ArticleBMC bioinformatics2018
CONFOLD2: improved contact-driven ab initio protein structure modeling.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints.Communications biology · 2025Article
- Computational Strategies in Drug Discovery: Leveraging Subtractive Genomic Analysis for Target Identification.Current pharmaceutical biotechnology · 2025Review
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Molecular docking in organic, inorganic, and hybrid systems: a tutorial review.Monatshefte fur chemie · 2023Review
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks.Nature communications · 2022Article
- A combinedCell cycle (Georgetown, Tex.) · 2022Article
- Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins.PLoS computational biology · 2022Article
- Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.Scientific reports · 2022Article
- Protein Design with Deep Learning.International journal of molecular sciences · 2021Review
- Improving deep learning-based protein distance prediction in CASP14.Bioinformatics (Oxford, England) · 2021Article
- Interdependence of a mechanosensitive anion channel and glutamate receptors in distal wound signaling.Science advances · 2021Article
- Protein Structure Prediction: Conventional and Deep Learning Perspectives.The protein journal · 2021Review
- MULTICOM2 open-source protein structure prediction system powered by deep learning and distance prediction.Scientific reports · 2021Article
- Evaluation of residue-residue contact prediction methods: From retrospective to prospective.PLoS computational biology · 2021Article
- Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy.International journal of molecular sciences · 2021Article
- Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction.Scientific reports · 2021Article
- DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures.Proteins · 2021Article
- Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins.PLoS computational biology · 2021Article
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2 authors.
Funding
Abstract
backgroundContact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed.
resultsWe develop an improved contact-driven protein modelling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain the top five models. CONFOLD2 obtains an average reconstruction accuracy of 0.57 TM-score for the 150 proteins in the PSICOV contact prediction dataset. When benchmarked on the CASP11 contacts predicted using CONSIP2 and CASP12 contacts predicted using Raptor-X, CONFOLD2 achieves a mean TM-score of 0.41 on both datasets.
conclusionCONFOLD2 allows to quickly generate top five structural models for a protein sequence when its secondary structures and contacts predictions at hand. The source code of CONFOLD2 is publicly available at https://github.com/multicom-toolbox/CONFOLD2/ .
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