Combining automated peak tracking in SAR by NMR with structure-based backbone assignment from 15N-NOESY
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Structural and Functional Bioinformatics Group
Online Publication Date2012-03-22
Print Publication Date2012
Permanent link to this recordhttp://hdl.handle.net/10754/325471
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AbstractBackground: Chemical shift mapping is an important technique in NMR-based drug screening for identifying the atoms of a target protein that potentially bind to a drug molecule upon the molecule's introduction in increasing concentrations. The goal is to obtain a mapping of peaks with known residue assignment from the reference spectrum of the unbound protein to peaks with unknown assignment in the target spectrum of the bound protein. Although a series of perturbed spectra help to trace a path from reference peaks to target peaks, a one-to-one mapping generally is not possible, especially for large proteins, due to errors, such as noise peaks, missing peaks, missing but then reappearing, overlapped, and new peaks not associated with any peaks in the reference. Due to these difficulties, the mapping is typically done manually or semi-automatically, which is not efficient for high-throughput drug screening.Results: We present PeakWalker, a novel peak walking algorithm for fast-exchange systems that models the errors explicitly and performs many-to-one mapping. On the proteins: hBclXL, UbcH5B, and histone H1, it achieves an average accuracy of over 95% with less than 1.5 residues predicted per target peak. Given these mappings as input, we present PeakAssigner, a novel combined structure-based backbone resonance and NOE assignment algorithm that uses just 15N-NOESY, while avoiding TOCSY experiments and 13C-labeling, to resolve the ambiguities for a one-to-one mapping. On the three proteins, it achieves an average accuracy of 94% or better.Conclusions: Our mathematical programming approach for modeling chemical shift mapping as a graph problem, while modeling the errors directly, is potentially a time- and cost-effective first step for high-throughput drug screening based on limited NMR data and homologous 3D structures. 2012 Jang et al.; licensee BioMed Central Ltd.
CitationJang R, Gao X, Li M (2012) Combining automated peak tracking in SAR by NMR with structure-based backbone assignment from 15N-NOESY. BMC Bioinformatics 13: S4. doi:10.1186/1471-2105-13-S3-S4.
PubMed Central IDPMC3402924
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- Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA.
- Authors: Herrmann T, Güntert P, Wüthrich K
- Issue date: 2002 May 24
- Protein NMR structure determination with automated NOE-identification in the NOESY spectra using the new software ATNOS.
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- Towards fully automated structure-based NMR resonance assignment of ¹⁵N-labeled proteins from automatically picked peaks.
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- Issue date: 2011 Mar
- Automated amino acid side-chain NMR assignment of proteins using (13)C- and (15)N-resolved 3D [ (1)H, (1)H]-NOESY.
- Authors: Fiorito F, Herrmann T, Damberger FF, Wüthrich K
- Issue date: 2008 Sep
- A new algorithm for reliable and general NMR resonance assignment.
- Authors: Schmidt E, Güntert P
- Issue date: 2012 Aug 1
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CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure predictionCui, Xuefeng; Lu, Zhiwu; wang, sheng; Wang, Jim Jing-Yan; Gao, Xin (Bioinformatics, Oxford University Press (OUP), 2016-06-15) [Article]Motivation: Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information. Method: We present a novel network-based protein homology detection method, CMsearch, based on cross-modal learning. Instead of exploring a single network built from the mixture of sequence and structure space information, CMsearch builds two separate networks to represent the sequence space and the structure space. It then learns sequence–structure correlation by simultaneously taking sequence information, structure information, sequence space information and structure space information into consideration. Results: We tested CMsearch on two challenging tasks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 proteins. Our results demonstrate that CMsearch is insensitive to the similarity metrics used to define the sequence and the structure spaces. By using HMM–HMM alignment as the sequence similarity metric, CMsearch clearly outperforms state-of-the-art homology detection methods and the CASP-winning template-based protein structure prediction methods.
PREPUBLICATION: From structure topology to chemical composition. XXIII. Revision of the crystal structure and chemical formula of zvyaginite, a seidozerite-supergroup mineral from the Lovozero alkaline massif, Kola peninsula, RussiaSokolova, E.; Genovese, Alessandro; Falqui, Andrea; Hawthorne, F. C.; Cámara, F. (Mineralogical Magazine, Mineralogical Society, 2018-01-26) [Article]The crystal structure and chemical formula of zvyaginite, ideally Na2ZnTiNb2(Si2O7)2O2(OH)2 (H2O)4, a lamprophyllite-group mineral of the seidozerite supergroup from the type locality, Mt. Malyi Punkaruaiv, Lovozero alkaline massif, Kola Peninsula, Russia have been revised. The crystal structure was refined with a new origin in space group C⎯1, a = 10.769(2), b = 14.276(3), c = 12.101(2) Å, α = 105.45(3), β = 95.17(3), γ = 90.04(3)°, V = 1785.3(3.2) Å3, R1 = 9.23%. The electron-microprobe analysis gave the following empirical formula [calculated on 22 (O + F)]:(Na0.75Ca0.09K0.04��1.12)Σ2 (Na1.12Zn0.88Mn0.17Fe2+0.04��0.79)Σ3(Nb1.68Ti1.25Al0.07)Σ3 (Si4.03O14)O2 [(OH)1.11F0.89]Σ2(H2O)4, Z = 4. Electron-diffraction patterns have prominent streaking along c* and HRTEM images show an intergrowth of crystalline zvyaginite with two distinct phases, both of which are partially amorphous. The crystal structure of zvyaginite is an array of TS (Titanium Silicate) blocks connected via hydrogen bonds between H2O groups. The TS block consists of HOH sheets (H = heteropolyhedral, O = octahedral) parallel to (001). In the O sheet, the MO(1,4,5) sites are occupied mainly by Ti, Zn and Na and the MO(2,3) sites are occupied by Na at less than 50%. In the H sheet, the MH(1,2) sites are occupied mainly by Nb and the AP(1) and AP(2) sites are occupied mainly by Na and ��. The MH and AP polyhedra and Si2O7 groups constitute the H sheet. The ideal structural formula is Na��Nb2NaZn��Ti(Si2O7)2O2(OH)2(H2O)4. Zvyaginite is a Zn-bearing and Na-poor analogue of epistolite, ideally (Na��)Nb2Na3Ti(Si2O7)2O2(OH)2(H2O)4. Epistolite and zvyaginite are related by the following substitution in the O sheet of the TS-block: (Na+2)epi ↔ Zn2+ zvy + ��zvy. The doubling of the t1 and t2 translations of zvyaginite relative to those of epistolite is due to the order of Zn and Na along a (t1) and b (t2) in the O sheet of zvyaginite.
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