Residue-wise local quality estimation for protein models from cryo-EM maps

Terashi, Genki; Wang, Xiao; Subramaniya, Sai Raghavendra Maddhuri Venkata; Tesmer, John J.G.

doi:10.1038/s41592-022-01574-4

Cited by 35 publications

(37 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A.g DAQ. 35 This algorithm uses deep learning that can estimate the residue-wise local quality for protein models from cryo-electron microscopy (EM) maps. The method calculates the likelihood that a given density feature corresponds to an amino acid, atom, and secondary structure.…”

Section: Level 5: +Micrographs and Coordinatesmentioning

confidence: 99%

Image processing tools for the validation of CryoEM maps

et al. 2022

View full text Add to dashboard Cite

show abstract

Section: Level 5: +Micrographs and Coordinatesmentioning

confidence: 99%

Image processing tools for the validation of CryoEM maps

et al. 2022

View full text Add to dashboard Cite

show abstract

“…DAQ score quantifies how well residues in an atomic model agree with density features detected by deep learning. Local density features of an EM map are captured by deep learning: The input EM map is scanned with a box 11*11*11 Å 3 in size with an interval of 1 Å along the three coordinates axes directions and a trained deep neural network outputs probabilities that the box contains 20 amino acid types at the center position the box (Terashi et al ., 2022).…”

Section: Methodsmentioning

confidence: 99%

“…We tested the DAQ-refine protocol on thirteen PDB chain models with corresponding EM maps, which were taken from datasets used in the DAQ score paper (Terashi et al, 2022). These targets S1.…”

Section: Dataset Of Pdb Entries To Refinementioning

confidence: 99%

“…Cryo-EM has unique strengths including the ability to determine macromolecular structures, such as large complexes or membrane proteins, that are often difficult to crystallize for X-ray crystallography (Cheng, 2018). As an increasing number of protein structures are being determined by cryo-EM, however, it has been observed that a substantial number of structure models determined with cryo-EM and deposited in PDB (Berman et al ., 2000) have potential errors (Terashi et al ., 2022). Errors include cases where modeled local conformation itself is wrong and situations where misassignment of amino acids occurred with otherwise correct main-chain conformation.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, we developed a novel validation method, Deep learning based Amino acid-wise model Quality (DAQ) score, which uses deep learning to capture local density features to assess the likelihood that each residue in a model is correct. It is demonstrated that DAQ score was very effective in finding the positions in the structure model where amino acid assignments to a local density are likely to be incorrect (Terashi et al ., 2022).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Protein Model Refinement for Cryo-EM Maps Using DAQ score

Terashi¹,

Wang

2022

Preprint

Self Cite

View full text Add to dashboard Cite

As more protein structure models have been determined from cryo-electron microscopy (cryo-EM) density maps, establishing how to evaluate the model accuracy and how to correct models in case they contain errors is becoming crucial to ensuring the quality of structure models deposited to the public database, PDB. Here, we present a new protocol for evaluating a protein model built from a cryo-EM map and for applying local structure refinement in case the model has potential errors. Model evaluation is performed with a deep learning-based model-local map assessment score, DAQ, which we developed recently. Then, the subsequent local refinement is performed by a modified procedure of AlphaFold2, where we provide a trimmed template and trimmed multiple sequence alignment as input to control which structure regions to refine while leaving other more confident regions in the model intact. A benchmark study showed that our protocol, DAQ-refine, consistently improves low-quality regions of initial models. Among about 20 refined models generated for an initial structure, DAQ score was able to identify most accurate models. The observed improvements by DAQ-refine were on average larger than other existing methods.

show abstract

Unveiling the stochastic nature of human heteropolymer ferritin self‐assembly mechanism

Bou‐Abdallah,

Fish,

Terashi

et al. 2024

Protein Science

Self Cite

View full text Add to dashboard Cite

Despite ferritin's critical role in regulating cellular and systemic iron levels, our understanding of the structure and assembly mechanism of isoferritins, discovered over eight decades ago, remains limited. Unveiling how the composition and molecular architecture of hetero‐oligomeric ferritins confer distinct functionality to isoferritins is essential to understanding how the structural intricacies of H and L subunits influence their interactions with cellular machinery. In this study, ferritin heteropolymers with specific H to L subunit ratios were synthesized using a uniquely engineered plasmid design, followed by high‐resolution cryo‐electron microscopy analysis and deep learning‐based amino acid modeling. Our structural examination revealed unique architectural features during the self‐assembly mechanism of heteropolymer ferritins and demonstrated a significant preference for H‐L heterodimer formation over H‐H or L‐L homodimers. Unexpectedly, while dimers seem essential building blocks in the protein self‐assembly process, the overall mechanism of ferritin self‐assembly is observed to proceed randomly through diverse pathways. The physiological significance of these findings is discussed including how ferritin microheterogeneity could represent a tissue‐specific adaptation process that imparts distinctive tissue‐specific functions to isoferritins.

show abstract

Residue-wise local quality estimation for protein models from cryo-EM maps

Cited by 35 publications

References 28 publications

Image processing tools for the validation of CryoEM maps

Image processing tools for the validation of CryoEM maps

Protein Model Refinement for Cryo-EM Maps Using DAQ score

Unveiling the stochastic nature of human heteropolymer ferritin self‐assembly mechanism

Contact Info

Product

Resources

About