1
|
A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification. PLoS One 2022; 17:e0275195. [PMID: 36201724 PMCID: PMC9536844 DOI: 10.1371/journal.pone.0275195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 09/12/2022] [Indexed: 11/18/2022] Open
Abstract
Plasmodium falciparum is a parasitic protozoan that can cause malaria, which is a deadly disease. Therefore, the accurate identification of malaria parasite mitochondrial proteins is essential for understanding their functions and identifying novel drug targets. For classifying protein sequences, several adaptive statistical techniques have been devised. Despite significant gains, prediction performance is still constrained by the lack of appropriate feature descriptors and learning strategies in current systems. Moreover, good ground truth data is important for Artificial Intelligence (AI)-based models but there is a lack of that data in the literature. Therefore, in this work, we propose a novel hybrid network that combines 1D Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BGRU) to classify the malaria parasite mitochondrial proteins. Furthermore, we curate a sequential data that are collected from National Center for Biotechnology Information (NCBI) and UniProtKB/Swiss-Prot proteins databanks to prepare a dataset that can be used by the research community for AI-based algorithms evaluation. We obtain 4204 cases after preprocessing of the collected data and denote this set of proteins as PF4204. Finally, we conduct an ablation study on several conventional and deep models using PF4204 and the benchmark PF2095 datasets. The proposed model 'CNN-BGRU' obtains the accuracy values of 0.9096 and 0.9857 on PF4204 and PF2095 datasets, respectively. In addition, the CNN-BGRU is compared with state-of-the-arts, where the results illustrate that it can extract robust features and identify proteins accurately.
Collapse
|
2
|
Zou H. iRNA5hmC-HOC: High-order correlation information for identifying RNA 5-hydroxymethylcytosine modification. J Bioinform Comput Biol 2022; 20:2250017. [DOI: 10.1142/s0219720022500172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
3
|
Zhang J, Lv L, Lu D, Kong D, Al-Alashaari MAA, Zhao X. Variable selection from a feature representing protein sequences: a case of classification on bacterial type IV secreted effectors. BMC Bioinformatics 2020; 21:480. [PMID: 33109082 PMCID: PMC7590791 DOI: 10.1186/s12859-020-03826-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 10/19/2020] [Indexed: 12/13/2022] Open
Abstract
Background Classification of certain proteins with specific functions is momentous for biological research. Encoding approaches of protein sequences for feature extraction play an important role in protein classification. Many computational methods (namely classifiers) are used for classification on protein sequences according to various encoding approaches. Commonly, protein sequences keep certain labels corresponding to different categories of biological functions (e.g., bacterial type IV secreted effectors or not), which makes protein prediction a fantasy. As to protein prediction, a kernel set of protein sequences keeping certain labels certified by biological experiments should be existent in advance. However, it has been hardly ever seen in prevailing researches. Therefore, unsupervised learning rather than supervised learning (e.g. classification) should be considered. As to protein classification, various classifiers may help to evaluate the effectiveness of different encoding approaches. Besides, variable selection from an encoded feature representing protein sequences is an important issue that also needs to be considered. Results Focusing on the latter problem, we propose a new method for variable selection from an encoded feature representing protein sequences. Taking a benchmark dataset containing 1947 protein sequences as a case, experiments are made to identify bacterial type IV secreted effectors (T4SE) from protein sequences, which are composed of 399 T4SE and 1548 non-T4SE. Comparable and quantified results are obtained only using certain components of the encoded feature, i.e., position-specific scoring matix, and that indicates the effectiveness of our method. Conclusions Certain variables other than an encoded feature they belong to do work for discrimination between different types of proteins. In addition, ensemble classifiers with an automatic assignment of different base classifiers do achieve a better classification result.
Collapse
Affiliation(s)
- Jian Zhang
- College of Artificial Intelligence, Wuxi Vocational College of Science and Technology, No. 8 Xinxi Road, Wuxi, 214028, China
| | - Lixin Lv
- College of Artificial Intelligence, Wuxi Vocational College of Science and Technology, No. 8 Xinxi Road, Wuxi, 214028, China
| | - Donglei Lu
- College of Artificial Intelligence, Wuxi Vocational College of Science and Technology, No. 8 Xinxi Road, Wuxi, 214028, China
| | - Denan Kong
- College of Information and Computer Engineering, Northeast Forestry University, No. 26 Hexing Road, Harbin, 150040, China
| | | | - Xudong Zhao
- College of Information and Computer Engineering, Northeast Forestry University, No. 26 Hexing Road, Harbin, 150040, China.
| |
Collapse
|
4
|
Bian H, Guo M, Wang J. Recognition of Mitochondrial Proteins in Plasmodium Based on the Tripeptide Composition. Front Cell Dev Biol 2020; 8:578901. [PMID: 33043014 PMCID: PMC7525148 DOI: 10.3389/fcell.2020.578901] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 08/13/2020] [Indexed: 01/31/2023] Open
Abstract
Mitochondria play essential roles in eukaryotic cells, especially in Plasmodium cells. They have several unusual evolutionary and functional features that are incredibly vital for disease diagnosis and drug design. Thus, predicting mitochondrial proteins of Plasmodium has become a worthwhile work. However, existing computational methods can only predict mitochondrial proteins of Plasmodium falciparum (P. falciparum for short), and these methods have low accuracy. It is highly desirable to design a classifier with high accuracy for predicting mitochondrial proteins for all Plasmodium species, not only P. falciparum. We proposed a novel method, named as PM-OTC, for predicting mitochondrial proteins in Plasmodium. PM-OTC uses the Support Vector Machine (SVM) as the classifier and the selected tripeptide composition as the features. We adopted the 5-fold cross-validation method to train and test PM-OTC. Results demonstrate that PM-OTC achieves an accuracy of 94.91%, and performances of PM-OTC are superior to other methods.
Collapse
Affiliation(s)
- Haodong Bian
- School of Computer Science, Inner Mongolia University, Hohhot, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.,Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing, China
| | - Juan Wang
- School of Computer Science, Inner Mongolia University, Hohhot, China.,Stage Key Laboratories of Reproductive Regulation & Breeding of Grassland Livestock, Hohhot, China
| |
Collapse
|
5
|
Dong H, He D, Wang F. SMOTE-XGBoost using Tree Parzen Estimator optimization for copper flotation method classification. POWDER TECHNOL 2020. [DOI: 10.1016/j.powtec.2020.07.065] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
6
|
MPPIF-Net: Identification of Plasmodium Falciparum Parasite Mitochondrial Proteins Using Deep Features with Multilayer Bi-directional LSTM. Processes (Basel) 2020. [DOI: 10.3390/pr8060725] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Mitochondrial proteins of Plasmodium falciparum (MPPF) are an important target for anti-malarial drugs, but their identification through manual experimentation is costly, and in turn, their related drugs production by pharmaceutical institutions involves a prolonged time duration. Therefore, it is highly desirable for pharmaceutical companies to develop computationally automated and reliable approach to identify proteins precisely, resulting in appropriate drug production in a timely manner. In this direction, several computationally intelligent techniques are developed to extract local features from biological sequences using machine learning methods followed by various classifiers to discriminate the nature of proteins. Unfortunately, these techniques demonstrate poor performance while capturing contextual features from sequence patterns, yielding non-representative classifiers. In this paper, we proposed a sequence-based framework to extract deep and representative features that are trust-worthy for Plasmodium mitochondrial proteins identification. The backbone of the proposed framework is MPPF identification-net (MPPFI-Net), that is based on a convolutional neural network (CNN) with multilayer bi-directional long short-term memory (MBD-LSTM). MPPIF-Net inputs protein sequences, passes through various convolution and pooling layers to optimally extract learned features. We pass these features into our sequence learning mechanism, MBD-LSTM, that is particularly trained to classify them into their relevant classes. Our proposed model is experimentally evaluated on newly prepared dataset PF2095 and two existing benchmark datasets i.e., PF175 and MPD using the holdout method. The proposed method achieved 97.6%, 97.1%, and 99.5% testing accuracy on PF2095, PF175, and MPD datasets, respectively, which outperformed the state-of-the-art approaches.
Collapse
|
7
|
Liu T, Tang H. A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite. Curr Pharm Des 2020; 26:3049-3058. [PMID: 32156226 DOI: 10.2174/1381612826666200310122324] [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: 01/01/2020] [Accepted: 02/10/2020] [Indexed: 11/22/2022]
Abstract
The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.
Collapse
Affiliation(s)
- Ting Liu
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou 646000, China
| | - Hua Tang
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou 646000, China
| |
Collapse
|
8
|
Dou L, Li X, Ding H, Xu L, Xiang H. Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem? MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 19:293-303. [PMID: 31865116 PMCID: PMC6931122 DOI: 10.1016/j.omtn.2019.11.014] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 10/29/2019] [Accepted: 11/11/2019] [Indexed: 01/01/2023]
Abstract
Pseudouridine (Ψ) is the most abundant RNA modification and has been found in many kinds of RNAs, including snRNA, rRNA, tRNA, mRNA, and snoRNA. Thus, Ψ sites play a significant role in basic research and drug development. Although some experimental techniques have been developed to identify Ψ sites, they are expensive and time consuming, especially in the post-genomic era with the explosive growth of known RNA sequences. Thus, highly accurate computational methods are urgently required to quickly detect the Ψ sites on uncharacterized RNA sequences. Several predictors have been proposed using multifarious features, but their evaluated performances are still unsatisfactory. In this study, we first identified Ψ sites for H. sapiens, S. cerevisiae, and M. musculus using the sequence features from the bi-profile Bayes (BPB) method based on the random forest (RF) and support vector machine (SVM) algorithms, where the performances were evaluated using 5-fold cross-validation and independent tests. It was found that the SVM-based accuracies were 3.55% and 5.09% lower than the iPseU-CUU predictor for the H_990 and S_628 datasets, respectively. Almost the same-level results were obtained for M_994 and an independent H_200 dataset, even showing a 5.0% improvement for S_200. Then, three different kinds of features, including basic Kmer, general parallel correlation pseudo-dinucleotide composition (PC-PseDNC-General), and nucleotide chemical property (NCP) and nucleotide density (ND) from the iRNA-PseU method, were combined with BPB to show their comprehensive performances, where the effective features are selected by the max-relevance-max-distance (MRMD) method. The best evaluated accuracies of the combined features for the S_628 and M_994 datasets were achieved at 70.54% and 72.45%, which were 2.39% and 0.65% higher than iPseU-CUU. For the S_200 dataset, it was also improved 8% from 69% to 77%. However, there was no obvious improvement for H. sapiens, which was evaluated as approximately 63.23% and 72.0% for the H_990 and H_200 datasets, respectively. The overall performances for Ψ identification using BPB features as well as the combined features were not obviously improved. Although some kinds of feature extraction methods based on the RNA sequence information have been applied to construct the predictors in previous studies, the corresponding accuracies are generally in the range of 60%-70%. Thus, researchers need to reconsider whether there is any sequence feature in the RNA Ψ modification prediction problem.
Collapse
Affiliation(s)
- Lijun Dou
- School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoling Li
- Department of Oncology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Hui Ding
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China.
| | - Huaikun Xiang
- School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China.
| |
Collapse
|
9
|
Ju Z, Wang SY. Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou's 5-steps Rule and General Pseudo Components. Curr Genomics 2019; 20:592-601. [PMID: 32581647 PMCID: PMC7290059 DOI: 10.2174/1389202921666191223154629] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/19/2019] [Accepted: 11/07/2019] [Indexed: 01/06/2023] Open
Abstract
Introduction Neddylation is a highly dynamic and reversible post-translational modification. The abnormality of neddylation has previously been shown to be closely related to some human diseases. The detection of neddylation sites is essential for elucidating the regulation mechanisms of protein neddylation. Objective As the detection of the lysine neddylation sites by the traditional experimental method is often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites. Methods In this study, a bioinformatics tool named NeddPred is developed to identify underlying protein neddylation sites. A bi-profile bayes feature extraction is used to encode neddylation sites and a fuzzy support vector machine model is utilized to overcome the problem of noise and class imbalance in the prediction. Results Matthew's correlation coefficient of NeddPred achieved 0.7082 and an area under the receiver operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms existing lysine neddylation sites predictor NeddyPreddy. Conclusion Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation sites. A user-friendly webserver for NeddPred is accessible at 123.206.31.171/NeddPred/.
Collapse
Affiliation(s)
- Zhe Ju
- College of Science, Shenyang Aerospace University, Shenyang110136, P.R. China
| | - Shi-Yun Wang
- College of Science, Shenyang Aerospace University, Shenyang110136, P.R. China
| |
Collapse
|
10
|
Yonge F, Weixia X. Identification of Mitochondrial Proteins of Malaria Parasite Adding the New Parameter. LETT ORG CHEM 2019. [DOI: 10.2174/1570178615666180608100348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Malaria has been one of the serious infectious diseases caused by Plasmodium falciparum (P. falciparum). Mitochondrial proteins of P. falciparum are regarded as effective drug targets against malaria. Thus, it is necessary to accurately identify mitochondrial proteins of malaria parasite. Many algorithms have been proposed for the prediction of mitochondrial proteins of malaria parasite and yielded the better results. However, the parameters used by these methods were primarily based on amino acid sequences. In this study, we added a novel parameter for predicting mitochondrial proteins of malaria parasite based on protein secondary structure. Firstly, we extracted three feature parameters, namely, three kinds of protein secondary structures compositions (3PSS), 20 amino acid compositions (20AAC) and 400 dipeptide compositions (400DC), and used the analysis of variance (ANOVA) to screen 400 dipeptides. Secondly, we adopted these features to predict mitochondrial proteins of malaria parasite by using support vector machine (SVM). Finally, we found that 1) adding the feature of protein secondary structure (3PSS) can indeed improve the prediction accuracy. This result demonstrated that the parameter of protein secondary structure is a valid feature in the prediction of mitochondrial proteins of malaria parasite; 2) feature combination can improve the prediction’s results; feature selection can reduce the dimension and simplify the calculation. We achieved the sensitivity (Sn) of 98.16%, the specificity (Sp) of 97.64% and overall accuracy (Acc) of 97.88% with 0.957 of Mathew’s correlation coefficient (MCC) by using 3PSS+ 20AAC+ 34DC as a feature in 15-fold cross-validation. This result is compared with that of the similar work in the same dataset, showing the superiority of our work.
Collapse
Affiliation(s)
- Feng Yonge
- College of Science, Inner Mongolia Agriculture University, Hohhot 010018, China
| | - Xie Weixia
- College of Science, Inner Mongolia Agriculture University, Hohhot 010018, China
| |
Collapse
|
11
|
Yang Q, Jia C, Li T. Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier. Math Biosci 2019; 311:103-108. [PMID: 30880100 DOI: 10.1016/j.mbs.2019.01.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 01/29/2019] [Accepted: 01/29/2019] [Indexed: 10/27/2022]
Abstract
Aptamer-protein interacting pairs play important roles in physiological functions and structural characterization. Identifying aptamer-protein interacting pairs is challenging and limited, despite of the tremendous applications of aptamers. Therefore, it is vital to construct a high prediction performance model for identifying aptamer-target interacting pairs. In this study, a novel ensemble method is presented to predict aptamer-protein interacting pairs by integrating sequence characteristics derived from aptamers and the target proteins. The features extracted for aptamers were the compositions of amino acids and pseudo K-tuple nucleotides. In addition, a sparse autoencoder was used to characterize features for the target protein sequences. To remove redundant features, gradient boosting decision tree (GBDT) and incremental feature selection (IFS) methods were used to obtain the optimum combination of sequence characters. Based on 616 selected features, an ensemble of three sub- support vector machine (SVM) classifiers was used to construct our prediction model. Evaluated on an independent dataset, our predictor obtained an accuracy of 75.7%, Matthew's Correlation Coefficient of 0.478, and Youden's Index of 0.538, which were superior to the values reached using other existing predictors. The results show that our model can be used to distinguishing novel aptamer-protein interacting pairs and revealing the interrelation between aptamers and proteins.
Collapse
Affiliation(s)
- Qing Yang
- Institute of Environmental Systems Biology, College of Environmental and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China
| | - Cangzhi Jia
- School of Science, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China
| | - Taoying Li
- Department of Maritime Economics and Management, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China.
| |
Collapse
|
12
|
Li T, Song R, Yin Q, Gao M, Chen Y. Identification of S-nitrosylation sites based on multiple features combination. Sci Rep 2019; 9:3098. [PMID: 30816267 PMCID: PMC6395632 DOI: 10.1038/s41598-019-39743-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 02/01/2019] [Indexed: 01/24/2023] Open
Abstract
Protein S-nitrosylation (SNO) is a typical reversible, redox-dependent and post-translational modification that involves covalent modification of cysteine residues with nitric oxide (NO) for the thiol group. Numerous experiments have shown that SNO plays a major role in cell function and pathophysiology. In order to rapidly analysis the big sets of data, the computing methods for identifying the SNO sites are being considered as necessary auxiliary tools. In this study, multiple features including Parallel correlation pseudo amino acid composition (PC-PseAAC), Basic kmer1 (kmer1), Basic kmer2 (kmer2), General parallel correlation pseudo amino acid composition (PC-PseAAC_G), Adapted Normal distribution Bi-Profile Bayes (ANBPB), Double Bi-Profile Bayes (DBPB), Bi-Profile Bayes (BPB), Incorporating Amino Acid Pairwise (IAAPair) and Position-specific Tri-Amino Acid Propensity(PSTAAP) were employed to extract the sequence information. To remove information redundancy, information gain (IG) was applied to evaluate the importance of amino acids, which is the information entropy of class after subtracting the conditional entropy for the given amino acid. The prediction performance of the SNO sites was found to be best by using the cross-validation and independent tests. In addition, we also calculated four commonly used performance measurements, i.e. Sensitivity (Sn), Specificity (Sp), Accuracy (Acc), and the Matthew's Correlation Coefficient (MCC). For the training dataset, the overall Acc was 83.11%, the MCC was 0.6617. For an independent test dataset, Acc was 73.17%, and MCC was 0.3788. The results indicate that our method is likely to complement the existing prediction methods and is a useful tool for effective identification of the SNO sites.
Collapse
Affiliation(s)
- Taoying Li
- Department of Maritime Economics and Management, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China.
| | - Runyu Song
- Department of Maritime Economics and Management, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China
| | - Qian Yin
- Department of Maritime Economics and Management, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China
| | - Mingyue Gao
- Department of Maritime Economics and Management, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China
| | - Yan Chen
- Department of Maritime Economics and Management, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China
| |
Collapse
|
13
|
Support Vector Machine Classifier for Accurate Identification of piRNA. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8112204] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Piwi-interacting RNA (piRNA) is a newly identified class of small non-coding RNAs. It can combine with PIWI proteins to regulate the transcriptional gene silencing process, heterochromatin modifications, and to maintain germline and stem cell function in animals. To better understand the function of piRNA, it is imperative to improve the accuracy of identifying piRNAs. In this study, the sequence information included the single nucleotide composition, and 16 dinucleotides compositions, six physicochemical properties in RNA, the position specificities of nucleotides both in N-terminal and C-terminal, and the proportions of the similar peptide sequence of both N-terminal and C-terminal in positive and negative samples, which were used to construct the feature vector. Then, the F-Score was applied to choose an optimal single type of features. By combining these selected features, we achieved the best results on the jackknife and the 5-fold cross-validation running 10 times based on the support vector machine algorithm. Moreover, we further evaluated the stability and robustness of our new method.
Collapse
|
14
|
Ju Z, Wang SY. Predicting lysine lipoylation sites using bi-profile bayes feature extraction and fuzzy support vector machine algorithm. Anal Biochem 2018; 561-562:11-17. [DOI: 10.1016/j.ab.2018.09.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Revised: 09/09/2018] [Accepted: 09/09/2018] [Indexed: 12/14/2022]
|
15
|
He J, Fang T, Zhang Z, Huang B, Zhu X, Xiong Y. PseUI: Pseudouridine sites identification based on RNA sequence information. BMC Bioinformatics 2018; 19:306. [PMID: 30157750 PMCID: PMC6114832 DOI: 10.1186/s12859-018-2321-0] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 08/21/2018] [Indexed: 01/28/2023] Open
Abstract
Background Pseudouridylation is the most prevalent type of posttranscriptional modification in various stable RNAs of all organisms, which significantly affects many cellular processes that are regulated by RNA. Thus, accurate identification of pseudouridine (Ψ) sites in RNA will be of great benefit for understanding these cellular processes. Due to the low efficiency and high cost of current available experimental methods, it is highly desirable to develop computational methods for accurately and efficiently detecting Ψ sites in RNA sequences. However, the predictive accuracy of existing computational methods is not satisfactory and still needs improvement. Results In this study, we developed a new model, PseUI, for Ψ sites identification in three species, which are H. sapiens, S. cerevisiae, and M. musculus. Firstly, five different kinds of features including nucleotide composition (NC), dinucleotide composition (DC), pseudo dinucleotide composition (pseDNC), position-specific nucleotide propensity (PSNP), and position-specific dinucleotide propensity (PSDP) were generated based on RNA segments. Then, a sequential forward feature selection strategy was used to gain an effective feature subset with a compact representation but discriminative prediction power. Based on the selected feature subsets, we built our model by using a support vector machine (SVM). Finally, the generalization of our model was validated by both the jackknife test and independent validation tests on the benchmark datasets. The experimental results showed that our model is more accurate and stable than the previously published models. We have also provided a user-friendly web server for our model at http://zhulab.ahu.edu.cn/PseUI, and a brief instruction for the web server is provided in this paper. By using this instruction, the academic users can conveniently get their desired results without complicated calculations. Conclusion In this study, we proposed a new predictor, PseUI, to detect Ψ sites in RNA sequences. It is shown that our model outperformed the existing state-of-art models. It is expected that our model, PseUI, will become a useful tool for accurate identification of RNA Ψ sites. Electronic supplementary material The online version of this article (10.1186/s12859-018-2321-0) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Jingjing He
- School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China
| | - Ting Fang
- School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China
| | - Zizheng Zhang
- School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China
| | - Bei Huang
- School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China
| | - Xiaolei Zhu
- School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China.
| | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| |
Collapse
|
16
|
Jia C, Yang Q, Zou Q. NucPosPred: Predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC. J Theor Biol 2018; 450:15-21. [PMID: 29678692 DOI: 10.1016/j.jtbi.2018.04.025] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 04/13/2018] [Accepted: 04/16/2018] [Indexed: 11/20/2022]
Abstract
The nucleosome is the basic structure of chromatin in eukaryotic cells, with essential roles in the regulation of many biological processes, such as DNA transcription, replication and repair, and RNA splicing. Because of the importance of nucleosomes, the factors that determine their positioning within genomes should be investigated. High-resolution nucleosome-positioning maps are now available for organisms including Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans, enabling the identification of nucleosome positioning by application of computational tools. Here, we describe a novel predictor called NucPosPred, which was specifically designed for large-scale identification of nucleosome positioning in C. elegans and D. melanogaster genomes. NucPosPred was separately optimized for each species for four types of DNA sequence feature extraction, with consideration of two classification algorithms (gradient-boosting decision tree and support vector machine). The overall accuracy obtained with NucPosPred was 92.29% for C. elegans and 88.26% for D. melanogaster, outperforming previous methods and demonstrating the potential for species-specific prediction of nucleosome positioning. For the convenience of most experimental scientists, a web-server for the predictor NucPosPred is available at http://121.42.167.206/NucPosPred/index.jsp.
Collapse
Affiliation(s)
- Cangzhi Jia
- Science of College, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China.
| | - Qing Yang
- Science of College, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China
| | - Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin, China.
| |
Collapse
|
17
|
Poonam, Gupta Y, Gupta N, Singh S, Wu L, Chhikara BS, Rawat M, Rathi B. Multistage inhibitors of the malaria parasite: Emerging hope for chemoprotection and malaria eradication. Med Res Rev 2018; 38:1511-1535. [DOI: 10.1002/med.21486] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 12/09/2017] [Accepted: 12/26/2017] [Indexed: 12/13/2022]
Affiliation(s)
- Poonam
- Department of Chemistry; Miranda House, University of Delhi; India
| | - Yash Gupta
- National Institute of Malaria Research (ICMR); New Delhi India
| | - Nikesh Gupta
- Special Centre for Nanosciences; Jawaharlal Nehru University; New Delhi India
| | - Snigdha Singh
- Laboratory for Translational Chemistry and Drug Discovery, Department of Chemistry; Hansraj College University Enclave, University of Delhi; Delhi India
| | - Lidong Wu
- Department of Chemistry; Massachusetts Institute of Technology; Cambridge MA USA
- Key Laboratory of Control of Quality and Safety for Aquatic Products; Ministry of Agriculture, Chinese Academy of Fishery Sciences; Beijing China
| | | | - Manmeet Rawat
- Department of Internal Medicine; University of New Mexico School of Medicine; Albuquerque NM USA
| | - Brijesh Rathi
- Laboratory for Translational Chemistry and Drug Discovery, Department of Chemistry; Hansraj College University Enclave, University of Delhi; Delhi India
- Department of Chemistry; Massachusetts Institute of Technology; Cambridge MA USA
| |
Collapse
|
18
|
Ju Z, Sun J, Li Y, Wang L. Predicting lysine glycation sites using bi-profile bayes feature extraction. Comput Biol Chem 2017; 71:98-103. [PMID: 29040908 DOI: 10.1016/j.compbiolchem.2017.10.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 09/14/2017] [Accepted: 10/07/2017] [Indexed: 12/21/2022]
Abstract
Glycation is a nonenzymatic post-translational modification which has been found to be involved in various biological processes and closely associated with many metabolic diseases. The accurate identification of glycation sites is important to understand the underlying molecular mechanisms of glycation. As the traditional experimental methods are often labor-intensive and time-consuming, it is desired to develop computational methods to predict glycation sites. In this study, a novel predictor named BPB_GlySite is proposed to predict lysine glycation sites by using bi-profile bayes feature extraction and support vector machine algorithm. As illustrated by 10-fold cross-validation, BPB_GlySite achieves a satisfactory performance with a Sensitivity of 63.68%, a Specificity of 72.60%, an Accuracy of 69.63% and a Matthew's correlation coefficient of 0.3499. Experimental results also indicate that BPB_GlySite significantly outperforms three existing glycation sites predictors: NetGlycate, PreGly and Gly-PseAAC. Therefore, BPB_GlySite can be a useful bioinformatics tool for the prediction of glycation sites. A user-friendly web-server for BPB_GlySite is established at 123.206.31.171/BPB_GlySite/.
Collapse
Affiliation(s)
- Zhe Ju
- College of Science, Shenyang Aerospace University, 110136, People's Republic of China.
| | - Juhe Sun
- College of Science, Shenyang Aerospace University, 110136, People's Republic of China
| | - Yanjie Li
- College of Science, Shenyang Aerospace University, 110136, People's Republic of China
| | - Li Wang
- College of Science, Shenyang Aerospace University, 110136, People's Republic of China
| |
Collapse
|
19
|
He W, Jia C. EnhancerPred2.0: predicting enhancers and their strength based on position-specific trinucleotide propensity and electron–ion interaction potential feature selection. MOLECULAR BIOSYSTEMS 2017; 13:767-774. [DOI: 10.1039/c7mb00054e] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Enhancers arecis-acting elements that play major roles in upregulating eukaryotic gene expression by providing binding sites for transcription factors and their complexes.
Collapse
Affiliation(s)
- Wenying He
- Department of Mathematics
- Dalian Maritime University
- Dalian 116026
- China
| | - Cangzhi Jia
- Department of Mathematics
- Dalian Maritime University
- Dalian 116026
- China
| |
Collapse
|
20
|
Jia C, He W. EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features. Sci Rep 2016; 6:38741. [PMID: 27941893 PMCID: PMC5150536 DOI: 10.1038/srep38741] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 11/11/2016] [Indexed: 12/31/2022] Open
Abstract
Enhancers are cis elements that play an important role in regulating gene expression by enhancing it. Recent study of modifications revealed that enhancers are a large group of functional elements with many different subgroups, which have different biological activities and regulatory effects on target genes. As powerful auxiliary tools, several computational methods have been proposed to distinguish enhancers from other regulatory elements, but only one method has been considered to clustering them into subgroups. In this study, we developed a predictor (called EnhancerPred) to distinguish between enhancers and nonenhancers and to determine enhancers' strength. A two-step wrapper-based feature selection method was applied in high dimension feature vector from bi-profile Bayes and pseudo-nucleotide composition. Finally, the combination of 104 features from bi-profile Bayes, 1 feature from nucleotide composition and 9 features from pseudo-nucleotide composition yielded the best performance for identifying enhancers and nonenhancers, with overall Acc of 77.39%. The combination of 89 features from bi-profile Bayes and 10 features from pseudo-nucleotide composition yielded the best performance for identifying strong and weak enhancers, with overall Acc of 68.19%. The process and steps of feature optimization illustrated that it is necessary to construct a particular model for identifying strong enhancers and weak enhancers.
Collapse
Affiliation(s)
- Cangzhi Jia
- Department of Mathematics, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China
| | - Wenying He
- Department of Mathematics, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China
| |
Collapse
|
21
|
Jia CZ, Zhang JJ, Gu WZ. RNA-MethylPred: A high-accuracy predictor to identify N6-methyladenosine in RNA. Anal Biochem 2016; 510:72-75. [DOI: 10.1016/j.ab.2016.06.012] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 06/07/2016] [Accepted: 06/08/2016] [Indexed: 10/21/2022]
|
22
|
Ding H, Li D. Identification of mitochondrial proteins of malaria parasite using analysis of variance. Amino Acids 2014; 47:329-33. [DOI: 10.1007/s00726-014-1862-4] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Accepted: 10/27/2014] [Indexed: 10/24/2022]
|
23
|
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.
Collapse
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
| |
Collapse
|
24
|
Jia C, Lin X, Wang Z. Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and Chou's pseudo amino acid composition. Int J Mol Sci 2014; 15:10410-23. [PMID: 24918295 PMCID: PMC4100159 DOI: 10.3390/ijms150610410] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Revised: 05/12/2014] [Accepted: 05/20/2014] [Indexed: 11/16/2022] Open
Abstract
Protein S-nitrosylation is a reversible post-translational modification by covalent modification on the thiol group of cysteine residues by nitric oxide. Growing evidence shows that protein S-nitrosylation plays an important role in normal cellular function as well as in various pathophysiologic conditions. Because of the inherent chemical instability of the S-NO bond and the low abundance of endogenous S-nitrosylated proteins, the unambiguous identification of S-nitrosylation sites by commonly used proteomic approaches remains challenging. Therefore, computational prediction of S-nitrosylation sites has been considered as a powerful auxiliary tool. In this work, we mainly adopted an adapted normal distribution bi-profile Bayes (ANBPB) feature extraction model to characterize the distinction of position-specific amino acids in 784 S-nitrosylated and 1568 non-S-nitrosylated peptide sequences. We developed a support vector machine prediction model, iSNO-ANBPB, by incorporating ANBPB with the Chou’s pseudo amino acid composition. In jackknife cross-validation experiments, iSNO-ANBPB yielded an accuracy of 65.39% and a Matthew’s correlation coefficient (MCC) of 0.3014. When tested on an independent dataset, iSNO-ANBPB achieved an accuracy of 63.41% and a MCC of 0.2984, which are much higher than the values achieved by the existing predictors SNOSite, iSNO-PseAAC, the Li et al. algorithm, and iSNO-AAPair. On another training dataset, iSNO-ANBPB also outperformed GPS-SNO and iSNO-PseAAC in the 10-fold crossvalidation test.
Collapse
Affiliation(s)
- Cangzhi Jia
- Department of Mathematics, Dalian Maritime University, Dalian 116026, China.
| | - Xin Lin
- Department of Mathematics, Dalian Maritime University, Dalian 116026, China.
| | - Zhiping Wang
- Department of Mathematics, Dalian Maritime University, Dalian 116026, China.
| |
Collapse
|
25
|
Das Roy R, Dash D. Selection of relevant features from amino acids enables development of robust classifiers. Amino Acids 2014; 46:1343-51. [PMID: 24604165 DOI: 10.1007/s00726-014-1697-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2013] [Accepted: 02/14/2014] [Indexed: 12/30/2022]
Abstract
Machine learning (ML) has been extensively applied to develop models and to understand high-throughput data of biological processes. However, new ML models, trained with novel experimental results, are required to build regularly for more precise predictions. ML methods can build models from numeric data, whereas biological data are generally textual (DNA, protein sequences) or images and needs feature calculation algorithms to generate quantitative features. Programming skills along with domain knowledge are required to develop these algorithms. Therefore, the process of knowledge discovery through ML is decelerated due to lack of generic tools to construct features and to build models directly from the data. Hence, we developed a schema that calculates about 5,000 features, selects relevant features and develops protein classifiers from the training data. To demonstrate the general applicability and robustness of our method, fungal adhesins and nuclear receptor proteins were used for building classifiers which outperformed existing classifiers when tested on independent data. Next, we built a classifier for mitochondrial proteins of Plasmodium falciparum which causes human malaria because the latest corresponding classifiers are not publically accessible. Our classifier attained 98.18 % accuracy and 0.95 Matthews correlation coefficient by fivefold cross-validation and outperformed existing classifiers on independent test set. We implemented this schema as user-friendly and open source application Pro-Gyan ( http://code.google.com/p/pro-gyan/ ), to build and share executable classifiers without programming knowledge.
Collapse
Affiliation(s)
- Rishi Das Roy
- GN Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, Mall Road, Delhi, 110007, India,
| | | |
Collapse
|
26
|
Mirza MT, Khan A, Tahir M, Lee YS. MitProt-Pred: Predicting mitochondrial proteins of Plasmodium falciparum parasite using diverse physiochemical properties and ensemble classification. Comput Biol Med 2013; 43:1502-11. [PMID: 24034742 DOI: 10.1016/j.compbiomed.2013.07.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2013] [Revised: 07/19/2013] [Accepted: 07/24/2013] [Indexed: 10/26/2022]
Abstract
Mitochondrial protein of Plasmodium falciparum is an important target for anti-malarial drugs. Experimental approaches for detecting mitochondrial proteins are costly and time consuming. Therefore, MitProt-Pred is developed that utilizes Bi-profile Bayes, Pseudo Average Chemical Shift, Split Amino Acid Composition, and Pseudo Amino Acid Composition based features of the protein sequences. Hybrid feature space is also developed by combining different individual feature spaces. These feature spaces are learned and exploited through SVM based ensemble. MitProt-Pred achieved significantly improved prediction performance for two standard datasets. We also developed the score level ensemble, which outperforms the feature level ensemble.
Collapse
Affiliation(s)
- Muhammad Tayyeb Mirza
- Pattern Recognition Laboratory, Department of Computer and Information Sciences, PIEAS, Nilore, Islamabad, Pakistan
| | | | | | | |
Collapse
|
27
|
Shao J, Xu D, Hu L, Kwan YW, Wang Y, Kong X, Ngai SM. Systematic analysis of human lysine acetylation proteins and accurate prediction of human lysine acetylation through bi-relative adapted binomial score Bayes feature representation. MOLECULAR BIOSYSTEMS 2013; 8:2964-73. [PMID: 22936054 DOI: 10.1039/c2mb25251a] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Lysine acetylation is a reversible post-translational modification (PTM) which has been linked to many biological and pathological implications. Hence, localization of lysine acetylation is essential for deciphering the mechanism of such implications. Whereas many acetylated lysines in human proteins have been localized through experimental approaches in wet lab, it still fails to reach completion. In the present study, we proposed a novel feature extraction approach, bi-relative adapted binomial score Bayes (BRABSB), combined with support vector machines (SVMs) to construct a human-specific lysine acetylation predictor, which yields, on average, a sensitivity of 83.91%, a specificity of 87.25% and an accuracy of 85.58%, in the case of 5-fold cross validation experiments. Results obtained through the validation on independent data sets show that the proposed approach here outperforms other existing lysine acetylation predictors. Furthermore, due to the fact that global analysis of human lysine acetylproteins, which would ultimately facilitate the systematic investigation of the biological and pathological consequences associated with lysine acetylation events, remains to be resolved, we made an attempt to systematically analyze human lysine acetylproteins, demonstrating their diversity with respect to subcellular localization as well as biological process and predominance by "binding" in terms of molecular function. Our analysis also revealed that human lysine acetylproteins are significantly enriched in neurodegenerative disorders and cancer pathways. Remarkably, lysine acetylproteins in mitochondria are significantly related to neurodegenerative disorders and those in the nucleus are instead significantly involved in pathways in cancers, all of which might ultimately provide novel global insights into such pathological processes for the therapeutic purpose. The web server is deployed at http://www.bioinfo.bio.cuhk.edu.hk/bpbphka.
Collapse
Affiliation(s)
- Jianlin Shao
- Institute of Health Sciences, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | | | | | | | | | | | | |
Collapse
|
28
|
Jia CZ, Liu T, Wang ZP. O-GlcNAcPRED: a sensitive predictor to capture protein O-GlcNAcylation sites. MOLECULAR BIOSYSTEMS 2013; 9:2909-13. [DOI: 10.1039/c3mb70326f] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
29
|
Deponte M, Hoppe HC, Lee MC, Maier AG, Richard D, Rug M, Spielmann T, Przyborski JM. Wherever I may roam: Protein and membrane trafficking in P. falciparum-infected red blood cells. Mol Biochem Parasitol 2012; 186:95-116. [DOI: 10.1016/j.molbiopara.2012.09.007] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2012] [Revised: 09/21/2012] [Accepted: 09/24/2012] [Indexed: 11/27/2022]
|