DOCKING-CALCULATION-BASED METHOD FOR PREDICTING PROTEIN-RNA INTERACTIONS
Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Prot...
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| Published in | Genome Informatics Vol. 25; no. 1; pp. 25 - 39 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Japan
Japanese Society for Bioinformatics
2011
日本バイオインフォマティクス学会 |
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| Online Access | Get full text |
| ISSN | 0919-9454 2185-842X |
| DOI | 10.11234/gi.25.25 |
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| Abstract | Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Protein Data Bank. We predicted the interactions for pairs in 78×78 combinations. Of these, 78 original complexes were defined as positive pairs, and the other 6,006 complexes were defined as negative pairs; then an F-measure value of 0.465 was obtained with our prediction system. |
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| AbstractList | Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Protein Data Bank. We predicted the interactions for pairs in 78A78 combinations. Of these, 78 original complexes were defined as positive pairs, and the other 6,006 complexes were defined as negative pairs; then an F-measure value of 0.465 was obtained with our prediction system. Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Protein Data Bank. We predicted the interactions for pairs in 78×78 combinations. Of these, 78 original complexes were defined as positive pairs, and the other 6,006 complexes were defined as negative pairs; then an F-measure value of 0.465 was obtained with our prediction system.Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Protein Data Bank. We predicted the interactions for pairs in 78×78 combinations. Of these, 78 original complexes were defined as positive pairs, and the other 6,006 complexes were defined as negative pairs; then an F-measure value of 0.465 was obtained with our prediction system. Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Protein Data Bank. We predicted the interactions for pairs in 78×78 combinations. Of these, 78 original complexes were defined as positive pairs, and the other 6,006 complexes were defined as negative pairs; then an F-measure value of 0.465 was obtained with our prediction system. |
| Author | AKIYAMA, YUTAKA MATSUZAKI, YURI OHUE, MASAHITO |
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| References | [14] Matsuzaki, Yuri, Matsuzaki, Yusuke, Sato, T. and Akiyama, Y., In silico screening of protein-protein interactions with all-to-all rigid docking and clustering: an application to pathway analysis, Journal of Bioinformatics and Computational Biology, 7(6), 991-1012, 2009. [3] Chen, R., Li, L. and Weng, Z., ZDOCK: an initial-stage protein-docking algorithm, Proteins, 52(1), 80-87, 2003. [12] Lensink, M.F. and Wodak, S.J., Docking and scoring protein interactions: CAPRI 2009, Proteins, 78(15), 3073-3084, 2010. [9] Katchalski-Katzir, E., Shariv, I., Eisenstein, M., Friesem, A.A., Aflalo, C. and Vakser, I.A., Molecular surface recognition: Determination of geometric fit between proteins and their ligands by correlation techniques, Proc. Natl. Acad. Sci. USA, 89(6), 2195-2199, 1992. [4] Ellis, J.J., Broom, M. and Jones, S., Protein-RNA Interactions : Structural Analysis and Functional Classes, Proteins, 66(4), 903-911, 2007. [19] Reiher, III., W.H., Theoretical studies of hydrogen bonding, Ph.D. Thesis at Harvard University, 1985. [16] Peréz-Cano, L., Solernou, A., Pons, C. and Fernández-Recio, J., Structural prediction of protein-RNA interaction by computational docking with propensity-based statistical potentials, Pacific Symposium on Biocomputing 2010, 293-301, 2010. [13] MacKerell, Jr. A.D, Banavali, N. and Foloppe, N., Development and current status of the CHARMM force field for nucleic acids, Biopolymers, 56(4), 257-265, 2001. [10] Keene, J.D., Ribonucleoprotein infrastructure regulating the flow of genetic information between the genome and the proteome, Proc. Natl. Acad. Sci. USA, 98(13), 7018-7024, 2001. [15] Ohue, M., Matsuzaki, Yuri, Matsuzaki, Yusuke, Sato, T. and Akiyama, Y., MEGADOCK: an all-to-all protein-protein interaction prediction system using tertiary structure data and its application to systems biology study, IPSJ Transactions on Mathematical Modeling and its Applications, 3(3), 91-106, 2010 (in Japanese). [5] Fanelli, F. and Ferrari, S., Prediction of MEF2A-DNA interface by rigid body docking: a tool for fast estimation of protein mutational effects on DNA binding, Journal of Structural Biology, 153(3), 278-283, 2006. [8] Jeong, E., Kim, H., Lee, S. and Han, K., Discovering the interaction propensities of amino acids and nucleotides from protein-RNA complexes. Molecules and cells, 16(2), 161-167, 2003. [6] Gabb, H.A., Jackson, R.M. and Sternberg, M.J., Modelling protein docking using shape complementarity, electrostatics and biochemical information, Journal of Molecular Biology, 272, 106-120, 1997. [18] Pons, C., Solernou, A., Peréz-Cano, L., Grosdidier, S., and Fernández-Recio, J., Optimization of pyDock for the new CAPRI challenges: Docking of homology-based models, domain-domain assembly and protein-RNA binding, Proteins, 78(15), 3182-3138, 2010. [2] Chen, R. and Weng, Z., A novel shape complementarity scoring function for protein-protein docking, Proteins, 51(3), 397-408, 2003. [21] Yi, H., Qiu, S., Cao, Z., Wu, Y. and Li, W., Molecular basis of inhibitory peptide maurotoxin recognizing Kv1.2 channel explored by ZDOCK and molecular dynamic simulations, Proteins, 70(3), 844-854, 2010. [22] Zweig, M.H. and Campbell, G., Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine, Clinical Chemistry, 39, 561-577, 1993. [17] Peréz-Cano, L. and Fernández-Recio, J., Optimal protein-RNA area, OPRA: a propensity-based method to identify RNA-binding sites on proteins, Proteins, 78(1), 25-35, 2010. [20] Wang, G. and Dunbrack, R.L., PISCES: recent improvements to a PDB sequence culling server, Nucleic Acids Research, 33(Web Server issue), W94-8, 2005. [11] Lejeune, D., Delsaux, N., Charloteaux, B., Thomas, A. and Brasseur, R., Protein-nucleic acid recognition: statistical analysis of atomic interactions and influence of DNA structure, Proteins, 61(2), 258-271, 2005. [1] Brooks, B.R., Bruccoleri, R.E., Olafson, B.D., States, D.J., Swaminathan, S. and Karplus, M., CHARMM: A program for macromolecular energy, minimization, and dynamics calculations, Journal of Computational Chemistry, 4, 187-217, 1983. [7] Janin, J., Henrick, K., Moult, J., Eyck, L.T., Sternberg, M.J.E., Vajda, S., Vakser, I. and Wodak, S.J., CAPRI: a Critical Assessment of PRedicted Interactions, Proteins, 52(1), 2-9, 2003. |
| References_xml | – reference: [4] Ellis, J.J., Broom, M. and Jones, S., Protein-RNA Interactions : Structural Analysis and Functional Classes, Proteins, 66(4), 903-911, 2007. – reference: [7] Janin, J., Henrick, K., Moult, J., Eyck, L.T., Sternberg, M.J.E., Vajda, S., Vakser, I. and Wodak, S.J., CAPRI: a Critical Assessment of PRedicted Interactions, Proteins, 52(1), 2-9, 2003. – reference: [13] MacKerell, Jr. A.D, Banavali, N. and Foloppe, N., Development and current status of the CHARMM force field for nucleic acids, Biopolymers, 56(4), 257-265, 2001. – reference: [15] Ohue, M., Matsuzaki, Yuri, Matsuzaki, Yusuke, Sato, T. and Akiyama, Y., MEGADOCK: an all-to-all protein-protein interaction prediction system using tertiary structure data and its application to systems biology study, IPSJ Transactions on Mathematical Modeling and its Applications, 3(3), 91-106, 2010 (in Japanese). – reference: [3] Chen, R., Li, L. and Weng, Z., ZDOCK: an initial-stage protein-docking algorithm, Proteins, 52(1), 80-87, 2003. – reference: [21] Yi, H., Qiu, S., Cao, Z., Wu, Y. and Li, W., Molecular basis of inhibitory peptide maurotoxin recognizing Kv1.2 channel explored by ZDOCK and molecular dynamic simulations, Proteins, 70(3), 844-854, 2010. – reference: [22] Zweig, M.H. and Campbell, G., Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine, Clinical Chemistry, 39, 561-577, 1993. – reference: [10] Keene, J.D., Ribonucleoprotein infrastructure regulating the flow of genetic information between the genome and the proteome, Proc. Natl. Acad. Sci. USA, 98(13), 7018-7024, 2001. – reference: [16] Peréz-Cano, L., Solernou, A., Pons, C. and Fernández-Recio, J., Structural prediction of protein-RNA interaction by computational docking with propensity-based statistical potentials, Pacific Symposium on Biocomputing 2010, 293-301, 2010. – reference: [5] Fanelli, F. and Ferrari, S., Prediction of MEF2A-DNA interface by rigid body docking: a tool for fast estimation of protein mutational effects on DNA binding, Journal of Structural Biology, 153(3), 278-283, 2006. – reference: [8] Jeong, E., Kim, H., Lee, S. and Han, K., Discovering the interaction propensities of amino acids and nucleotides from protein-RNA complexes. Molecules and cells, 16(2), 161-167, 2003. – reference: [14] Matsuzaki, Yuri, Matsuzaki, Yusuke, Sato, T. and Akiyama, Y., In silico screening of protein-protein interactions with all-to-all rigid docking and clustering: an application to pathway analysis, Journal of Bioinformatics and Computational Biology, 7(6), 991-1012, 2009. – reference: [12] Lensink, M.F. and Wodak, S.J., Docking and scoring protein interactions: CAPRI 2009, Proteins, 78(15), 3073-3084, 2010. – reference: [11] Lejeune, D., Delsaux, N., Charloteaux, B., Thomas, A. and Brasseur, R., Protein-nucleic acid recognition: statistical analysis of atomic interactions and influence of DNA structure, Proteins, 61(2), 258-271, 2005. – reference: [1] Brooks, B.R., Bruccoleri, R.E., Olafson, B.D., States, D.J., Swaminathan, S. and Karplus, M., CHARMM: A program for macromolecular energy, minimization, and dynamics calculations, Journal of Computational Chemistry, 4, 187-217, 1983. – reference: [6] Gabb, H.A., Jackson, R.M. and Sternberg, M.J., Modelling protein docking using shape complementarity, electrostatics and biochemical information, Journal of Molecular Biology, 272, 106-120, 1997. – reference: [17] Peréz-Cano, L. and Fernández-Recio, J., Optimal protein-RNA area, OPRA: a propensity-based method to identify RNA-binding sites on proteins, Proteins, 78(1), 25-35, 2010. – reference: [19] Reiher, III., W.H., Theoretical studies of hydrogen bonding, Ph.D. Thesis at Harvard University, 1985. – reference: [20] Wang, G. and Dunbrack, R.L., PISCES: recent improvements to a PDB sequence culling server, Nucleic Acids Research, 33(Web Server issue), W94-8, 2005. – reference: [2] Chen, R. and Weng, Z., A novel shape complementarity scoring function for protein-protein docking, Proteins, 51(3), 397-408, 2003. – reference: [18] Pons, C., Solernou, A., Peréz-Cano, L., Grosdidier, S., and Fernández-Recio, J., Optimization of pyDock for the new CAPRI challenges: Docking of homology-based models, domain-domain assembly and protein-RNA binding, Proteins, 78(15), 3182-3138, 2010. – reference: [9] Katchalski-Katzir, E., Shariv, I., Eisenstein, M., Friesem, A.A., Aflalo, C. and Vakser, I.A., Molecular surface recognition: Determination of geometric fit between proteins and their ligands by correlation techniques, Proc. Natl. Acad. Sci. USA, 89(6), 2195-2199, 1992. |
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| Snippet | Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body... |
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| SubjectTerms | all-to-all prediction Amino Acid Sequence Base Sequence Data banks Data processing FFT-based rigid-body docking Informatics Molecular Docking Simulation Molecular Sequence Data Protein Binding Protein structure protein-RNA interaction RNA - chemistry RNA - metabolism RNA-Binding Proteins - chemistry RNA-Binding Proteins - metabolism Software Tertiary structure |
| Title | DOCKING-CALCULATION-BASED METHOD FOR PREDICTING PROTEIN-RNA INTERACTIONS |
| URI | https://www.jstage.jst.go.jp/article/gi/25/1/25_1_25/_article/-char/en https://cir.nii.ac.jp/crid/1390282679465557376 https://www.ncbi.nlm.nih.gov/pubmed/22230937 https://www.proquest.com/docview/1038602564 https://www.proquest.com/docview/915385147 |
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