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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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Chen Y, Li Z, Li Z. Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework. FRONTIERS IN PLANT SCIENCE 2022; 13:912599. [PMID: 35712582 PMCID: PMC9194944 DOI: 10.3389/fpls.2022.912599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
Plant resistance proteins (R proteins) recognize effector proteins secreted by pathogenic microorganisms and trigger an immune response against pathogenic microbial infestation. Accurate identification of plant R proteins is an important research topic in plant pathology. Plant R protein prediction has achieved many research results. Recently, some machine learning-based methods have emerged to identify plant R proteins. Still, most of them only rely on protein sequence features, which ignore inter-amino acid features, thus limiting the further improvement of plant R protein prediction performance. In this manuscript, we propose a method called StackRPred to predict plant R proteins. Specifically, the StackRPred first obtains plant R protein feature information from the pairwise energy content of residues; then, the obtained feature information is fed into the stacking framework for training to construct a prediction model for plant R proteins. The results of both the five-fold cross-validation and independent test validation show that our proposed method outperforms other state-of-the-art methods, indicating that StackRPred is an effective tool for predicting plant R proteins. It is expected to bring some favorable contribution to the study of plant R proteins.
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Affiliation(s)
- Yifan Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Zejun Li
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
| | - Zhiyong Li
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
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Mishra A, Khanal R, Kabir WU, Hoque T. AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques. Artif Intell Med 2021; 113:102034. [PMID: 33685590 DOI: 10.1016/j.artmed.2021.102034] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 01/19/2021] [Accepted: 02/09/2021] [Indexed: 12/25/2022]
Abstract
Identification of RNA-binding proteins (RBPs) that bind to ribonucleic acid molecules is an important problem in Computational Biology and Bioinformatics. It becomes indispensable to identify RBPs as they play crucial roles in post-transcriptional control of RNAs and RNA metabolism as well as have diverse roles in various biological processes such as splicing, mRNA stabilization, mRNA localization, and translation, RNA synthesis, folding-unfolding, modification, processing, and degradation. The existing experimental techniques for identifying RBPs are time-consuming and expensive. Therefore, identifying RBPs directly from the sequence using computational methods can be useful to annotate RBPs and assist the experimental design efficiently. In this work, we present a method called AIRBP, which is designed using an advanced machine learning technique, called stacking, to effectively predict RBPs by utilizing features extracted from evolutionary information, physiochemical properties, and disordered properties. Moreover, our method, AIRBP, use the majority vote from RBPPred, DeepRBPPred, and the stacking model for the prediction for RBPs. The results show that AIRBP attains Accuracy (ACC), Balanced Accuracy (BACC), F1-score, and Mathews Correlation Coefficient (MCC) of 95.84 %, 94.71 %, 0.928, and 0.899, respectively, based on the training dataset, using 10-fold cross-validation (CV). Further evaluation of AIRBP on independent test set reveals that it achieves ACC, BACC, F1-score, and MCC of 94.36 %, 94.28 %, 0.897, and 0.860, for Human test set; 91.25 %, 93.00 %, 0.896, and 0.835 for S. cerevisiae test set; and 90.60 %, 90.41 %, 0.934, and 0.775 for A. thaliana test set, respectively. These results indicate that the AIRBP outperforms the existing Deep- and TriPepSVM methods. Therefore, the proposed better-performing AIRBP can be useful for accurate identification and annotation of RBPs directly from the sequence and help gain valuable insight to treat critical diseases. Availability: Code-data is available here: http://cs.uno.edu/∼tamjid/Software/AIRBP/code_data.zip.
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Affiliation(s)
- Avdesh Mishra
- Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX, USA
| | - Reecha Khanal
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
| | - Wasi Ul Kabir
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
| | - Tamjidul Hoque
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA.
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Pražnikar J, Tomić M, Turk D. Validation and quality assessment of macromolecular structures using complex network analysis. Sci Rep 2019; 9:1678. [PMID: 30737447 PMCID: PMC6368557 DOI: 10.1038/s41598-019-38658-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 01/07/2019] [Indexed: 02/06/2023] Open
Abstract
Validation of three-dimensional structures is at the core of structural determination methods. The local validation criteria, such as deviations from ideal bond length and bonding angles, Ramachandran plot outliers and clashing contacts, are a standard part of structure analysis before structure deposition, whereas the global and regional packing may not yet have been addressed. In the last two decades, three-dimensional models of macromolecules such as proteins have been successfully described by a network of nodes and edges. Amino acid residues as nodes and close contact between the residues as edges have been used to explore basic network properties, to study protein folding and stability and to predict catalytic sites. Using complex network analysis, we introduced common network parameters to distinguish between correct and incorrect three-dimensional protein structures. The analysis showed that correct structures have a higher average node degree, higher graph energy, and lower shortest path length than their incorrect counterparts. Thus, correct protein models are more densely intra-connected, and in turn, the transfer of information between nodes/amino acids is more efficient. Moreover, protein graph spectra were used to investigate model bias in protein structure.
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Affiliation(s)
- Jure Pražnikar
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper, Slovenia.
- Department of Biochemistry, Molecular and Structural Biology, Institute Jožef Stefan, Jamova 39, Ljubljana, Slovenia.
| | - Miloš Tomić
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper, Slovenia
| | - Dušan Turk
- Department of Biochemistry, Molecular and Structural Biology, Institute Jožef Stefan, Jamova 39, Ljubljana, Slovenia
- Center of excellence for Integrated Approaches in Chemistry and Biology of Proteins, Jamova 39, Ljubljana, Slovenia
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Mishra A, Pokhrel P, Hoque MT. StackDPPred: a stacking based prediction of DNA-binding protein from sequence. Bioinformatics 2018; 35:433-441. [DOI: 10.1093/bioinformatics/bty653] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 07/18/2018] [Indexed: 12/12/2022] Open
Affiliation(s)
- Avdesh Mishra
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
| | - Pujan Pokhrel
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
| | - Md Tamjidul Hoque
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
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Gao S, Song S, Cheng J, Todo Y, Zhou M. Incorporation of Solvent Effect into Multi-Objective Evolutionary Algorithm for Improved Protein Structure Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1365-1378. [PMID: 28534784 DOI: 10.1109/tcbb.2017.2705094] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The problem of predicting the three-dimensional (3-D) structure of a protein from its one-dimensional sequence has been called the "holy grail of molecular biology", and it has become an important part of structural genomics projects. Despite the rapid developments in computer technology and computational intelligence, it remains challenging and fascinating. In this paper, to solve it we propose a multi-objective evolutionary algorithm. We decompose the protein energy function Chemistry at HARvard Macromolecular Mechanics force fields into bond and non-bond energies as the first and second objectives. Considering the effect of solvent, we innovatively adopt a solvent-accessible surface area as the third objective. We use 66 benchmark proteins to verify the proposed method and obtain better or competitive results in comparison with the existing methods. The results suggest the necessity to incorporate the effect of solvent into a multi-objective evolutionary algorithm to improve protein structure prediction in terms of accuracy and efficiency.
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dos Santos RN, Ferrari AJR, de Jesus HCR, Gozzo FC, Morcos F, Martínez L. Enhancing protein fold determination by exploring the complementary information of chemical cross-linking and coevolutionary signals. Bioinformatics 2018; 34:2201-2208. [DOI: 10.1093/bioinformatics/bty074] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 02/10/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Ricardo N dos Santos
- Institute of Chemistry, University of Campinas, Campinas, Brazil
- Center for Computational Engineering and Sciences, University of Campinas, Campinas, Brazil
| | | | | | - Fábio C Gozzo
- Institute of Chemistry, University of Campinas, Campinas, Brazil
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, USA
| | - Leandro Martínez
- Institute of Chemistry, University of Campinas, Campinas, Brazil
- Center for Computational Engineering and Sciences, University of Campinas, Campinas, Brazil
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Ligabue-Braun R, Borguesan B, Verli H, Krause MJ, Dorn M. Everyone Is a Protagonist: Residue Conformational Preferences in High-Resolution Protein Structures. J Comput Biol 2017; 25:451-465. [PMID: 29267011 DOI: 10.1089/cmb.2017.0182] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
In many structural bioinformatics problems, there is a broad range of unanswered questions about protein dynamics and amino acid properties. Proteins are not strictly static objects, but rather populate ensembles of conformations. One way to understand these particularities is to analyze the information available in experimental databases. The Ramachandran plot, despite being more than half a century old, remains an utterly useful tool in the study of protein conformation. Based on its assumptions, we inspected a large data set (11,130 protein structures, amounting to 5,255,768 residues) and discriminated the conformational preferences of each residue type regarding their secondary structure participation. These data were studied for phi [Formula: see text], psi [Formula: see text], and side chain chi [Formula: see text] angles, being presented in non-Ramachandranian plots. In the largest analysis of protein conformation made so far, we propose an original plot to depict conformational preferences in relation to different secondary structure elements. Despite confirming previous observations, our results strongly support a unique character for each residue type, whereas also reinforcing the observation that side chains have a major contribution to secondary structure and, by consequence, on protein conformation. This information can be further used in the development of more robust methods and computational strategies for structural bioinformatics problems.
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Affiliation(s)
- Rodrigo Ligabue-Braun
- 1 Center for Biotechnology, PPGBCM, Federal University of Rio Grande do Sul , Porto Alegre, Brazil
| | - Bruno Borguesan
- 2 Institute of Informatics, PPGC, Federal University of Rio Grande do Sul , Porto Alegre, Brazil
| | - Hugo Verli
- 1 Center for Biotechnology, PPGBCM, Federal University of Rio Grande do Sul , Porto Alegre, Brazil
| | - Mathias J Krause
- 3 Institute for Mechanical Process Engineering and Mechanics (MVM), Institute for Applied and Numerical Mathematics (IANM), Karlsruhe Institute of Technology (KIT) , Karlsruhe, Germany
| | - Márcio Dorn
- 1 Center for Biotechnology, PPGBCM, Federal University of Rio Grande do Sul , Porto Alegre, Brazil .,2 Institute of Informatics, PPGC, Federal University of Rio Grande do Sul , Porto Alegre, Brazil
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