Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2013 Oct 15;14(10):20635-57.
doi: 10.3390/ijms141020635.

Mass spectrometry coupled experiments and protein structure modeling methods

Affiliations
Review

Mass spectrometry coupled experiments and protein structure modeling methods

Jaewoo Pi et al. Int J Mol Sci. .

Abstract

With the accumulation of next generation sequencing data, there is increasing interest in the study of intra-species difference in molecular biology, especially in relation to disease analysis. Furthermore, the dynamics of the protein is being identified as a critical factor in its function. Although accuracy of protein structure prediction methods is high, provided there are structural templates, most methods are still insensitive to amino-acid differences at critical points that may change the overall structure. Also, predicted structures are inherently static and do not provide information about structural change over time. It is challenging to address the sensitivity and the dynamics by computational structure predictions alone. However, with the fast development of diverse mass spectrometry coupled experiments, low-resolution but fast and sensitive structural information can be obtained. This information can then be integrated into the structure prediction process to further improve the sensitivity and address the dynamics of the protein structures. For this purpose, this article focuses on reviewing two aspects: the types of mass spectrometry coupled experiments and structural data that are obtainable through those experiments; and the structure prediction methods that can utilize these data as constraints. Also, short review of current efforts in integrating experimental data in the structural modeling is provided.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Number of solved structures versus number of identified protein sequences. Numbers of sequences and protein structures are obtained through Uniprot (http://www.ebi.ac.uk/uniprot/) and RCBS PDB (http://www.rcsb.org), respectively.
Figure 2
Figure 2
Four types of cross-links (adapted from figure 3 of [26]). (A) Homo-bifunctional; (B) Hetero-bifunctional; (C) zero-length; and (D) hetero-trifunctional cross-link.
Figure 3
Figure 3
Structure prediction pipeline (A) Rosetta [54]; and (B) I-TASSER pipeline (adapted from Figure 1 of [55]).

References

    1. Apweiler R., Bairoch A., Wu C.H. Protein sequence databases. Curr. Opin. Chem. Biol. 2004;8:76–80. - PubMed
    1. Gao X. Ph.D. Thesis. University of Waterloo; Waterloo, ON, Canada: 2009. Towards Automating Protein Structure Determination from NMR Data.
    1. Skolnick J., Zhang Y., Arakaki A.K., Kolinski A., Boniecki M., Szilágyi A., Kihara D. TOUCHSTONE: A unified approach to protein structure prediction. Proteins. 2003;53:469–479. - PubMed
    1. Xu D., Zhang Y. Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins. 2012;80:1715–1735. - PMC - PubMed
    1. Venkatraman V., Yang Y.D., Sael L., Kihara D. Protein-protein docking using region-based 3D Zernike descriptors. BMC Bioinf. 2009;10:407. - PMC - PubMed

Publication types

LinkOut - more resources