and accordingly classified as single-stranded DNA-binding proteins (SSBs) or double-stranded DNA-binding proteins (DSBs). Computational prediction of SSBs and DSBs helps in annotating protein functions and understanding of protein-binding domains. RESULTS Performance is reported using the DNA-binding protein dataset that was recently introduced by Wang et al., [1]. The proposed method achieved a sensitivity of 0.600, specificity of 0.792, AUC of 0.758, MCC of 0.369, accuracy of 0.744, and F-measure of 0.536, on the independent test set. CONCLUSION The proposed method with the hidden Markov model (HMM) profiles for feature extraction, outperformed the benchmark method in the literature and achieved an overall improvement of approximately 3%. The source code and supplementary information of the proposed method is available at https://github.com/roneshsharma/Predict-DNA-binding-proteins/wiki.
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Affiliation(s)
- Ronesh Sharma
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.
| | - Shiu Kumar
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.
| | - Tatsuhiko Tsunoda
- Laboratory of Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan; Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan; Laboratory of Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, 113-0033, Japan.
| | - Thirumananseri Kumarevel
- Laboratory for Transcription Structural Biology, RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.
| | - Alok Sharma
- Laboratory of Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan; Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan; School of Engineering and Physics, The University of the South Pacific, Suva, Fiji; Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, Australia.
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12
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Refahi MS, Mir A, Nasiri JA. A novel fusion based on the evolutionary features for protein fold recognition using support vector machines. Sci Rep 2020; 10:14368. [PMID: 32873824 PMCID: PMC7463267 DOI: 10.1038/s41598-020-71172-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 08/10/2020] [Indexed: 11/29/2022] Open
Abstract
Protein fold recognition plays a crucial role in discovering three-dimensional structure of proteins and protein functions. Several approaches have been employed for the prediction of protein folds. Some of these approaches are based on extracting features from protein sequences and using a strong classifier. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In recent years, finding an efficient technique for integrating discriminate features have been received advancing attention. In this study, we integrate Auto-Cross-Covariance and Separated dimer evolutionary feature extraction methods. The results’ features are scored by Information gain to define and select several discriminated features. According to three benchmark datasets, DD, RDD ,and EDD, the results of the support vector machine show more than 6\documentclass[12pt]{minimal}
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\begin{document}$$\%$$\end{document}% improvement in accuracy on these benchmark datasets.
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Affiliation(s)
- Mohammad Saleh Refahi
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - A Mir
- Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran
| | - Jalal A Nasiri
- Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran.
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13
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PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method. BIOMED RESEARCH INTERNATIONAL 2020; 2020:7297631. [PMID: 32352006 PMCID: PMC7174956 DOI: 10.1155/2020/7297631] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 04/01/2020] [Indexed: 12/02/2022]
Abstract
DNA-binding proteins (DBPs) play vital roles in all aspects of genetic activities. However, the identification of DBPs by using wet-lab experimental approaches is often time-consuming and laborious. In this study, we develop a novel computational method, called PredDBP-Stack, to predict DBPs solely based on protein sequences. First, amino acid composition (AAC) and transition probability composition (TPC) extracted from the hidden markov model (HMM) profile are adopted to represent a protein. Next, we establish a stacked ensemble model to identify DBPs, which involves two stages of learning. In the first stage, the four base classifiers are trained with the features of HMM-based compositions. In the second stage, the prediction probabilities of these base classifiers are used as inputs to the meta-classifier to perform the final prediction of DBPs. Based on the PDB1075 benchmark dataset, we conduct a jackknife cross validation with the proposed PredDBP-Stack predictor and obtain a balanced sensitivity and specificity of 92.47% and 92.36%, respectively. This outcome outperforms most of the existing classifiers. Furthermore, our method also achieves superior performance and model robustness on the PDB186 independent dataset. This demonstrates that the PredDBP-Stack is an effective classifier for accurately identifying DBPs based on protein sequence information alone.
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14
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Zhang L, Kong L. A Novel Amino Acid Properties Selection Method for Protein Fold Classification. Protein Pept Lett 2020; 27:287-294. [PMID: 32207399 DOI: 10.2174/0929866526666190718151753] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 04/17/2019] [Accepted: 06/10/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND Amino acid physicochemical properties encoded in protein primary structure play a crucial role in protein folding. However, it is not yet clear which of the properties are the most suitable for protein fold classification. OBJECTIVE To avoid exhaustively searching the total properties space, an amino acid properties selection method was proposed in this study to rapidly obtain a suitable properties combination for protein fold classification. METHODS The proposed amino acid properties selection method was based on sequential floating forward selection strategy. Beginning with an empty set, variable number of features were added iteratively until achieving the iteration termination condition. RESULTS The experimental results indicate that the proposed method improved prediction accuracies by 0.26-5% on a widely used benchmark dataset with appropriately selected amino acid properties. CONCLUSION The proposed properties selection method can be extended to other biomolecule property related classification problems in bioinformatics.
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Affiliation(s)
- Lichao Zhang
- School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao, China.,College of Sciences, Northeastern University, Shenyang, China
| | - Liang Kong
- School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Qinhuangdao, China
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15
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Patil K, Chouhan U. Relevance of Machine Learning Techniques and Various Protein Features in Protein Fold Classification: A Review. Curr Bioinform 2019. [DOI: 10.2174/1574893614666190204154038] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background:
Protein fold prediction is a fundamental step in Structural Bioinformatics.
The tertiary structure of a protein determines its function and to predict its tertiary structure, fold
prediction serves an important role. Protein fold is simply the arrangement of the secondary
structure elements relative to each other in space. A number of studies have been carried out till
date by different research groups working worldwide in this field by using the combination of
different benchmark datasets, different types of descriptors, features and classification techniques.
Objective:
In this study, we have tried to put all these contributions together, analyze their study
and to compare different techniques used by them.
Methods:
Different features are derived from protein sequence, its secondary structure, different
physicochemical properties of amino acids, domain composition, Position Specific Scoring Matrix,
profile and threading techniques.
Conclusion:
Combination of these different features can improve classification accuracy to a
large extent. With the help of this survey, one can know the most suitable feature/attribute set and
classification technique for this multi-class protein fold classification problem.
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Affiliation(s)
- Komal Patil
- Department of Mathematics, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003 M.P, India
| | - Usha Chouhan
- Department of Mathematics, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003 M.P, India
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Peyravi F, Latif A, Moshtaghioun SM. Protein tertiary structure prediction using hidden Markov model based on lattice. J Bioinform Comput Biol 2019; 17:1950007. [PMID: 31057069 DOI: 10.1142/s0219720019500070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The prediction of protein structure from its amino acid sequence is one of the most prominent problems in computational biology. The biological function of a protein depends on its tertiary structure which is determined by its amino acid sequence via the process of protein folding. We propose a novel fold recognition method for protein tertiary structure prediction based on a hidden Markov model and 3D coordinates of amino acid residues. The method introduces states based on the basis vectors in Bravais cubic lattices to learn the path of amino acids of the proteins of each fold. Three hidden Markov models are considered based on simple cubic, body-centered cubic (BCC) and face-centered cubic (FCC) lattices. A 10-fold cross validation was performed on a set of 42 fold SCOP dataset. The proposed composite methodology is compared to fold recognition methods which have HMM as base of their algorithms having approaches on only amino acid sequence or secondary structure. The accuracy of proposed model based on face-centered cubic lattices is quite better in comparison with SAM, 3-HMM optimized and Markov chain optimized in overall experiment. The huge data of 3D space help the model to have greater performance in comparison to methods which use only primary structures or only secondary structures.
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Affiliation(s)
- Farzad Peyravi
- * Department of Computer Engineering, Yazd University, Yazd, Iran
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17
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Sharma A, Lysenko A, López Y, Dehzangi A, Sharma R, Reddy H, Sattar A, Tsunoda T. HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues. BMC Genomics 2019; 19:982. [PMID: 30999862 PMCID: PMC7402407 DOI: 10.1186/s12864-018-5206-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 10/28/2018] [Indexed: 02/06/2023] Open
Abstract
Background Post-translational modifications are viewed as an important mechanism for controlling protein function and are believed to be involved in multiple important diseases. However, their profiling using laboratory-based techniques remain challenging. Therefore, making the development of accurate computational methods to predict post-translational modifications is particularly important for making progress in this area of research. Results This work explores the use of four half-sphere exposure-based features for computational prediction of sumoylation sites. Unlike most of the previously proposed approaches, which focused on patterns of amino acid co-occurrence, we were able to demonstrate that protein structural based features could be sufficiently informative to achieve good predictive performance. The evaluation of our method has demonstrated high sensitivity (0.9), accuracy (0.89) and Matthew’s correlation coefficient (0.78–0.79). We have compared these results to the recently released pSumo-CD method and were able to demonstrate better performance of our method on the same evaluation dataset. Conclusions The proposed predictor HseSUMO uses half-sphere exposures of amino acids to predict sumoylation sites. It has shown promising results on a benchmark dataset when compared with the state-of-the-art method. The extracted data of this study can be accessed at https://github.com/YosvanyLopez/HseSUMO. Electronic supplementary material The online version of this article (10.1186/s12864-018-5206-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Q, Brisbane, LD-4111, Australia. .,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan. .,School of Engineering and Physics, Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji Islands.
| | - Artem Lysenko
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Yosvany López
- Genesis Institute of Genetic Research, Genesis Healthcare Co, Tokyo, Japan
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, MD, USA
| | - Ronesh Sharma
- School of Engineering and Physics, Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji Islands.,School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji
| | - Hamendra Reddy
- School of Engineering and Physics, Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji Islands
| | - Abdul Sattar
- Institute for Integrated and Intelligent Systems, Griffith University, Q, Brisbane, LD-4111, Australia
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan. .,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan. .,CREST, JST, Tokyo, 113-8510, Japan.
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18
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Dehzangi A, López Y, Taherzadeh G, Sharma A, Tsunoda T. SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure. Molecules 2018; 23:E3260. [PMID: 30544729 PMCID: PMC6320791 DOI: 10.3390/molecules23123260] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Revised: 11/30/2018] [Accepted: 12/05/2018] [Indexed: 12/13/2022] Open
Abstract
Post Translational Modification (PTM) is defined as the modification of amino acids along the protein sequences after the translation process. These modifications significantly impact on the functioning of proteins. Therefore, having a comprehensive understanding of the underlying mechanism of PTMs turns out to be critical in studying the biological roles of proteins. Among a wide range of PTMs, sumoylation is one of the most important modifications due to its known cellular functions which include transcriptional regulation, protein stability, and protein subcellular localization. Despite its importance, determining sumoylation sites via experimental methods is time-consuming and costly. This has led to a great demand for the development of fast computational methods able to accurately determine sumoylation sites in proteins. In this study, we present a new machine learning-based method for predicting sumoylation sites called SumSec. To do this, we employed the predicted secondary structure of amino acids to extract two types of structural features from neighboring amino acids along the protein sequence which has never been used for this task. As a result, our proposed method is able to enhance the sumoylation site prediction task, outperforming previously proposed methods in the literature. SumSec demonstrated high sensitivity (0.91), accuracy (0.94) and MCC (0.88). The prediction accuracy achieved in this study is 21% better than those reported in previous studies. The script and extracted features are publicly available at: https://github.com/YosvanyLopez/SumSec.
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Affiliation(s)
- Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA.
| | - Yosvany López
- Genesis Institute of Genetic Research, Genesis Healthcare Co., Tokyo 150-6015, Japan.
| | - Ghazaleh Taherzadeh
- School of Information and Communication Technology, Griffith University, Gold Coast 4222, Australia.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane 4111, Australia.
- School of Engineering & Physics, University of the South Pacific, Suva, Fiji.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan.
- CREST, JST, Tokyo 102-0076, Japan.
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo 113-8510, Japan.
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan.
- CREST, JST, Tokyo 102-0076, Japan.
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo 113-8510, Japan.
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19
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Sudha P, Ramyachitra D, Manikandan P. Enhanced Artificial Neural Network for Protein Fold Recognition and Structural Class Prediction. GENE REPORTS 2018. [DOI: 10.1016/j.genrep.2018.07.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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20
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López Y, Sharma A, Dehzangi A, Lal SP, Taherzadeh G, Sattar A, Tsunoda T. Success: evolutionary and structural properties of amino acids prove effective for succinylation site prediction. BMC Genomics 2018; 19:923. [PMID: 29363424 PMCID: PMC5781056 DOI: 10.1186/s12864-017-4336-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Post-translational modification is considered an important biological mechanism with critical impact on the diversification of the proteome. Although a long list of such modifications has been studied, succinylation of lysine residues has recently attracted the interest of the scientific community. The experimental detection of succinylation sites is an expensive process, which consumes a lot of time and resources. Therefore, computational predictors of this covalent modification have emerged as a last resort to tackling lysine succinylation. RESULTS In this paper, we propose a novel computational predictor called 'Success', which efficiently uses the structural and evolutionary information of amino acids for predicting succinylation sites. To do this, each lysine was described as a vector that combined the above information of surrounding amino acids. We then designed a support vector machine with a radial basis function kernel for discriminating between succinylated and non-succinylated residues. We finally compared the Success predictor with three state-of-the-art predictors in the literature. As a result, our proposed predictor showed a significant improvement over the compared predictors in statistical metrics, such as sensitivity (0.866), accuracy (0.838) and Matthews correlation coefficient (0.677) on a benchmark dataset. CONCLUSIONS The proposed predictor effectively uses the structural and evolutionary information of the amino acids surrounding a lysine. The bigram feature extraction approach, while retaining the same number of features, facilitates a better description of lysines. A support vector machine with a radial basis function kernel was used to discriminate between modified and unmodified lysines. The aforementioned aspects make the Success predictor outperform three state-of-the-art predictors in succinylation detection.
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Affiliation(s)
- Yosvany López
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan. .,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
| | - Alok Sharma
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan. .,Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia. .,School of Engineering & Physics, University of the South Pacific, Suva, Fiji.
| | - Abdollah Dehzangi
- Department of Computer Science, School of Computer, Mathematical, and Natural Sciences, Morgan State University, Baltimore, Maryland, USA
| | - Sunil Pranit Lal
- School of Engineering & Advanced Technology, Massey University, Palmerston North, New Zealand
| | - Ghazaleh Taherzadeh
- School of Information and Communication Technology, Griffith University, Brisbane, Australia
| | - Abdul Sattar
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.,School of Information and Communication Technology, Griffith University, Brisbane, Australia
| | - Tatsuhiko Tsunoda
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.,CREST, JST, Tokyo, 113-8510, Japan
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21
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Ibrahim W, Abadeh MS. Protein fold recognition using Deep Kernelized Extreme Learning Machine and linear discriminant analysis. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3346-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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22
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Sharma R, Bayarjargal M, Tsunoda T, Patil A, Sharma A. MoRFPred-plus: Computational Identification of MoRFs in Protein Sequences using Physicochemical Properties and HMM profiles. J Theor Biol 2018; 437:9-16. [DOI: 10.1016/j.jtbi.2017.10.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 10/12/2017] [Accepted: 10/13/2017] [Indexed: 11/26/2022]
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23
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Yan K, Xu Y, Fang X, Zheng C, Liu B. Protein fold recognition based on sparse representation based classification. Artif Intell Med 2017; 79:1-8. [DOI: 10.1016/j.artmed.2017.03.006] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 03/06/2017] [Accepted: 03/07/2017] [Indexed: 12/13/2022]
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Sharma R, Kumar S, Tsunoda T, Patil A, Sharma A. Predicting MoRFs in protein sequences using HMM profiles. BMC Bioinformatics 2016; 17:504. [PMID: 28155710 PMCID: PMC5259822 DOI: 10.1186/s12859-016-1375-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Intrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task. METHODS In this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others). RESULTS To evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors. CONCLUSIONS Using HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.
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Affiliation(s)
- Ronesh Sharma
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.,School of Engineering and Physics, The University of the South Pacific, Suva, Fiji
| | - Shiu Kumar
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.,School of Engineering and Physics, The University of the South Pacific, Suva, Fiji
| | - Tatsuhiko Tsunoda
- CREST, JST, Yokohama, 230-0045, Japan.,RIKEN Center for Integrative Medical Science, Yokohama, 230-0045, Japan.,Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
| | - Ashwini Patil
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
| | - Alok Sharma
- School of Engineering and Physics, The University of the South Pacific, Suva, Fiji. .,CREST, JST, Yokohama, 230-0045, Japan. .,RIKEN Center for Integrative Medical Science, Yokohama, 230-0045, Japan. .,Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
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ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier. BIOMED RESEARCH INTERNATIONAL 2016; 2016:6802832. [PMID: 27660761 PMCID: PMC5021882 DOI: 10.1155/2016/6802832] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 07/15/2016] [Accepted: 08/07/2016] [Indexed: 11/17/2022]
Abstract
Protein fold classification plays an important role in both protein functional analysis and drug design. The number of proteins in PDB is very large, but only a very small part is categorized and stored in the SCOPe database. Therefore, it is necessary to develop an efficient method for protein fold classification. In recent years, a variety of classification methods have been used in many protein fold classification studies. In this study, we propose a novel classification method called proFold. We import protein tertiary structure in the period of feature extraction and employ a novel ensemble strategy in the period of classifier training. Compared with existing similar ensemble classifiers using the same widely used dataset (DD-dataset), proFold achieves 76.2% overall accuracy. Another two commonly used datasets, EDD-dataset and TG-dataset, are also tested, of which the accuracies are 93.2% and 94.3%, higher than the existing methods. ProFold is available to the public as a web-server.
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Improving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acids. J Theor Biol 2016; 402:117-28. [PMID: 27164998 DOI: 10.1016/j.jtbi.2016.05.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Revised: 04/20/2016] [Accepted: 05/02/2016] [Indexed: 11/24/2022]
Abstract
Predicting the three-dimensional (3-D) structure of a protein is an important task in the field of bioinformatics and biological sciences. However, directly predicting the 3-D structure from the primary structure is hard to achieve. Therefore, predicting the fold or structural class of a protein sequence is generally used as an intermediate step in determining the protein's 3-D structure. For protein fold recognition (PFR) and structural class prediction (SCP), two steps are required - feature extraction step and classification step. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In this study, we explore the importance of utilizing the physicochemical properties of amino acids for improving PFR and SCP accuracies. For this, we propose a Forward Consecutive Search (FCS) scheme which aims to strategically select physicochemical attributes that will supplement the existing feature extraction techniques for PFR and SCP. An exhaustive search is conducted on all the existing 544 physicochemical attributes using the proposed FCS scheme and a subset of physicochemical attributes is identified. Features extracted from these selected attributes are then combined with existing syntactical-based and evolutionary-based features, to show an improvement in the recognition and prediction performance on benchmark datasets.
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