Computational Simulations to Predict Creatine Kinase-Associated Factors: Protein-Protein Interaction Studies of Brain and Muscle Types of Creatine Kinases

Hu, Wei-Jiang; Zhou, Sheng-Mei; Yang, Joshua SungWoo; Meng, Fan-Guo

doi:https://doi.org/10.4061/2011/328249

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Skin Diseases-Related Enzymes: Mechanisms and Clinical Applications

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Volume 2011 | Article ID 328249 | https://doi.org/10.4061/2011/328249

Computational Simulations to Predict Creatine Kinase-Associated Factors: Protein-Protein Interaction Studies of Brain and Muscle Types of Creatine Kinases

Wei-Jiang Hu,¹Sheng-Mei Zhou,²Joshua SungWoo Yang,^3,4and Fan-Guo Meng¹

Academic Editor: Jun-Mo Yang

Received17 May 2011

Accepted26 May 2011

Published03 Aug 2011

Abstract

Creatine kinase (CK; EC 2.7.3.2) is related to several skin diseases such as psoriasis and dermatomyositis. CK is important in skin energy homeostasis because it catalyzes the reversible transfer of a phosphoryl group from MgATP to creatine. In this study, we predicted CK binding proteins via the use of bioinformatic tools such as protein-protein interaction (PPI) mappings and suggest the putative hub proteins for CK interactions. We obtained 123 proteins for brain type CK and 85 proteins for muscle type CK in the interaction networks. Among them, several hub proteins such as NFKB1, FHL2, MYOC, and ASB9 were predicted. Determination of the binding factors of CK can further promote our understanding of the roles of CK in physiological conditions.

1. Introduction

Creatine kinase (CK) (ATP: creatine kinase N-phosphotransferase, EC 2.7.3.2) is thought to be crucial for intracellular transport and the storage of high energy phosphate because it catalyzes the reversible transfer of a phosphoryl group from MgATP to creatine, which leads to the creation of phosphocreatine and MgADP [1]. CK plays an important role in the cellular energy metabolism of vertebrates, and it is widely distributed in tissues that require a lot of energy [2]. Several types of CK are expressed in various tissues: the muscle and brain types of CK are the most common, and three different isoenzymes that include CK-MM (the muscle type homodimer), CK-BB (the brain type homodimer), and CK-MB (the muscle plus brain type heterodimer) originate from these two common types. CK is an important serum marker for myocardial infarction. Various types of CKs (the muscle, brain, and mitochondrial types) are thought to be important not only in the diagnosis of myocardial infarction, cardiac hypertrophy, and muscular dystrophy but also for studies of some other serious diseases, including Alzheimer’s disease, Parkinson's disease, and psoriasis [3–8].

CK-BB is associated with several pathologies, including neurodegenerative and age-related diseases. Recently, Chang et al. [9] reported an important role for CK-BB in osteoclast-mediated bone resorption, which was found using a proteomics approach. They found that CK-BB is greatly increased during osteoclastogenesis and suggested that it represents a potential target for antiresorptive drug development. CK-BB interacts with the potassium-chloride cotransporter 3, which is involved in the pathophysiology of hereditary motor and sensory neuropathy with agenesis of the corpus callosum [10]. Previous studies [11, 12] have reported that CK-BB is involved in Alzheimer's disease (AD) as an oxidatively modified protein. This suggests that oxidatively damaged CK-BB may be associated with aging and age-related neurodegenerative disorders such as AD.

CK-MM is a good model to use for studying folding pathways because of several characteristics: (i) it is a dimer that consists of two identical subunits, each with an N-terminal domain with about 100 residues and a C-terminal domain with about 250 residues connected by a long linker [13]; (ii) extensively denatured CK can be renatured spontaneously with restoration of its enzymatic activity in the absence of any external assistance [14]; (iii) its folding pathway is complicated and involves several intermediates [15, 16]; (iv) conformational changes of the secondary and tertiary structures can be easily measured by monitoring activity changes [14, 15]; (v) protein-protein interactions, including molecular chaperones, are observed during refolding [17, 18].

In this study, we obtained computational predictions of the binding proteins by using two types of CK (CK-BB and CK-MM) as hub proteins in bioinformatic algorithms. As a result, we obtained 208 protein lists in the interaction networks via application of both muscle and brain types of CK. Determination of the binding factors and functions of CK can further promote our understanding of the physiological roles of CK.

2. Materials and Methods

2.1. PPI Mappings: PEIMAP and PSIMAP Algorithms

We present the functionally classified protein-protein interactions on the basis of the cell cycle, cell transport, oxidoreductase, and apoptosis. PPI resources were assembled from a combination of several experimental protein interaction databases. The protein interaction resources included six databases: DIP [19], BIND [20], IntAct [21], MINT [22], HPRD [23], and BioGrid [24]. We performed a redundancy test to remove identical protein sequences from the interaction databases. The databases contain 116,773 proteins and 229,799 interactions.

PPI prediction uses most of the major types of PPI algorithms. They are (1) Protein Structural Interactome MAP (PSIMAP), a method that uses the structural domain of the SCOP (Structural Classification of Proteins) database [25] and (2) Protein Experimental Interactome MAP (PEIMAP), a common method that uses public resources of experimental protein interaction information such as HPRD, BIND, DIP, MINT, IntAct; and BioGrid. The basic procedure of PSIMAP is to infer interactions between proteins by using their homologs. Interactions among domains or proteins for known PDB (Protein Data Bank) structures are the basis for the prediction. If an unknown protein has a homolog to a domain, then PSIMAP assumes that the query has the probability to interact with its homolog’s partners. This concept is called “homologous interaction.” The original interaction between two proteins or domains is based on the Euclidean distance. Therefore, PSIMAP gives a structure-based interaction prediction [26]. PEIMAP was constructed by combining several experimental protein-protein interaction databases. We carried out a redundancy check to remove identical protein sequences from the source interaction databases. The image was made by the Pajak2.00 program (http://vlado.fmf.uni-lj.si/pub/networks/pajek/).

3. Results and Discussion

We identified potential candidates through protein-protein interaction predictions made using various protein interaction resources. By analyzing the hub protein of the networks with metrics such as degree and centrality, we detected 123 potential candidates for CKB interacting (direct or indirect) factors and 85 candidates for CKM.

In Figure 1, interacting factors such as NFKB1 (NP_003989, nuclear factor of kappa light polypeptide gene enhancer in B-cells 1), MYOC (NP_000252; myocilin, trabecular meshwork inducible glucocorticoid response), MYOM2 (NP_003961; myomesin (M-protein) 2, 165 kDa), FHL2 (NP_001034581, four-and-a-half LIM domains 2), HIF1AN (NP_060372, hypoxia-inducible factor 1, alpha subunit inhibitor), ASB9 (NP_076992, ankyrin repeat and SOCS box-containing 9), and CKM (NP_001815, creatine kinase, muscle) were elucidated. Interestingly, NFKB1 was detected as a hub protein interacting with CK-BB in our results. In Figure 2, we obtained results similar to those from Figure 1, where NFKB1, MYOC, MYOM2, FHL2, HIF1AN, ASB9, and CKM were detected as interacting factors that were directly or indirectly associated with CKB. NFKB1, CKM, and ASB9 interacted with CKB directly.

In the same way, we detected the CKM-associated proteins as shown in Figure 3 with 80% sequence identity. As a result, we found that CKB, FHL2, MYOC, ASB9, HIF1AN, NFKB1, TTN (NP_596870, titin), MYH9 (NP_002464, myosin, heavy chain 9, non-muscle), and ITGA7 (NP_002197, integrin, alpha 7) mainly interacted with CKM at 80% sequence identity. At the level of 100% identity, we found that MYOM2, CKB, FHL2, and MYOC directly interacted with CKM as shown in Figure 4. In addition to these factors, complete lists of factors that interacted with CKB and CKM in a direct or indirect manner are shown in Tables 1 and 2. After overlapping the results from Figures 1 to 4, we found that NFKB1, FHL2, and MYOC were still detected as hub proteins in Figure 5.

NFKB1 (also known as p50 or NF-kappaB) is a well-known transcription regulator that is responsible for the expression and regulation of many genes for immune response, cell adhesion, differentiation, proliferation, angiogenesis, and apoptosis [27–31]. It translocates into the nucleus and stimulates the expression of many genes involved in various biological functions. NFKB1 is also associated with a number of inflammatory diseases such as lymphoma [32], Alzheimer disease [33], psoriatic arthritis [34], breast cancer [35, 36], and rheumatoid arthritis [37]. Activation of NFKB1 requires binding of NF-kappaB essential modulator (NEMO) to ubiquitinated substrates [38]. With respect to an association with CK, it has been reported that NFKB1 is mostly associated with myocardial ischemia/reperfusion. During reperfusion, the absence of poly(ADP-ribose) polymerase-1 (PARP-1) leads to a reduction of myocardial apoptosis, which is associated with reduced NFKB1 activation [39, 40], and proteasome inhibition ablates activation of NFKB1 in myocardial reperfusion and reduces reperfusion injury [41]. Myocardial injury was assessed by measuring the serum levels of CK, and CK was reduced in serum along with reduction of NFKB1 activation.

FHL2 is a member of the human four-and-a-half-LIM-only protein family, which consists of the members FHL1, FHL2, FHL3, FHL4, and ACT. These proteins function in various cellular processes, including regulation of cell survival, transcription, and signal transduction [42]. FHL2 contains an LIM domain, one of the protein-protein interaction motifs, which allows specific proteins to combine with certain partners. The specificity of a protein-protein interaction can be obtained by an interaction code predicted by conserved amino acid sequences. The interaction of FHL2 with transcription factors and other proteins involved in cancer development was examined. Since transcription factors control all fundamental developmental and homeostatic processes, transcriptional cofactors such as FHL2 are likely to contribute to human carcinogenesis and are of clinical importance in various forms of cancer [43], including leukemia [44]. With respect to an association with CK, Chung et al. [45] reported that FHL2 (developmentally enhanced phosphotransfer enzyme-anchoring protein) amalgamated the myofibrillar CK metabolic signaling circuit, providing an energetic continuum between mitochondria and the nascent contractile machinery in a murine embryonic stem cell cardiac differentiation model. They reported that CK-M clustered around developing myofibrils, sarcolemma, and the perinuclear compartment, whereas CK-B was tightly associated with myofibrillar alpha-actinin, forming wire-like structures extending from the nuclear compartment to the sarcolemma. FHL2 was also increased in myocardial ischemia-reperfusion injury, where IL-6 and IL-8 mRNA are upregulated in human cardiac myocytes [46].

Recently, ASB9 was found to interact with ubiquitous mitochondrial CK [47]. The ankyrin repeat domains of ASB9 can associate with the substrate binding site of CK in a SOCS box-independent manner. The overexpression of ASB9 induces ubiquitination of CK. ASB9 reduces CK activities and cell growth and negatively regulates cell growth. ASB9 is a member of the ankyrin repeat and is a suppressor of the cytokine signaling (SOCS) box protein family. It can interact with the SOCS box domain of the elongin B-C adapter complex and can further complex with the cullin and ring box proteins to form E3 ubiquitin ligase complexes [48]. These complexes may be involved in specific substrate-recognition for ubiquitination and degradation and mediate the substrate-recognition of the E3 ubiquitin ligases.

The interaction between CK and MYOC has not been elucidated. However, MYOC has a cytoskeletal function, and this implies that it may interact with CK somehow. MYOC is expressed in many ocular tissues including the trabecular meshwork [49], which is a specialized eye tissue that is essential in regulating intraocular pressure. MYOC mutations have been identified as the cause of hereditary juvenile-onset open-angle glaucoma [50].

Researchers could apply computational prediction by PPI mapping to help determine target proteins. Since the next step in the functional study of interesting proteins/genes is a time- and cost-consuming process, the number of target proteins is limited; hence, for the right choice, computational prediction on the basis of database information could be critical at this step. Functional studies can be further conducted using a mouse model and a large number of clinical samples. Final confirmation and CK mechanisms could then be more clearly evaluated for developing drugs to effectively treat CK-related diseases.

The functions of most of the candidate proteins predicted in this study have not been well reported in skin diseases or in the pathogenesis of other diseases. We provide new information regarding these candidate proteins’ interaction with CK, as well as the involvement of several hub proteins such as NFKB1, FHL2, ASB9, and MYOC. Although we do not suggest a direct role of any candidate protein in skin diseases, we provide candidate proteins to be targeted in further studies of CK-associated diagnostic markers and/or treatment of corresponding skin conditions. Furthermore, we also provide some insights into understanding the responses of CK in skin.

Abbreviations

PPI:	Protein-protein interaction
CK-MM:	Muscle type homodimer
CK-BB:	Brain type homodimer.

Acknowledgments

This study was supported by the Grants from the Science and Technology Bureau of Jiaxing, Zhejiang (no. 2008AY2032) and the Science and Technology Planning Project of Zhejiang Province (no. 2010C33139). Dr. W.-J. Hu was supported by a Grant from China Postdoctoral Science Foundation (no. 20060400467). Dr. S.-M. Zhou was supported by a grant from the Science and Technology Bureau of Jiaxing (no. 2007AY2021).

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Copyright © 2011 Wei-Jiang Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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