@article{DHINGRA2025,

title = {Using protein blocks to build custom fragment libraries from protein structures},

author = {Surbhi Dhingra and Stéphane Téletchéa and Ramanathan Sowdhamini and Yves-Henri Sanejouand and Alexandre G. Brevern and Frédéric Cadet and Bernard Offmann},

url = {https://www.sciencedirect.com/science/article/pii/S0300908425001907},

doi = {https://doi.org/10.1016/j.biochi.2025.08.011},

issn = {0300-9084},

year  = {2025},

date = {2025-08-13},

urldate = {2025-01-01},

journal = {Biochimie},

abstract = {The remarkable structural diversity of modern proteins reflects millions of years of evolution, during which sequence space has expanded while many structural features remain conserved. This conservation is evident not only among homologous proteins but also in the recurrence of supersecondary motifs across unrelated proteins, underscoring the abundance and robustness of these structural units. Here, we present a novel pipeline for generating customized protein fragment libraries using protein blocks (PBs)—a structural alphabet that encodes local backbone conformations. Our method efficiently extracts structurally similar fragments from a curated, non-redundant protein structure database by converting three-dimensional structures into one-dimensional PB sequences. By integrating predicted PB sequences with the PB-ALIGN and PB-kPRED tools, our approach identifies relevant fragments independently of sequence homology. Fragment quality is further assessed using a new scoring function that combines secondary structure similarity and PB alignment metrics. The resulting libraries contain fragments of at least seven PBs (11 amino acid residues), covering over 70% of the local backbone structure. Our results demonstrate that PBs enable efficient mining of high-quality structural fragments from diverse protein spaces, including proteins with disordered regions. The pipeline is accessible as an online tool (PB-Frag, http://pbpred-us2b.univ-nantes.fr/pbfrag).},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@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}

}

@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}

}