Evidence map›Paper›PMID 34944417›Full record

ArticleBiomolecules2021

Three-Dimensional Graph Matching to Identify Secondary Structure Correspondence of Medium-Resolution Cryo-EM Density Maps.

Bahareh Behkamal, Mahmoud Naghibzadeh, Mohammad Reza Saberi, Zeinab Amiri Tehranizadeh, Andrea Pagnani, Kamal Al Nasr

Abstract read
In one paragraph

Article in Biomolecules, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Bahareh BehkamalDepartment of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran.ORCID 0000-0003-4630-1178
Mahmoud NaghibzadehDepartment of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran.
Mohammad Reza SaberiMedicinal Chemistry Department, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad 9177899191, Iran.
Zeinab Amiri TehranizadehMedicinal Chemistry Department, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad 9177899191, Iran.ORCID 0000-0002-5024-2521
Andrea PagnaniPolitecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy.
Kamal Al NasrDepartment of Computer Science, Tennessee State University, Nashville, TN 37209, USA.

Funding

A Computational Tool for Modeling of Protein Macromolecules Guided by Cryo-Electron Microscopy DataR15GM126509 · NIGMS · TENNESSEE STATE UNIVERSITY · PI AL NASR, KAMAL, CHEN, WEI · 2017 to 2017
$399k
NIGMS NIH HHS R15 GM126509
6 · The paper itself

Abstract

Cryo-electron microscopy (cryo-EM) is a structural technique that has played a significant role in protein structure determination in recent years. Compared to the traditional methods of X-ray crystallography and NMR spectroscopy, cryo-EM is capable of producing images of much larger protein complexes. However, cryo-EM reconstructions are limited to medium-resolution (~4-10 Å) for some cases. At this resolution range, a cryo-EM density map can hardly be used to directly determine the structure of proteins at atomic level resolutions, or even at their amino acid residue backbones. At such a resolution, only the position and orientation of secondary structure elements (SSEs) such as α-helices and β-sheets are observable. Consequently, finding the mapping of the secondary structures of the modeled structure (SSEs-A) to the cryo-EM map (SSEs-C) is one of the primary concerns in cryo-EM modeling. To address this issue, this study proposes a novel automatic computational method to identify SSEs correspondence in three-dimensional (3D) space. Initially, through a modeling of the target sequence with the aid of extracting highly reliable features from a generated 3D model and map, the SSEs matching problem is formulated as a 3D vector matching problem. Afterward, the 3D vector matching problem is transformed into a 3D graph matching problem. Finally, a similarity-based voting algorithm combined with the principle of least conflict (PLC) concept is developed to obtain the SSEs correspondence. To evaluate the accuracy of the method, a testing set of 25 experimental and simulated maps with a maximum of 65 SSEs is selected. Comparative studies are also conducted to demonstrate the superiority of the proposed method over some state-of-the-art techniques. The results demonstrate that the method is efficient, robust, and works well in the presence of errors in the predicted secondary structures of the cryo-EM images.

Indexed as

Computational BiologyCryoelectron MicroscopyCrystallography, X-RayModels, MolecularProteinsProtein Structure, SecondarySupport Vector MachineProteins3D graph matching3D vector matchingcryo-electron microscopymodeled structureproteinsecondary structure elementssimilarity-based voting algorithm

Identifiers

PMID34944417
PMCPMC8698881

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.