Molecular architecture of nucleosome remodeling and deacetylase sub-complexes by integrative structure determination

Abstract

The Nucleosome Remodeling and Deacetylase (NuRD) complex is a chromatin-modifying assembly that regulates gene expression and DNA damage repair. Despite its importance, limited structural information is available on the complex and a detailed understanding of its mechanism is lacking. We investigated the molecular architecture of three NuRD sub-complexes: MTA1-HDAC1-RBBP4 (MHR), MTA1N-HDAC1-MBD3GATAD2CC (MHM), and MTA1-HDAC1-RBBP4-MBD3-GATAD2 (NuDe) using Bayesian integrative structure determination with IMP (Integrative Modeling Platform), drawing on information from SEC-MALLS, DIA-MS, XLMS, negative stain EM, X-ray crystallography, NMR spectroscopy, secondary structure and homology predictions. The structures were corroborated by independent cryo-EM maps, biochemical assays, and known cancer-associated mutations. Our integrative structure of the 2:2:2 MHM complex shows asymmetric binding of MBD3, whereas our structure of the NuDe complex shows MBD3 localized precisely to a single position distant from the MTA1 dimerization interface. Our models suggest a possible mechanism by which asymmetry is introduced in NuRD, and indicate three previously unrecognized subunit interfaces in NuDe: HDAC1C-MTA1BAH, MTA1BAH-MBD3, and HDAC160-100-MBD3. We observed that a significant number of cancer-associated mutations mapped to protein-protein interfaces in NuDe. Our approach also allows us to localize regions of unknown structure, such as HDAC1C and MBD3IDR, thereby resulting in the most complete structural characterization of these NuRD sub-complexes so far.

PrISM: Precision for Integrative Structural Models

“…PrISM is benchmarked on twelve systems and shown to be fast (Supplementary Results, Table S1) [6], [8], [9], [17], [18], [30], [32]. The annotated precision is shown to be consistent with measures such as root mean-square fluctuation (RMSF) and localization density maps, providing more fine-grained information than the latter in some cases (Supplementary Results, Table S2-S3, Fig.…”

“…PrISM is benchmarked on twelve systems and shown to be fast (Supplementary Results, Table S1) (Viswanath, Chemmama, et al ., 2017; Saltzberg et al ., 2019, 2021; Viswanath and Sali, 2018; Brilot et al . ; Luo et al ., 2015; Arvindekar et al ., 2021). The annotated precision is shown to be consistent with root mean-square fluctuation (RMSF) and localization density maps, providing more fine-grained information than the latter in some cases (Supplementary Results, Table S2, Fig.…”

“…Integrative modeling has emerged as the method of choice for determining the structures of large macromolecular assemblies which are challenging to characterize using a single experimental method [1]- [6]. Several assemblies have been determined by this approach, yielding insights on transcription [7], gene regulation and DNA repair [8], [9], intra-cellular transport [10], [11], cell cycle progression [12], [13], immune response and metabolism [14], [15]. Integrative modeling often relies on sparse, noisy, and ambiguous data from heterogenous samples [16].…”

“…Integrative modeling has emerged as the method of choice for determining the structures of macromolecular assemblies which are challenging to characterize using a single experimental method (Alber et al ., 2007; Russel et al ., 2012; Ward et al ., 2013; Webb et al ., 2018; Rout and Sali, 2019; Saltzberg et al ., 2021). Several assemblies have been determined by this approach, yielding insights on transcription (Robinson et al ., 2015), gene regulation and DNA repair (Luo et al ., 2015; Arvindekar et al ., 2021), intra-cellular transport (Kim et al ., 2018; Ganesan et al ., 2020), cell cycle progression (Viswanath, Bonomi, et al ., 2017; Pasani and Viswanath, 2021), immune response and metabolism (Lasker et al ., 2012; Gutierrez et al ., 2020). Integrative modeling often relies on sparse, noisy, and ambiguous data from heterogenous samples (Schneidman-Duhovny et al ., 2014).…”