ArticleBriefings in bioinformatics2023
Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
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- Protein Loop Modeling via the Discretizable Distance Geometry Problem with Hydrogen-Based NMR Constraints.ACS omega · 2026Article
- AI-guided epitope engineering of a SARS-CoV-2 spike antigen for broad sarbecovirus neutralization.Frontiers in immunology · 2026Article
- Chemosensory Receptors in Vertebrates: Structure and Computational Modeling Insights.International journal of molecular sciences · 2025Review
- TRAP: a contrastive learning-enhanced framework for robust TCR-pMHC binding prediction with improved generalizability.Chemical science · 2025Article
- ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties.Nature communications · 2025Article
- Genetic Evidence of Active Circulation and Evolution of Diverse Penguin Siadenoviruses in Antarctica Based on Partial DNA Sequences.Transboundary and emerging diseases · 2025Article
- In silico drug repurposing of potential antiviral inhibitors targeting methyltransferase (2'-O-MTase) domain of Marburg virus.In silico pharmacology · 2025Article
- Tackling Undruggable Targets with Designer Peptidomimetics and Synthetic Biologics.Chemical reviews · 2024Review
- Highly Accurate and Efficient Deep Learning Paradigm for Full-Atom Protein Loop Modeling with KarmaLoop.Research (Washington, D.C.) · 2024Article
- Orphan G protein-coupled receptors: the ongoing search for a home.Frontiers in pharmacology · 2024Review
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17 authors.
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Abstract
Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.
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