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Journal Articles
2017
51.
Alexander Miguel Monzon; Diego Javier Zea; Cristina Marino-Buslje; Gustavo Parisi
Homology modeling in a dynamical world Journal Article
In: Protein Science, vol. 26, no. 11, pp. 2195-2206, 2017, (Cited by: 22; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:85032200484,
title = {Homology modeling in a dynamical world},
author = {Alexander Miguel Monzon and Diego Javier Zea and Cristina Marino-Buslje and Gustavo Parisi},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85032200484&origin=inward},
doi = {10.1002/pro.3274},
year = {2017},
date = {2017-01-01},
journal = {Protein Science},
volume = {26},
number = {11},
pages = {2195-2206},
publisher = {Blackwell Publishing Ltdcustomerservices@oxonblackwellpublishing.com},
abstract = {© 2017 The Protein SocietyA key concept in template-based modeling (TBM) is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity. In this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair TBM results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.},
note = {Cited by: 22; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© 2017 The Protein SocietyA key concept in template-based modeling (TBM) is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity. In this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair TBM results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.
52.
Alexander Miguel Monzon; Marcia A. Hasenahuer; Estefanía Mancini; Nilson Coimbra; Fiorella Cravero; Javier Cáceres-Molina; César A Ramírez-Sarmiento; Nicolas Palopoli; Pieter Meysman; R Gonzalo Parra
Second ISCB Latin American Student Council Symposium (LA-SCS) 2016 Journal Article
In: F1000Research, vol. 6, 2017, (Cited by: 4; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:85029008824,
title = {Second ISCB Latin American Student Council Symposium (LA-SCS) 2016},
author = {Alexander Miguel Monzon and Marcia A. Hasenahuer and Estefanía Mancini and Nilson Coimbra and Fiorella Cravero and Javier Cáceres-Molina and César A Ramírez-Sarmiento and Nicolas Palopoli and Pieter Meysman and R Gonzalo Parra},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85029008824&origin=inward},
doi = {10.12688/f1000research.12321.1},
year = {2017},
date = {2017-01-01},
journal = {F1000Research},
volume = {6},
publisher = {NLM (Medline)},
abstract = {This report summarizes the scientific content and activities of the second edition of the Latin American Symposium (LA-SCS), organized by the Student Council (SC) of the International Society for Computational Biology (ISCB), held in conjunction with the Fourth Latin American conference from the International Society for Computational Biology (ISCB-LA 2016) in Buenos Aires, Argentina, on November 19, 2016.},
note = {Cited by: 4; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This report summarizes the scientific content and activities of the second edition of the Latin American Symposium (LA-SCS), organized by the Student Council (SC) of the International Society for Computational Biology (ISCB), held in conjunction with the Fourth Latin American conference from the International Society for Computational Biology (ISCB-LA 2016) in Buenos Aires, Argentina, on November 19, 2016.
53.
Alexander Miguel Monzon; Diego Javier Zea; María Silvina Fornasari; Tadeo E. Saldaño; Sebastian Fernandez-Alberti; Silvio C. E. Tosatto; Gustavo Parisi
Conformational diversity analysis reveals three functional mechanisms in proteins Journal Article
In: PLoS Computational Biology, vol. 13, no. 2, 2017, (Cited by: 44; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:85014243730,
title = {Conformational diversity analysis reveals three functional mechanisms in proteins},
author = {Alexander Miguel Monzon and Diego Javier Zea and María Silvina Fornasari and Tadeo E. Saldaño and Sebastian Fernandez-Alberti and Silvio C. E. Tosatto and Gustavo Parisi},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85014243730&origin=inward},
doi = {10.1371/journal.pcbi.1005398},
year = {2017},
date = {2017-01-01},
journal = {PLoS Computational Biology},
volume = {13},
number = {2},
publisher = {Public Library of Science},
abstract = {© 2017 Monzon et al.Protein motions are a key feature to understand biological function. Recently, a large-scale analysis of protein conformational diversity showed a positively skewed distribution with a peak at 0.5 Å C-alpha root-mean-square-deviation (RMSD). To understand this distribution in terms of structure-function relationships, we studied a well curated and large dataset of textasciitilde 5,000 proteins with experimentally determined conformational diversity. We searched for global behaviour patterns studying how structure-based features change among the available conformer population for each protein. This procedure allowed us to describe the RMSD distribution in terms of three main protein classes sharing given properties. The largest of these protein subsets (textasciitilde 60%), which we call “rigid” (average RMSD = 0.83 Å), has no disordered regions, shows low conformational diversity, the largest tunnels and smaller and buried cavities. The two additional subsets contain disordered regions, but with differential sequence composition and behaviour. Partially disordered proteins have on average 67% of their conformers with disordered regions, average RMSD = 1.1 Å, the highest number of hinges and the longest disordered regions. In contrast, malleable proteins have on average only 25% of disordered conformers and average RMSD = 1.3 Å, flexible cavities affected in size by the presence of disordered regions and show the highest diversity of cognate ligands. Proteins in each set are mostly non-homologous to each other, share no given fold class, nor functional similarity but do share features derived from their conformer population. These shared features could represent conformational mechanisms related with biological functions.},
note = {Cited by: 44; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© 2017 Monzon et al.Protein motions are a key feature to understand biological function. Recently, a large-scale analysis of protein conformational diversity showed a positively skewed distribution with a peak at 0.5 Å C-alpha root-mean-square-deviation (RMSD). To understand this distribution in terms of structure-function relationships, we studied a well curated and large dataset of textasciitilde 5,000 proteins with experimentally determined conformational diversity. We searched for global behaviour patterns studying how structure-based features change among the available conformer population for each protein. This procedure allowed us to describe the RMSD distribution in terms of three main protein classes sharing given properties. The largest of these protein subsets (textasciitilde 60%), which we call “rigid” (average RMSD = 0.83 Å), has no disordered regions, shows low conformational diversity, the largest tunnels and smaller and buried cavities. The two additional subsets contain disordered regions, but with differential sequence composition and behaviour. Partially disordered proteins have on average 67% of their conformers with disordered regions, average RMSD = 1.1 Å, the highest number of hinges and the longest disordered regions. In contrast, malleable proteins have on average only 25% of disordered conformers and average RMSD = 1.3 Å, flexible cavities affected in size by the presence of disordered regions and show the highest diversity of cognate ligands. Proteins in each set are mostly non-homologous to each other, share no given fold class, nor functional similarity but do share features derived from their conformer population. These shared features could represent conformational mechanisms related with biological functions.
2016
54.
Nicolas Palopoli; Alexander Miguel Monzon; Gustavo Parisi; Maria Silvina Fornasari
Addressing the role of conformational diversity in protein structure prediction Journal Article
In: PLoS ONE, vol. 11, no. 5, 2016, (Cited by: 13; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:84968752860,
title = {Addressing the role of conformational diversity in protein structure prediction},
author = {Nicolas Palopoli and Alexander Miguel Monzon and Gustavo Parisi and Maria Silvina Fornasari},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84968752860&origin=inward},
doi = {10.1371/journal.pone.0154923},
year = {2016},
date = {2016-01-01},
journal = {PLoS ONE},
volume = {11},
number = {5},
publisher = {Public Library of Scienceplos@plos.org},
abstract = {© 2016 Palopoli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.},
note = {Cited by: 13; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© 2016 Palopoli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.
55.
Alexander Miguel Monzon; Cristian Oscar Rohr; María Silvina Fornasari; Gustavo Parisi
CoDNaS 2.0: A comprehensive database of protein conformational diversity in the native state Journal Article
In: Database, vol. 2016, 2016, (Cited by: 55; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:84970969226,
title = {CoDNaS 2.0: A comprehensive database of protein conformational diversity in the native state},
author = {Alexander Miguel Monzon and Cristian Oscar Rohr and María Silvina Fornasari and Gustavo Parisi},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84970969226&origin=inward},
doi = {10.1093/database/baw038},
year = {2016},
date = {2016-01-01},
journal = {Database},
volume = {2016},
publisher = {Oxford University Pressjnl.info@oup.co.uk},
abstract = {© The Author(s) 2016. Published by Oxford University Press.CoDNaS (conformational diversity of the native state) is a protein conformational diversity database. Conformational diversity describes structural differences between conformers that define the native state of proteins. It is a key concept to understand protein function and biological processes related to protein functions. CoDNaS offers a well curated database that is experimentally driven, thoroughly linked, and annotated. CoDNaS facilitates the extraction of key information on small structural differences based on protein movements. CoDNaS enables users to easily relate the degree of conformational diversity with physical, chemical and biological properties derived from experiments on protein structure and biological characteristics. The new version of CoDNaS includes 70% of all available protein structures, and new tools have been added that run sequence searches, display structural flexibility profiles and allow users to browse the database for different structural classes. These tools facilitate the exploration of protein conformational diversity and its role in protein function.},
note = {Cited by: 55; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© The Author(s) 2016. Published by Oxford University Press.CoDNaS (conformational diversity of the native state) is a protein conformational diversity database. Conformational diversity describes structural differences between conformers that define the native state of proteins. It is a key concept to understand protein function and biological processes related to protein functions. CoDNaS offers a well curated database that is experimentally driven, thoroughly linked, and annotated. CoDNaS facilitates the extraction of key information on small structural differences based on protein movements. CoDNaS enables users to easily relate the degree of conformational diversity with physical, chemical and biological properties derived from experiments on protein structure and biological characteristics. The new version of CoDNaS includes 70% of all available protein structures, and new tools have been added that run sequence searches, display structural flexibility profiles and allow users to browse the database for different structural classes. These tools facilitate the exploration of protein conformational diversity and its role in protein function.
56.
Diego Javier Zea; Alexander Miguel Monzon; Claudia Gonzalez; María Silvina Fornasari; Silvio C. E. Tosatto; Gustavo Parisi
Disorder transitions and conformational diversity cooperatively modulate biological function in proteins Journal Article
In: Protein Science, vol. 25, no. 6, pp. 1138-1146, 2016, (Cited by: 16; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:84963973482,
title = {Disorder transitions and conformational diversity cooperatively modulate biological function in proteins},
author = {Diego Javier Zea and Alexander Miguel Monzon and Claudia Gonzalez and María Silvina Fornasari and Silvio C. E. Tosatto and Gustavo Parisi},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84963973482&origin=inward},
doi = {10.1002/pro.2931},
year = {2016},
date = {2016-01-01},
journal = {Protein Science},
volume = {25},
number = {6},
pages = {1138-1146},
publisher = {Blackwell Publishing Ltdcustomerservices@oxonblackwellpublishing.com},
abstract = {© 2016 The Protein Society.Structural differences between conformers sustain protein biological function. Here, we studied in a large dataset of 745 intrinsically disordered proteins, how ordered-disordered transitions modulate structural differences between conformers as derived from crystallographic data. We found that almost 50% of the proteins studied show no transitions and have low conformational diversity while the rest show transitions and a higher conformational diversity. In this last subset, 60% of the proteins become more ordered after ligand binding, while 40% more disordered. As protein conformational diversity is inherently connected with protein function our analysis suggests differences in structure-function relationships related to order-disorder transitions.},
note = {Cited by: 16; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© 2016 The Protein Society.Structural differences between conformers sustain protein biological function. Here, we studied in a large dataset of 745 intrinsically disordered proteins, how ordered-disordered transitions modulate structural differences between conformers as derived from crystallographic data. We found that almost 50% of the proteins studied show no transitions and have low conformational diversity while the rest show transitions and a higher conformational diversity. In this last subset, 60% of the proteins become more ordered after ligand binding, while 40% more disordered. As protein conformational diversity is inherently connected with protein function our analysis suggests differences in structure-function relationships related to order-disorder transitions.
57.
Tadeo E. Saldaño; Alexander M. Monzon; Gustavo Parisi; Sebastian Fernandez-Alberti
Evolutionary Conserved Positions Define Protein Conformational Diversity Journal Article
In: PLoS Computational Biology, vol. 12, no. 3, 2016, (Cited by: 23; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:84962061561,
title = {Evolutionary Conserved Positions Define Protein Conformational Diversity},
author = {Tadeo E. Saldaño and Alexander M. Monzon and Gustavo Parisi and Sebastian Fernandez-Alberti},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84962061561&origin=inward},
doi = {10.1371/journal.pcbi.1004775},
year = {2016},
date = {2016-01-01},
journal = {PLoS Computational Biology},
volume = {12},
number = {3},
publisher = {Public Library of Science},
abstract = {© 2016 Saldaño et al.Conformational diversity of the native state plays a central role in modulating protein function. The selection paradigm sustains that different ligands shift the conformational equilibrium through their binding to highest-affinity conformers. Intramolecular vibrational dynamics associated to each conformation should guarantee conformational transitions, which due to its importance, could possibly be associated with evolutionary conserved traits. Normal mode analysis, based on a coarse-grained model of the protein, can provide the required information to explore these features. Herein, we present a novel procedure to identify key positions sustaining the conformational diversity associated to ligand binding. The method is applied to an adequate refined dataset of 188 paired protein structures in their bound and unbound forms. Firstly, normal modes most involved in the conformational change are selected according to their corresponding overlap with structural distortions introduced by ligand binding. The subspace defined by these modes is used to analyze the effect of simulated point mutations on preserving the conformational diversity of the protein. We find a negative correlation between the effects of mutations on these normal mode subspaces associated to ligand-binding and position-specific evolutionary conservations obtained from multiple sequence-structure alignments. Positions whose mutations are found to alter the most these subspaces are defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. These positions are shown to be evolutionary conserved, mostly buried aliphatic residues localized in regular structural regions of the protein like β-sheets and α-helix.},
note = {Cited by: 23; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© 2016 Saldaño et al.Conformational diversity of the native state plays a central role in modulating protein function. The selection paradigm sustains that different ligands shift the conformational equilibrium through their binding to highest-affinity conformers. Intramolecular vibrational dynamics associated to each conformation should guarantee conformational transitions, which due to its importance, could possibly be associated with evolutionary conserved traits. Normal mode analysis, based on a coarse-grained model of the protein, can provide the required information to explore these features. Herein, we present a novel procedure to identify key positions sustaining the conformational diversity associated to ligand binding. The method is applied to an adequate refined dataset of 188 paired protein structures in their bound and unbound forms. Firstly, normal modes most involved in the conformational change are selected according to their corresponding overlap with structural distortions introduced by ligand binding. The subspace defined by these modes is used to analyze the effect of simulated point mutations on preserving the conformational diversity of the protein. We find a negative correlation between the effects of mutations on these normal mode subspaces associated to ligand-binding and position-specific evolutionary conservations obtained from multiple sequence-structure alignments. Positions whose mutations are found to alter the most these subspaces are defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. These positions are shown to be evolutionary conserved, mostly buried aliphatic residues localized in regular structural regions of the protein like β-sheets and α-helix.
2015
58.
Gustavo Parisi; Diego Javier Zea; Alexander Miguel Monzon; Cristina Marino-Buslje
Conformational diversity and the emergence of sequence signatures during evolution Journal Article
In: Current Opinion in Structural Biology, vol. 32, pp. 58-65, 2015, (Cited by: 36).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:84923886677,
title = {Conformational diversity and the emergence of sequence signatures during evolution},
author = {Gustavo Parisi and Diego Javier Zea and Alexander Miguel Monzon and Cristina Marino-Buslje},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84923886677&origin=inward},
doi = {10.1016/j.sbi.2015.02.005},
year = {2015},
date = {2015-01-01},
journal = {Current Opinion in Structural Biology},
volume = {32},
pages = {58-65},
publisher = {Elsevier Ltd},
abstract = {© 2015 Elsevier Ltd.Proteins' native structure is an ensemble of conformers in equilibrium, including all their respective functional states and intermediates. The induced-fit first and the pre-equilibrium theories later, described how structural changes are required to explain the allosteric and cooperative behaviours in proteins, which are key to protein function. The conformational ensemble concept has become a key tool in explaining an endless list of essential protein properties such as function, enzyme and antibody promiscuity, signal transduction, protein-protein recognition, origin of diseases, origin of new protein functions, evolutionary rate and order-disorder transitions, among others. Conformational diversity is encoded by the amino acid sequence and such a signature can be evidenced through evolutionary studies as evolutionary rate, conservation and coevolution.},
note = {Cited by: 36},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© 2015 Elsevier Ltd.Proteins’ native structure is an ensemble of conformers in equilibrium, including all their respective functional states and intermediates. The induced-fit first and the pre-equilibrium theories later, described how structural changes are required to explain the allosteric and cooperative behaviours in proteins, which are key to protein function. The conformational ensemble concept has become a key tool in explaining an endless list of essential protein properties such as function, enzyme and antibody promiscuity, signal transduction, protein-protein recognition, origin of diseases, origin of new protein functions, evolutionary rate and order-disorder transitions, among others. Conformational diversity is encoded by the amino acid sequence and such a signature can be evidenced through evolutionary studies as evolutionary rate, conservation and coevolution.
2013
59.
Diego Javier Zea; Alexander Miguel Monzon; Maria Silvina Fornasari; Cristina Marino-Buslje; Gustavo Parisi
Protein conformational diversity correlates with evolutionary rate Journal Article
In: Molecular Biology and Evolution, vol. 30, no. 7, pp. 1500-1503, 2013, (Cited by: 29; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:84879373603,
title = {Protein conformational diversity correlates with evolutionary rate},
author = {Diego Javier Zea and Alexander Miguel Monzon and Maria Silvina Fornasari and Cristina Marino-Buslje and Gustavo Parisi},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84879373603&origin=inward},
doi = {10.1093/molbev/mst065},
year = {2013},
date = {2013-01-01},
journal = {Molecular Biology and Evolution},
volume = {30},
number = {7},
pages = {1500-1503},
abstract = {Native state of proteins is better represented by an ensemble of conformers in equilibrium than by only one structure. The extension of structural differences between conformers characterizes the conformational diversity of the protein. In this study, we found a negative correlation between conformational diversity and protein evolutionary rate. Conformational diversity was expressed as the maximum root mean square deviation (RMSD) between the available conformers in Conformational Diversity of Native State database. Evolutionary rate estimations were calculated using 16 different species compared with human sharing at least 700 orthologous proteins with known conformational diversity extension. The negative correlation found is independent of the protein expression level and comparable in magnitude and sign with the correlation between gene expression level and evolutionary rate. Our findings suggest that the structural constraints underlying protein dynamism, essential for protein function, could modulate protein divergence. © 2013 The Author.},
note = {Cited by: 29; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Native state of proteins is better represented by an ensemble of conformers in equilibrium than by only one structure. The extension of structural differences between conformers characterizes the conformational diversity of the protein. In this study, we found a negative correlation between conformational diversity and protein evolutionary rate. Conformational diversity was expressed as the maximum root mean square deviation (RMSD) between the available conformers in Conformational Diversity of Native State database. Evolutionary rate estimations were calculated using 16 different species compared with human sharing at least 700 orthologous proteins with known conformational diversity extension. The negative correlation found is independent of the protein expression level and comparable in magnitude and sign with the correlation between gene expression level and evolutionary rate. Our findings suggest that the structural constraints underlying protein dynamism, essential for protein function, could modulate protein divergence. © 2013 The Author.
60.
Alexander Miguel Monzon; Ezequiel Juritz; María Silvina Fornasari; Gustavo Parisi
CoDNaS: A database of conformational diversity in the native state of proteins Journal Article
In: Bioinformatics, vol. 29, no. 19, pp. 2512-2514, 2013, (Cited by: 28; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:84897366399,
title = {CoDNaS: A database of conformational diversity in the native state of proteins},
author = {Alexander Miguel Monzon and Ezequiel Juritz and María Silvina Fornasari and Gustavo Parisi},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84897366399&origin=inward},
doi = {10.1093/bioinformatics/btt405},
year = {2013},
date = {2013-01-01},
journal = {Bioinformatics},
volume = {29},
number = {19},
pages = {2512-2514},
publisher = {Oxford University Press},
abstract = {Motivation: Conformational diversity is a key concept in the understanding of different issues related with protein function such as the study of catalytic processes in enzymes, protein-protein recognition, protein evolution and the origins of new biological functions. Here, we present a database of proteins with different degrees of conformational diversity. Conformational Diversity of Native State (CoDNaS) is a redundant collection of three-dimensional structures for the same protein derived from protein data bank. Structures for the same protein obtained under different crystallographic conditions have been associated with snapshots of protein dynamism and consequently could characterize protein conformers. CoDNaS allows the user to explore global and local structural differences among conformers as a function of different parameters such as presence of ligand, post-translational modifications, changes in oligomeric states and differences in pH and temperature. Additionally, CoDNaS contains information about protein taxonomy and function, disorder level and structural classification offering useful information to explore the underlying mechanism of conformational diversity and its close relationship with protein function. Currently, CoDNaS has 122 122 structures integrating 12 684 entries, with an average of 9.63 conformers per protein. © The Author 2013.},
note = {Cited by: 28; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Motivation: Conformational diversity is a key concept in the understanding of different issues related with protein function such as the study of catalytic processes in enzymes, protein-protein recognition, protein evolution and the origins of new biological functions. Here, we present a database of proteins with different degrees of conformational diversity. Conformational Diversity of Native State (CoDNaS) is a redundant collection of three-dimensional structures for the same protein derived from protein data bank. Structures for the same protein obtained under different crystallographic conditions have been associated with snapshots of protein dynamism and consequently could characterize protein conformers. CoDNaS allows the user to explore global and local structural differences among conformers as a function of different parameters such as presence of ligand, post-translational modifications, changes in oligomeric states and differences in pH and temperature. Additionally, CoDNaS contains information about protein taxonomy and function, disorder level and structural classification offering useful information to explore the underlying mechanism of conformational diversity and its close relationship with protein function. Currently, CoDNaS has 122 122 structures integrating 12 684 entries, with an average of 9.63 conformers per protein. © The Author 2013.
