Développement, à l’aide des blocs protéiques (un alphabet structural), d’une méthode d’assemblage de fragments structuraux pour la modélisation ab-initio des structures des protéines.
Publications
2 publications
Dhingra, Surbhi; Sowdhamini, Ramanathan; Sanejouand, Yves-Henri; Cadet, Frédéric; Offmann, Bernard
@article{Dhingra2020,
title = {Customised fragment libraries for ab initio protein structure prediction using a structural alphabet},
author = {Surbhi Dhingra and Ramanathan Sowdhamini and Yves-Henri Sanejouand and Frédéric Cadet and Bernard Offmann},
url = {https://arxiv.org/pdf/2005.01696.pdf},
year = {2020},
date = {2020-05-01},
journal = {arXiv:2005.01696},
abstract = {Motivation: Computational protein structure prediction has taken over the structural community in past few decades, mostly focusing on the development of Template-Free modelling (TFM) or ab initio modelling protocols. Fragment-based assembly (FBA), falls under this category and is by far the most popular approach to solve the spatial arrangements of proteins. FBA approaches usually rely on sequence based profile comparison to generate fragments from a representative structural database. Here we report the use of Protein Blocks (PBs), a structural alphabet (SA) to perform such sequence comparison and to build customised fragment libraries for TFM. Results: We demonstrate that predicted PB sequences for a query protein can be used to search for high quality fragments that overall cover above 90% of the query. The fragments generated are of minimum length of 11 residues, and fragments that cover more than 30% of the query length were often obtained. Our work shows that PBs can serve as a good way to extract structurally similar fragments from a database of representatives of non-homologous structures and of the proteins that contain less ordered regions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Motivation: Computational protein structure prediction has taken over the structural community in past few decades, mostly focusing on the development of Template-Free modelling (TFM) or ab initio modelling protocols. Fragment-based assembly (FBA), falls under this category and is by far the most popular approach to solve the spatial arrangements of proteins. FBA approaches usually rely on sequence based profile comparison to generate fragments from a representative structural database. Here we report the use of Protein Blocks (PBs), a structural alphabet (SA) to perform such sequence comparison and to build customised fragment libraries for TFM. Results: We demonstrate that predicted PB sequences for a query protein can be used to search for high quality fragments that overall cover above 90% of the query. The fragments generated are of minimum length of 11 residues, and fragments that cover more than 30% of the query length were often obtained. Our work shows that PBs can serve as a good way to extract structurally similar fragments from a database of representatives of non-homologous structures and of the proteins that contain less ordered regions.
@article{DHINGRA202085,
title = {A glance into the evolution of template-free protein structure prediction methodologies},
author = {Surbhi Dhingra and Ramanathan Sowdhamini and Frédéric Cadet and Bernard Offmann},
url = {http://www.sciencedirect.com/science/article/pii/S0300908420300961},
doi = {https://doi.org/10.1016/j.biochi.2020.04.026},
issn = {0300-9084},
year = {2020},
date = {2020-01-01},
journal = {Biochimie},
volume = {175},
pages = {85 - 92},
abstract = {Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in template-based modelling protocols, whereas strategies that involve template-free modelling still lag behind, specifically for larger proteins (>150 a.a.). Various improvements have been observed in ab initio protein structure prediction methodologies overtime, with recent ones attributed to the usage of deep learning approaches to construct protein backbone structure from its amino acid sequence. This review highlights the major strategies undertaken for template-free modelling of protein structures while discussing few tools developed under each strategy. It will also briefly comment on the progress observed in the field of ab initio modelling of proteins over the course of time as seen through the evolution of CASP platform.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in template-based modelling protocols, whereas strategies that involve template-free modelling still lag behind, specifically for larger proteins (>150 a.a.). Various improvements have been observed in ab initio protein structure prediction methodologies overtime, with recent ones attributed to the usage of deep learning approaches to construct protein backbone structure from its amino acid sequence. This review highlights the major strategies undertaken for template-free modelling of protein structures while discussing few tools developed under each strategy. It will also briefly comment on the progress observed in the field of ab initio modelling of proteins over the course of time as seen through the evolution of CASP platform.
