ArticleBiophysical journal2017
Assessing Exhaustiveness of Stochastic Sampling for Integrative Modeling of Macromolecular Structures.
Article in Biophysical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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58 citing papers in PubMed.
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- 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
- Multiscale simulations reveal architecture of NOTCH protein and ligand specific features.Biophysical journal · 2025Article
- Structural insights into distinct mechanisms of RNA polymerase II and III recruitment to snRNA promoters.Nature communications · 2025Article
- The molecular architecture of the desmosomal outer dense plaque by integrative structural modeling.Protein science : a publication of the Protein Society · 2024Article
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- An integrated structural model of the DNA damage-responsive H3K4me3 binding WDR76:SPIN1 complex with the nucleosome.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex.bioRxiv : the preprint server for biology · 2024Article
- The Molecular Architecture of the Nuclear Basket.bioRxiv : the preprint server for biology · 2024Article
- The molecular architecture of the desmosomal outer dense plaque by integrative structural modeling.bioRxiv : the preprint server for biology · 2024Article
- Optimizing representations for integrative structural modeling using Bayesian model selection.Bioinformatics (Oxford, England) · 2024Article
- Optimizing representations for integrative structural modeling using Bayesian model selection.bioRxiv : the preprint server for biology · 2023Article
- Integrated modeling of the Nexin-dynein regulatory complex reveals its regulatory mechanism.Nature communications · 2023Article
- Integrated modeling of the Nexin-dynein regulatory complex reveals its regulatory mechanism.bioRxiv : the preprint server for biology · 2023Article
- Integrative structure determination of histones H3 and H4 using genetic interactions.The FEBS journal · 2023Article
- Does AlphaFold2 model proteins' intracellular conformations? An experimental test using cross-linking mass spectrometry of endogenous ciliary proteins.Communications biology · 2023Article
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4 authors.
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Abstract
Modeling of macromolecular structures involves structural sampling guided by a scoring function, resulting in an ensemble of good-scoring models. By necessity, the sampling is often stochastic, and must be exhaustive at a precision sufficient for accurate modeling and assessment of model uncertainty. Therefore, the very first step in analyzing the ensemble is an estimation of the highest precision at which the sampling is exhaustive. Here, we present an objective and automated method for this task. As a proxy for sampling exhaustiveness, we evaluate whether two independently and stochastically generated sets of models are sufficiently similar. The protocol includes testing 1) convergence of the model score, 2) whether model scores for the two samples were drawn from the same parent distribution, 3) whether each structural cluster includes models from each sample proportionally to its size, and 4) whether there is sufficient structural similarity between the two model samples in each cluster. The evaluation also provides the sampling precision, defined as the smallest clustering threshold that satisfies the third, most stringent test. We validate the protocol with the aid of enumerated good-scoring models for five illustrative cases of binary protein complexes. Passing the proposed four tests is necessary, but not sufficient for thorough sampling. The protocol is general in nature and can be applied to the stochastic sampling of any set of models, not just structural models. In addition, the tests can be used to stop stochastic sampling as soon as exhaustiveness at desired precision is reached, thereby improving sampling efficiency; they may also help in selecting a model representation that is sufficiently detailed to be informative, yet also sufficiently coarse for sampling to be exhaustive.
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