ArticleBioinformatics (Oxford, England)2017
EigenTHREADER: analogous protein fold recognition by efficient contact map threading.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- Alternative Conformation Prediction Using Deep Learning With Multi-MSA Strategy and Structural Clustering in CASP16.Proteins · 2026Article
- PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network.BMC bioinformatics · 2024Article
- PLMSearch: Protein language model powers accurate and fast sequence search for remote homology.Nature communications · 2024Article
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- An interactive visualization tool for educational outreach in protein contact map overlap analysis.Frontiers in bioinformatics · 2024Article
- Article
- Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader.Communications biology · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment Motifs.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins.Proteins · 2022Article
- Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14.Proteins · 2021Article
- Improving protein fold recognition using triplet network and ensemble deep learning.Briefings in bioinformatics · 2021Article
- FoldHSphere: deep hyperspherical embeddings for protein fold recognition.BMC bioinformatics · 2021Article
- Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretation.Briefings in bioinformatics · 2021Article
- Protein Structure Prediction: Conventional and Deep Learning Perspectives.The protein journal · 2021Review
- Toward the solution of the protein structure prediction problem.The Journal of biological chemistry · 2021Review
- CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction.Nature communications · 2021Article
- Deep template-based protein structure prediction.PLoS computational biology · 2021Article
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2 authors.
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Abstract
motivationProtein 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.
resultsEigenTHREADER 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.
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