Literature DB >> 28062450

The structural bioinformatics library: modeling in biomolecular science and beyond.

Frédéric Cazals, Tom Dreyfus.   

Abstract

Motivation: Software in structural bioinformatics has mainly been application driven. To favor practitioners seeking off-the-shelf applications, but also developers seeking advanced building blocks to develop novel applications, we undertook the design of the Structural Bioinformatics Library ( SBL , http://sbl.inria.fr ), a generic C ++/python cross-platform software library targeting complex problems in structural bioinformatics. Its tenet is based on a modular design offering a rich and versatile framework allowing the development of novel applications requiring well specified complex operations, without compromising robustness and performances.
Results: The SBL involves four software components (1-4 thereafter). For end-users, the SBL provides ready to use, state-of-the-art (1) applications to handle molecular models defined by unions of balls, to deal with molecular flexibility, to model macro-molecular assemblies. These applications can also be combined to tackle integrated analysis problems. For developers, the SBL provides a broad C ++ toolbox with modular design, involving core (2) algorithms , (3) biophysical models and (4) modules , the latter being especially suited to develop novel applications. The SBL comes with a thorough documentation consisting of user and reference manuals, and a bugzilla platform to handle community feedback. Availability and Implementation: The SBL is available from http://sbl.inria.fr. Contact: Frederic.Cazals@inria.fr. Supplementary information: Supplementary data are available at Bioinformatics online.
© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

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Year:  2017        PMID: 28062450     DOI: 10.1093/bioinformatics/btw752

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  4 in total

1.  From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction.

Authors:  Nasrin Akhter; Amarda Shehu
Journal:  Molecules       Date:  2018-01-19       Impact factor: 4.411

2.  Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.

Authors:  Nasrin Akhter; Gopinath Chennupati; Kazi Lutful Kabir; Hristo Djidjev; Amarda Shehu
Journal:  Biomolecules       Date:  2019-10-14

3.  Decoy selection for protein structure prediction via extreme gradient boosting and ranking.

Authors:  Nasrin Akhter; Gopinath Chennupati; Hristo Djidjev; Amarda Shehu
Journal:  BMC Bioinformatics       Date:  2020-12-09       Impact factor: 3.169

4.  Fewer Dimensions, More Structures for Improved Discrete Models of Dynamics of Free versus Antigen-Bound Antibody.

Authors:  Kazi Lutful Kabir; Buyong Ma; Ruth Nussinov; Amarda Shehu
Journal:  Biomolecules       Date:  2022-07-21
  4 in total

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