ArticlePLoS computational biology2014
Fast protein loop sampling and structure prediction using distance-guided sequential chain-growth Monte Carlo method.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Simulation of pH-Dependent, Loop-Based Membrane Protein Gating Using Pretzel.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Highly Accurate and Efficient Deep Learning Paradigm for Full-Atom Protein Loop Modeling with KarmaLoop.Research (Washington, D.C.) · 2024Article
- Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency.Briefings in bioinformatics · 2023Article
- Enhancing Ligand and Protein Sampling Using Sequential Monte Carlo.Journal of chemical theory and computation · 2022Article
- Thermodynamics of unfolding mechanisms of mouse mammary tumor virus pseudoknot from a coarse-grained loop-entropy model.Journal of biological physics · 2022Article
- Article
- Simulation of pH-Dependent, Loop-Based Membrane Protein Gating Using Pretzel.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Current approaches to flexible loop modeling.Current research in structural biology · 2021Review
- ART-RRT: As-Rigid-As-Possible search for protein conformational transition paths.Journal of computer-aided molecular design · 2019Article
- Effect of Protein Conformation and AMP Protonation State on Fireflies' Bioluminescent Emission.Molecules (Basel, Switzerland) · 2019Article
- A new clustering and nomenclature for beta turns derived from high-resolution protein structures.PLoS computational biology · 2019Article
- Computational design of structured loops for new protein functions.Biological chemistry · 2019Review
- Mechanism of OmpG pH-Dependent Gating from Loop Ensemble and Single Channel Studies.Journal of the American Chemical Society · 2018Article
- Modeling Chemical Reactions by QM/MM Calculations: The Case of the Tautomerization in Fireflies Bioluminescent Systems.Frontiers in chemistry · 2018Article
- A fragment based method for modeling of protein segments into cryo-EM density maps.BMC bioinformatics · 2017Article
- Distance-Guided Forward and Backward Chain-Growth Monte Carlo Method for Conformational Sampling and Structural Prediction of Antibody CDR-H3 Loops.Journal of chemical theory and computation · 2017Article
- Structure Prediction of RNA Loops with a Probabilistic Approach.PLoS computational biology · 2016Article
- SL2: an interactive webtool for modeling of missing segments in proteins.Nucleic acids research · 2016Article
- Toward high-resolution computational design of the structure and function of helical membrane proteins.Nature structural & molecular biology · 2016Article
- A fast loop-closure algorithm to accelerate residue matching in computational enzyme design.Journal of molecular modeling · 2016Article
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3 authors.
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Abstract
Loops in proteins are flexible regions connecting regular secondary structures. They are often involved in protein functions through interacting with other molecules. The irregularity and flexibility of loops make their structures difficult to determine experimentally and challenging to model computationally. Conformation sampling and energy evaluation are the two key components in loop modeling. We have developed a new method for loop conformation sampling and prediction based on a chain growth sequential Monte Carlo sampling strategy, called Distance-guided Sequential chain-Growth Monte Carlo (DISGRO). With an energy function designed specifically for loops, our method can efficiently generate high quality loop conformations with low energy that are enriched with near-native loop structures. The average minimum global backbone RMSD for 1,000 conformations of 12-residue loops is 1:53 A° , with a lowest energy RMSD of 2:99 A° , and an average ensembleRMSD of 5:23 A° . A novel geometric criterion is applied to speed up calculations. The computational cost of generating 1,000 conformations for each of the x loops in a benchmark dataset is only about 10 cpu minutes for 12-residue loops, compared to ca 180 cpu minutes using the FALCm method. Test results on benchmark datasets show that DISGRO performs comparably or better than previous successful methods, while requiring far less computing time. DISGRO is especially effective in modeling longer loops (10-17 residues).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.