Pcons: A neural‐network–based consensus predictor that improves fold recognition

By Jesper Lundström, Leszek Rychlewski, Janusz M. Bujnicki, Arne Elofsson. Protein Science, 2001

326 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bc330fa7b4fba0646c198539d2aab844685a7b6e

How researchers see this paper

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

In Computational Theory and Mathematics

1. Liam J. McGuffin
2. Arne Elofsson
3. Björn Wallner
4. Yang Zhang
5. Daniel B. Roche
6. Janusz M. Bujnicki
7. David T. Jones
8. Sitao Wu

In Molecular Biology

1. Liam J. McGuffin
2. Yang Zhang
3. Björn Wallner
4. Arne Elofsson
5. Torsten Schwede
6. Daniel B. Roche
7. Jianlin Cheng
8. David T. Jones

Citations in the corpus

• GraphQA: protein model quality assessment using graph convolutional networks (2020, unknown): “This work focuses on quality assessment (QA) of computationally derived protein models (Lundstrom et al. , 2001; Won et al. , 2019).”
• Can correct protein models be identified? (2003, methodology): “The final neural network model, ProQ, was compared with three other methods for structural evaluation as described in Table 4 and also combined with the fold recognition consensus predictor Pcons (Lundström et al. 2001).”
• QMEAN server for protein model quality estimation (2009, methodology): “QMEANclust differs from other consensus methods such as Pcons (12) or the consensus method included in the ModFOLD server (20) in that it takes advantage of an initial ranking of the individual models obtained by QMEAN in order to weight the contribution of the models in the clustering process.”
• Pcons5: combining consensus, structural evaluation and fold recognition scores (2005, background): “Pcons [20] was the first fully automated consensus predictor, followed by several others [8, 9].”
• Improving sequence-based fold recognition by using 3D model quality assessment (2005, methodology): “It is important to make the distinction that the term ‘model sim-ilarity’ is used in this context to signify this particular group of methods, whereas, MQAP methods describe the set of tools that calculate a model quality score from the predicted structure alone.”
• GeneSilico protein structure prediction meta-server (2003, methodology): “Thereby, all FR alignments obtained from different servers undergo unified assessment by energetic criteria implemented in VERIFY3D in addition to the ranking criterion offered by the PCONS server.”
• Improvement of 3D protein models using multiple templates guided by single-template model quality assessment (2012, methodology): “PconsM (Larsson et al., 2011), for example, uses ProQ (Wallner and Elofsson, 2003) for model quality assessment of the top six models drawn from the top six template sequences of Pcons.net (Wallner et al., 2007).”
• pyconsFold: a fast and easy tool for modeling and docking using distance predictions (2021, background): “CNS internal NOE energy, but Quality Assessment score pcons [5] will also be calculated.”