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Jia J, Wu G, Li M. iGly-IDN: Identifying Lysine Glycation Sites in Proteins Based on Improved DenseNet. J Comput Biol 2024; 31:161-174. [PMID: 38016151 DOI: 10.1089/cmb.2023.0112] [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] [Indexed: 11/30/2023] Open
Abstract
Lysine glycation is one of the most significant protein post-translational modifications, which changes the properties of the proteins and causes them to be dysfunctional. Accurately identifying glycation sites helps to understand the biological function and potential mechanism of glycation in disease treatments. Nonetheless, the experimental methods are ordinarily inefficient and costly, so effective computational methods need to be developed. In this study, we proposed the new model called iGly-IDN based on the improved densely connected convolutional networks (DenseNet). First, one hot encoding was adopted to obtain the original feature maps. Afterward, the improved DenseNet was adopted to capture feature information with the importance degrees during the feature learning. According to the experimental results, Acc reaches 66%, and Mathews correlation coefficient reaches 0.33 on the independent testing data set, which indicates that the iGly-IDN can provide more effective glycation site identification than the current predictors.
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Affiliation(s)
- Jianhua Jia
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
| | - Genqiang Wu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
- College of Modern Economics and Management, Jiangxi University of Finance and Economics, Nanchang, China
| | - Meifang Li
- School of Computer Information Engineering, Nanchang Institute of Technology, Nanchang, China
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2
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Ahmed F, Dehzangi I, Hasan MM, Shatabda S. Accurately predicting microbial phosphorylation sites using evolutionary and structural features. Gene 2023; 851:146993. [DOI: 10.1016/j.gene.2022.146993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/05/2022] [Accepted: 10/14/2022] [Indexed: 11/27/2022]
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3
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MethEvo: an accurate evolutionary information-based methylation site predictor. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07738-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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4
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Que-Salinas U, Martinez-Peon D, Reyes-Figueroa AD, Ibarra I, Scheckhuber CQ. On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks. SENSORS (BASEL, SWITZERLAND) 2022; 22:5237. [PMID: 35890916 PMCID: PMC9324327 DOI: 10.3390/s22145237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/08/2022] [Accepted: 07/11/2022] [Indexed: 06/15/2023]
Abstract
One of the hallmarks of diabetes is an increased modification of cellular proteins. The most prominent type of modification stems from the reaction of methylglyoxal with arginine and lysine residues, leading to structural and functional impairments of target proteins. For lysine glycation, several algorithms allow a prediction of occurrence; thus, making it possible to pinpoint likely targets. However, according to our knowledge, no approaches have been published for predicting the likelihood of arginine glycation. There are indications that arginine and not lysine is the most prominent target for the toxic dialdehyde. One of the reasons why there is no arginine glycation predictor is the limited availability of quantitative data. Here, we used a recently published high-quality dataset of arginine modification probabilities to employ an artificial neural network strategy. Despite the limited data availability, our results achieve an accuracy of about 75% of correctly predicting the exact value of the glycation probability of an arginine-containing peptide without setting thresholds upon whether it is decided if a given arginine is modified or not. This contribution suggests a solution for predicting arginine glycation of short peptides.
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Affiliation(s)
- Ulices Que-Salinas
- Centro de Ciencias de la Tierra, Universidad Veracruzana, Xalapa 91090, VER, Mexico;
| | - Dulce Martinez-Peon
- Department of Electrical and Electronic Engineering, National Technological Institute of Mexico/IT, Monterrey 67170, NL, Mexico;
| | - Angel D. Reyes-Figueroa
- Consejo Nacional de Ciencia y Tecnología, Av. Insurgentes Sur 1582, Col. Crédito Constructor, Benito Juárez, Mexico City 03940, DF, Mexico;
- Centro de Investigación en Matemáticas Unidad Monterrey, Parque de Investigación e Innovación Tecnológica (PIIT), Av. Alianza Centro No. 502, Apodaca 66628, NL, Mexico
| | - Ivonne Ibarra
- Independent Researcher, Monterrey 66620, NL, Mexico;
| | - Christian Quintus Scheckhuber
- Departamento de Bioingeniería, Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey 64849, NL, Mexico
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5
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Suresh SA, Ethiraj S, Rajnish KN. A systematic review of recent trends in research on therapeutically significant L-asparaginase and acute lymphoblastic leukemia. Mol Biol Rep 2022; 49:11281-11287. [PMID: 35816224 DOI: 10.1007/s11033-022-07688-4] [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] [Received: 03/03/2022] [Accepted: 06/08/2022] [Indexed: 12/01/2022]
Abstract
L-asparaginases are mostly obtained from bacterial sources for their application in the therapy and food industry. Bacterial L-asparaginases are employed in the treatment of Acute Lymphoblastic Leukemia (ALL) and its subtypes, a type of blood and bone marrow cancer that results in the overproduction of immature blood cells. It also plays a role in the food industry in reducing the acrylamide formed during baking, roasting, and frying starchy foods. This importance of the enzyme makes it to be of constant interest to the researchers to isolate novel sources. Presently L-asparaginases from E. coli native and PEGylated form, Dickeya chrysanthemi (Erwinia chrysanthemi) are in the treatment regime. In therapy, the intrinsic glutaminase activity of the enzyme is a major drawback as the patients in treatment experience side effects like fever, skin rashes, anaphylaxis, pancreatitis, steatosis in the liver, and many complications. Its significance in the food industry in mitigating acrylamide is also a major reason. Acrylamide, a potent carcinogen was formed when treating starchy foods at higher temperatures. Acrylamide content in food was analyzed and pre-treatment was considered a valuable option. Immobilization of the enzyme is an advancing and promising technique in the effective delivery of the enzyme than in free form. The concept of machine learning by employing the Artificial Network and Genetic Algorithm has paved the way to optimize the production of L-asparaginase from its sources. Gene-editing tools are gaining momentum in the study of several diseases and this review focuses on the CRISPR-Cas9 gene-editing tool in ALL.
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Affiliation(s)
| | | | - K N Rajnish
- SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
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6
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Kang Q, Meng J, Luan Y. RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction. Brief Bioinform 2022; 23:6555402. [PMID: 35352114 DOI: 10.1093/bib/bbac107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/22/2022] [Accepted: 03/02/2022] [Indexed: 11/12/2022] Open
Abstract
Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experiments can confirm RNA-RNA interactions to facilitate the exploration of their biological functions, but they are expensive and time-consuming. Machine learning models can predict potential RNA-RNA interactions, which provide candidates for molecular biology experiments to save a lot of time and cost. Using a set of suitable features to represent the sample is crucial for training powerful models, but there is a lack of effective feature representation for RNA-RNA interaction. This study proposes a novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction (named RNAI-FRID). Diverse base features are first extracted from RNA data to contain more sample information. Then, the extracted base features are used to construct the complex features through an arithmetic-level method. It greatly reduces the feature dimension while keeping the relationship between molecule features. Since the dimension reduction may cause information loss, in the process of complex feature construction, the arithmetic mean strategy is adopted to enhance the sample information further. Finally, three feature ranking methods are integrated for feature selection on constructed complex features. It can adaptively retain important features and remove redundant ones. Extensive experiment results show that RNAI-FRID can provide reliable feature representation for RNA-RNA interaction with higher efficiency and the model trained with generated features obtain better performance than other deep neural network predictors.
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Affiliation(s)
- Qiang Kang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116024, China
| | - Jun Meng
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116024, China
| | - Yushi Luan
- School of Bioengineering, Dalian University of Technology, Dalian, Liaoning, 116024, China
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7
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Liu Y, Liu Y, Wang GA, Cheng Y, Bi S, Zhu X. BERT-Kgly: A Bidirectional Encoder Representations From Transformers (BERT)-Based Model for Predicting Lysine Glycation Site for Homo sapiens. FRONTIERS IN BIOINFORMATICS 2022; 2:834153. [PMID: 36304324 PMCID: PMC9580886 DOI: 10.3389/fbinf.2022.834153] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/20/2022] [Indexed: 12/21/2022] Open
Abstract
As one of the most important posttranslational modifications (PTMs), protein lysine glycation changes the characteristics of the proteins and leads to the dysfunction of the proteins, which may cause diseases. Accurately detecting the glycation sites is of great benefit for understanding the biological function and potential mechanism of glycation in the treatment of diseases. However, experimental methods are expensive and time-consuming for lysine glycation site identification. Instead, computational methods, with their higher efficiency and lower cost, could be an important supplement to the experimental methods. In this study, we proposed a novel predictor, BERT-Kgly, for protein lysine glycation site prediction, which was developed by extracting embedding features of protein segments from pretrained Bidirectional Encoder Representations from Transformers (BERT) models. Three pretrained BERT models were explored to get the embeddings with optimal representability, and three downstream deep networks were employed to build our models. Our results showed that the model based on embeddings extracted from the BERT model pretrained on 556,603 protein sequences of UniProt outperforms other models. In addition, an independent test set was used to evaluate and compare our model with other existing methods, which indicated that our model was superior to other existing models.
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Affiliation(s)
| | | | | | | | - Shoudong Bi
- *Correspondence: Shoudong Bi, ; Xiaolei Zhu,
| | - Xiaolei Zhu
- *Correspondence: Shoudong Bi, ; Xiaolei Zhu,
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8
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Taherzadeh G, Campbell M, Zhou Y. Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins. Methods Mol Biol 2022; 2499:177-186. [PMID: 35696081 DOI: 10.1007/978-1-0716-2317-6_9] [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] [Indexed: 06/15/2023]
Abstract
Protein glycosylation is one of the most complex posttranslational modifications (PTM) that play a fundamental role in protein function. Identification and annotation of these sites using experimental approaches are challenging and time consuming. Hence, there is a demand to build fast and efficient computational methods to address this problem. Here, we present the SPRINT-Gly framework containing the largest dataset and a prediction model of glycosylation sites for a given protein sequence. In this framework, we construct a large dataset containing N- and O-linked glycosylation sites of human and mouse proteins, collected from different sources. We then introduce the SPRINT-Gly method to predict putative N- and O-linked sites. SPRINT-Gly is a machine learning-based approach consisting of a number of trained predictive models for glycosylation sites in both human and mouse proteins, separately. The method is built by incorporating sequence-based, predicted structural, and physicochemical information of the neighboring residues of each N- and O-linked glycosylation site and by training deep learning neural network and support vector machine as classifiers. SPRINT-Gly outperformed other existing methods by achieving 18% and 50% higher Matthew's correlation coefficient for N- and O-linked glycosylation site prediction, respectively. SPRINT-Gly is publicly available as an online and stand-alone predictor at https://sparks-lab.org/server/sprint-gly/ .
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Affiliation(s)
- Ghazaleh Taherzadeh
- Department of Mathematics and Computer Science, Wilkes University, Wilkes-Barre, PA, USA.
| | - Matthew Campbell
- Institute for Glycomics, Griffith University, Southport, QLD, Australia
| | - Yaoqi Zhou
- Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China
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9
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Dehzangi I, Sharma A, Shatabda S. iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features. Methods Mol Biol 2022; 2499:125-134. [PMID: 35696077 DOI: 10.1007/978-1-0716-2317-6_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: 06/15/2023]
Abstract
Posttranslational modification (PTM) is an important biological mechanism to promote functional diversity among the proteins. So far, a wide range of PTMs has been identified. Among them, glycation is considered as one of the most important PTMs. Glycation is associated with different neurological disorders including Parkinson and Alzheimer. It is also shown to be responsible for different diseases, including vascular complications of diabetes mellitus. Despite all the efforts have been made so far, the prediction performance of glycation sites using computational methods remains limited. Here we present a newly developed machine learning tool called iProtGly-SS that utilizes sequential and structural information as well as Support Vector Machine (SVM) classifier to enhance lysine glycation site prediction accuracy. The performance of iProtGly-SS was investigated using the three most popular benchmarks used for this task. Our results demonstrate that iProtGly-SS is able to achieve 81.61%, 93.62%, and 92.95% prediction accuracies on these benchmarks, which are significantly better than those results reported in the previous studies. iProtGly-SS is implemented as a web-based tool which is publicly available at http://brl.uiu.ac.bd/iprotgly-ss/ .
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Affiliation(s)
- Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, USA.
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia.
- Department of Medical Science Mathematics, Tokyo Medical and Dental University (TMDU), Tokyo, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.
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10
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Azim SM, Haque MR, Shatabda S. OriC-ENS: A sequence-based ensemble classifier for predicting origin of replication in S. cerevisiae. Comput Biol Chem 2021; 92:107502. [PMID: 33962169 DOI: 10.1016/j.compbiolchem.2021.107502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/21/2021] [Indexed: 01/08/2023]
Abstract
DNA Replication plays the most crucial part in biological inheritance, ensuring an even flow of genetic information from parent to offspring. The beginning site of DNA Replication which is called the Origin of Replication (ORI), plays a significant role in understanding the molecular mechanisms and genomic analysis of DNA. Hence, it is paramount to accurately identify the origin of replication to gain a more accurate understanding of the biochemical and genomic properties of DNA. In this paper, We have proposed a new approach named OriC-ENS that uses sequence-based feature extraction techniques, K-mer, K-gapped Mono-Di, and Di Mono, and an ensemble classification technique that uses majority voting for the identification of Origin of Replication. We have used three SVM classifiers, one for the K-mer features and two more for K-Gapped Mono-Di and K-Gapped Di-mono features. Finally, we used majority voting to combine the prediction by each predictor. Experimental results on the S. Cerevisiae dataset have shown that our method achieves an accuracy of 91.62 % which outperforms other state-of-the-art methods by a significant margin. We have also tested our method using other evaluation metrics such as Matthews Correlation Coefficient (MCC), Area Under Curve(AUC), Sensitivity, and Specificity, where it has achieved a score of 0.83, 0.98, 0.90, and 0.92 respectively. We have further evaluated our model on an independent test set collected from OriDB, consisting of the sequences of Schizosaccharomyces pombe where we have seen that our model can predict the origin of replication efficiently and with great precision. We have made our python-based source code available at https://github.com/MehediAzim/OriC-ENS.
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Affiliation(s)
- Sayed Mehedi Azim
- Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Badda, Dhaka, 1212, Bangladesh
| | - Md Rakibul Haque
- Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Badda, Dhaka, 1212, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Badda, Dhaka, 1212, Bangladesh.
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11
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Convolutional neural networks with image representation of amino acid sequences for protein function prediction. Comput Biol Chem 2021; 92:107494. [PMID: 33930742 DOI: 10.1016/j.compbiolchem.2021.107494] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/21/2021] [Indexed: 01/11/2023]
Abstract
Proteins are one of the most important molecules that govern the cellular processes in most of the living organisms. Various functions of the proteins are of paramount importance to understand the basics of life. Several supervised learning approaches are applied in this field to predict the functionality of proteins. In this paper, we propose a convolutional neural network based approach ProtConv to predict the functionality of proteins by converting the amino-acid sequences to a two dimensional image. We have used a protein embedding technique using transfer learning to generate the feature vector. Feature vector is then converted into a square sized single channel image to be fed into a convolutional network. The neural network architecture used here is a combination of convolutional filters and average pooling layers followed by dense fully connected layers to predict a binary function. We have performed experiments on standard benchmark datasets taken from two very important protein function prediction task: proinflammatory cytokines and anticancer peptides. Our experiments show that the proposed method, ProtConv achieves state-of-the-art performances on both of the datasets. All necessary details about implementation with source code and datasets are made available at: https://github.com/swakkhar/ProtConv.
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12
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Haque HMF, Rafsanjani M, Arifin F, Adilina S, Shatabda S. SubFeat: Feature subspacing ensemble classifier for function prediction of DNA, RNA and protein sequences. Comput Biol Chem 2021; 92:107489. [PMID: 33932779 DOI: 10.1016/j.compbiolchem.2021.107489] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 03/07/2021] [Accepted: 04/19/2021] [Indexed: 11/16/2022]
Abstract
The information of a cell is primarily contained in deoxyribonucleic acid (DNA). There is a flow of DNA information to protein sequences via ribonucleic acids (RNA) through transcription and translation. These entities are vital for the genetic process. Recent epigenetics developments also show the importance of the genetic material and knowledge of their attributes and functions. However, the growth in these entities' available features or functionalities is still slow due to the time-consuming and expensive in vitro experimental methods. In this paper, we have proposed an ensemble classification algorithm called SubFeat to predict biological entities' functionalities from different types of datasets. Our model uses a feature subspace-based novel ensemble method. It divides the feature space into sub-spaces, which are then passed to learn individual classifier models. The ensemble is built on these base classifiers that use a weighted majority voting mechanism. SubFeat tested on four datasets comprising two DNA, one RNA, and one protein dataset, and it outperformed all the existing single classifiers and the ensemble classifiers. SubFeat is made available as a Python-based tool. We have made the package SubFeat available online along with a user manual. It is freely accessible from here: https://github.com/fazlulhaquejony/SubFeat.
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Affiliation(s)
- H M Fazlul Haque
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
| | - Muhammod Rafsanjani
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
| | - Fariha Arifin
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
| | - Sheikh Adilina
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh.
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13
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Yang Y, Wang H, Li W, Wang X, Wei S, Liu Y, Xu Y. Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks. BMC Bioinformatics 2021; 22:171. [PMID: 33789579 PMCID: PMC8010967 DOI: 10.1186/s12859-021-04101-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 03/23/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein's function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins. METHOD We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories. RESULTS In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN . CONCLUSIONS The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.
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Affiliation(s)
- Yingxi Yang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Hui Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China
| | - Wen Li
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xiaobo Wang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Shizhao Wei
- No. 15 Research Institute, China Electronics Technology Group Corporation, Beijing, 100083, China
| | - Yulong Liu
- No. 15 Research Institute, China Electronics Technology Group Corporation, Beijing, 100083, China
| | - Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China.
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14
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Dipta SR, Taherzadeh G, Ahmad MW, Arafat ME, Shatabda S, Dehzangi A. SEMal: Accurate protein malonylation site predictor using structural and evolutionary information. Comput Biol Med 2020; 125:104022. [PMID: 33022522 DOI: 10.1016/j.compbiomed.2020.104022] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 09/24/2020] [Accepted: 09/25/2020] [Indexed: 10/23/2022]
Abstract
Post Transactional Modification (PTM) is a vital process which plays an important role in a wide range of biological interactions. One of the most recently identified PTMs is Malonylation. It has been shown that Malonylation has an important impact on different biological pathways including glucose and fatty acid metabolism. Malonylation can be detected experimentally using mass spectrometry. However, this process is both costly and time-consuming which has inspired research to find more efficient and fast computational methods to solve this problem. This paper proposes a novel approach, called SEMal, to identify Malonylation sites in protein sequences. It uses both structural and evolutionary-based features to solve this problem. It also uses Rotation Forest (RoF) as its classification technique to predict Malonylation sites. To the best of our knowledge, our extracted features as well as our employed classifier have never been used for this problem. Compared to the previously proposed methods, SEMal outperforms them in all metrics such as sensitivity (0.94 and 0.89), accuracy (0.94 and 0.91), and Matthews correlation coefficient (0.88 and 0.82), for Homo Sapiens and Mus Musculus species, respectively. SEMal is publicly available as an online predictor at: http://brl.uiu.ac.bd/SEMal/.
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Affiliation(s)
- Shubhashis Roy Dipta
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Ghazaleh Taherzadeh
- Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD, 20742, USA
| | - Md Wakil Ahmad
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Md Easin Arafat
- Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, 08102, USA; Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08102, USA.
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15
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Wang YG, Huang SY, Wang LN, Zhou ZY, Qiu JD. Accurate prediction of species-specific 2-hydroxyisobutyrylation sites based on machine learning frameworks. Anal Biochem 2020; 602:113793. [DOI: 10.1016/j.ab.2020.113793] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 04/25/2020] [Accepted: 05/20/2020] [Indexed: 12/17/2022]
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16
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AHMAD WAKIL, ARAFAT EASIN, TAHERZADEH GHAZALEH, SHARMA ALOK, DIPTA SHUBHASHISROY, DEHZANGI ABDOLLAH, SHATABDA SWAKKHAR. Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:77888-77902. [PMID: 33354488 PMCID: PMC7751949 DOI: 10.1109/access.2020.2989713] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/).
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Affiliation(s)
- WAKIL AHMAD
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
| | - EASIN ARAFAT
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
| | - GHAZALEH TAHERZADEH
- Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD, 20742, USA
| | - ALOK SHARMA
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD-4111, Australia
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Kanagawa, Japan
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji
- CREST, JST, Tokyo, 102-8666, Japan
| | - SHUBHASHIS ROY DIPTA
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
| | - ABDOLLAH DEHZANGI
- Department of Computer Science, Morgan State University, Baltimore, MD, 21251, USA
| | - SWAKKHAR SHATABDA
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
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Delmar JA, Wang J, Choi SW, Martins JA, Mikhail JP. Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate. MOLECULAR THERAPY-METHODS & CLINICAL DEVELOPMENT 2019; 15:264-274. [PMID: 31890727 PMCID: PMC6923510 DOI: 10.1016/j.omtm.2019.09.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 09/16/2019] [Indexed: 12/20/2022]
Abstract
The spontaneous conversion of asparagine residues to aspartic acid or iso-aspartic acid, via deamidation, is a major pathway of protein degradation and is often seriously disruptive to biological systems. Deamidation has been shown to negatively affect both in vitro stability and in vivo biological function of diverse classes of proteins. During protein therapeutics development, deamidation liabilities that are overlooked necessitate expensive and time-consuming remediation strategies, sometimes leading to termination of the project. In this paper, we apply machine learning to a large (n = 776) liquid chromatography-tandem mass spectrometry (LC-MS/MS) dataset of monoclonal antibody peptides to create computational models for the post-translational modification asparagine deamidation, using the random decision forest method. We show that our categorical model predicts antibody deamidation with nearly 5% increased accuracy and 0.2 MCC over the best currently available models. Surprisingly, our model also paces or outperforms advanced and conventional models on an independent non-antibody dataset. In addition to deamidation probability, we are able to accurately predict deamidation rate (R2 = 0.963 and Q2 = 0.822), a capability with no peer in current models. This method should enable significant improvement in protein candidate selection, especially in biopharmaceutical development, and can be applied with similar accuracy to enzymes, monoclonal antibodies, next-generation formats, vaccine component antigens, and gene therapy vectors such as adeno-associated virus.
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Affiliation(s)
- Jared A Delmar
- Analytical Sciences, Biopharmaceutical Development, AstraZeneca, One MedImmune Way, Gaithersburg, MD 20878, USA
| | - Jihong Wang
- Analytical Sciences, Biopharmaceutical Development, AstraZeneca, One MedImmune Way, Gaithersburg, MD 20878, USA
| | - Seo Woo Choi
- David H. Koch School of Chemical Engineering Practice, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jason A Martins
- David H. Koch School of Chemical Engineering Practice, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - John P Mikhail
- David H. Koch School of Chemical Engineering Practice, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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18
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Ahmad A, Shatabda S. EPAI-NC: Enhanced prediction of adenosine to inosine RNA editing sites using nucleotide compositions. Anal Biochem 2019; 569:16-21. [DOI: 10.1016/j.ab.2019.01.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 01/03/2019] [Accepted: 01/11/2019] [Indexed: 01/24/2023]
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19
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Reddy HM, Sharma A, Dehzangi A, Shigemizu D, Chandra AA, Tsunoda T. GlyStruct: glycation prediction using structural properties of amino acid residues. BMC Bioinformatics 2019; 19:547. [PMID: 30717650 PMCID: PMC7394324 DOI: 10.1186/s12859-018-2547-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 11/29/2018] [Indexed: 02/06/2023] Open
Abstract
Background Glycation is a one of the post-translational modifications (PTM) where sugar molecules and residues in protein sequences are covalently bonded. It has become one of the clinically important PTM in recent times attributed to many chronic and age related complications. Being a non-enzymatic reaction, it is a great challenge when it comes to its prediction due to the lack of significant bias in the sequence motifs. Results We developed a classifier, GlyStruct based on support vector machine, to predict glycated and non-glycated lysine residues using structural properties of amino acid residues. The features used were secondary structure, accessible surface area and the local backbone torsion angles. For this work, a benchmark dataset was extracted containing 235 glycated and 303 non-glycated lysine residues. GlyStruct demonstrated improved performance of approximately 10% in comparison to benchmark method of Gly-PseAAC. The performance for GlyStruct on the metrics, sensitivity, specificity, accuracy and Mathew’s correlation coefficient were 0.7013, 0.7989, 0.7562, and 0.5065, respectively for 10-fold cross-validation. Conclusion Glycation has emerged to be one of the clinically important PTM of proteins in recent times. Therefore, the development of computational tools become necessary to predict glycation, which could help medical professionals administer drugs and manage patients more effectively. The proposed predictor manages to classify glycated and non-glycated lysine residues with promising results consistently on various cross-validation schemes and outperforms other state of the art methods. Electronic supplementary material The online version of this article (10.1186/s12859-018-2547-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Alok Sharma
- School of Engineering & Physics, University of the South Pacific, Suva, Fiji. .,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan. .,Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia. .,CREST, JST, Tokyo, Japan.
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, MD, USA
| | - Daichi Shigemizu
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.,CREST, JST, Tokyo, Japan.,Division of Genomic Medicine, Medical Genome Center, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | | | - Tatushiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.,CREST, JST, Tokyo, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
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Yu J, Shi S, Zhang F, Chen G, Cao M. PredGly: predicting lysine glycation sites for Homo sapiens based on XGboost feature optimization. Bioinformatics 2018; 35:2749-2756. [DOI: 10.1093/bioinformatics/bty1043] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 12/13/2018] [Accepted: 12/20/2018] [Indexed: 01/22/2023] Open
Abstract
Abstract
Motivation
Protein glycation is a familiar post-translational modification (PTM) which is a two-step non-enzymatic reaction. Glycation not only impairs the function but also changes the characteristics of the proteins so that it is related to many human diseases. It is still much more difficult to systematically detect glycation sites due to the glycated residues without crucial patterns. Computational approaches, which can filter supposed sites prior to experimental verification, can extremely increase the efficiency of experiment work. However, the previous lysine glycation prediction method uses a small number of training datasets. Hence, the model is not generalized or pervasive.
Results
By searching from a new database, we collected a large dataset in Homo sapiens. PredGly, a novel software, can predict lysine glycation sites for H.sapiens, which was developed by combining multiple features. In addition, XGboost was adopted to optimize feature vectors and to improve the model performance. Through comparing various classifiers, support vector machine achieved an optimal performance. On the basis of a new independent test set, PredGly outperformed other glycation tools. It suggests that PredGly can provide more instructive guidance for further experimental research of lysine glycation.
Availability and implementation
https://github.com/yujialinncu/PredGly
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jialin Yu
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China
| | - Shaoping Shi
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China
| | - Fang Zhang
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China
| | - Guodong Chen
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China
| | - Man Cao
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China
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21
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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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22
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Adilina S, Farid DM, Shatabda S. Effective DNA binding protein prediction by using key features via Chou's general PseAAC. J Theor Biol 2018; 460:64-78. [PMID: 30316822 DOI: 10.1016/j.jtbi.2018.10.027] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 10/07/2018] [Accepted: 10/10/2018] [Indexed: 11/18/2022]
Abstract
DNA-binding proteins (DBPs) are responsible for several cellular functions, starting from our immunity system to the transport of oxygen. In the recent studies, scientists have used supervised machine learning based methods that use information from the protein sequence only to classify the DBPs. Most of the methods work effectively on the train sets but performance of most of them degrades in the independent test set. It shows a room for improving the prediction method by reducing over-fitting. In this paper, we have extracted several features solely using the protein sequence and carried out two different types of feature selection on them. Our results have proven comparable on training set and significantly improved on the independent test set. On the independent test set our accuracy was 82.26% which is 1.62% improved compared to the previous best state-of-the-art methods. Performance in terms of sensitivity and area under receiver operating characteristic curve for the independent test set was also higher and they were 0.95 and 0.823 respectively.
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Affiliation(s)
- Sheikh Adilina
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh.
| | - Dewan Md Farid
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh.
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23
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Rahman MS, Aktar U, Jani MR, Shatabda S. iPromoter-FSEn: Identification of bacterial σ 70 promoter sequences using feature subspace based ensemble classifier. Genomics 2018; 111:1160-1166. [PMID: 30059731 DOI: 10.1016/j.ygeno.2018.07.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 07/07/2018] [Accepted: 07/12/2018] [Indexed: 10/28/2022]
Abstract
Sigma promoter sequences in bacterial genomes are important due to their role in transcription initiation. Sigma 70 is one of the most important and crucial sigma factors. In this paper, we address the problem of identification of σ70 promoter sequences in bacterial genome. We propose iPromoter-FSEn, a novel predictor for identification of σ70 promoter sequences. Our proposed method is based on a feature subspace based ensemble classifier. A large set of of features extracted from the sequence of nucleotides are divided into subsets and each subset is given to individual single classifiers to learn. Based on the decisions of the ensemble an aggregate decision is made by the ensemble voting classifier. We tested our method on a standard benchmark dataset extracted from experimentally validated results. Experimental results shows that iPromoter-FSEn significantly improves over the state-of-the art σ70 promoter sequence predictors. The accuracy and area under receiver operating characteristic curve of iPromoter-FSEn are 86.32% and 0.9319 respectively. We have also made our method readily available for use as an web application from: http://ipromoterfsen.pythonanywhere.com/server.
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Affiliation(s)
- Md Siddiqur Rahman
- Department of Computer Science and Engineering, United International University Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh
| | - Usma Aktar
- Department of Computer Science and Engineering, United International University Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh
| | - Md Rafsan Jani
- Department of Computer Science and Engineering, United International University Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh.
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24
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Al Maruf MA, Shatabda S. iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components. Genomics 2018; 111:966-972. [PMID: 29935224 DOI: 10.1016/j.ygeno.2018.06.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 06/09/2018] [Accepted: 06/13/2018] [Indexed: 11/28/2022]
Abstract
Recombination hotspots in a genome are unevenly distributed. Hotspots are regions in a genome that show higher rates of meiotic recombinations. Computational methods for recombination hotspot prediction often use sophisticated features that are derived from physico-chemical or structure based properties of nucleotides. In this paper, we propose iRSpot-SF that uses sequence based features which are computationally cheap to generate. Four feature groups are used in our method: k-mer composition, gapped k-mer composition, TF-IDF of k-mers and reverse complement k-mer composition. We have used recursive feature elimination to select 17 top features for hotspot prediction. Our analysis shows the superiority of gapped k-mer composition and reverse complement k-mer composition features over others. We have used SVM with RBF kernel as a classification algorithm. We have tested our algorithm on standard benchmark datasets. Compared to other methods iRSpot-SF is able to produce significantly better results in terms of accuracy, Mathew's Correlation Coefficient and sensitivity which are 84.58%, 0.6941 and 84.57%. We have made our method readily available to use as a python based tool and made the datasets and source codes available at: https://github.com/abdlmaruf/iRSpot-SF. An web application is developed based on iRSpot-SF and freely available to use at: http://irspot.pythonanywhere.com/server.html.
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Affiliation(s)
- Md Abdullah Al Maruf
- Department of Computer Science and Engineering, United International University, Madani Aveneue, Satarkul, Badda, Dhaka 1212, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Madani Aveneue, Satarkul, Badda, Dhaka 1212, Bangladesh.
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