Recent Methods from Statistical Inference for Integrative Structural Modeling
| Title | Recent Methods from Statistical Inference for Integrative Structural Modeling |
| Publication Type | Book Chapter |
| Year of Publication | 2026 |
| Authors | Arvindekar S, Majila K, Viswanath S |
| Book Title | Springer Handbook of Chem- and Bioinformatics |
| Pagination | 1075–1103 |
| Publisher | Springer, Cham |
| ISBN Number | Print ISBN 978-3-031-81727-4 |
| ISBN | Online ISBN 978-3-031-81728-1 |
| Keywords | AlphaFold, Crosslinking coupled with mass spectrometry, Deep learning, Dynamical Integrative Structure Modeling, Electron cryo-microscopy, Electron cryo-tomography, Integrative structure modeling, Whole-cell modeling |
| Abstract |
Integrative modeling of macromolecular assemblies allows for structural characterization of large assemblies that are recalcitrant to direct experimental observation. A Bayesian inference approach facilitates combining data from complementary experiments along with physical principles, statistics of known structures, and prior models, for structure determination. Here, we review recent methods for integrative modeling based on statistical inference and machine learning. These methods improve over the current state-of-the-art by improving data collection, optimizing coarse-grained model representations, making scoring functions more accurate, sampling more efficient, and model analysis more rigorous. We also discuss three new frontiers in integrative modeling: incorporating recent deep learning-based methods, integrative modeling with in situ data, and metamodeling.
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| URL | https://link.springer.com/chapter/10.1007/978-3-031-81728-1_47 |
| DOI | 10.1007/978-3-031-81728-1_47 |