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Malik A, Kamli MR, Sabir JSM, Rather IA, Phan LT, Kim CB, Manavalan B. APLpred: A machine learning-based tool for accurate prediction and characterization of asparagine peptide lyases using sequence-derived optimal features. Methods 2024:S1046-2023(24)00133-6. [PMID: 38944134 DOI: 10.1016/j.ymeth.2024.05.014] [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: 03/24/2024] [Revised: 05/08/2024] [Accepted: 05/19/2024] [Indexed: 07/01/2024] Open
Abstract
Asparagine peptide lyase (APL) is among the seven groups of proteases, also known as proteolytic enzymes, which are classified according to their catalytic residue. APLs are synthesized as precursors or propeptides that undergo self-cleavage through autoproteolytic reaction. At present, APLs are grouped into 10 families belonging to six different clans of proteases. Recognizing their critical roles in many biological processes including virus maturation, and virulence, accurate identification and characterization of APLs is indispensable. Experimental identification and characterization of APLs is laborious and time-consuming. Here, we developed APLpred, a novel support vector machine (SVM) based predictor that can predict APLs from the primary sequences. APLpred was developed using Boruta-based optimal features derived from seven encodings and subsequently trained using five machine learning algorithms. After evaluating each model on an independent dataset, we selected APLpred (an SVM-based model) due to its consistent performance during cross-validation and independent evaluation. We anticipate APLpred will be an effective tool for identifying APLs. This could aid in designing inhibitors against these enzymes and exploring their functions. The APLpred web server is freely available at https://procarb.org/APLpred/.
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Affiliation(s)
- Adeel Malik
- Institute of Intelligence Informatics Technology, Sangmyung University, Seoul 03016, Republic of Korea
| | - Majid Rasool Kamli
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Jamal S M Sabir
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
| | - Irfan Ahmad Rather
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Le Thi Phan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Chang-Bae Kim
- Department of Biotechnology, Sangmyung University, Seoul 03016, Republic of Korea.
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea.
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Feng J, Sun M, Liu C, Zhang W, Xu C, Wang J, Wang G, Wan S. SAMP: Identifying Antimicrobial Peptides by an Ensemble Learning Model Based on Proportionalized Split Amino Acid Composition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.25.590553. [PMID: 38712184 PMCID: PMC11071531 DOI: 10.1101/2024.04.25.590553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
It is projected that 10 million deaths could be attributed to drug-resistant bacteria infections in 2050. To address this concern, identifying new-generation antibiotics is an effective way. Antimicrobial peptides (AMPs), a class of innate immune effectors, have received significant attention for their capacity to eliminate drug-resistant pathogens, including viruses, bacteria, and fungi. Recent years have witnessed widespread applications of computational methods especially machine learning (ML) and deep learning (DL) for discovering AMPs. However, existing methods only use features including compositional, physiochemical, and structural properties of peptides, which cannot fully capture sequence information from AMPs. Here, we present SAMP, an ensemble random projection (RP) based computational model that leverages a new type of features called Proportionalized Split Amino Acid Composition (PSAAC) in addition to conventional sequence-based features for AMP prediction. With this new feature set, SAMP captures the residue patterns like sorting signals at around both the N-terminus and the C-terminus, while also retaining the sequence order information from the middle peptide fragments. Benchmarking tests on different balanced and imbalanced datasets demonstrate that SAMP consistently outperforms existing state-of-the-art methods, such as iAMPpred and AMPScanner V2, in terms of accuracy, MCC, G-measure and F1-score. In addition, by leveraging an ensemble RP architecture, SAMP is scalable to processing large-scale AMP identification with further performance improvement, compared to those models without RP. To facilitate the use of SAMP, we have developed a Python package freely available at https://github.com/wan-mlab/SAMP .
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Gabriel DB, Havugimana F, Liley AE, Aguilar I, Yeasin M, Simon NW. Lateral Orbitofrontal Cortex Encodes Presence of Risk and Subjective Risk Preference During Decision-Making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.08.588332. [PMID: 38645204 PMCID: PMC11030364 DOI: 10.1101/2024.04.08.588332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Adaptive decision-making requires consideration of objective risks and rewards associated with each option, as well as subjective preference for risky/safe alternatives. Inaccurate risk/reward estimations can engender excessive risk-taking, a central trait in many psychiatric disorders. The lateral orbitofrontal cortex (lOFC) has been linked to many disorders associated with excessively risky behavior and is ideally situated to mediate risky decision-making. Here, we used single-unit electrophysiology to measure neuronal activity from lOFC of freely moving rats performing in a punishment-based risky decision-making task. Subjects chose between a small, safe reward and a large reward associated with either 0% or 50% risk of concurrent punishment. lOFC activity repeatedly encoded current risk in the environment throughout the decision-making sequence, signaling risk before, during, and after a choice. In addition, lOFC encoded reward magnitude, although this information was only evident during action selection. A Random Forest classifier successfully used neural data accurately to predict the risk of punishment in any given trial, and the ability to predict choice via lOFC activity differentiated between and risk-preferring and risk-averse rats. Finally, risk preferring subjects demonstrated reduced lOFC encoding of risk and increased encoding of reward magnitude. These findings suggest lOFC may serve as a central decision-making hub in which external, environmental information converges with internal, subjective information to guide decision-making in the face of punishment risk.
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Affiliation(s)
- Daniel B.K. Gabriel
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202
| | - Felix Havugimana
- Department of Computer Engineering, University of Memphis, Memphis, TN, 38152
| | - Anna E. Liley
- Institut du Cerveau/Paris Brain Institute, Paris, France, 75013
| | - Ivan Aguilar
- Department of Psychology, University of Memphis, Memphis, TN, 38152
| | - Mohammed Yeasin
- Department of Computer Engineering, University of Memphis, Memphis, TN, 38152
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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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Shen J, Xia Y, Lu Y, Lu W, Qian M, Wu H, Fu Q, Chen J. Identification of membrane protein types via deep residual hypergraph neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:20188-20212. [PMID: 38052642 DOI: 10.3934/mbe.2023894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
A membrane protein's functions are significantly associated with its type, so it is crucial to identify the types of membrane proteins. Conventional computational methods for identifying the species of membrane proteins tend to ignore two issues: High-order correlation among membrane proteins and the scenarios of multi-modal representations of membrane proteins, which leads to information loss. To tackle those two issues, we proposed a deep residual hypergraph neural network (DRHGNN), which enhances the hypergraph neural network (HGNN) with initial residual and identity mapping in this paper. We carried out extensive experiments on four benchmark datasets of membrane proteins. In the meantime, we compared the DRHGNN with recently developed advanced methods. Experimental results showed the better performance of DRHGNN on the membrane protein classification task on four datasets. Experiments also showed that DRHGNN can handle the over-smoothing issue with the increase of the number of model layers compared with HGNN. The code is available at https://github.com/yunfighting/Identification-of-Membrane-Protein-Types-via-deep-residual-hypergraph-neural-network.
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Affiliation(s)
- Jiyun Shen
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Yiyi Xia
- Tianping College of Suzhou University of Science and Technology, Suzhou, China
| | - Yiming Lu
- Tianping College of Suzhou University of Science and Technology, Suzhou, China
| | - Weizhong Lu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
- Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China
| | - Meiling Qian
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Hongjie Wu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Qiming Fu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Jing Chen
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
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DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet Transform. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2987407. [PMID: 36211019 PMCID: PMC9534628 DOI: 10.1155/2022/2987407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/19/2022] [Accepted: 09/09/2022] [Indexed: 11/17/2022]
Abstract
DNA-binding proteins (DBPs) have crucial biotic activities including DNA replication, recombination, and transcription. DBPs are highly concerned with chronic diseases and are used in the manufacturing of antibiotics and steroids. A series of predictors were established to identify DBPs. However, researchers are still working to further enhance the identification of DBPs. This research designed a novel predictor to identify DBPs more accurately. The features from the sequences are transformed by F-PSSM (Filtered position-specific scoring matrix), PSSM-DPC (Position specific scoring matrix-dipeptide composition), and R-PSSM (Reduced position-specific scoring matrix). To eliminate the noisy attributes, we extended DWT (discrete wavelet transform) to F-PSSM, PSSM-DPC, and R-PSSM and introduced three novel descriptors, namely, F-PSSM-DWT, PSSM-DPC-DWT, and R-PSSM-DWT. Onward, the training of the four models were performed using LiXGB (Light eXtreme gradient boosting), XGB (eXtreme gradient boosting, ERT (extremely randomized trees), and Adaboost. LiXGB with R-PSSM-DWT has attained 6.55% higher accuracy on training and 5.93% on testing dataset than the best existing predictors. The results reveal the excellent performance of our novel predictor over the past studies. DBP-iDWT would be fruitful for establishing more operative therapeutic strategies for fatal disease treatment.
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Ali F, Kumar H, Patil S, Kotecha K, Banjar A, Daud A. Target-DBPPred: An intelligent model for prediction of DNA-binding proteins using discrete wavelet transform based compression and light eXtreme gradient boosting. Comput Biol Med 2022; 145:105533. [PMID: 35447463 DOI: 10.1016/j.compbiomed.2022.105533] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/11/2022] [Accepted: 04/13/2022] [Indexed: 11/03/2022]
Abstract
DNA-protein interaction is a critical biological process that performs influential activities, including DNA transcription and recombination. DBPs (DNA-binding proteins) are closely associated with different kinds of human diseases (asthma, cancer, and AIDS), while some of the DBPs are used in the production of antibiotics, steroids, and anti-inflammatories. Several methods have been reported for the prediction of DBPs. However, a more intelligent method is still highly desirable for the accurate prediction of DBPs. This study presents an intelligent computational method, Target-DBPPred, to improve DBPs prediction. Important features from primary protein sequences are investigated via a novel feature descriptor, called EDF-PSSM-DWT (Evolutionary difference formula position-specific scoring matrix-discrete wavelet transform) and several other multi-evolutionary methods, including F-PSSM (Filtered position-specific scoring matrix), EDF-PSSM (Evolutionary difference formula position-specific scoring matrix), PSSM-DPC (Position-specific scoring matrix-dipeptide composition), and Lead-BiPSSM (Lead-bigram-position specific scoring matrix) to encapsulate diverse multivariate features. The best feature set from the features of each descriptor is selected using sequential forward selection (SFS). Further, four models are trained using Adaboost, XGB (eXtreme gradient boosting), ERT (extremely randomized trees), and LiXGB (Light eXtreme gradient boosting) classifiers. LiXGB, with the best feature set of EDF-PSSM-DWT, has attained 6.69% and 15.07% higher performance in terms of accuracies using training and testing datasets, respectively. The obtained results verify the improved performance of our proposed predictor over the existing predictors.
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Affiliation(s)
- Farman Ali
- Department of Elementary and Secondary Education, Peshawar, Khyber Pakhtunkhwa, Pakistan; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Harish Kumar
- Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | - Shruti Patil
- Symbiosis Institute of Technology, Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune, India
| | - Ketan Kotecha
- Symbiosis Institute of Technology, Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune, India.
| | - Ameen Banjar
- Department of Information Systems, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Ali Daud
- Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, School of Information Engineering, Zhejiang Ocean University, Zhoushan, 316022, China; Department of Computer Science and Artificial Intelligence, University of Jeddah, Jeddah, Saudi Arabia.
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Amerifar S, Norouzi M, Ghandi M. A tool for feature extraction from biological sequences. Brief Bioinform 2022; 23:6563937. [PMID: 35383372 DOI: 10.1093/bib/bbac108] [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: 11/19/2021] [Revised: 03/01/2022] [Accepted: 03/03/2022] [Indexed: 11/12/2022] Open
Abstract
With the advances in sequencing technologies, a huge amount of biological data is extracted nowadays. Analyzing this amount of data is beyond the ability of human beings, creating a splendid opportunity for machine learning methods to grow. The methods, however, are practical only when the sequences are converted into feature vectors. Many tools target this task including iLearnPlus, a Python-based tool which supports a rich set of features. In this paper, we propose a holistic tool that extracts features from biological sequences (i.e. DNA, RNA and Protein). These features are the inputs to machine learning models that predict properties, structures or functions of the input sequences. Our tool not only supports all features in iLearnPlus but also 30 additional features which exist in the literature. Moreover, our tool is based on R language which makes an alternative for bioinformaticians to transform sequences into feature vectors. We have compared the conversion time of our tool with that of iLearnPlus: we transform the sequences much faster. We convert small nucleotides by a median of 2.8X faster, while we outperform iLearnPlus by a median of 6.3X for large sequences. Finally, in amino acids, our tool achieves a median speedup of 23.9X.
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Affiliation(s)
- Sare Amerifar
- Bioinformatics, Tatbiat Modares University, Jalal Al Ahmad, 14115-111, Tehran, Iran
| | - Mahammad Norouzi
- Computer Science, Technical University of Darmstadt, Hochschulstr. 1, 64293, Hesse, Germany
| | - Mahmoud Ghandi
- Bioinformatics, Monte Rosa Therapeutics, Summer Street, 02210, Boston, United States
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Akbar S, Ahmad A, Hayat M, Rehman AU, Khan S, Ali F. iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model. Comput Biol Med 2021; 137:104778. [PMID: 34481183 DOI: 10.1016/j.compbiomed.2021.104778] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 08/16/2021] [Accepted: 08/17/2021] [Indexed: 11/26/2022]
Abstract
Tuberculosis (TB) is a worldwide illness caused by the bacteria Mycobacterium tuberculosis. Owing to the high prevalence of multidrug-resistant tuberculosis, numerous traditional strategies for developing novel alternative therapies have been presented. The effectiveness and dependability of these procedures are not always consistent. Peptide-based therapy has recently been regarded as a preferable alternative due to its excellent selectivity in targeting specific cells without affecting the normal cells. However, due to the rapid growth of the peptide samples, predicting TB accurately has become a challenging task. To effectively identify antitubercular peptides, an intelligent and reliable prediction model is indispensable. An ensemble learning approach was used in this study to improve expected results by compensating for the shortcomings of individual classification algorithms. Initially, three distinct representation approaches were used to formulate the training samples: k-space amino acid composition, composite physiochemical properties, and one-hot encoding. The feature vectors of the applied feature extraction methods are then combined to generate a heterogeneous vector. Finally, utilizing individual and heterogeneous vectors, five distinct nature classification models were used to evaluate prediction rates. In addition, a genetic algorithm-based ensemble model was used to improve the suggested model's prediction and training capabilities. Using Training and independent datasets, the proposed ensemble model achieved an accuracy of 94.47% and 92.68%, respectively. It was observed that our proposed "iAtbP-Hyb-EnC" model outperformed and reported ~10% highest training accuracy than existing predictors. The "iAtbP-Hyb-EnC" model is suggested to be a reliable tool for scientists and might play a valuable role in academic research and drug discovery. The source code and all datasets are publicly available at https://github.com/Farman335/iAtbP-Hyb-EnC.
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Affiliation(s)
- Shahid Akbar
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP, 23200, Pakistan.
| | - Ashfaq Ahmad
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP, 23200, Pakistan.
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP, 23200, Pakistan.
| | - Ateeq Ur Rehman
- Department of Information Technology, The University of Haripur, KP, Pakistan.
| | - Salman Khan
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP, 23200, Pakistan.
| | - Farman Ali
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
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10
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Identification of antioxidant proteins using a discriminative intelligent model of k-space amino acid pairs based descriptors incorporating with ensemble feature selection. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.10.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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11
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Alphonse AS, Mary NAB, Starvin MS. Classification of membrane protein using Tetra Peptide Pattern. Anal Biochem 2020; 606:113845. [PMID: 32739352 DOI: 10.1016/j.ab.2020.113845] [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: 04/16/2020] [Revised: 06/17/2020] [Accepted: 06/22/2020] [Indexed: 11/29/2022]
Abstract
Membrane proteins play an important role in the life activities of organisms. The mechanism of cell structures and biological activities can be identified only by knowing the functional types of membrane proteins which accelerate the process. Therefore, it is greatly necessary to build up computational approaches for timely and accurate prediction of the functional types of membrane protein. The proposed method analyzes the structure of the membrane proteins using novel Tetra Peptide Pattern (TPP)-based feature extraction technique. A frequency occurrence matrix is created from which a feature vector is formed. This feature vector captures the pattern among amino acids in a membrane protein sequence. The feature vector is reduced in the dimension using General Kernel-based Supervised Principal Component Analysis (GKSPCA). Stacked Restricted Boltzmann Machines (RBM) in Deep Belief Network (DBN) is used for classification. The RBM is the building block of Deep Belief Network. The proposed method achieves good results on two datasets. The performance of the proposed method was analyzed using Accuracy, Specificity, Sensitivity and Mathew's correlation coefficient. The proposed method achieves good results when compared to other state-of-the-art techniques.
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Affiliation(s)
| | | | - M S Starvin
- University College of Engineering, Nagercoil, 629004, India.
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Arif M, Ahmad S, Ali F, Fang G, Li M, Yu DJ. TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree. J Comput Aided Mol Des 2020; 34:841-856. [PMID: 32180124 DOI: 10.1007/s10822-020-00307-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 03/09/2020] [Indexed: 02/08/2023]
Abstract
Cell-penetrating peptides (CPPs) are short length permeable proteins have emerged as drugs delivery tool of therapeutic agents including genetic materials and macromolecules into cells. Recently, CPP has become a hotspot avenue for life science research and paved a new way of disease treatment without harmful impact on cell viability due to nontoxic characteristic. Therefore, the correct identification of CPPs will provide hints for medical applications. Considering the shortcomings of traditional experimental CPPs identification, it is urgently needed to design intelligent predictor for accurate identification of CPPs for the large scale uncharacterized sequences. We develop a novel computational method, called TargetCPP, to discriminate CPPs from Non-CPPs with improved accuracy. In TargetCPP, first the peptide sequences are formulated with four distinct encoding methods i.e., composite protein sequence representation, composition transition and distribution, split amino acid composition, and information theory features. These dominant feature vectors were fused and applied intelligent minimum redundancy and maximum relevancy feature selection method to choose an optimal subset of features. Finally, the predictive model is learned through different classification algorithms on the optimized features. Among these classifiers, gradient boost decision tree algorithm achieved excellent performance throughout the experiments. Notably, the TargetCPP tool attained high prediction Accuracy of 93.54% and 88.28% using jackknife and independent test, respectively. Empirical outcomes prove the superiority and potency of proposed bioinformatics method over state-of-the-art methods. It is highly anticipated that the outcomes of this study will provide a strong background for large scale prediction of CPPs and instructive guidance in clinical therapy and medical applications.
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Affiliation(s)
- Muhammad Arif
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Saeed Ahmad
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Farman Ali
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Ge Fang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Min Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
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13
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Identification of membrane protein types via multivariate information fusion with Hilbert–Schmidt Independence Criterion. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.11.103] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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14
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Javed F, Hayat M. Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC. Genomics 2019; 111:1325-1332. [DOI: 10.1016/j.ygeno.2018.09.004] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2018] [Accepted: 09/04/2018] [Indexed: 12/13/2022]
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15
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Arif M, Ali F, Ahmad S, Kabir M, Ali Z, Hayat M. Pred-BVP-Unb: Fast prediction of bacteriophage Virion proteins using un-biased multi-perspective properties with recursive feature elimination. Genomics 2019; 112:1565-1574. [PMID: 31526842 DOI: 10.1016/j.ygeno.2019.09.006] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 08/27/2019] [Accepted: 09/11/2019] [Indexed: 10/26/2022]
Abstract
Bacteriophage virion proteins (BVPs) are bacterial viruses that have a great impact on different biological functions of bacteria. They are significantly used in genetic engineering and phage therapy applications. Correct identification of BVP through conventional pathogen methods are slow and expensive. Thus, designing a Bioinformatics predictor is urgently desirable to accelerate correct identification of BVPs within a huge volume of proteins. However, available prediction tools performance is inadequate due to the lack of useful feature representation and severe imbalance issue. In the present study, we propose an intelligent model, called Pred-BVP-Unb for discrimination of BVPs that employed three nominal sequences-driven descriptors, i.e. Bi-PSSM evolutionary information, composition & translation, and split amino acid composition. The imbalance phenomena between classes were coped with the help of a synthetic minority oversampling technique. The essential attributes are selected by a robust algorithm called recursive feature elimination. Finally, the optimal feature space is provided to support vector machine classifier using a radial base kernel in order to train the model. Our predictor remarkably outperforms than existing approaches in the literature by achieving the highest accuracy of 92.54% and 83.06% respectively on the benchmark and independent datasets. We expect that Pred-BVP-Unb tool can provide useful hints for designing antibacterial drugs and also helpful to expedite large scale discovery of new bacteriophage virion proteins. The source code and all datasets are publicly available at https://github.com/Muhammad-Arif-NUST/BVP_Pred_Unb.
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Affiliation(s)
- Muhammad Arif
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Department of Computer Science, Abdul Wali Khan University Mardan, KP, Pakistan.
| | - Farman Ali
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
| | - Saeed Ahmad
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Muhammad Kabir
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Zakir Ali
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, KP, Pakistan.
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Kabir M, Ahmad S, Iqbal M, Hayat M. iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families. Genomics 2019; 112:276-285. [PMID: 30779939 DOI: 10.1016/j.ygeno.2019.02.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Revised: 01/09/2019] [Accepted: 02/07/2019] [Indexed: 12/25/2022]
Abstract
Nuclear receptor proteins (NRPs) perform a vital role in regulating gene expression. With the rapidity growth of NRPs in post-genomic era, it is highly recommendable to identify NRPs and their sub-families accurately from their primary sequences. Several conventional methods have been used for discrimination of NRPs and their sub-families, but did not achieve considerable results. In a sequel, a two-level new computational model "iNR-2 L" is developed. Two discrete methods namely: Dipeptide Composition and Tripeptide Composition were used to formulate NRPs sequences. Further, both the descriptor spaces were merged to construct hybrid space. Furthermore, feature selection technique minimum redundancy and maximum relevance was employed in order to select salient features as well as reduce the noise and redundancy. The experiential outcomes exhibited that the proposed model iNR-2 L achieved outstanding results. It is anticipated that the proposed computational model might be a practical and effective tool for academia and research community.
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Affiliation(s)
- Muhammad Kabir
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
| | - Saeed Ahmad
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Muhammad Iqbal
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
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17
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Prediction of membrane protein types by exploring local discriminative information from evolutionary profiles. Anal Biochem 2019; 564-565:123-132. [DOI: 10.1016/j.ab.2018.10.027] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Revised: 10/23/2018] [Accepted: 10/25/2018] [Indexed: 11/17/2022]
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18
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Sankari ES, Manimegalai D. Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC. J Theor Biol 2018; 455:319-328. [DOI: 10.1016/j.jtbi.2018.07.032] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 06/27/2018] [Accepted: 07/23/2018] [Indexed: 10/28/2022]
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Butt AH, Rasool N, Khan YD. Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC. Mol Biol Rep 2018; 45:2295-2306. [PMID: 30238411 DOI: 10.1007/s11033-018-4391-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 09/14/2018] [Indexed: 11/30/2022]
Abstract
For many biological functions membrane proteins (MPs) are considered crucial. Due to this nature of MPs, many pharmaceutical agents have reflected them as attractive targets. It bears indispensable importance that MPs are predicted with accurate measures using effective and efficient computational models (CMs). Annotation of MPs using in vitro analytical techniques is time-consuming and expensive; and in some cases, it can prove to be intractable. Due to this scenario, automated prediction and annotation of MPs through CM based techniques have appeared to be useful. Based on the use of computational intelligence and statistical moments based feature set, an MP prediction framework is proposed. Furthermore, the previously used dataset has been enhanced by incorporating new MPs from the latest release of UniProtKB. Rigorous experimentation proves that the use of statistical moments with a multilayer neural network, trained using back-propagation based prediction techniques allows more thorough results.
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Affiliation(s)
- Ahmad Hassan Butt
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, C-II, Johar Town, P.O. Box 10033, Lahore, 54770, Pakistan.
| | - Nouman Rasool
- Department of Life Sciences, School of Science, University of Management and Technology, C-II, Johar Town, P.O. Box 10033, Lahore, 54770, Pakistan
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, C-II, Johar Town, P.O. Box 10033, Lahore, 54770, Pakistan
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20
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Akbar S, Hayat M. iMethyl-STTNC: Identification of N 6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences. J Theor Biol 2018; 455:205-211. [PMID: 30031793 DOI: 10.1016/j.jtbi.2018.07.018] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 07/14/2018] [Accepted: 07/17/2018] [Indexed: 11/17/2022]
Abstract
N6- methyladenosine (m6A) is a vital post-transcriptional modification, which adds another layer of epigenetic regulation at RNA level. It chemically modifies mRNA that effects protein expression. RNA sequence contains many genetic code motifs (GAC). Among these codes, identification of methylated or not methylated GAC motif is highly indispensable. However, with a large number of RNA sequences generated in post-genomic era, it becomes a challenging task how to accurately and speedily characterize these sequences. In view of this, the concept of an intelligent is incorporated with a computational model that truly and fast reflects the motif of the desired classes. An intelligent computational model "iMethyl-STTNC" model is proposed for identification of methyladenosine sites in RNA. In the proposed study, four feature extraction techniques, such as; Pseudo-dinucleotide-composition, Pseudo-trinucleotide-composition, split-trinucleotide-composition, and split-tetra-nucleotides-composition (STTNC) are utilized for genuine numerical descriptors. Three different classification algorithms including probabilistic neural network, Support vector machine (SVM), and K-nearest neighbor are adopted for prediction. After examining the outcomes of prediction model on each feature spaces, SVM using STTNC feature space reported the highest accuracy of 69.84%, 91.84% on dataset1 and dataset2, respectively. The reported results show that our proposed predictor has achieved encouraging results compared to the present approaches, so far in the research. It is finally reckoned that our developed model might be beneficial for in-depth analysis of genomes and drug development.
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Affiliation(s)
- Shahid Akbar
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
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21
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iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition. J Theor Biol 2018; 442:11-21. [DOI: 10.1016/j.jtbi.2018.01.008] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 12/23/2017] [Accepted: 01/10/2018] [Indexed: 02/08/2023]
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22
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Sankari ES, Manimegalai D. Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets. J Theor Biol 2017; 435:208-217. [PMID: 28941868 DOI: 10.1016/j.jtbi.2017.09.018] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Revised: 09/15/2017] [Accepted: 09/18/2017] [Indexed: 12/19/2022]
Abstract
Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier.
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Affiliation(s)
- E Siva Sankari
- Department of CSE, Government College of Engineering, Tirunelveli, Tamil Nadu, India.
| | - D Manimegalai
- Department of IT, National Engineering College, Kovilpatti, Tamil Nadu, India.
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23
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Kumar R, Kumari B, Kumar M. Prediction of endoplasmic reticulum resident proteins using fragmented amino acid composition and support vector machine. PeerJ 2017; 5:e3561. [PMID: 28890846 PMCID: PMC5588793 DOI: 10.7717/peerj.3561] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 06/20/2017] [Indexed: 12/15/2022] Open
Abstract
Background The endoplasmic reticulum plays an important role in many cellular processes, which includes protein synthesis, folding and post-translational processing of newly synthesized proteins. It is also the site for quality control of misfolded proteins and entry point of extracellular proteins to the secretory pathway. Hence at any given point of time, endoplasmic reticulum contains two different cohorts of proteins, (i) proteins involved in endoplasmic reticulum-specific function, which reside in the lumen of the endoplasmic reticulum, called as endoplasmic reticulum resident proteins and (ii) proteins which are in process of moving to the extracellular space. Thus, endoplasmic reticulum resident proteins must somehow be distinguished from newly synthesized secretory proteins, which pass through the endoplasmic reticulum on their way out of the cell. Approximately only 50% of the proteins used in this study as training data had endoplasmic reticulum retention signal, which shows that these signals are not essentially present in all endoplasmic reticulum resident proteins. This also strongly indicates the role of additional factors in retention of endoplasmic reticulum-specific proteins inside the endoplasmic reticulum. Methods This is a support vector machine based method, where we had used different forms of protein features as inputs for support vector machine to develop the prediction models. During training leave-one-out approach of cross-validation was used. Maximum performance was obtained with a combination of amino acid compositions of different part of proteins. Results In this study, we have reported a novel support vector machine based method for predicting endoplasmic reticulum resident proteins, named as ERPred. During training we achieved a maximum accuracy of 81.42% with leave-one-out approach of cross-validation. When evaluated on independent dataset, ERPred did prediction with sensitivity of 72.31% and specificity of 83.69%. We have also annotated six different proteomes to predict the candidate endoplasmic reticulum resident proteins in them. A webserver, ERPred, was developed to make the method available to the scientific community, which can be accessed at http://proteininformatics.org/mkumar/erpred/index.html. Discussion We found that out of 124 proteins of the training dataset, only 66 proteins had endoplasmic reticulum retention signals, which shows that these signals are not an absolute necessity for endoplasmic reticulum resident proteins to remain inside the endoplasmic reticulum. This observation also strongly indicates the role of additional factors in retention of proteins inside the endoplasmic reticulum. Our proposed predictor, ERPred, is a signal independent tool. It is tuned for the prediction of endoplasmic reticulum resident proteins, even if the query protein does not contain specific ER-retention signal.
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Affiliation(s)
- Ravindra Kumar
- Department of Biophysics, University of Delhi South Campus, New Delhi, India.,Current affiliation: Newe-Ya'ar Research Center, Agricultural Research Organization, Ramat Yishay, Israel
| | - Bandana Kumari
- Department of Biophysics, University of Delhi South Campus, New Delhi, India
| | - Manish Kumar
- Department of Biophysics, University of Delhi South Campus, New Delhi, India
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24
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Tahir M, Hayat M. iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou's PseAAC. MOLECULAR BIOSYSTEMS 2017; 12:2587-93. [PMID: 27271822 DOI: 10.1039/c6mb00221h] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
The nucleosome is the fundamental unit of eukaryotic chromatin, which participates in regulating different cellular processes. Owing to the huge exploration of new DNA primary sequences, it is indispensable to develop an automated model. However, identification of novel protein sequences using conventional methods is difficult or sometimes impossible because of vague motifs and the intricate structure of DNA. In this regard, an effective and high throughput automated model "iNuc-STNC" has been proposed in order to identify accurately and reliably nucleosome positioning in genomes. In this proposed model, DNA sequences are expressed into three distinct feature extraction strategies containing dinucleotide composition, trinucleotide composition and split trinucleotide composition (STNC). Various statistical models were utilized as learner hypotheses. Jackknife test was employed to evaluate the success rates of the proposed model. The experiential results expressed that SVM, in combination with STNC, has obtained an outstanding performance on all benchmark datasets. The predicted outcomes of the proposed model "iNuc-STNC" is higher than current state of the art methods in the literature so far. It is ascertained that the "iNuc-STNC" model will provide a rudimentary framework for the pharmaceutical industry in the development of drug design.
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Affiliation(s)
- Muhammad Tahir
- Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan.
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan.
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25
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Tahir M, Hayat M, Kabir M. Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 146:69-75. [PMID: 28688491 DOI: 10.1016/j.cmpb.2017.05.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 05/05/2017] [Accepted: 05/19/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVES Enhancers are pivotal DNA elements, which are widely used in eukaryotes for activation of transcription genes. On the basis of enhancer strength, they are further classified into two groups; strong enhancers and weak enhancers. Due to high availability of huge amount of DNA sequences, it is needed to develop fast, reliable and robust intelligent computational method, which not only identify enhancers but also determines their strength. Considerable progress has been achieved in this regard; however, timely and precisely identification of enhancers is still a challenging task. METHODS Two-level intelligent computational model for identification of enhancers and their subgroups is proposed. Two different feature extraction techniques including di-nucleotide composition and tri-nucleotide composition were adopted for extraction of numerical descriptors. Four classification methods including probabilistic neural network, support vector machine, k-nearest neighbor and random forest were utilized for classification. RESULTS The proposed method yielded 77.25% of accuracy for dataset S1 contains enhancers and non-enhancers, whereas 64.70% of accuracy for dataset S2 comprises of strong enhancer and weak enhancer sequences using jackknife cross-validation test. CONCLUSION The predictive results validated that the proposed method is better than that of existing approaches so far reported in the literature. It is thus highly observed that the developed method will be useful and expedient for basic research and academia.
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Affiliation(s)
- Muhammad Tahir
- Department of Computer Science, Abdul Wali Khan University Mardan, KP Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, KP Pakistan.
| | - Muhammad Kabir
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
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26
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Akbar S, Hayat M, Iqbal M, Jan MA. iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space. Artif Intell Med 2017; 79:62-70. [PMID: 28655440 DOI: 10.1016/j.artmed.2017.06.008] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 06/12/2017] [Accepted: 06/16/2017] [Indexed: 01/10/2023]
Abstract
Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers.
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Affiliation(s)
- Shahid Akbar
- Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
| | - Muhammad Iqbal
- Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
| | - Mian Ahmad Jan
- Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
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27
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Khan M, Hayat M, Khan SA, Iqbal N. Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC. J Theor Biol 2017; 415:13-19. [DOI: 10.1016/j.jtbi.2016.12.004] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Revised: 10/24/2016] [Accepted: 12/07/2016] [Indexed: 01/22/2023]
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28
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Butt AH, Rasool N, Khan YD. A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes. J Membr Biol 2016; 250:55-76. [DOI: 10.1007/s00232-016-9937-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Accepted: 11/02/2016] [Indexed: 10/20/2022]
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29
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Wan S, Mak MW, Kung SY. Ensemble Linear Neighborhood Propagation for Predicting Subchloroplast Localization of Multi-Location Proteins. J Proteome Res 2016; 15:4755-4762. [DOI: 10.1021/acs.jproteome.6b00686] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Shibiao Wan
- Department
of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Man-Wai Mak
- Department
of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Sun-Yuan Kung
- Department
of Electrical Engineering, Princeton University, New Jersey 08540, United States
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30
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Qiu WR, Zheng QS, Sun BQ, Xiao X. Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou′s General PseAAC via Grey System Theory. Mol Inform 2016; 36. [DOI: 10.1002/minf.201600085] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 09/07/2016] [Indexed: 01/19/2023]
Affiliation(s)
- Wang-Ren Qiu
- Computer Department; Jingdezhen Ceramic Institute; Jingdezhen 333403 China
- Department of Computer Science; University of Missouri; Columbia, MO USA
- Bond Life Science Center; University of Missouri; Columbia, MO USA
| | - Quan-Shu Zheng
- Computer Department; Jingdezhen Ceramic Institute; Jingdezhen 333403 China
| | - Bi-Qian Sun
- Computer Department; Jingdezhen Ceramic Institute; Jingdezhen 333403 China
| | - Xuan Xiao
- Computer Department; Jingdezhen Ceramic Institute; Jingdezhen 333403 China
- Gordon Life Science Institute; Boston, Massachusetts 02478 United States of America
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31
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Ali F, Hayat M. Machine learning approaches for discrimination of Extracellular Matrix proteins using hybrid feature space. J Theor Biol 2016; 403:30-37. [DOI: 10.1016/j.jtbi.2016.05.011] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2015] [Revised: 05/02/2016] [Accepted: 05/03/2016] [Indexed: 01/12/2023]
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32
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Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.03.025] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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33
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Wan S, Mak MW, Kung SY. Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:706-718. [PMID: 26336143 DOI: 10.1109/tcbb.2015.2474407] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Membrane proteins play important roles in various biological processes within organisms. Predicting the functional types of membrane proteins is indispensable to the characterization of membrane proteins. Recent studies have extended to predicting single- and multi-type membrane proteins. However, existing predictors perform poorly and more importantly, they are often lack of interpretability. To address these problems, this paper proposes an efficient predictor, namely Mem-mEN, which can produce sparse and interpretable solutions for predicting membrane proteins with single- and multi-label functional types. Given a query membrane protein, its associated gene ontology (GO) information is retrieved by searching a compact GO-term database with its homologous accession number, which is subsequently classified by a multi-label elastic net (EN) classifier. Experimental results show that Mem-mEN significantly outperforms existing state-of-the-art membrane-protein predictors. Moreover, by using Mem-mEN, 338 out of more than 7,900 GO terms are found to play more essential roles in determining the functional types. Based on these 338 essential GO terms, Mem-mEN can not only predict the functional type of a membrane protein, but also explain why it belongs to that type. For the reader's convenience, the Mem-mEN server is available online at http://bioinfo.eie.polyu.edu.hk/MemmENServer/.
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34
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Ali S, Majid A, Javed SG, Sattar M. Can-CSC-GBE: Developing Cost-sensitive Classifier with Gentleboost Ensemble for breast cancer classification using protein amino acids and imbalanced data. Comput Biol Med 2016; 73:38-46. [DOI: 10.1016/j.compbiomed.2016.04.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Revised: 03/31/2016] [Accepted: 04/02/2016] [Indexed: 01/10/2023]
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35
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Wan S, Mak MW, Kung SY. Mem-ADSVM: A two-layer multi-label predictor for identifying multi-functional types of membrane proteins. J Theor Biol 2016; 398:32-42. [DOI: 10.1016/j.jtbi.2016.03.013] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Revised: 03/07/2016] [Accepted: 03/07/2016] [Indexed: 02/06/2023]
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36
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Protein subcellular localization of fluorescence microscopy images: Employing new statistical and Texton based image features and SVM based ensemble classification. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.01.064] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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37
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Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou’s General Pseudo Amino Acid Composition. J Membr Biol 2016; 249:551-7. [DOI: 10.1007/s00232-016-9904-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Accepted: 04/11/2016] [Indexed: 10/21/2022]
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38
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Hayat M, Tahir M. PSOFuzzySVM-TMH: identification of transmembrane helix segments using ensemble feature space by incorporated fuzzy support vector machine. MOLECULAR BIOSYSTEMS 2016; 11:2255-62. [PMID: 26054033 DOI: 10.1039/c5mb00196j] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Membrane protein is a central component of the cell that manages intra and extracellular processes. Membrane proteins execute a diversity of functions that are vital for the survival of organisms. The topology of transmembrane proteins describes the number of transmembrane (TM) helix segments and its orientation. However, owing to the lack of its recognized structures, the identification of TM helix and its topology through experimental methods is laborious with low throughput. In order to identify TM helix segments reliably, accurately, and effectively from topogenic sequences, we propose the PSOFuzzySVM-TMH model. In this model, evolutionary based information position specific scoring matrix and discrete based information 6-letter exchange group are used to formulate transmembrane protein sequences. The noisy and extraneous attributes are eradicated using an optimization selection technique, particle swarm optimization, from both feature spaces. Finally, the selected feature spaces are combined in order to form ensemble feature space. Fuzzy-support vector Machine is utilized as a classification algorithm. Two benchmark datasets, including low and high resolution datasets, are used. At various levels, the performance of the PSOFuzzySVM-TMH model is assessed through 10-fold cross validation test. The empirical results reveal that the proposed framework PSOFuzzySVM-TMH outperforms in terms of classification performance in the examined datasets. It is ascertained that the proposed model might be a useful and high throughput tool for academia and research community for further structure and functional studies on transmembrane proteins.
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Affiliation(s)
- Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan.
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39
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Robust segmentation and intelligent decision system for cerebrovascular disease. Med Biol Eng Comput 2016; 54:1903-1920. [DOI: 10.1007/s11517-016-1481-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Accepted: 02/28/2016] [Indexed: 12/15/2022]
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40
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Kabir M, Hayat M. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Mol Genet Genomics 2015; 291:285-96. [DOI: 10.1007/s00438-015-1108-5] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Accepted: 08/19/2015] [Indexed: 10/23/2022]
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41
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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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42
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Khan ZU, Hayat M, Khan MA. Discrimination of acidic and alkaline enzyme using Chou’s pseudo amino acid composition in conjunction with probabilistic neural network model. J Theor Biol 2015; 365:197-203. [DOI: 10.1016/j.jtbi.2014.10.014] [Citation(s) in RCA: 110] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Revised: 09/09/2014] [Accepted: 10/11/2014] [Indexed: 12/11/2022]
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43
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HBC-Evo: predicting human breast cancer by exploiting amino acid sequence-based feature spaces and evolutionary ensemble system. Amino Acids 2014; 47:217-21. [DOI: 10.1007/s00726-014-1871-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2014] [Accepted: 11/04/2014] [Indexed: 10/24/2022]
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44
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Hayat M, Iqbal N. Discriminating protein structure classes by incorporating Pseudo Average Chemical Shift to Chou's general PseAAC and Support Vector Machine. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 116:184-192. [PMID: 24997484 DOI: 10.1016/j.cmpb.2014.06.007] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Revised: 06/09/2014] [Accepted: 06/13/2014] [Indexed: 06/03/2023]
Abstract
Proteins control all biological functions in living species. Protein structure is comprised of four major classes including all-α class, all-β class, α+β, and α/β. Each class performs different function according to their nature. Owing to the large exploration of protein sequences in the databanks, the identification of protein structure classes is difficult through conventional methods with respect to cost and time. Looking at the importance of protein structure classes, it is thus highly desirable to develop a computational model for discriminating protein structure classes with high accuracy. For this purpose, we propose a silco method by incorporating Pseudo Average Chemical Shift and Support Vector Machine. Two feature extraction schemes namely Pseudo Amino Acid Composition and Pseudo Average Chemical Shift are used to explore valuable information from protein sequences. The performance of the proposed model is assessed using four benchmark datasets 25PDB, 1189, 640 and 399 employing jackknife test. The success rates of the proposed model are 84.2%, 85.0%, 86.4%, and 89.2%, respectively on the four datasets. The empirical results reveal that the performance of our proposed model compared to existing models is promising in the literature so far and might be useful for future research.
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Affiliation(s)
- Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
| | - Nadeem Iqbal
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
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45
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DNA-LCEB: a high-capacity and mutation-resistant DNA data-hiding approach by employing encryption, error correcting codes, and hybrid twofold and fourfold codon-based strategy for synonymous substitution in amino acids. Med Biol Eng Comput 2014; 52:945-961. [PMID: 25195035 DOI: 10.1007/s11517-014-1194-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2013] [Accepted: 08/25/2014] [Indexed: 10/24/2022]
Abstract
Data-hiding in deoxyribonucleic acid (DNA) sequences can be used to develop an organic memory and to track parent genes in an offspring as well as in genetically modified organism. However, the main concerns regarding data-hiding in DNA sequences are the survival of organism and successful extraction of watermark from DNA. This implies that the organism should live and reproduce without any functional disorder even in the presence of the embedded data. Consequently, performing synonymous substitution in amino acids for watermarking becomes a primary option. In this regard, a hybrid watermark embedding strategy that employs synonymous substitution in both twofold and fourfold codons of amino acids is proposed. This work thus presents a high-capacity and mutation-resistant watermarking technique, DNA-LCEB, for hiding secret information in DNA of living organisms. By employing the different types of synonymous codons of amino acids, the data storage capacity has been significantly increased. It is further observed that the proposed DNA-LCEB employing a combination of synonymous substitution, lossless compression, encryption, and Bose-Chaudary-Hocquenghem coding is secure and performs better in terms of both capacity and robustness compared to existing DNA data-hiding schemes. The proposed DNA-LCEB is tested against different mutations, including silent, miss-sense, and non-sense mutations, and provides substantial improvement in terms of mutation detection/correction rate and bits per nucleotide. A web application for DNA-LCEB is available at http://111.68.99.218/DNA-LCEB.
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Prediction of multi-type membrane proteins in human by an integrated approach. PLoS One 2014; 9:e93553. [PMID: 24676214 PMCID: PMC3968155 DOI: 10.1371/journal.pone.0093553] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2013] [Accepted: 03/05/2014] [Indexed: 11/29/2022] Open
Abstract
Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computational tools predicting membrane protein types have been developed, most of them can only recognize one kind of type. Therefore, they are not as effective as one membrane protein can have several types at the same time. To our knowledge, few methods handling multiple types of membrane proteins were reported. In this study, we proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network. As a result, the prediction accuracies reached 87.65%, 81.39% and 70.79%, respectively, by the leave-one-out test on three datasets. It outperformed the nearest neighbor algorithm adopting pseudo amino acid composition. The method is anticipated to be an alternative tool for identifying membrane protein types. New metrics for evaluating performances of methods dealing with multi-label problems were also presented. The program of the method is available upon request.
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47
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Ali S, Majid A, Khan A. IDM-PhyChm-Ens: intelligent decision-making ensemble methodology for classification of human breast cancer using physicochemical properties of amino acids. Amino Acids 2014; 46:977-93. [PMID: 24390396 DOI: 10.1007/s00726-013-1659-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2013] [Accepted: 12/20/2013] [Indexed: 12/21/2022]
Abstract
Development of an accurate and reliable intelligent decision-making method for the construction of cancer diagnosis system is one of the fast growing research areas of health sciences. Such decision-making system can provide adequate information for cancer diagnosis and drug discovery. Descriptors derived from physicochemical properties of protein sequences are very useful for classifying cancerous proteins. Recently, several interesting research studies have been reported on breast cancer classification. To this end, we propose the exploitation of the physicochemical properties of amino acids in protein primary sequences such as hydrophobicity (Hd) and hydrophilicity (Hb) for breast cancer classification. Hd and Hb properties of amino acids, in recent literature, are reported to be quite effective in characterizing the constituent amino acids and are used to study protein foldings, interactions, structures, and sequence-order effects. Especially, using these physicochemical properties, we observed that proline, serine, tyrosine, cysteine, arginine, and asparagine amino acids offer high discrimination between cancerous and healthy proteins. In addition, unlike traditional ensemble classification approaches, the proposed 'IDM-PhyChm-Ens' method was developed by combining the decision spaces of a specific classifier trained on different feature spaces. The different feature spaces used were amino acid composition, split amino acid composition, and pseudo amino acid composition. Consequently, we have exploited different feature spaces using Hd and Hb properties of amino acids to develop an accurate method for classification of cancerous protein sequences. We developed ensemble classifiers using diverse learning algorithms such as random forest (RF), support vector machines (SVM), and K-nearest neighbor (KNN) trained on different feature spaces. We observed that ensemble-RF, in case of cancer classification, performed better than ensemble-SVM and ensemble-KNN. Our analysis demonstrates that ensemble-RF, ensemble-SVM and ensemble-KNN are more effective than their individual counterparts. The proposed 'IDM-PhyChm-Ens' method has shown improved performance compared to existing techniques.
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Affiliation(s)
- Safdar Ali
- Department of Computer and Information Sciences, Pakistan Institute of Engineering, and Applied Sciences, Nilore, Islamabad, 45650, Pakistan,
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48
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Protein subcellular localization in human and hamster cell lines: Employing local ternary patterns of fluorescence microscopy images. J Theor Biol 2014; 340:85-95. [DOI: 10.1016/j.jtbi.2013.08.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Revised: 07/09/2013] [Accepted: 08/15/2013] [Indexed: 11/21/2022]
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49
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Hayat M, Tahir M, Khan SA. Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces. J Theor Biol 2013; 346:8-15. [PMID: 24384128 DOI: 10.1016/j.jtbi.2013.12.015] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2013] [Revised: 10/30/2013] [Accepted: 12/12/2013] [Indexed: 11/28/2022]
Abstract
Proteins are the executants of biological functions in living organisms. Comprehension of protein structure is a challenging problem in the era of proteomics, computational biology, and bioinformatics because of its pivotal role in protein folding patterns. Owing to the large exploration of protein sequences in protein databanks and intricacy of protein structures, experimental and theoretical methods are insufficient for prediction of protein structure classes. Therefore, it is highly desirable to develop an accurate, reliable, and high throughput computational model to predict protein structure classes correctly from polygenetic sequences. In this regard, we propose a promising model employing hybrid descriptor space in conjunction with optimized evidence-theoretic K-nearest neighbor algorithm. Hybrid space is the composition of two descriptor spaces including Multi-profile Bayes and bi-gram probability. In order to enhance the generalization power of the classifier, we have selected high discriminative descriptors from the hybrid space using particle swarm optimization, a well-known evolutionary feature selection technique. Performance evaluation of the proposed model is performed using the jackknife test on three low similarity benchmark datasets including 25PDB, 1189, and 640. The success rates of the proposed model are 87.0%, 86.6%, and 88.4%, respectively on the three benchmark datasets. The comparative analysis exhibits that our proposed model has yielded promising results compared to the existing methods in the literature. In addition, our proposed prediction system might be helpful in future research particularly in cases where the major focus of research is on low similarity datasets.
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Affiliation(s)
- Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
| | - Muhammad Tahir
- Department of Computer Science, National University of Computer and Emerging Science, Peshawar, Pakistan
| | - Sher Afzal Khan
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
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50
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Hayat M, Khan A. WRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids. Amino Acids 2013; 44:1317-28. [PMID: 23494269 DOI: 10.1007/s00726-013-1466-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2012] [Accepted: 01/23/2013] [Indexed: 02/05/2023]
Abstract
Membrane protein is the prime constituent of a cell, which performs a role of mediator between intra and extracellular processes. The prediction of transmembrane (TM) helix and its topology provides essential information regarding the function and structure of membrane proteins. However, prediction of TM helix and its topology is a challenging issue in bioinformatics and computational biology due to experimental complexities and lack of its established structures. Therefore, the location and orientation of TM helix segments are predicted from topogenic sequences. In this regard, we propose WRF-TMH model for effectively predicting TM helix segments. In this model, information is extracted from membrane protein sequences using compositional index and physicochemical properties. The redundant and irrelevant features are eliminated through singular value decomposition. The selected features provided by these feature extraction strategies are then fused to develop a hybrid model. Weighted random forest is adopted as a classification approach. We have used two benchmark datasets including low and high-resolution datasets. tenfold cross validation is employed to assess the performance of WRF-TMH model at different levels including per protein, per segment, and per residue. The success rates of WRF-TMH model are quite promising and are the best reported so far on the same datasets. It is observed that WRF-TMH model might play a substantial role, and will provide essential information for further structural and functional studies on membrane proteins. The accompanied web predictor is accessible at http://111.68.99.218/WRF-TMH/ .
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