ReviewPLoS computational biology2014
Combining experiments and simulations using the maximum entropy principle.
Review 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 77 papers.
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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.
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Who cites it
77 citing papers in PubMed.
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Minimum-Excess-Work Guidance: Score-Based Sampling with Experimental Data or Sparse Restraints.Journal of chemical theory and computation · 2026Article
- From possibility to precision in macromolecular ensemble prediction.Nature methods · 2026Review
- Advances and challenges in multiscale biomolecular simulations: artificial intelligence-driven paradigm shift.Quantitative biology (Beijing, China) · 2026Article
- Evaluating the Impact of Phosphorylation on the Dynamics of the Tau Protein Proline-Rich Region.Journal of chemical theory and computation · 2026Article
- Mapping the Ligand-dependent Remodeling of the Conformational Entropy Landscape in Neurotensin Receptor 1 by NMR-guided Molecular Simulations.bioRxiv : the preprint server for biology · 2026Article
- Dissecting the RAD51-BRC4 Interaction Landscape through Integrative Molecular Simulations and Experimental Biophysics.Journal of chemical information and modeling · 2025Article
- Review
- Combining simulations and experiments - a perspective on maximum entropy methods.Physical chemistry chemical physics : PCCP · 2025Review
- Umbrella Refinement of Ensembles-An Alternative View of Ensemble Optimization.Molecules (Basel, Switzerland) · 2025Article
- NMR-driven structure-based drug discovery by unveiling molecular interactions.Communications chemistry · 2025Review
- Integrative spatiotemporal modeling of biomolecular processes: Application to the assembly of the nuclear pore complex.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex.bioRxiv : the preprint server for biology · 2024Article
- Determination of the Structure and Dynamics of the Fuzzy Coat of an Amyloid Fibril of IAPP Using Cryo-Electron Microscopy.Biochemistry · 2023Article
- Emerging Time-Resolved X-Ray Diffraction Approaches for Protein Dynamics.Annual review of biophysics · 2023Review
- BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations.Journal of chemical information and modeling · 2023Article
- Development of hidden Markov modeling method for molecular orientations and structure estimation from high-speed atomic force microscopy time-series images.PLoS computational biology · 2022Article
- Maximum Entropy Technique and Regularization Functional for Determining the Pharmacokinetic Parameters in DCE-MRI.Journal of digital imaging · 2022Article
- Modeling biomolecular kinetics with large-scale simulation.Current opinion in structural biology · 2022Review
17 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A key component of computational biology is to compare the results of computer modelling with experimental measurements. Despite substantial progress in the models and algorithms used in many areas of computational biology, such comparisons sometimes reveal that the computations are not in quantitative agreement with experimental data. The principle of maximum entropy is a general procedure for constructing probability distributions in the light of new data, making it a natural tool in cases when an initial model provides results that are at odds with experiments. The number of maximum entropy applications in our field has grown steadily in recent years, in areas as diverse as sequence analysis, structural modelling, and neurobiology. In this Perspectives article, we give a broad introduction to the method, in an attempt to encourage its further adoption. The general procedure is explained in the context of a simple example, after which we proceed with a real-world application in the field of molecular simulations, where the maximum entropy procedure has recently provided new insight. Given the limited accuracy of force fields, macromolecular simulations sometimes produce results that are at not in complete and quantitative accordance with experiments. A common solution to this problem is to explicitly ensure agreement between the two by perturbing the potential energy function towards the experimental data. So far, a general consensus for how such perturbations should be implemented has been lacking. Three very recent papers have explored this problem using the maximum entropy approach, providing both new theoretical and practical insights to the problem. We highlight each of these contributions in turn and conclude with a discussion on remaining challenges.
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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.