Automated tertiary structure prediction with accurate local model quality assessment using the intfold‐ts method

By Liam J. McGuffin, Daniel B. Roche. 2011

35 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

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

In Materials Chemistry

1. Liam J. McGuffin
2. Ali Hassan Ahmed Maghrabi
3. Ahmad N. Shuid
4. Maria T. Buenavista
5. Daniel B. Roche
6. Jennifer D. Atkins
7. Bajuna R. Salehe
8. John O Nealon

In Molecular Biology

1. Liam J. McGuffin
2. Ahmad N. Shuid
3. Ali Hassan Ahmed Maghrabi
4. Daniel B. Roche
5. Jennifer D. Atkins
6. Maria T. Buenavista
7. John O Nealon
8. Bajuna R. Salehe

Citations in the corpus

• IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, background): “org) and the performance has been competitive (9,18).”
• IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences (2015, methodology): “The original IntFOLD-TS method used a single-template local consensus fold recognition approach to predict protein tertiary structure from sequence (6).”
• ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models (2017, unknown): “We initially implemented a quasi-single model approach with our ModFOLD3 method (18), which generated reference sets of models from the target sequence, using IntFOLDTS (19), for comparison with the submitted model using ModFOLDclust2 (16).”
• Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies (2019, methodology): “Starting from the input sequence, IntFOLD2-TS [77] generates a set of templates.”
• Improvement of 3D protein models using multiple templates guided by single-template model quality assessment (2012, methodology): “Our own IntFOLD-TS method (McGuffin and Roche, 2011; Roche et al., 2011) integrates ModFOLDclust2 (McGuffin and Roche, 2009) into the core of its pipeline.”
• The ModFOLD4 server for the quality assessment of 3D protein models (2013, methodology): “The target sequence is then processed via the IntFOLD2 tertiary structure prediction pipeline (IntFOLD2-TS) (18,19).”