Structural and functional characteristics of cGMP-dependent methionine oxidation in Arabidopsis thaliana proteins
Article - Full Text
Supplemental File 1
Parrott, Brian Jonathan
Jankovic, Boris R.
Lilley, Kathryn S
Gehring, Christoph A
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Bioscience Core Lab
Chemical Engineering Program
Computational Bioscience Research Center (CBRC)
Molecular Signalling Group
Online Publication Date2013-01-05
Print Publication Date2013
Permanent link to this recordhttp://hdl.handle.net/10754/325257
MetadataShow full item record
AbstractBackground: Increasing structural and biochemical evidence suggests that post-translational methionine oxidation of proteins is not just a result of cellular damage but may provide the cell with information on the cellular oxidative status. In addition, oxidation of methionine residues in key regulatory proteins, such as calmodulin, does influence cellular homeostasis. Previous findings also indicate that oxidation of methionine residues in signaling molecules may have a role in stress responses since these specific structural modifications can in turn change biological activities of proteins. Findings. Here we use tandem mass spectrometry-based proteomics to show that treatment of Arabidopsis thaliana cells with a non-oxidative signaling molecule, the cell-permeant second messenger analogue, 8-bromo-3,5-cyclic guanosine monophosphate (8-Br-cGMP), results in a time-dependent increase in the content of oxidised methionine residues. Interestingly, the group of proteins affected by cGMP-dependent methionine oxidation is functionally enriched for stress response proteins. Furthermore, we also noted distinct signatures in the frequency of amino acids flanking oxidised and un-oxidised methionine residues on both the C- and N-terminus. Conclusions: Given both a structural and functional bias in methionine oxidation events in response to a signaling molecule, we propose that these are indicative of a specific role of such post-translational modifications in the direct or indirect regulation of cellular responses. The mechanisms that determine the specificity of the modifications remain to be elucidated. 2013 Marondedze et al.; licensee BioMed Central Ltd.
CitationMarondedze C, Turek I, Parrott B, Thomas L, Jankovic B, et al. (2013) Structural and functional characteristics of cGMP-dependent methionine oxidation in Arabidopsis thaliana proteins. Cell Communication and Signaling 11: 1. doi:10.1186/1478-811X-11-1.
JournalCell Communication and Signaling
PubMed Central IDPMC3544604
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Protein Methionine Sulfoxide Dynamics in Arabidopsis thaliana under Oxidative Stress.
- Authors: Jacques S, Ghesquière B, De Bock PJ, Demol H, Wahni K, Willems P, Messens J, Van Breusegem F, Gevaert K
- Issue date: 2015 May
- Thioredoxin-dependent redox regulation of cellular signaling and stress response through reversible oxidation of methionines.
- Authors: Bigelow DJ, Squier TC
- Issue date: 2011 Jul
- Redox modulation of cellular signaling and metabolism through reversible oxidation of methionine sensors in calcium regulatory proteins.
- Authors: Bigelow DJ, Squier TC
- Issue date: 2005 Jan 17
- Site-specific methionine oxidation in calmodulin affects structural integrity and interaction with Ca2+/calmodulin-dependent protein kinase II.
- Authors: Snijder J, Rose RJ, Raijmakers R, Heck AJ
- Issue date: 2011 Apr
- Loss of conformational stability in calmodulin upon methionine oxidation.
- Authors: Gao J, Yin DH, Yao Y, Sun H, Qin Z, Schöneich C, Williams TD, Squier TC
- Issue date: 1998 Mar
Showing items related by title, author, creator and subject.
ProDis-ContSHC: Learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrievalWang, Jim Jing-Yan; Gao, Xin; Wang, Quanquan; Li, Yongping (BMC Bioinformatics, Springer Nature, 2012-05-08) [Article]Background: The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.Results: In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure dij by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (i, j), if their context N (i) and N (j) is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing dij by a factor learned from the context N (i) and N (j). Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new Supervised learned Dissimilarity measure, we update the Protein Hierarchial Context Coherently in an iterative algorithm--ProDis-ContSHC.We test the performance of ProDis-ContSHC on two benchmark sets, i.e., the ASTRAL 1.73 database and the FSSP/DALI database. Experimental results demonstrate that plugging our supervised contextual dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarity measures that do not use the class label information.Conclusions: Using the contextual proteins with their class labels in the database, we can improve the accuracy of the pairwise dissimilarity/similarity measures dramatically for the protein retrieval tasks. In this work, for the first time, we propose the idea of supervised contextual dissimilarity learning, resulting in the ProDis-ContSHC algorithm. Among different contextual dissimilarity learning approaches that can be used to compare a pair of proteins, ProDis-ContSHC provides the highest accuracy. Finally, ProDis-ContSHC compares favorably with other methods reported in the recent literature. 2012 Wang et al.; licensee BioMed Central Ltd.
The human interactome knowledge base (hint-kb): An integrative human protein interaction database enriched with predicted protein–protein interaction scores using a novel hybrid techniqueTheofilatos, Konstantinos A.; Dimitrakopoulos, Christos M.; Likothanassis, Spiridon D.; Kleftogiannis, Dimitrios A.; Moschopoulos, Charalampos N.; Alexakos, Christos; Papadimitriou, Stergios; Mavroudi, Seferina P. (Artificial Intelligence Review, Springer Nature, 2013-07-12) [Article]Proteins are the functional components of many cellular processes and the identification of their physical protein–protein interactions (PPIs) is an area of mature academic research. Various databases have been developed containing information about experimentally and computationally detected human PPIs as well as their corresponding annotation data. However, these databases contain many false positive interactions, are partial and only a few of them incorporate data from various sources. To overcome these limitations, we have developed HINT-KB (http://biotools.ceid.upatras.gr/hint-kb/), a knowledge base that integrates data from various sources, provides a user-friendly interface for their retrieval, cal-culatesasetoffeaturesofinterest and computesaconfidence score for every candidate protein interaction. This confidence score is essential for filtering the false positive interactions which are present in existing databases, predicting new protein interactions and measuring the frequency of each true protein interaction. For this reason, a novel machine learning hybrid methodology, called (Evolutionary Kalman Mathematical Modelling—EvoKalMaModel), was used to achieve an accurate and interpretable scoring methodology. The experimental results indicated that the proposed scoring scheme outperforms existing computational methods for the prediction of PPIs.
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.