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Backbone and side chain resonance assignment of the intrinsically disordered human DBNDD1 protein.

ABSTRACT: The dysbindin domain-containing protein 1 (DBNDD1) is a conserved protein among higher eukaryotes whose structure and function are poorly investigated so far. Here, we present the backbone and side chain nuclear magnetic resonance assignments for the human DBNDD1 protein. Our chemical-shift based secondary structure analysis reveals the human DBNDD1 as an intrinsically disordered protein.

SUBMITTER: Wiedemann C

PROVIDER: S-EPMC9510119 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Backbone and side chain resonance assignment of the intrinsically disordered human DBNDD1 protein.

Wiedemann Christoph C Obika Kingsley Benjamin KB Liebscher Sandra S Jirschitzka Jan J Ohlenschläger Oliver O Bordusa Frank F

Biomolecular NMR assignments 20220426 2

The dysbindin domain-containing protein 1 (DBNDD1) is a conserved protein among higher eukaryotes whose structure and function are poorly investigated so far. Here, we present the backbone and side chain nuclear magnetic resonance assignments for the human DBNDD1 protein. Our chemical-shift based secondary structure analysis reveals the human DBNDD1 as an intrinsically disordered protein. ...[more]

PMID: 35474152

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Project description:One bottleneck in NMR structure determination lies in the laborious and time-consuming process of side-chain resonance and NOE assignments. Compared to the well-studied backbone resonance assignment problem, automated side-chain resonance and NOE assignments are relatively less explored. Most NOE assignment algorithms require nearly complete side-chain resonance assignments from a series of through-bond experiments such as HCCH-TOCSY or HCCCONH. Unfortunately, these TOCSY experiments perform poorly on large proteins. To overcome this deficiency, we present a novel algorithm, called NASCA: (NOE Assignment and Side-Chain Assignment), to automate both side-chain resonance and NOE assignments and to perform high-resolution protein structure determination in the absence of any explicit through-bond experiment to facilitate side-chain resonance assignment, such as HCCH-TOCSY. After casting the assignment problem into a Markov Random Field (MRF), NASCA: extends and applies combinatorial protein design algorithms to compute optimal assignments that best interpret the NMR data. The MRF captures the contact map information of the protein derived from NOESY spectra, exploits the backbone structural information determined by RDCs, and considers all possible side-chain rotamers. The complexity of the combinatorial search is reduced by using a dead-end elimination (DEE) algorithm, which prunes side-chain resonance assignments that are provably not part of the optimal solution. Then an A* search algorithm is employed to find a set of optimal side-chain resonance assignments that best fit the NMR data. These side-chain resonance assignments are then used to resolve the NOE assignment ambiguity and compute high-resolution protein structures. Tests on five proteins show that NASCA: assigns resonances for more than 90% of side-chain protons, and achieves about 80% correct assignments. The final structures computed using the NOE distance restraints assigned by NASCA: have backbone RMSD 0.8-1.5 Å from the reference structures determined by traditional NMR approaches.

Project description:Experimental residual dipolar couplings (RDCs) in combination with structural models have the potential for accelerating the protein backbone resonance assignment process because RDCs can be measured accurately and interpreted quantitatively. However, this application has been limited due to the need for very high-resolution structural templates. Here, we introduce a new approach to resonance assignment based on optimal agreement between the experimental and calculated RDCs from a structural template that contains all assignable residues. To overcome the inherent computational complexity of such a global search, we have adopted an efficient two-stage search algorithm and included connectivity data from conventional assignment experiments. In the first stage, a list of strings of resonances (CA-links) is generated via exhaustive searches for short segments of sequentially connected residues in a protein (local templates), and then ranked by the agreement of the experimental (13)C(α) chemical shifts and (15)N-(1)H RDCs to the predicted values for each local template. In the second stage, the top CA-links for different local templates in stage I are combinatorially connected to produce CA-links for all assignable residues. The resulting CA-links are ranked for resonance assignment according to their measured RDCs and predicted values from a tertiary structure. Since the final RDC ranking of CA-links includes all assignable residues and the assignment is derived from a "global minimum", our approach is far less reliant on the quality of experimental data and structural templates. The present approach is validated with the assignments of several proteins, including a 42 kDa maltose binding protein (MBP) using RDCs and structural templates of varying quality. Since backbone resonance assignment is an essential first step for most of biomolecular NMR applications and is often a bottleneck for large systems, we expect that this new approach will improve the efficiency of the assignment process for small and medium size proteins and will extend the size limits assignable by current methods for proteins with structural models.

Dataset Information

Backbone and side chain resonance assignment of the intrinsically disordered human DBNDD1 protein.

Publications

Backbone and side chain resonance assignment of the intrinsically disordered human DBNDD1 protein.

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