- Email: [email protected]
Computational methods for protein interaction and structural prediction
Biochimica et Biophysica Acta 1824 (2012) 1416–1417
Contents lists available at SciVerse ScienceDirect
Biochimica et Biophysica Acta journal homepage: www.elsevier.com/locate/bbapap
Preface
Computational methods for protein interaction and structural prediction
Experimental methods produce diverse data on these interactions collection from high throughput protein–protein interactions (PPIs) to the crystallized structures of complexes [1,2]. In model cellular systems should provide novel insights into the structure and properties of these systems for comprehensive knowledge of the whole network of protein–protein interactions. High-throughput technologies have formed large-scale data on protein–protein interactions (PPI) across human and most model species. A fundamental challenge to bioinformatics is how to interpret these riches of data to elucidate the interaction of patterns and the biological characteristics of the proteins [3,4]. The prediction of the interactions and structures of biological macromolecules and the design of new structures and interactions are crucial tests of our thoughtful of the interatomic interactions that underlie molecular biology [5,6]. This issue Computational Methods for Protein Interaction and Structural Prediction of the BBA — Proteins and Proteomics features the 9 selected papers at APBC2012, the Tenth Asia Pacific Bioinformatics Conference, held at Melbourne, Australia, 17–19 January 2012. The Asia Pacific Bioinformatics Conference (APBC) is a leading conference in the Bioinformatics community and has grown rapidly since its inception in 2003. The goal of the annual conference series is to enable high quality interaction on bioinformatics research. The past APBC conferences were held in: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
APBC 2003 4–7 Feb 2003: Adelaide, Australia APBC 2004 18–22 Jan 2004: Dunedin, New Zealand APBC 2005 17–21 Jan 2005: Singapore APBC 2006 13–16 Feb 2006: Taipei, Taiwan APBC 2007 15–17 Jan 2007: Hong Kong APBC 2008 14–17 Jan 2008: Kyoto, Japan APBC 2009 13–16 Jan 2009: Beijing, China APBC 2010 18–21 Jan 2010: Bangalore, India APBC 2011 11–14 Jan 2011: Incheon, Korea APBC2012 17–19 Jan, 2012: Melbourne, Australia.
2012, of the 129 submitted full papers; each paper was sent to three Program Committee members to review and with an acceptance rate of 32.5%. We wish to thank and acknowledge the Program Committee members and their contributions. These selected papers show of recent research that they have shown great teamwork in the completion of some challenging Bioinformatics' tasks and studies on Computational Methods for Protein Interaction and Structural Prediction. The authors of Protein Complex Prediction based on Maximum Matching with Domain–domain Interaction, Lusheng Wang, Wenji Ma, and Craig McAnulla, found that most of the existing methods only make use of the protein–protein interaction networks without considering the structural limitations of proteins to bind together. In this paper, they designed a new protein complex prediction method by extending the 1570-9639/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.bbapap.2012.09.011
idea of using domain–domain interaction information. They combined their method with three other existing methods, such as COACH, MCL and MCODE. Experiments show that the precision of the combined method is improved. The authors, Marcin Magnus, Marcin Pawlowski, and Janusz M. Bujnicki, investigated on MetaLocGramN: a meta-predictor of protein subcellular localization for gram-negative bacteria. Subcellular localization is a key functional characteristic of proteins. It is determined by signals encoded in the protein sequence. They found that PSORTb3 performs best on the average, but is outperformed by other methods in predictions of extracellular proteins. This had motivated them into developing a meta-predictor, which combines the primary methods by using the logistic regression models, to take advantage of their combined strengths, and to eliminate their individual weaknesses. The authors Nguyen Xuan Vinh, Madhu Chetty, Ross L Coppel and Pramod P Wangikar, studied the Issues Impacting Genetic Network Reverse Engineering Algorithm Validation Using Small Networks. Genetic network reverse engineering has been an area of intensive research within the systems biology community during the last decade. With many techniques currently available, the task of validating them and choosing the best one for a certain problem is a complex issue. An important issue highlighted was that with short time series, a small variation in the pre-processing procedure might yield large differences in the inferred networks. To demonstrate these issues, they have selected as their case study, the IRMA in-vivo synthetic yeast network recently published in cell. The authors Yamamotoya, Hitomi Dose, Zhongyuan Tian, Adrien Fauré, Yoshihiro Toya, Masayuki Honma, Kaori Igarashi, Kenji Nakahigashi, Tomoyoshi Soga, Hirotada Mori, and Hiroshi Matsunoa investigated on glycogen being the primary source of glucose during the lag phase of Escherichia coli proliferation. In the studies of E. coli (Escherichia coli), metabolomics analyses have mainly been performed using steady state culture. However, to analyze the dynamic changes in cellular metabolism, they performed a profiling of concentration of metabolites by using batch culture. The existence of another carbon source was suggested from the computational result. They confirmed their prediction experimentally. The authors Periyanaina Kesika and Krishnaswamy Balamurugan investigated in the studies on Shigella boydii infection in Caenorhabditis elegans and bioinformatics analysis of immune regulatory protein interactions. S. boydii causes bacillary dysentery or shigellosis and generates a significant burden in the developing nations. S. boydii-mediated infection assays were performed at both physiological and molecular levels using C. elegans as a host. Their results using the lowest eukaryotic model system and human database indicated that the major players involved in immunity related processes appear to be common in cases of Shigella sp. mediated immune responses.
Preface
The authors Qian Liu, Limsoon Wong, and Jinyan Li, studied the Z-score biological significance of binding hot spots of protein interfaces by using crystal packing as the reference state. Characterization of binding hot spots of protein interfaces is a fundamental study in molecular biology. Many computational methods have been proposed to identify binding hot spots. They propose to use Z-score to predict whether a contact residue is a hot spot residue. Comparison with previously reported methods on two benchmark datasets shows that this Z-score method is mostly superior to earlier methods. The authors Yi-Tsung Tang and Hung-Yu Kao, studied Augmented Transitive Relationships with High Impact Protein Distillation in Protein Interaction Prediction. Predicting new protein–protein interactions is important for discovering novel functions of various biological pathways. Predicting these interactions is a crucial and challenging task. Moreover, discovering new protein–protein interactions through biological experiments is still difficult. Their results demonstrate that ATRP can effectively predict protein–protein interactions. ATRP achieves an 81% precision, a 74% recall and a 77% F-measure in average rate in the prediction of direct protein–protein interactions. The authors Abhinav Grover, Shashank P Katiyar, Sanjeev K Singh, Vikash K Dubey, and D. Sundar, studied a leishmaniasis study: structure-based screening and molecular dynamics mechanistic analysis for discovering potent inhibitors of spermidine synthase. Protozoa Leishmania donovani (Ld) is the main cause of the endemic disease leishmaniasis. Spermidine synthase (SS), an important enzyme in the synthetic pathway of polyamines in Ld, is an essential element for the survival of this protozoan. They modelled the tertiary structure of LSS using homology modelling approach making use of homologous X-ray crystallographic structure of spermidine synthase of Trypanosoma cruzi (TSS) (2.5 Å resolution). The modelled structure was stabilized using Molecular Dynamics simulations. The authors Keunwan Park and Dongsup Kim investigated on the Structure-based Rebuilding of Coevolutionary Information Reveals Functional Modules in Rhodopsin Structure. Correlated mutation analysis (CMA) has been used to investigate protein functional sites. However, CMA has suffered from low signal-to-noise ratio caused by meaningless phylogenetic signals or structural constraints. They presented a new method, Structure-based Correlated Mutation Analysis (SCMA), which encodes coevolution scores into the protein structure network. This model intrinsically assumes that residues in physical contact have a more reliable coevolution score than distant residues, and that coevolution in distant residues likely arises from a series of contacting and coevolving residues.
1417
References [1] M.N. Wass, A. David, M.J.E. Sternberg, Challenges for the prediction of macromolecular interactions, Curr. Opin. Struct. Biol. 21 (3) (2011) 382–390. [2] B. Xue, R.L. Dunbrack, R.W. Williams, A.K. Dunker, V.N. Uversky, PONDR-FIT: a meta-predictor of intrinsically disordered amino acids, Biochim. Biophys. Acta, Proteins Proteomics 1804 (4) (2010) 996–1010. [3] W. Zhu, J. Hou, Y.P.P. Chen, Semantic and layered protein function prediction from PPI networks, J. Theor. Biol. 267 (2) (2010) 129–136. [4] B.D. Allena, A. Nisthalb, S.L. Mayo, Experimental library screening demonstrates the successful application of computational protein design to large structural ensembles, PNAS 107 (46) (2010) 19838–19843. [5] O. Schueler-Furman, C. Wang, P. Bradley, K. Misura, D. Baker, Progress in modeling of protein structures and interactions, Science 28 (October 2005) 638–642. [6] G. Grigoryan, A.W. Reinke, A.E. Keating, Design of protein-interaction specificity gives selective bZIP-binding peptides, Nature 458 (April 16 2009) 859–864.
Yi-Ping Phoebe Chen is a Professor and Chair & Director of Research at the Department of Computer Science and Computer Engineering, La Trobe University, Melbourne Australia. She was the Head of Department of Department of Computer Science and Computer Engineering, La Trobe University from Sep 2010 to April 2012. Professor Chen is the Chief Investigator of ARC Centre of Excellence in Bioinformatics. Phoebe received her BInfTech degree with First Class Honors and PhD in Computer Science (Bioinformatics) from the University of Queensland. Before she joined La Trobe, Phoebe was an Associate Professor (Reader) in Deakin University from Dec 2003 to April 2010. She worked as an Associate Lecturer/Lecturer/Senior Lecturer in Queensland University of Technology from Jul 1999 to Nov 2003. She is currently working on knowledge discovery technologies and is especially interested in their application to genomics and biomedical science. Her research focus is to find best solutions for mining, integrating and analyzing complex data structure and functions for scientific and biomedical applications. She has been working in the area of bioinformatics, health informatics, multimedia databases, query system and systems biology, co-authored over 180 research papers with many published in top journals and conferences such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Information Technology in Biomedicine, Aging Cell, Nucleic Acids Research, BMC Genomics, BMC Bioinformatics, Current Drug Metabolism, Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Information Systems and ACM Transactions. She is a steering committee chair of Asia-Pacific Bioinformatics Conference (founder) and International conference on Multimedia Modelling. She has been on the program committees of over 100 international conferences, including top ranking conferences such as ICDE, ICPR, ISMB and CIKM.
Yi-Ping Phoebe Chen Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086, Australia E-mail address: [email protected].
Contents lists available at SciVerse ScienceDirect
Biochimica et Biophysica Acta journal homepage: www.elsevier.com/locate/bbapap
Preface
Computational methods for protein interaction and structural prediction
Experimental methods produce diverse data on these interactions collection from high throughput protein–protein interactions (PPIs) to the crystallized structures of complexes [1,2]. In model cellular systems should provide novel insights into the structure and properties of these systems for comprehensive knowledge of the whole network of protein–protein interactions. High-throughput technologies have formed large-scale data on protein–protein interactions (PPI) across human and most model species. A fundamental challenge to bioinformatics is how to interpret these riches of data to elucidate the interaction of patterns and the biological characteristics of the proteins [3,4]. The prediction of the interactions and structures of biological macromolecules and the design of new structures and interactions are crucial tests of our thoughtful of the interatomic interactions that underlie molecular biology [5,6]. This issue Computational Methods for Protein Interaction and Structural Prediction of the BBA — Proteins and Proteomics features the 9 selected papers at APBC2012, the Tenth Asia Pacific Bioinformatics Conference, held at Melbourne, Australia, 17–19 January 2012. The Asia Pacific Bioinformatics Conference (APBC) is a leading conference in the Bioinformatics community and has grown rapidly since its inception in 2003. The goal of the annual conference series is to enable high quality interaction on bioinformatics research. The past APBC conferences were held in: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
APBC 2003 4–7 Feb 2003: Adelaide, Australia APBC 2004 18–22 Jan 2004: Dunedin, New Zealand APBC 2005 17–21 Jan 2005: Singapore APBC 2006 13–16 Feb 2006: Taipei, Taiwan APBC 2007 15–17 Jan 2007: Hong Kong APBC 2008 14–17 Jan 2008: Kyoto, Japan APBC 2009 13–16 Jan 2009: Beijing, China APBC 2010 18–21 Jan 2010: Bangalore, India APBC 2011 11–14 Jan 2011: Incheon, Korea APBC2012 17–19 Jan, 2012: Melbourne, Australia.
2012, of the 129 submitted full papers; each paper was sent to three Program Committee members to review and with an acceptance rate of 32.5%. We wish to thank and acknowledge the Program Committee members and their contributions. These selected papers show of recent research that they have shown great teamwork in the completion of some challenging Bioinformatics' tasks and studies on Computational Methods for Protein Interaction and Structural Prediction. The authors of Protein Complex Prediction based on Maximum Matching with Domain–domain Interaction, Lusheng Wang, Wenji Ma, and Craig McAnulla, found that most of the existing methods only make use of the protein–protein interaction networks without considering the structural limitations of proteins to bind together. In this paper, they designed a new protein complex prediction method by extending the 1570-9639/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.bbapap.2012.09.011
idea of using domain–domain interaction information. They combined their method with three other existing methods, such as COACH, MCL and MCODE. Experiments show that the precision of the combined method is improved. The authors, Marcin Magnus, Marcin Pawlowski, and Janusz M. Bujnicki, investigated on MetaLocGramN: a meta-predictor of protein subcellular localization for gram-negative bacteria. Subcellular localization is a key functional characteristic of proteins. It is determined by signals encoded in the protein sequence. They found that PSORTb3 performs best on the average, but is outperformed by other methods in predictions of extracellular proteins. This had motivated them into developing a meta-predictor, which combines the primary methods by using the logistic regression models, to take advantage of their combined strengths, and to eliminate their individual weaknesses. The authors Nguyen Xuan Vinh, Madhu Chetty, Ross L Coppel and Pramod P Wangikar, studied the Issues Impacting Genetic Network Reverse Engineering Algorithm Validation Using Small Networks. Genetic network reverse engineering has been an area of intensive research within the systems biology community during the last decade. With many techniques currently available, the task of validating them and choosing the best one for a certain problem is a complex issue. An important issue highlighted was that with short time series, a small variation in the pre-processing procedure might yield large differences in the inferred networks. To demonstrate these issues, they have selected as their case study, the IRMA in-vivo synthetic yeast network recently published in cell. The authors Yamamotoya, Hitomi Dose, Zhongyuan Tian, Adrien Fauré, Yoshihiro Toya, Masayuki Honma, Kaori Igarashi, Kenji Nakahigashi, Tomoyoshi Soga, Hirotada Mori, and Hiroshi Matsunoa investigated on glycogen being the primary source of glucose during the lag phase of Escherichia coli proliferation. In the studies of E. coli (Escherichia coli), metabolomics analyses have mainly been performed using steady state culture. However, to analyze the dynamic changes in cellular metabolism, they performed a profiling of concentration of metabolites by using batch culture. The existence of another carbon source was suggested from the computational result. They confirmed their prediction experimentally. The authors Periyanaina Kesika and Krishnaswamy Balamurugan investigated in the studies on Shigella boydii infection in Caenorhabditis elegans and bioinformatics analysis of immune regulatory protein interactions. S. boydii causes bacillary dysentery or shigellosis and generates a significant burden in the developing nations. S. boydii-mediated infection assays were performed at both physiological and molecular levels using C. elegans as a host. Their results using the lowest eukaryotic model system and human database indicated that the major players involved in immunity related processes appear to be common in cases of Shigella sp. mediated immune responses.
Preface
The authors Qian Liu, Limsoon Wong, and Jinyan Li, studied the Z-score biological significance of binding hot spots of protein interfaces by using crystal packing as the reference state. Characterization of binding hot spots of protein interfaces is a fundamental study in molecular biology. Many computational methods have been proposed to identify binding hot spots. They propose to use Z-score to predict whether a contact residue is a hot spot residue. Comparison with previously reported methods on two benchmark datasets shows that this Z-score method is mostly superior to earlier methods. The authors Yi-Tsung Tang and Hung-Yu Kao, studied Augmented Transitive Relationships with High Impact Protein Distillation in Protein Interaction Prediction. Predicting new protein–protein interactions is important for discovering novel functions of various biological pathways. Predicting these interactions is a crucial and challenging task. Moreover, discovering new protein–protein interactions through biological experiments is still difficult. Their results demonstrate that ATRP can effectively predict protein–protein interactions. ATRP achieves an 81% precision, a 74% recall and a 77% F-measure in average rate in the prediction of direct protein–protein interactions. The authors Abhinav Grover, Shashank P Katiyar, Sanjeev K Singh, Vikash K Dubey, and D. Sundar, studied a leishmaniasis study: structure-based screening and molecular dynamics mechanistic analysis for discovering potent inhibitors of spermidine synthase. Protozoa Leishmania donovani (Ld) is the main cause of the endemic disease leishmaniasis. Spermidine synthase (SS), an important enzyme in the synthetic pathway of polyamines in Ld, is an essential element for the survival of this protozoan. They modelled the tertiary structure of LSS using homology modelling approach making use of homologous X-ray crystallographic structure of spermidine synthase of Trypanosoma cruzi (TSS) (2.5 Å resolution). The modelled structure was stabilized using Molecular Dynamics simulations. The authors Keunwan Park and Dongsup Kim investigated on the Structure-based Rebuilding of Coevolutionary Information Reveals Functional Modules in Rhodopsin Structure. Correlated mutation analysis (CMA) has been used to investigate protein functional sites. However, CMA has suffered from low signal-to-noise ratio caused by meaningless phylogenetic signals or structural constraints. They presented a new method, Structure-based Correlated Mutation Analysis (SCMA), which encodes coevolution scores into the protein structure network. This model intrinsically assumes that residues in physical contact have a more reliable coevolution score than distant residues, and that coevolution in distant residues likely arises from a series of contacting and coevolving residues.
1417
References [1] M.N. Wass, A. David, M.J.E. Sternberg, Challenges for the prediction of macromolecular interactions, Curr. Opin. Struct. Biol. 21 (3) (2011) 382–390. [2] B. Xue, R.L. Dunbrack, R.W. Williams, A.K. Dunker, V.N. Uversky, PONDR-FIT: a meta-predictor of intrinsically disordered amino acids, Biochim. Biophys. Acta, Proteins Proteomics 1804 (4) (2010) 996–1010. [3] W. Zhu, J. Hou, Y.P.P. Chen, Semantic and layered protein function prediction from PPI networks, J. Theor. Biol. 267 (2) (2010) 129–136. [4] B.D. Allena, A. Nisthalb, S.L. Mayo, Experimental library screening demonstrates the successful application of computational protein design to large structural ensembles, PNAS 107 (46) (2010) 19838–19843. [5] O. Schueler-Furman, C. Wang, P. Bradley, K. Misura, D. Baker, Progress in modeling of protein structures and interactions, Science 28 (October 2005) 638–642. [6] G. Grigoryan, A.W. Reinke, A.E. Keating, Design of protein-interaction specificity gives selective bZIP-binding peptides, Nature 458 (April 16 2009) 859–864.
Yi-Ping Phoebe Chen is a Professor and Chair & Director of Research at the Department of Computer Science and Computer Engineering, La Trobe University, Melbourne Australia. She was the Head of Department of Department of Computer Science and Computer Engineering, La Trobe University from Sep 2010 to April 2012. Professor Chen is the Chief Investigator of ARC Centre of Excellence in Bioinformatics. Phoebe received her BInfTech degree with First Class Honors and PhD in Computer Science (Bioinformatics) from the University of Queensland. Before she joined La Trobe, Phoebe was an Associate Professor (Reader) in Deakin University from Dec 2003 to April 2010. She worked as an Associate Lecturer/Lecturer/Senior Lecturer in Queensland University of Technology from Jul 1999 to Nov 2003. She is currently working on knowledge discovery technologies and is especially interested in their application to genomics and biomedical science. Her research focus is to find best solutions for mining, integrating and analyzing complex data structure and functions for scientific and biomedical applications. She has been working in the area of bioinformatics, health informatics, multimedia databases, query system and systems biology, co-authored over 180 research papers with many published in top journals and conferences such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Information Technology in Biomedicine, Aging Cell, Nucleic Acids Research, BMC Genomics, BMC Bioinformatics, Current Drug Metabolism, Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Information Systems and ACM Transactions. She is a steering committee chair of Asia-Pacific Bioinformatics Conference (founder) and International conference on Multimedia Modelling. She has been on the program committees of over 100 international conferences, including top ranking conferences such as ICDE, ICPR, ISMB and CIKM.
Yi-Ping Phoebe Chen Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086, Australia E-mail address: [email protected].