ArticlePloS one2010
Potentials of mean force for protein structure prediction vindicated, formalized and generalized.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Assessing Molecular Contacts Using Atom Environments Described by Ranked Lists.Journal of chemical information and modeling · 2026Article
- Temporal fluctuations of non-equilibrium water accessible pathways in polyamide reverse osmosis active layers.Journal of membrane science letters · 2025Article
- Unlocking the specificity of antimicrobial peptide interactions for membrane-targeted therapies.Computational and structural biotechnology journal · 2024Article
- Multi-Journal of chemical theory and computation · 2024Article
- BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations.Journal of chemical information and modeling · 2023Article
- Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions.Molecules (Basel, Switzerland) · 2022Review
- Structure-conditioned amino-acid couplings: How contact geometry affects pairwise sequence preferences.Protein science : a publication of the Protein Society · 2022Article
- Representations of protein structure for exploring the conformational space: A speed-accuracy trade-off.Computational and structural biotechnology journal · 2021Article
- Reconciling Simulations and Experiments With BICePs: A Review.Frontiers in molecular biosciences · 2021Review
- MHCII3D-Robust Structure Based Prediction of MHC II Binding Peptides.International journal of molecular sciences · 2020Article
- An information gain-based approach for evaluating protein structure models.Computational and structural biotechnology journal · 2020Article
- Accurate calculation of side chain packing and free energy with applications to protein molecular dynamics.PLoS computational biology · 2018Article
- Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model.Biophysical journal · 2018Article
- Model Selection Using BICePs: A Bayesian Approach for Force Field Validation and Parameterization.The journal of physical chemistry. B · 2018Article
- Finding the needle in the haystack: towards solving the protein-folding problem computationally.Critical reviews in biochemistry and molecular biology · 2018Review
- Modeling transcriptional activation changes to Gal4 variants via structure-based computational mutagenesis.PeerJ · 2018Article
- A Generative Angular Model of Protein Structure Evolution.Molecular biology and evolution · 2017Article
- Establishing knowledge on the sequence arrangement pattern of nucleated protein folding.PloS one · 2017Article
- SARAH Domain-Mediated MST2-RASSF Dimeric Interactions.PLoS computational biology · 2016Article
- Predicting RNA 3D structure using a coarse-grain helix-centered model.RNA (New York, N.Y.) · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge-based potentials based on pairwise distances--so-called "potentials of mean force" (PMFs)--have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state--a necessary component of these potentials--is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities "reference ratio distributions" deriving from the application of the "reference ratio method." This new view is not only of theoretical relevance but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures.
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.