Simulation-based inference with approximately correct parameters via maximum entropy

Barrett, Rainier; Ansari, Mehrad; Ghoshal, Gourab; White, Andrew D.

doi:10.1088/2632-2153/ac6286

Cited by 3 publications

(3 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typical experimental data used for ensemble refinement take the form of observables that are ensemble averages, such as those obtained from nuclear magnetic resonance or X-ray scattering experiments. Many methodologies have been proposed as general approaches to reweight ensembles using these averaged observables. − …”

Section: Introductionmentioning

confidence: 99%

Ensemble Reweighting Using Cryo-EM Particle Images

et al. 2023

View full text Add to dashboard Cite

Cryo-electron microscopy (cryo-EM) has recently become a leading method for obtaining high-resolution structures of biological macromolecules. However, cryo-EM is limited to biomolecular samples with low conformational heterogeneity, where most conformations can be well-sampled at various projection angles. While cryo-EM provides single-molecule data for heterogeneous molecules, most existing reconstruction tools cannot retrieve the ensemble distribution of possible molecular conformations from these data. To overcome these limitations, we build on a previous Bayesian approach and develop an ensemble refinement framework that estimates the ensemble density from a set of cryo-EM particle images by reweighting a prior conformational ensemble, e.g., from molecular dynamics simulations or structure prediction tools. Our work provides a general approach to recovering the equilibrium probability density of the biomolecule directly in conformational space from single-molecule data. To validate the framework, we study the extraction of state populations and free energies for a simple toy model and from synthetic cryo-EM particle images of a simulated protein that explores multiple folded and unfolded conformations.

show abstract

Section: Introductionmentioning

confidence: 99%

Ensemble Reweighting Using Cryo-EM Particle Images

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Advances in density estimation due to neural networks and deep learning enabled a new generation of powerful SBI methods, which produce surrogate models of the likelihood or posterior using simulated data [33]. SBI is a general approach [34,35] and a growing field with broad applications ranging from particle physics [36] to cosmology and astrophysics [37][38][39][40], nuclear fusion [41], genomics [42], and neuroscience [43,44].…”

Section: Introductionmentioning

confidence: 99%

Simulation-based inference of single-molecule force spectroscopy

Dingeldein

Cossio

Covino

2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

Single-molecule force spectroscopy (smFS) is a powerful approach to studying molecular self-organization. However, the coupling of the molecule with the ever-present experimental device introduces artifacts, that complicate the interpretation of these experiments. Performing statistical inference to learn hidden molecular properties is challenging because these measurements produce non-Markovian time series, and even minimal models lead to intractable likelihoods. To overcome these challenges, we developed a computational framework built on novel statistical methods called simulation-based inference (SBI). SBI enabled us to directly estimate the Bayesian posterior, and extract reduced quantitative models from smFS, by encoding a mechanistic model into a simulator in combination with probabilistic deep learning. Using synthetic data, we could systematically disentangle the measurement of hidden molecular properties from experimental artifacts. The integration of physical models with machine-learning density estimation is general, transparent, easy to use, and broadly applicable to other types of biophysical experiments.

show abstract

“…Many approaches focus on reweighting the structures in a given prior ensemble, e.g. from an MD trajectory, see Crehuet et al (2019); Bottaro et al (2020); Barrett et al (2022) for some recent examples. Related approaches attempt to achieve better uncertainty treatment using a Bayesian approach, see Hummer and Köfinger (2015); Köfinger et al (2019), or apply reweighting to entire MD trajectories in a set of multiple trajectories, see Bolhuis et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Maximum Entropy Ensemble Refinement

Eltzner,

Hofstadler,

Rudolf

et al. 2023

Preprint

View full text Add to dashboard Cite

The principle of maximum entropy provides a canonical way to include measurement results into a thermodynamic ensemble. Observable features of a thermodynamic system, which are measured as averages over an ensemble are included into the partition function by using Lagrange multipliers. Applying this principle to the system's energy leads to the well-known exponential form of the Boltzmann probability density. Here, we present a Bayesian approach to the estimation of maximum entropy parameters from nuclear Overhauser effect measurements in order to achieve a refined ensemble in molecular dynamics simulations. To achieve this goal, we leverage advances in the treatment of doubly intractable Bayesian inference problems by adaptive Markov Chain Monte Carlo methods. We illustrate the properties and viability of our method for alanine dipeptide as a simple model system and trp-cage as an example for a more complex peptide.

show abstract

Simulation-based inference with approximately correct parameters via maximum entropy

Cited by 3 publications

References 66 publications

Ensemble Reweighting Using Cryo-EM Particle Images

Ensemble Reweighting Using Cryo-EM Particle Images

Simulation-based inference of single-molecule force spectroscopy

Bayesian Maximum Entropy Ensemble Refinement

Contact Info

Product

Resources

About