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Mufassirin MMM, Newton MAH, Sattar A. Artificial intelligence for template-free protein structure prediction: a comprehensive review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10350-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Artificial intelligence in drug design: algorithms, applications, challenges and ethics. FUTURE DRUG DISCOVERY 2021. [DOI: 10.4155/fdd-2020-0028] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.
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Abstract
During the last three decades or so, many efforts have been made to study the protein cleavage
sites by some disease-causing enzyme, such as HIV (Human Immunodeficiency Virus) protease
and SARS (Severe Acute Respiratory Syndrome) coronavirus main proteinase. It has become increasingly
clear <i>via</i> this mini-review that the motivation driving the aforementioned studies is quite wise,
and that the results acquired through these studies are very rewarding, particularly for developing peptide
drugs.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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Chou KC. An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule. Curr Pharm Des 2020; 25:4223-4234. [PMID: 31782354 DOI: 10.2174/1381612825666191129164042] [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: 09/18/2019] [Accepted: 11/25/2019] [Indexed: 11/22/2022]
Abstract
OBJECTIVE One of the most challenging and also the most difficult problems is how to formulate a biological sequence with a vector but considerably keep its sequence order information. METHODS To address such a problem, the approach of Pseudo Amino Acid Components or PseAAC has been developed. RESULTS AND CONCLUSION It has become increasingly clear via the 10-year recollection that the aforementioned proposal has been indeed very powerful.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, Massachusetts 02478, United States.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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Kwon E, Cho M, Kim H, Son HS. A Study on Host Tropism Determinants of Influenza Virus Using Machine Learning. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191104160927] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Background:
The host tropism determinants of influenza virus, which cause changes in
the host range and increase the likelihood of interaction with specific hosts, are critical for
understanding the infection and propagation of the virus in diverse host species.
Methods:
Six types of protein sequences of influenza viral strains isolated from three classes of
hosts (avian, human, and swine) were obtained. Random forest, naïve Bayes classification, and knearest
neighbor algorithms were used for host classification. The Java language was used for
sequence analysis programming and identifying host-specific position markers.
Results:
A machine learning technique was explored to derive the physicochemical properties of
amino acids used in host classification and prediction. HA protein was found to play the most
important role in determining host tropism of the influenza virus, and the random forest method
yielded the highest accuracy in host prediction. Conserved amino acids that exhibited host-specific
differences were also selected and verified, and they were found to be useful position markers for
host classification. Finally, ANOVA analysis and post-hoc testing revealed that the
physicochemical properties of amino acids, comprising protein sequences combined with position
markers, differed significantly among hosts.
Conclusion:
The host tropism determinants and position markers described in this study can be
used in related research to classify, identify, and predict the hosts of influenza viruses that are
currently susceptible or likely to be infected in the future.
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Affiliation(s)
- Eunmi Kwon
- Laboratory of Computational Biology & Bioinformatics, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
| | - Myeongji Cho
- Laboratory of Computational Biology & Bioinformatics, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
| | - Hayeon Kim
- Department of Biomedical Laboratory Science, Kyungdong University, 815 Gyeonhwon-ro, Munmak, Wonju, Gangwondo, 24695, Korea
| | - Hyeon S. Son
- Laboratory of Computational Biology & Bioinformatics, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
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Some illuminating remarks on molecular genetics and genomics as well as drug development. Mol Genet Genomics 2020; 295:261-274. [PMID: 31894399 DOI: 10.1007/s00438-019-01634-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 12/05/2019] [Indexed: 02/07/2023]
Abstract
Facing the explosive growth of biological sequences unearthed in the post-genomic age, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, but still keep it with considerable sequence-order information or its special pattern. To deal with such a challenging problem, the ideas of "pseudo amino acid components" and "pseudo K-tuple nucleotide composition" have been proposed. The ideas and their approaches have further stimulated the birth for "distorted key theory", "wenxing diagram", and substantially strengthening the power in treating the multi-label systems, as well as the establishment of the famous "5-steps rule". All these logic developments are quite natural that are very useful not only for theoretical scientists but also for experimental scientists in conducting genetics/genomics analysis and drug development. Presented in this review paper are also their future perspectives; i.e., their impacts will become even more significant and propounding.
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Shao YT, Liu XX, Lu Z, Chou KC. pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.127042] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Shao Y, Chou KC. pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.126034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Chou KC. Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs. Curr Med Chem 2019; 26:4918-4943. [PMID: 31060481 DOI: 10.2174/0929867326666190507082559] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 01/29/2019] [Accepted: 01/31/2019] [Indexed: 12/16/2022]
Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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12
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Chou KC. Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09910-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Xiao X, Cheng X, Chen G, Mao Q, Chou KC. pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset. Med Chem 2019; 15:496-509. [DOI: 10.2174/1573406415666181217114710] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 10/23/2018] [Accepted: 12/12/2018] [Indexed: 12/17/2022]
Abstract
Background/Objective:Knowledge of protein subcellular localization is vitally important for both basic research and drug development. Facing the avalanche of protein sequences emerging in the post-genomic age, it is urgent to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mVirus” was developed for identifying the subcellular localization of virus proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, known as “multiplex proteins”, may simultaneously occur in, or move between two or more subcellular location sites. Despite the fact that it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mVirus was trained by an extremely skewed dataset in which some subset was over 10 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset.Methods:Using the Chou's general PseAAC (Pseudo Amino Acid Composition) approach and the IHTS (Inserting Hypothetical Training Samples) treatment to balance out the training dataset, we have developed a new predictor called “pLoc_bal-mVirus” for predicting the subcellular localization of multi-label virus proteins.Results:Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mVirus, the existing state-of-theart predictor for the same purpose.Conclusion:Its user-friendly web-server is available at http://www.jci-bioinfo.cn/pLoc_balmVirus/, by which the majority of experimental scientists can easily get their desired results without the need to go through the detailed complicated mathematics. Accordingly, pLoc_bal-mVirus will become a very useful tool for designing multi-target drugs and in-depth understanding of the biological process in a cell.
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Affiliation(s)
- Xuan Xiao
- Gordon Life Science Institute, Boston, MA 02478, United States
| | - Xiang Cheng
- Gordon Life Science Institute, Boston, MA 02478, United States
| | - Genqiang Chen
- College of Chemistry, Chemical Engineering and Biotechnology, Donghua University, Shanghai 201620, China
| | - Qi Mao
- College of Information Science and Technology, Donghua University, Shanghai, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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Akbar S, Hayat M, Kabir M, Iqbal M. iAFP-gap-SMOTE: An Efficient Feature Extraction Scheme Gapped Dipeptide Composition is Coupled with an Oversampling Technique for Identification of Antifreeze Proteins. LETT ORG CHEM 2019. [DOI: 10.2174/1570178615666180816101653] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Antifreeze proteins (AFPs) perform distinguishable roles in maintaining homeostatic conditions of living organisms and protect their cell and body from freezing in extremely cold conditions. Owing to high diversity in protein sequences and structures, the discrimination of AFPs from non- AFPs through experimental approaches is expensive and lengthy. It is, therefore, vastly desirable to propose a computational intelligent and high throughput model that truly reflects AFPs quickly and accurately. In a sequel, a new predictor called “iAFP-gap-SMOTE” is proposed for the identification of AFPs. Protein sequences are expressed by adopting three numerical feature extraction schemes namely; Split Amino Acid Composition, G-gap di-peptide Composition and Reduce Amino Acid alphabet composition. Usually, classification hypothesis biased towards majority class in case of the imbalanced dataset. Oversampling technique Synthetic Minority Over-sampling Technique is employed in order to increase the instances of the lower class and control the biasness. 10-fold cross-validation test is applied to appraise the success rates of “iAFP-gap-SMOTE” model. After the empirical investigation, “iAFP-gap-SMOTE” model obtained 95.02% accuracy. The comparison suggested that the accuracy of” iAFP-gap-SMOTE” model is higher than that of the present techniques in the literature so far. It is greatly recommended that our proposed model “iAFP-gap-SMOTE” might be helpful for the research community and academia.
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Affiliation(s)
- Shahid Akbar
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP 23200, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP 23200, Pakistan
| | - Muhammad Kabir
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP 23200, Pakistan
| | - Muhammad Iqbal
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP 23200, Pakistan
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MacCarthy E, Perry D, Kc DB. Advances in Protein Super-Secondary Structure Prediction and Application to Protein Structure Prediction. Methods Mol Biol 2019; 1958:15-45. [PMID: 30945212 DOI: 10.1007/978-1-4939-9161-7_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Due to the advancement in various sequencing technologies, the gap between the number of protein sequences and the number of experimental protein structures is ever increasing. Community-wide initiatives like CASP have resulted in considerable efforts in the development of computational methods to accurately model protein structures from sequences. Sequence-based prediction of super-secondary structure has direct application in protein structure prediction, and there have been significant efforts in the prediction of super-secondary structure in the last decade. In this chapter, we first introduce the protein structure prediction problem and highlight some of the important progress in the field of protein structure prediction. Next, we discuss recent methods for the prediction of super-secondary structures. Finally, we discuss applications of super-secondary structure prediction in structure prediction/analysis of proteins. We also discuss prediction of protein structures that are composed of simple super-secondary structure repeats and protein structures that are composed of complex super-secondary structure repeats. Finally, we also discuss the recent trends in the field.
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Affiliation(s)
- Elijah MacCarthy
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Derrick Perry
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Dukka B Kc
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA.
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Xiao X, Xu ZC, Qiu WR, Wang P, Ge HT, Chou KC. iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition. Genomics 2018; 111:1785-1793. [PMID: 30529532 DOI: 10.1016/j.ygeno.2018.12.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 11/20/2018] [Accepted: 12/04/2018] [Indexed: 12/20/2022]
Abstract
The promoter is a regulatory DNA region about 81-1000 base pairs long, usually located near the transcription start site (TSS) along upstream of a given gene. By combining a certain protein called transcription factor, the promoter provides the starting point for regulated gene transcription, and hence plays a vitally important role in gene transcriptional regulation. With explosive growth of DNA sequences in the post-genomic age, it has become an urgent challenge to develop computational method for effectively identifying promoters because the information thus obtained is very useful for both basic research and drug development. Although some prediction methods were developed in this regard, most of them were limited at merely identifying whether a query DNA sequence being of a promoter or not. However, based on their strength-distinct levels for transcriptional activation and expression, promoter should be divided into two categories: strong and weak types. Here a new two-layer predictor, called "iPSW(2L)-PseKNC", was developed by fusing the physicochemical properties of nucleotides and their nucleotide density into PseKNC (pseudo K-tuple nucleotide composition). Its 1st-layer serves to predict whether a query DNA sequence sample is of promoter or not, while its 2nd-layer is able to predict the strength of promoters. It has been observed through rigorous cross-validations that the 1st-layer sub-predictor is remarkably superior to the existing state-of-the-art predictors in identifying the promoters and non-promoters, and that the 2nd-layer sub-predictor can do what is beyond the reach of the existing predictors. Moreover, the web-server for iPSW(2L)-PseKNC has been established at http://www.jci-bioinfo.cn/iPSW(2L)-PseKNC, by which the majority of experimental scientists can easily get the results they need.
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Affiliation(s)
- Xuan Xiao
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China; The Gordon Life Science Institute, Boston, MA 02478, USA.
| | - Zhao-Chun Xu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.
| | - Wang-Ren Qiu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China; The Gordon Life Science Institute, Boston, MA 02478, USA
| | - Peng Wang
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Hui-Ting Ge
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Kuo-Chen Chou
- The Gordon Life Science Institute, Boston, MA 02478, USA; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017; 2017:9183796. [PMID: 28744305 PMCID: PMC5514333 DOI: 10.1155/2017/9183796] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2017] [Revised: 05/29/2017] [Accepted: 06/04/2017] [Indexed: 02/05/2023]
Abstract
Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction methods have been developed for apoptosis protein subcellular localization. However, these existing works only focus on the proteins that have one location; proteins with multiple locations are either not considered or assumed as not existing when constructing prediction models, so that they cannot completely predict all the locations of the apoptosis proteins with multiple locations. To address this problem, this paper proposes a novel multilabel predictor named MultiP-Apo, which can predict not only apoptosis proteins with single subcellular location but also those with multiple subcellular locations. Specifically, given a query protein, GO-based feature extraction method is used to extract its feature vector. Subsequently, the GO feature vector is classified by a new multilabel classifier based on the label-specific features. It is the first multilabel predictor ever established for identifying subcellular locations of multilocation apoptosis proteins. As an initial study, MultiP-Apo achieves an overall accuracy of 58.49% by jackknife test, which indicates that our proposed predictor may become a very useful high-throughput tool in this area.
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Wan S, Mak MW, Kung SY. Transductive Learning for Multi-Label Protein Subchloroplast Localization Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:212-224. [PMID: 26887009 DOI: 10.1109/tcbb.2016.2527657] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Predicting the localization of chloroplast proteins at the sub-subcellular level is an essential yet challenging step to elucidate their functions. Most of the existing subchloroplast localization predictors are limited to predicting single-location proteins and ignore the multi-location chloroplast proteins. While recent studies have led to some multi-location chloroplast predictors, they usually perform poorly. This paper proposes an ensemble transductive learning method to tackle this multi-label classification problem. Specifically, given a protein in a dataset, its composition-based sequence information and profile-based evolutionary information are respectively extracted. These two kinds of features are respectively compared with those of other proteins in the dataset. The comparisons lead to two similarity vectors which are weighted-combined to constitute an ensemble feature vector. A transductive learning model based on the least squares and nearest neighbor algorithms is proposed to process the ensemble features. We refer to the resulting predictor to as EnTrans-Chlo. Experimental results on a stringent benchmark dataset and a novel dataset demonstrate that EnTrans-Chlo significantly outperforms state-of-the-art predictors and particularly gains more than 4% (absolute) improvement on the overall actual accuracy. For readers' convenience, EnTrans-Chlo is freely available online at http://bioinfo.eie.polyu.edu.hk/EnTransChloServer/.
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Liu B, Wu H, Chou KC. Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences. ACTA ACUST UNITED AC 2017. [DOI: 10.4236/ns.2017.94007] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Nasrul Islam M, Iqbal S, Katebi AR, Tamjidul Hoque M. A balanced secondary structure predictor. J Theor Biol 2016; 389:60-71. [DOI: 10.1016/j.jtbi.2015.10.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 10/14/2015] [Accepted: 10/22/2015] [Indexed: 11/30/2022]
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22
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Kou G, Feng Y. Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts. J Theor Biol 2015; 380:392-8. [DOI: 10.1016/j.jtbi.2015.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Revised: 06/02/2015] [Accepted: 06/04/2015] [Indexed: 10/23/2022]
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Kabir M, Hayat M. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Mol Genet Genomics 2015; 291:285-96. [DOI: 10.1007/s00438-015-1108-5] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Accepted: 08/19/2015] [Indexed: 10/23/2022]
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Wang X, Zhang J, Li GZ. Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble. BMC Bioinformatics 2015; 16 Suppl 12:S1. [PMID: 26329681 PMCID: PMC4705491 DOI: 10.1186/1471-2105-16-s12-s1] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Background It has become a very important and full of challenge task to predict bacterial protein subcellular locations using computational methods. Although there exist a lot of prediction methods for bacterial proteins, the majority of these methods can only deal with single-location proteins. But unfortunately many multi-location proteins are located in the bacterial cells. Moreover, multi-location proteins have special biological functions capable of helping the development of new drugs. So it is necessary to develop new computational methods for accurately predicting subcellular locations of multi-location bacterial proteins. Results In this article, two efficient multi-label predictors, Gpos-ECC-mPLoc and Gneg-ECC-mPLoc, are developed to predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. The two multi-label predictors construct the GO vectors by using the GO terms of homologous proteins of query proteins and then adopt a powerful multi-label ensemble classifier to make the final multi-label prediction. The two multi-label predictors have the following advantages: (1) they improve the prediction performance of multi-label proteins by taking the correlations among different labels into account; (2) they ensemble multiple CC classifiers and further generate better prediction results by ensemble learning; and (3) they construct the GO vectors by using the frequency of occurrences of GO terms in the typical homologous set instead of using 0/1 values. Experimental results show that Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. Conclusions Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently improve prediction accuracy of subcellular localization of multi-location gram-positive and gram-negative bacterial proteins respectively. The online web servers for Gpos-ECC-mPLoc and Gneg-ECC-mPLoc predictors are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/gpos-ecc-mploc/ and http://biomed.zzuli.edu.cn/bioinfo/gneg-ecc-mploc/ respectively.
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Huang Q, You Z, Zhang X, Zhou Y. Prediction of protein-protein interactions with clustered amino acids and weighted sparse representation. Int J Mol Sci 2015; 16:10855-69. [PMID: 25984606 PMCID: PMC4463679 DOI: 10.3390/ijms160510855] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Revised: 05/06/2015] [Accepted: 05/07/2015] [Indexed: 01/22/2023] Open
Abstract
With the completion of the Human Genome Project, bioscience has entered into the era of the genome and proteome. Therefore, protein–protein interactions (PPIs) research is becoming more and more important. Life activities and the protein–protein interactions are inseparable, such as DNA synthesis, gene transcription activation, protein translation, etc. Though many methods based on biological experiments and machine learning have been proposed, they all spent a long time to learn and obtained an imprecise accuracy. How to efficiently and accurately predict PPIs is still a big challenge. To take up such a challenge, we developed a new predictor by incorporating the reduced amino acid alphabet (RAAA) information into the general form of pseudo-amino acid composition (PseAAC) and with the weighted sparse representation-based classification (WSRC). The remarkable advantages of introducing the reduced amino acid alphabet is being able to avoid the notorious dimensionality disaster or overfitting problem in statistical prediction. Additionally, experiments have proven that our method achieved good performance in both a low- and high-dimensional feature space. Among all of the experiments performed on the PPIs data of Saccharomyces cerevisiae, the best one achieved 90.91% accuracy, 94.17% sensitivity, 87.22% precision and a 83.43% Matthews correlation coefficient (MCC) value. In order to evaluate the prediction ability of our method, extensive experiments are performed to compare with the state-of-the-art technique, support vector machine (SVM). The achieved results show that the proposed approach is very promising for predicting PPIs, and it can be a helpful supplement for PPIs prediction.
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Affiliation(s)
- Qiaoying Huang
- Shenzhen Graduate School, Harbin Institute of Technology, HIT Campus of University Town of Shenzhen, Shenzhen 518055, China.
| | - Zhuhong You
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
| | - Xiaofeng Zhang
- Shenzhen Graduate School, Harbin Institute of Technology, HIT Campus of University Town of Shenzhen, Shenzhen 518055, China.
| | - Yong Zhou
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
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26
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Liu B, Chen J, Wang X. Protein remote homology detection by combining Chou’s distance-pair pseudo amino acid composition and principal component analysis. Mol Genet Genomics 2015; 290:1919-31. [DOI: 10.1007/s00438-015-1044-4] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 04/06/2015] [Indexed: 02/07/2023]
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27
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Wang X, Zhang W, Zhang Q, Li GZ. MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier. Bioinformatics 2015; 31:2639-45. [PMID: 25900916 DOI: 10.1093/bioinformatics/btv212] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Accepted: 04/13/2015] [Indexed: 01/11/2023] Open
Abstract
MOTIVATION Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations when constructing prediction models, so that they can predict only one of all subchloroplast locations of this kind of multilabel proteins. RESULTS To address this problem, through utilizing label-specific features and label correlations simultaneously, a novel multilabel classifier was developed for predicting protein subchloroplast location(s) with both single and multiple location sites. As an initial study, the overall accuracy of our proposed algorithm reaches 55.52%, which is quite high to be able to become a promising tool for further studies. AVAILABILITY AND IMPLEMENTATION An online web server for our proposed algorithm named MultiP-SChlo was developed, which are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/multip-schlo/. CONTACT pandaxiaoxi@gmail.com or gzli@tongji.edu.cn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiao Wang
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China and
| | - Weiwei Zhang
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China and
| | - Qiuwen Zhang
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China and
| | - Guo-Zheng Li
- Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
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28
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Chen L, Chu C, Huang T, Kong X, Cai YD. Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models. Amino Acids 2015; 47:1485-93. [PMID: 25894890 DOI: 10.1007/s00726-015-1974-5] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Accepted: 03/27/2015] [Indexed: 12/26/2022]
Abstract
Cell-penetrating peptides, a group of short peptides, can traverse cell membranes to enter cells and thus facilitate the uptake of various molecular cargoes. Thus, they have the potential to become powerful drug delivery systems. The correct identification of peptides as cell-penetrating or non-cell-penetrating would accelerate this application. In this study, we determined which features were important for a peptide to be cell-penetrating or non-cell-penetrating and built a predictive model based on the key features extracted from this analysis. The investigated peptides were retrieved from a previous study, and each was encoded as a numeric vector according to six properties of amino acids-amino acid frequency, codon diversity, electrostatic charge, molecular volume, polarity, and secondary structure-by the pseudo-amino acid composition method. Methods of minimum redundancy maximum relevance and incremental feature selection were then employed to analyze these features, and some were found to be key determinants of cell penetration. In parallel, an optimal random forest prediction model was built. We hope that our findings will provide new resources for the study of cell-penetrating peptides.
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Affiliation(s)
- Lei Chen
- College of Life Science, Shanghai University, Shanghai, 200444, People's Republic of China,
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29
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Chen W, Lin H, Chou KC. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. MOLECULAR BIOSYSTEMS 2015; 11:2620-34. [DOI: 10.1039/c5mb00155b] [Citation(s) in RCA: 262] [Impact Index Per Article: 29.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
With the avalanche of DNA/RNA sequences generated in the post-genomic age, it is urgent to develop automated methods for analyzing the relationship between the sequences and their functions.
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Affiliation(s)
- Wei Chen
- Department of Physics
- School of Sciences
- and Center for Genomics and Computational Biology
- Hebei United University
- Tangshan 063000
| | - Hao Lin
- Gordon Life Science Institute
- Boston
- USA
- Key Laboratory for Neuro-Information of Ministry of Education
- Center of Bioinformatics
| | - Kuo-Chen Chou
- Department of Physics
- School of Sciences
- and Center for Genomics and Computational Biology
- Hebei United University
- Tangshan 063000
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30
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Wen J, Zhang Y, Yau SS. k-mer Sparse matrix model for genetic sequence and its applications in sequence comparison. J Theor Biol 2014; 363:145-50. [DOI: 10.1016/j.jtbi.2014.08.028] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2014] [Revised: 07/14/2014] [Accepted: 08/17/2014] [Indexed: 10/24/2022]
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31
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Hayat M, Iqbal N. Discriminating protein structure classes by incorporating Pseudo Average Chemical Shift to Chou's general PseAAC and Support Vector Machine. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 116:184-192. [PMID: 24997484 DOI: 10.1016/j.cmpb.2014.06.007] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Revised: 06/09/2014] [Accepted: 06/13/2014] [Indexed: 06/03/2023]
Abstract
Proteins control all biological functions in living species. Protein structure is comprised of four major classes including all-α class, all-β class, α+β, and α/β. Each class performs different function according to their nature. Owing to the large exploration of protein sequences in the databanks, the identification of protein structure classes is difficult through conventional methods with respect to cost and time. Looking at the importance of protein structure classes, it is thus highly desirable to develop a computational model for discriminating protein structure classes with high accuracy. For this purpose, we propose a silco method by incorporating Pseudo Average Chemical Shift and Support Vector Machine. Two feature extraction schemes namely Pseudo Amino Acid Composition and Pseudo Average Chemical Shift are used to explore valuable information from protein sequences. The performance of the proposed model is assessed using four benchmark datasets 25PDB, 1189, 640 and 399 employing jackknife test. The success rates of the proposed model are 84.2%, 85.0%, 86.4%, and 89.2%, respectively on the four datasets. The empirical results reveal that the performance of our proposed model compared to existing models is promising in the literature so far and might be useful for future research.
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Affiliation(s)
- Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
| | - Nadeem Iqbal
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
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32
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Liu B, Xu J, Fan S, Xu R, Zhou J, Wang X. PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou’s PseAAC and Physicochemical Distance Transformation. Mol Inform 2014; 34:8-17. [DOI: 10.1002/minf.201400025] [Citation(s) in RCA: 135] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Accepted: 05/27/2014] [Indexed: 11/06/2022]
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33
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Liu B, Xu J, Lan X, Xu R, Zhou J, Wang X, Chou KC. iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PLoS One 2014; 9:e106691. [PMID: 25184541 PMCID: PMC4153653 DOI: 10.1371/journal.pone.0106691] [Citation(s) in RCA: 208] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2014] [Accepted: 07/31/2014] [Indexed: 11/18/2022] Open
Abstract
Playing crucial roles in various cellular processes, such as recognition of specific nucleotide sequences, regulation of transcription, and regulation of gene expression, DNA-binding proteins are essential ingredients for both eukaryotic and prokaryotic proteomes. With the avalanche of protein sequences generated in the postgenomic age, it is a critical challenge to develop automated methods for accurate and rapidly identifying DNA-binding proteins based on their sequence information alone. Here, a novel predictor, called "iDNA-Prot|dis", was established by incorporating the amino acid distance-pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) vector. The former can capture the characteristics of DNA-binding proteins so as to enhance its prediction quality, while the latter can reduce the dimension of PseAAC vector so as to speed up its prediction process. It was observed by the rigorous jackknife and independent dataset tests that the new predictor outperformed the existing predictors for the same purpose. As a user-friendly web-server, iDNA-Prot|dis is accessible to the public at http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step protocol guide is provided on how to use the web-server to get their desired results without the need to follow the complicated mathematic equations that are presented in this paper just for the integrity of its developing process. It is anticipated that the iDNA-Prot|dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard.
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Affiliation(s)
- Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China
- Gordon Life Science Institute, Belmont, Massachusetts, United States of America
- * E-mail: (BL); (KCC)
| | - Jinghao Xu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Xun Lan
- Stanford University, Stanford, California, United States of America
| | - Ruifeng Xu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Jiyun Zhou
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Xiaolong Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Belmont, Massachusetts, United States of America
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
- * E-mail: (BL); (KCC)
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34
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PSNO: predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou's PseAAC. Int J Mol Sci 2014; 15:11204-19. [PMID: 24968264 PMCID: PMC4139777 DOI: 10.3390/ijms150711204] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2014] [Revised: 05/26/2014] [Accepted: 05/27/2014] [Indexed: 11/16/2022] Open
Abstract
S-nitrosylation (SNO) is one of the most universal reversible post-translational modifications involved in many biological processes. Malfunction or dysregulation of SNO leads to a series of severe diseases, such as developmental abnormalities and various diseases. Therefore, the identification of SNO sites (SNOs) provides insights into disease progression and drug development. In this paper, a new bioinformatics tool, named PSNO, is proposed to identify SNOs from protein sequences. Firstly, we explore various promising sequence-derived discriminative features, including the evolutionary profile, the predicted secondary structure and the physicochemical properties. Secondly, rather than simply combining the features, which may bring about information redundancy and unwanted noise, we use the relative entropy selection and incremental feature selection approach to select the optimal feature subsets. Thirdly, we train our model by the technique of the k-nearest neighbor algorithm. Using both informative features and an elaborate feature selection scheme, our method, PSNO, achieves good prediction performance with a mean Mathews correlation coefficient (MCC) value of about 0.5119 on the training dataset using 10-fold cross-validation. These results indicate that PSNO can be used as a competitive predictor among the state-of-the-art SNOs prediction tools. A web-server, named PSNO, which implements the proposed method, is freely available at http://59.73.198.144:8088/PSNO/.
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35
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Prediction of four kinds of simple supersecondary structures in protein by using chemical shifts. ScientificWorldJournal 2014; 2014:978503. [PMID: 25050407 PMCID: PMC4090465 DOI: 10.1155/2014/978503] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Revised: 06/03/2014] [Accepted: 06/04/2014] [Indexed: 12/23/2022] Open
Abstract
Knowledge of supersecondary structures can provide important information about its spatial structure of protein. Some approaches have been developed for the prediction of protein supersecondary structure. However, the feature used by these approaches is primarily based on amino acid sequences. In this study, a novel model is presented to predict protein supersecondary structure by use of chemical shifts (CSs) information derived from nuclear magnetic resonance (NMR) spectroscopy. Using these CSs as inputs of the method of quadratic discriminant analysis (QD), we achieve the overall prediction accuracy of 77.3%, which is competitive with the same method for predicting supersecondary structures from amino acid compositions in threefold cross-validation. Moreover, our finding suggests that the combined use of different chemical shifts will influence the accuracy of prediction.
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36
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A set of descriptors for identifying the protein-drug interaction in cellular networking. J Theor Biol 2014; 359:120-8. [PMID: 24949993 DOI: 10.1016/j.jtbi.2014.06.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Revised: 06/02/2014] [Accepted: 06/06/2014] [Indexed: 12/24/2022]
Abstract
The study of protein-drug interactions is a significant issue for drug development. Unfortunately, it is both expensive and time-consuming to perform physical experiments to determine whether a drug and a protein are interacting with each other. Some previous attempts to design an automated system to perform this task were based on the knowledge of the 3D structure of a protein, which is not always available in practice. With the availability of protein sequences generated in the post-genomic age, however, a sequence-based solution to deal with this problem is necessary. Following other works in this area, we propose a new machine learning system based on several protein descriptors extracted from several protein representations, such as, variants of the position specific scoring matrix (PSSM) of proteins, the amino-acid sequence, and a matrix representation of a protein. The prediction engine is operated by an ensemble of support vector machines (SVMs), with each SVM trained on a specific descriptor and the results of each SVM combined by sum rule. The overall success rate achieved by our final ensemble is notably higher than previous results obtained on the same datasets using the same testing protocols reported in the literature. MATLAB code and the datasets used in our experiments are freely available for future comparison at http://www.dei.unipd.it/node/2357.
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37
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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.
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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.
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38
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iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. BIOMED RESEARCH INTERNATIONAL 2014; 2014:623149. [PMID: 24967386 PMCID: PMC4055483 DOI: 10.1155/2014/623149] [Citation(s) in RCA: 97] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Revised: 04/22/2014] [Accepted: 04/23/2014] [Indexed: 11/17/2022]
Abstract
In eukaryotic genes, exons are generally interrupted by introns. Accurately removing introns and joining exons together are essential processes in eukaryotic gene expression. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapid and effective detection of splice sites that play important roles in gene structure annotation and even in RNA splicing. Although a series of computational methods were proposed for splice site identification, most of them neglected the intrinsic local structural properties. In the present study, a predictor called “iSS-PseDNC” was developed for identifying splice sites. In the new predictor, the sequences were formulated by a novel feature-vector called “pseudo dinucleotide composition” (PseDNC) into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on two benchmark datasets that the overall success rates achieved by iSS-PseDNC in identifying splice donor site and splice acceptor site were 85.45% and 87.73%, respectively. It is anticipated that iSS-PseDNC may become a useful tool for identifying splice sites and that the six DNA local structural properties described in this paper may provide novel insights for in-depth investigations into the mechanism of RNA splicing.
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39
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Chen W, Lei TY, Jin DC, Lin H, Chou KC. PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Anal Biochem 2014; 456:53-60. [PMID: 24732113 DOI: 10.1016/j.ab.2014.04.001] [Citation(s) in RCA: 304] [Impact Index Per Article: 30.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2013] [Revised: 03/20/2014] [Accepted: 04/01/2014] [Indexed: 10/25/2022]
Abstract
The pseudo oligonucleotide composition, or pseudo K-tuple nucleotide composition (PseKNC), can be used to represent a DNA or RNA sequence with a discrete model or vector yet still keep considerable sequence order information, particularly the global or long-range sequence order information, via the physicochemical properties of its constituent oligonucleotides. Therefore, the PseKNC approach may hold very high potential for enhancing the power in dealing with many problems in computational genomics and genome sequence analysis. However, dealing with different DNA or RNA problems may need different kinds of PseKNC. Here, we present a flexible and user-friendly web server for PseKNC (at http://lin.uestc.edu.cn/pseknc/default.aspx) by which users can easily generate many different modes of PseKNC according to their need by selecting various parameters and physicochemical properties. Furthermore, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the current web server to generate their desired PseKNC without the need to follow the complicated mathematical equations, which are presented in this article just for the integrity of PseKNC formulation and its development. It is anticipated that the PseKNC web server will become a very useful tool in computational genomics and genome sequence analysis.
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Affiliation(s)
- Wei Chen
- School of Sciences, and Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China; Gordon Life Science Institute, Belmont, MA 02478, USA.
| | - Tian-Yu Lei
- School of Sciences, and Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China
| | - Dian-Chuan Jin
- School of Sciences, and Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China
| | - Hao Lin
- Gordon Life Science Institute, Belmont, MA 02478, USA; Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Kuo-Chen Chou
- School of Sciences, and Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China; Gordon Life Science Institute, Belmont, MA 02478, USA; Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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40
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Fan YN, Xiao X, Min JL, Chou KC. iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking. Int J Mol Sci 2014; 15:4915-37. [PMID: 24651462 PMCID: PMC3975431 DOI: 10.3390/ijms15034915] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 02/12/2014] [Accepted: 02/16/2014] [Indexed: 12/20/2022] Open
Abstract
Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
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Affiliation(s)
- Yue-Nong Fan
- Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen 333046, Jiangxi, China.
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen 333046, Jiangxi, China.
| | - Jian-Liang Min
- Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen 333046, Jiangxi, China.
| | - Kuo-Chen Chou
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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41
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Feng Y, Lin H, Luo L. Prediction of protein secondary structure using feature selection and analysis approach. Acta Biotheor 2014; 62:1-14. [PMID: 24052343 DOI: 10.1007/s10441-013-9203-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2012] [Accepted: 08/24/2013] [Indexed: 01/09/2023]
Abstract
The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. However, the accuracy of ab initio secondary structure prediction from sequence is about 80% currently, which is still far from satisfactory. In this study, we proposed a novel method that uses binomial distribution to optimize tetrapeptide structural words and increment of diversity with quadratic discriminant to perform prediction for protein three-state secondary structure. A benchmark dataset including 2,640 proteins with sequence identity of less than 25% was used to train and test the proposed method. The results indicate that overall accuracy of 87.8% was achieved in secondary structure prediction by using ten-fold cross-validation. Moreover, the accuracy of predicted secondary structures ranges from 84 to 89% at the level of residue. These results suggest that the feature selection technique can detect the optimized tetrapeptide structural words which affect the accuracy of predicted secondary structures.
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Du P, Gu S, Jiao Y. PseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets. Int J Mol Sci 2014; 15:3495-506. [PMID: 24577312 PMCID: PMC3975349 DOI: 10.3390/ijms15033495] [Citation(s) in RCA: 242] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2014] [Revised: 02/13/2014] [Accepted: 02/14/2014] [Indexed: 11/16/2022] Open
Abstract
The general form pseudo-amino acid composition (PseAAC) has been widely used to represent protein sequences in predicting protein structural and functional attributes. We developed the program PseAAC-General to generate various different modes of Chou’s general PseAAC, such as the gene ontology mode, the functional domain mode, and the sequential evolution mode. This program allows the users to define their own desired modes. In every mode, 544 physicochemical properties of the amino acids are available for choosing. The computing efficiency is at least 100 times that of existing programs, which makes it able to facilitate the extensive studies on proteins and peptides. The PseAAC-General is freely available via SourceForge. It runs on both Linux and Windows.
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Affiliation(s)
- Pufeng Du
- School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
| | - Shuwang Gu
- School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
| | - Yasen Jiao
- School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
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iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci 2014; 15:1746-66. [PMID: 24469313 PMCID: PMC3958819 DOI: 10.3390/ijms15021746] [Citation(s) in RCA: 211] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2014] [Revised: 01/14/2014] [Accepted: 01/16/2014] [Indexed: 01/22/2023] Open
Abstract
Meiosis and recombination are the two opposite aspects that coexist in a DNA system. As a driving force for evolution by generating natural genetic variations, meiotic recombination plays a very important role in the formation of eggs and sperm. Interestingly, the recombination does not occur randomly across a genome, but with higher probability in some genomic regions called “hotspots”, while with lower probability in so-called “coldspots”. With the ever-increasing amount of genome sequence data in the postgenomic era, computational methods for effectively identifying the hotspots and coldspots have become urgent as they can timely provide us with useful insights into the mechanism of meiotic recombination and the process of genome evolution as well. To meet the need, we developed a new predictor called “iRSpot-TNCPseAAC”, in which a DNA sample was formulated by combining its trinucleotide composition (TNC) and the pseudo amino acid components (PseAAC) of the protein translated from the DNA sample according to its genetic codes. The former was used to incorporate its local or short-rage sequence order information; while the latter, its global and long-range one. Compared with the best existing predictor in this area, iRSpot-TNCPseAAC achieved higher rates in accuracy, Mathew’s correlation coefficient, and sensitivity, indicating that the new predictor may become a useful tool for identifying the recombination hotspots and coldspots, or, at least, become a complementary tool to the existing methods. It has not escaped our notice that the aforementioned novel approach to incorporate the DNA sequence order information into a discrete model may also be used for many other genome analysis problems. The web-server for iRSpot-TNCPseAAC is available at http://www.jci-bioinfo.cn/iRSpot-TNCPseAAC. Furthermore, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the current web server to obtain their desired result without the need to follow the complicated mathematical equations.
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Min JL, Xiao X, Chou KC. iEzy-drug: a web server for identifying the interaction between enzymes and drugs in cellular networking. BIOMED RESEARCH INTERNATIONAL 2013; 2013:701317. [PMID: 24371828 PMCID: PMC3858977 DOI: 10.1155/2013/701317] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 09/17/2013] [Indexed: 01/16/2023]
Abstract
With the features of extremely high selectivity and efficiency in catalyzing almost all the chemical reactions in cells, enzymes play vitally important roles for the life of an organism and hence have become frequent targets for drug design. An essential step in developing drugs by targeting enzymes is to identify drug-enzyme interactions in cells. It is both time-consuming and costly to do this purely by means of experimental techniques alone. Although some computational methods were developed in this regard based on the knowledge of the three-dimensional structure of enzyme, unfortunately their usage is quite limited because three-dimensional structures of many enzymes are still unknown. Here, we reported a sequence-based predictor, called "iEzy-Drug," in which each drug compound was formulated by a molecular fingerprint with 258 feature components, each enzyme by the Chou's pseudo amino acid composition generated via incorporating sequential evolution information and physicochemical features derived from its sequence, and the prediction engine was operated by the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iEzy-Drug via rigorous cross-validations was about 91%. Moreover, to maximize the convenience for the majority of experimental scientists, a user-friendly web server was established, by which users can easily obtain their desired results.
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Affiliation(s)
- Jian-Liang Min
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333046, China
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333046, China
- Information School, ZheJiang Textile & Fashion College, NingBo 315211, China
- Gordon Life Science Institute, Belmont, MA 02478, USA
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Belmont, MA 02478, USA
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
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Feng PM, Chen W, Lin H, Chou KC. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal Biochem 2013; 442:118-25. [DOI: 10.1016/j.ab.2013.05.024] [Citation(s) in RCA: 230] [Impact Index Per Article: 20.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2013] [Revised: 05/21/2013] [Accepted: 05/22/2013] [Indexed: 01/22/2023]
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46
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Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou's pseudo amino acid compositions. J Theor Biol 2013; 335:205-12. [DOI: 10.1016/j.jtbi.2013.06.034] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Revised: 05/26/2013] [Accepted: 06/29/2013] [Indexed: 12/19/2022]
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Predicting drugs side effects based on chemical-chemical interactions and protein-chemical interactions. BIOMED RESEARCH INTERNATIONAL 2013; 2013:485034. [PMID: 24078917 PMCID: PMC3776367 DOI: 10.1155/2013/485034] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2013] [Accepted: 07/30/2013] [Indexed: 11/18/2022]
Abstract
A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.
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Xiaohui N, Nana L, Jingbo X, Dingyan C, Yuehua P, Yang X, Weiquan W, Dongming W, Zengzhen W. Using the concept of Chou's pseudo amino acid composition to predict protein solubility: An approach with entropies in information theory. J Theor Biol 2013; 332:211-7. [DOI: 10.1016/j.jtbi.2013.03.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2012] [Revised: 03/10/2013] [Accepted: 03/11/2013] [Indexed: 11/15/2022]
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Xiao X, Min JL, Wang P, Chou KC. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS One 2013; 8:e72234. [PMID: 24015221 PMCID: PMC3754978 DOI: 10.1371/journal.pone.0072234] [Citation(s) in RCA: 91] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2013] [Accepted: 07/08/2013] [Indexed: 11/19/2022] Open
Abstract
Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.
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Affiliation(s)
- Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
- Information School, ZheJiang Textile and Fashion College, NingBo, China
- Gordon Life Science Institute, Belmont, Massachusetts, United States of America
| | - Jian-Liang Min
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| | - Pu Wang
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| | - Kuo-Chen Chou
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
- Gordon Life Science Institute, Belmont, Massachusetts, United States of America
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Xiao X, Min JL, Wang P, Chou KC. iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. J Theor Biol 2013; 337:71-9. [PMID: 23988798 DOI: 10.1016/j.jtbi.2013.08.013] [Citation(s) in RCA: 104] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2013] [Revised: 07/26/2013] [Accepted: 08/14/2013] [Indexed: 12/29/2022]
Abstract
Many crucial functions in life, such as heartbeat, sensory transduction and central nervous system response, are controlled by cell signalings via various ion channels. Therefore, ion channels have become an excellent drug target, and study of ion channel-drug interaction networks is an important topic for drug development. However, it is both time-consuming and costly to determine whether a drug and a protein ion channel are interacting with each other in a cellular network by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (three-dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most protein ion channels are still unknown. With the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop the sequence-based computational method to address this problem. To take up the challenge, we developed a new predictor called iCDI-PseFpt, in which the protein ion-channel sample is formulated by the PseAAC (pseudo amino acid composition) generated with the gray model theory, the drug compound by the 2D molecular fingerprint, and the operation engine is the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iCDI-PseFpt via the jackknife cross-validation was 87.27%, which is remarkably higher than that by any of the existing predictors in this area. As a user-friendly web-server, iCDI-PseFpt is freely accessible to the public at the website http://www.jci-bioinfo.cn/iCDI-PseFpt/. Furthermore, for the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in the paper just for its integrity. It has not escaped our notice that the current approach can also be used to study other drug-target interaction networks.
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Affiliation(s)
- Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China; Information School, Zhe-Jiang Textile & Fashion College, Ning-Bo 315211, China; Gordon Life Science Institute, 53 South Cottage Road, Belmont, MA 02478, United States.
| | - Jian-Liang Min
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China.
| | - Pu Wang
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China.
| | - Kuo-Chen Chou
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia; Gordon Life Science Institute, 53 South Cottage Road, Belmont, MA 02478, United States.
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