EigenTHREADER: analogous protein fold recognition by efficient contact map threading
By Daniel W. A. Buchan, David T. Jones. 2017
53 citations recorded by the source corpus.
Motivation: Protein fold recognition when appropriate, evolutionarily‐related, structural templates can be identified is often trivial and may even be viewed as a solved problem. However in cases where no homologous structural templates can be detected, fold recognition is a notoriously difficult problem (Moult et al., 2014). Here we present EigenTHREADER, a novel fold recognition method capable of identifying folds where no homologous structures can be identified. EigenTHREADER takes a query amino acid sequence, generates a map of intra‐residue contacts, and then searches a library of contact maps of known structures. To allow the contact maps to be compared, we use eigenvector decomposition to resolve the principal eigenvectors these can then be aligned using standard dynamic programming algorithms. The approach is similar to the Al‐Eigen approach of Di Lena et al. (2010), but with improvements made both to speed and accuracy. With this search strategy, EigenTHREADER does not depend directly on sequence homology between the target protein and entries in the fold library to generate models. This in turn enables EigenTHREADER to correctly identify analogous folds where little or no sequence homology information is. Results: EigenTHREADER outperforms well‐established fold recognition methods such as pGenTHREADER and HHSearch in terms of True Positive Rate in the difficult task of analogous fold recognition. This should allow template‐based modelling to be extended to many new protein families that were previously intractable to homology based fold recognition methods. Availability and implementation: All code used to generate these results and the computational protocol can be downloaded from https://github.com/DanBuchan/eigen_scripts. EigenTHREADER, the benchmark code and the data this paper is based on can be downloaded from: http://bioinfadmin.cs.ucl.ac.uk/downloads/eigenTHREADER/. Contact: d.t.jones@ucl.ac.uk
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
- Robin Pearce
- David T. Jones
- Daniel W. A. Buchan
- Yang Zhang
- Dongbo Bu
- Jianwei Zhu
- Weinan Zheng
- Tie-Yan Liu
In Molecular Biology
- Robin Pearce
- Yang Zhang
- David T. Jones
- Daniel W. A. Buchan
- Jianlin Cheng
- Jinbo Xu
- Chengxin Zhang
- Sheng Wang
Citations in the corpus
- CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction (2020, background): “EigenThreader[22] and CEThreader[23] align target proteins with templates by considering eigenvector decomposition of the predicted inter-residue contacts.”
- Improved protein contact predictions with the MetaPSICOV2 server in CASP12 (2017, methodology): “Further to our previous work on EigenTHREADER (Buchan and Jones 2017), we note that with such high precision over the top L/5 contacts, it should be possible to uniquely specify the fold of the domain.”
- Toward the solution of the protein structure prediction problem (2021, background): “In fact, contact and distance map-guided threading approaches represent the state of the art in fold recognition, achieving superior accuracy to traditional pro fi le or local structural feature-based threading approaches (95, 96, 187).”