Cargando…
Massive non-natural proteins structure prediction using grid technologies
BACKGROUND: The number of natural proteins represents a small fraction of all the possible protein sequences and there is an enormous number of proteins never sampled by nature, the so called "never born proteins" (NBPs). A fundamental question in this regard is if the ensemble of natural...
Autores principales: | , , , , , , , |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2009
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2697646/ https://www.ncbi.nlm.nih.gov/pubmed/19534748 http://dx.doi.org/10.1186/1471-2105-10-S6-S22 |
_version_ | 1782168347356430336 |
---|---|
author | Minervini, Giovanni Evangelista, Giuseppe Villanova, Laura Slanzi, Debora De Lucrezia, Davide Poli, Irene Luisi, Pier Luigi Polticelli, Fabio |
author_facet | Minervini, Giovanni Evangelista, Giuseppe Villanova, Laura Slanzi, Debora De Lucrezia, Davide Poli, Irene Luisi, Pier Luigi Polticelli, Fabio |
author_sort | Minervini, Giovanni |
collection | PubMed |
description | BACKGROUND: The number of natural proteins represents a small fraction of all the possible protein sequences and there is an enormous number of proteins never sampled by nature, the so called "never born proteins" (NBPs). A fundamental question in this regard is if the ensemble of natural proteins possesses peculiar chemical and physical properties or if it is just the product of contingency coupled to functional selection. A key feature of natural proteins is their ability to form a well defined three-dimensional structure. Thus, the structural study of NBPs can help to understand if natural protein sequences were selected for their peculiar properties or if they are just one of the possible stable and functional ensembles. METHODS: The structural characterization of a huge number of random proteins cannot be approached experimentally, thus the problem has been tackled using a computational approach. A large random protein sequences library (2 × 10(4 )sequences) was generated, discarding amino acid sequences with significant similarity to natural proteins, and the corresponding structures were predicted using Rosetta. Given the highly computational demanding problem, Rosetta was ported in grid and a user friendly job submission environment was developed within the GENIUS Grid Portal. Protein structures generated were analysed in terms of net charge, secondary structure content, surface/volume ratio, hydrophobic core composition, etc. RESULTS: The vast majority of NBPs, according to the Rosetta model, are characterized by a compact three-dimensional structure with a high secondary structure content. Structure compactness and surface polarity are comparable to those of natural proteins, suggesting similar stability and solubility. Deviations are observed in α helix-β strands relative content and in hydrophobic core composition, as NBPs appear to be richer in helical structure and aromatic amino acids with respect to natural proteins. CONCLUSION: The results obtained suggest that the ability to form a compact, ordered and water-soluble structure is an intrinsic property of polypeptides. The tendency of random sequences to adopt α helical folds indicate that all-α proteins may have emerged early in pre-biotic evolution. Further, the lower percentage of aromatic residues observed in natural proteins has important evolutionary implications as far as tolerance to mutations is concerned. |
format | Text |
id | pubmed-2697646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-26976462009-06-16 Massive non-natural proteins structure prediction using grid technologies Minervini, Giovanni Evangelista, Giuseppe Villanova, Laura Slanzi, Debora De Lucrezia, Davide Poli, Irene Luisi, Pier Luigi Polticelli, Fabio BMC Bioinformatics Proceedings BACKGROUND: The number of natural proteins represents a small fraction of all the possible protein sequences and there is an enormous number of proteins never sampled by nature, the so called "never born proteins" (NBPs). A fundamental question in this regard is if the ensemble of natural proteins possesses peculiar chemical and physical properties or if it is just the product of contingency coupled to functional selection. A key feature of natural proteins is their ability to form a well defined three-dimensional structure. Thus, the structural study of NBPs can help to understand if natural protein sequences were selected for their peculiar properties or if they are just one of the possible stable and functional ensembles. METHODS: The structural characterization of a huge number of random proteins cannot be approached experimentally, thus the problem has been tackled using a computational approach. A large random protein sequences library (2 × 10(4 )sequences) was generated, discarding amino acid sequences with significant similarity to natural proteins, and the corresponding structures were predicted using Rosetta. Given the highly computational demanding problem, Rosetta was ported in grid and a user friendly job submission environment was developed within the GENIUS Grid Portal. Protein structures generated were analysed in terms of net charge, secondary structure content, surface/volume ratio, hydrophobic core composition, etc. RESULTS: The vast majority of NBPs, according to the Rosetta model, are characterized by a compact three-dimensional structure with a high secondary structure content. Structure compactness and surface polarity are comparable to those of natural proteins, suggesting similar stability and solubility. Deviations are observed in α helix-β strands relative content and in hydrophobic core composition, as NBPs appear to be richer in helical structure and aromatic amino acids with respect to natural proteins. CONCLUSION: The results obtained suggest that the ability to form a compact, ordered and water-soluble structure is an intrinsic property of polypeptides. The tendency of random sequences to adopt α helical folds indicate that all-α proteins may have emerged early in pre-biotic evolution. Further, the lower percentage of aromatic residues observed in natural proteins has important evolutionary implications as far as tolerance to mutations is concerned. BioMed Central 2009-06-16 /pmc/articles/PMC2697646/ /pubmed/19534748 http://dx.doi.org/10.1186/1471-2105-10-S6-S22 Text en Copyright © 2009 Minervini et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Minervini, Giovanni Evangelista, Giuseppe Villanova, Laura Slanzi, Debora De Lucrezia, Davide Poli, Irene Luisi, Pier Luigi Polticelli, Fabio Massive non-natural proteins structure prediction using grid technologies |
title | Massive non-natural proteins structure prediction using grid technologies |
title_full | Massive non-natural proteins structure prediction using grid technologies |
title_fullStr | Massive non-natural proteins structure prediction using grid technologies |
title_full_unstemmed | Massive non-natural proteins structure prediction using grid technologies |
title_short | Massive non-natural proteins structure prediction using grid technologies |
title_sort | massive non-natural proteins structure prediction using grid technologies |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2697646/ https://www.ncbi.nlm.nih.gov/pubmed/19534748 http://dx.doi.org/10.1186/1471-2105-10-S6-S22 |
work_keys_str_mv | AT minervinigiovanni massivenonnaturalproteinsstructurepredictionusinggridtechnologies AT evangelistagiuseppe massivenonnaturalproteinsstructurepredictionusinggridtechnologies AT villanovalaura massivenonnaturalproteinsstructurepredictionusinggridtechnologies AT slanzidebora massivenonnaturalproteinsstructurepredictionusinggridtechnologies AT delucreziadavide massivenonnaturalproteinsstructurepredictionusinggridtechnologies AT poliirene massivenonnaturalproteinsstructurepredictionusinggridtechnologies AT luisipierluigi massivenonnaturalproteinsstructurepredictionusinggridtechnologies AT polticellifabio massivenonnaturalproteinsstructurepredictionusinggridtechnologies |