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Abstract Structural, regulatory and enzymatic proteins interact with DNA to maintain a healthy and functional genome. Yet, our structural understanding of how proteins interact with DNA is limited. We present MELD-DNA, a novel computational approach to predict the structures of protein–DNA complexes. The method combines molecular dynamics simulations with general knowledge or experimental information through Bayesian inference. The physical model is sensitive to sequence-dependent properties and conformational changes required for binding, while information accelerates sampling of bound conformations. MELD-DNA can: (i) sample multiple binding modes; (ii) identify the preferred binding mode from the ensembles; and (iii) provide qualitative binding preferences between DNA sequences. We first assess performance on a dataset of 15 protein–DNA complexes and compare it with state-of-the-art methodologies. Furthermore, for three selected complexes, we show sequence dependence effects of binding in MELD predictions. We expect that the results presented herein, together with the freely available software, will impact structural biology (by complementing DNA structural databases) and molecular recognition (by bringing new insights into aspects governing protein–DNA interactions).
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NMR‐assisted protein structure prediction with MELDxMD
Abstract We describe the performance of MELD‐accelerated molecular dynamics (MELDxMD) in determining protein structures in the NMR‐data‐assisted category in CASP13. Seeded from web server predictions, MELDxMD was found best in the NMR category, over 17 targets, outperforming the next‐best groups by a factor of ~4 inz‐score. MELDxMD gives ensembles, not single structures; succeeds on a 326‐mer, near the current upper limit for NMR structures; and predicts structures that match experimental residual dipolar couplings even though the only NMR‐derived data used in the simulations was NOE‐based ambiguous atom–atom contacts and backbone dihedrals. MELD can use noisy and ambiguous experimental information to reduce the MD search space. We believe MELDxMD is a promising method for determining protein structures from NMR data.
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- PAR ID:
- 10459197
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Proteins: Structure, Function, and Bioinformatics
- Volume:
- 87
- Issue:
- 12
- ISSN:
- 0887-3585
- Page Range / eLocation ID:
- p. 1333-1340
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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