ReviewThe Journal of biological chemistry2020
Successes and challenges in simulating the folding of large proteins.
Review in The Journal of biological chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 80 citations in OpenAlex.
- The Shape of Things to Come: α-Helical Membrane Protein Folding on the Ribosome.Chemical reviews · 2026Review
- Structure-Based Simulations for Folding of a tRNA Isodecoder.Molecules (Basel, Switzerland) · 2026Article
- Unavailability of experimental 3D structural data on protein folding dynamics and necessity for a new generation of structure prediction methods in this context.Bioinformatics (Oxford, England) · 2026Article
- Bridging Scales: Coarse-Grained Protein Models in Computational Biology.Advances in experimental medicine and biology · 2026Review
- Full-Length Context Disrupts Folding of IgG-Binding Domains of Protein A.bioRxiv : the preprint server for biology · 2025Article
- Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development.Biological cybernetics · 2025Review
- Enzyme miniaturization: Revolutionizing future biocatalysts.Biotechnology advances · 2025Review
- Review
- Large-scale energy decomposition for the analysis of protein stability.Cell stress & chaperones · 2025Article
- Long-term dynamic simulation of cellular systems with inhomogeneous mass distribution.Multibody system dynamics · 2024Article
- Adaptive CVgen: Leveraging reinforcement learning for advanced sampling in protein folding and chemical reactions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A Perspective on Interdicting in Protein Misfolding for Therapeutic Drug Design: Modulating the Formation of Nonlocal Contacts in α-Synuclein as a Strategy against Parkinson's Disease.The journal of physical chemistry. B · 2024Review
- Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development.Biomolecules · 2024Review
- It is theoretically possible to avoid misfolding into non-covalent lasso entanglements using small molecule drugs.PLoS computational biology · 2024Article
- Molecular Free Energies, Rates, and Mechanisms from Data-Efficient Path Sampling Simulations.Journal of chemical theory and computation · 2023Article
- Scientific novelty beyond the experiment.Microbial biotechnology · 2023Article
- Systematic conformation-to-phenotype mapping via limited deep sequencing of proteins.Molecular cell · 2023Article
- Non-Native Structures of Apomyoglobin and Apoleghemoglobin in Folding Intermediates Related to the Protein Misfolding.Molecules (Basel, Switzerland) · 2023Review
- Long-term dynamic simulation of adipogenic differentiation of a human mesenchymal stem cell.Multibody system dynamics · 2023Article
- The conformational landscape of a serpin N-terminal subdomain facilitates folding and in-cell quality control.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational simulations of protein folding can be used to interpret experimental folding results, to design new folding experiments, and to test the effects of mutations and small molecules on folding. However, whereas major experimental and computational progress has been made in understanding how small proteins fold, research on larger, multidomain proteins, which comprise the majority of proteins, is less advanced. Specifically, large proteins often fold via long-lived partially folded intermediates, whose structures, potentially toxic oligomerization, and interactions with cellular chaperones remain poorly understood. Molecular dynamics based folding simulations that rely on knowledge of the native structure can provide critical, detailed information on folding free energy landscapes, intermediates, and pathways. Further, increases in computational power and methodological advances have made folding simulations of large proteins practical and valuable. Here, using serpins that inhibit proteases as an example, we review native-centric methods for simulating the folding of large proteins. These synergistic approaches range from Gō and related structure-based models that can predict the effects of the native structure on folding to all-atom-based methods that include side-chain chemistry and can predict how disease-associated mutations may impact folding. The application of these computational approaches to serpins and other large proteins highlights the successes and limitations of current computational methods and underscores how computational results can be used to inform experiments. These powerful simulation approaches in combination with experiments can provide unique insights into how large proteins fold and misfold, expanding our ability to predict and manipulate protein folding.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.