Structural Bioinformatics

By Kévin Darty, Alain Denise, Yann Ponty

727 citations recorded by the source corpus.

Motivation: Antibodies are able to recognize a wide range of antigens through their complementary determining regions formed by six hypervariable loops. Predicting the 3D structure of these loops is essential for the analysis and reengineering of novel antibodies with enhanced affinity and specificity. The canonical structure model allows high accuracy prediction for five of the loops. The third loop of the heavy chain, H3, is the hardest to predict because of its diversity in structure, length and sequence composition. Results: We describe a method, based on the Random Forest automatic learning technique, to select structural templates for H3 loops among a dataset of candidates. These can be used to predict the structure of the loop with a higher accuracy than that achieved by any of the presently available methods. The method also has the advantage of being extremely fast and returning a reliable estimate of the model quality. Availability and implementation: The source code is freely available at http://www.biocomputing.it/H3Loopred/ Contact: anna.tramontano@uniroma1.it Supplementary Information: Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Janusz M. Bujnicki
2. Paolo Marcatili
3. Marta Szachniuk
4. Eric Westhof
5. Maciej Antczak
6. Martin Closter Jespersen
7. Zhichao Miao
8. Tomasz Zok

In Radiology, Nuclear Medicine and Imaging

1. Paolo Marcatili
2. Morten Nielsen
3. Anna Tramontano
4. Pier Paolo Olimpieri
5. Bjoern Peters
6. Alessandro Sette

Citations in the corpus

• Flexible torsion-angle noncrystallographic symmetry restraints for improved macromolecular structure refinement (2014, neutral): “Through the iotbx.cif routines (Gildea et al., 2011), phenix.refine is also fully compliant with migration to mmCIF format (Westbrook & Fitzgerald, 2005), and will allow the propagation of explicit NCS restraint information.”
• Morphology, phylogeny and azaspiracid profile of Azadinium poporum (Dinophyceae) from the China Sea (2013, neutral): “8 (Darty et al., 2009) and edited.”
• LYRA, a webserver for lymphocyte receptor structural modeling (2015, background): “As expected (25,26,36), the weak point of the pipeline is the modeling of the third CDR loop of heavy, and chain, either because a proper template loop is not available or because the adopted sequence-based template selection scheme fails to select it.”
• PIGSPro: prediction of immunoGlobulin structures v2 (2017, methodology): “In summary, in terms of prediction accuracy we achieve significant improvements compared to PIGS (Table 1) and to other methods (22).”
• RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools (2019, methodology): “The functionalities include visualization of a sequence and a structure with VARNA (55), evaluation of free energy, parsing secondary structure into a list of pairs, and various tools for secondary structure format conversions, etc.”