Evidence map›Paper›PMID 27613298›Full record

ArticleJournal of biomolecular NMR2016

Predicting the redox state and secondary structure of cysteine residues using multi-dimensional classification analysis of NMR chemical shifts.

Ching-Cheng Wang, Wen-Chung Lai, Woei-Jer Chuang

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Article in Journal of biomolecular NMR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Ching-Cheng WangInstitute of Manufacturing Information and Systems, National Cheng Kung University College of Electrical Engineering and Computer Science, Tainan, 701, Taiwan.
Wen-Chung LaiInstitute of Manufacturing Information and Systems, National Cheng Kung University College of Electrical Engineering and Computer Science, Tainan, 701, Taiwan.
Woei-Jer ChuangDepartment of Biochemistry and Molecular Biology, National Cheng Kung University College of Medicine, Tainan, 701, Taiwan. wjcnmr@mail.ncku.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A tool for predicting the redox state and secondary structure of cysteine residues using multi-dimensional analyses of different combinations of nuclear magnetic resonance (NMR) chemical shifts has been developed. A data set of cysteine [Formula: see text], (13)C(α), (13)C(β), (1)H(α), (1)H(N), and (15)N(H) chemical shifts was created, classified according to redox state and secondary structure, using a library of 540 re-referenced BioMagResBank (BMRB) entries. Multi-dimensional analyses of three, four, five, and six chemical shifts were used to derive rules for predicting the structural states of cysteine residues. The results from 60 BMRB entries containing 122 cysteines showed that four-dimensional analysis of the C(α), C(β), H(α), and N(H) chemical shifts had the highest prediction accuracy of 100 and 95.9 % for the redox state and secondary structure, respectively. The prediction of secondary structure using 3D, 5D, and 6D analyses had the accuracy of ~90 %, suggesting that H(N) and [Formula: see text] chemical shifts may be noisy and made the discrimination worse. A web server (6DCSi) was established to enable users to submit NMR chemical shifts, either in BMRB or key-in formats, for prediction. 6DCSi displays predictions using sets of 3, 4, 5, and 6 chemical shifts, which shows their consistency and allows users to draw their own conclusions. This web-based tool can be used to rapidly obtain structural information regarding cysteine residues directly from experimental NMR data.

Indexed as

Nuclear Magnetic Resonance, BiomolecularOxidation-ReductionProtein Structure, SecondaryAlgorithmsCluster AnalysisCysteineModels, ChemicalProteinsSoftwareWeb BrowserCysteineProteinsChemical shiftCysteine residueNMRPredictionRedox stateSecondary structure

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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.