Comparative homology modeling and simulation studies of

Comparative homology modeling and simulation studies of protein c-jun

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International Journal of Integrative Biology A journal for biology beyond borders

ISSN 0973-8363

Comparative homology modeling and simulation studies of protein c-jun Pallavi Chauhan 1,*, Madhvi Shakya 2 1

Deptt. of Bioinformatics, MANIT, Bhopal, MP, India 2 Deptt. of Mathematics, MANIT,Bhopal, MP, India

Submitted: 9 Oct. 2009; Revised: 23 Nov. 2009; Accepted: 3 Dec. 2009

Abstract Signaling pathways leading to wrinkle formation have demonstrated c-jun to be one of the most prominent target against wrinkle formation. Designing inhibitors for c-jun requires a complete 3D structure. In this report an attempt has been made to model 3D structure of c-jun by comparative homology modeling. Through homology modeling four models were constructed using single and multiple templates. Based on single template approach two models were constructed differing in loop modeling. In multiple templates (segment based approach) approach two models were constructed differing in target-template identity and loop modeling. The models developed were evaluated by PROCHECK, WHAT CHECK and calculated RMSD and RMSF values. Among all Models, model developed using multiple templates having more than 40% sequence identity and loop modeled by fold recognition was found to be the best model for 3D structure of protein c-jun. Keywords: c-jun, homology modeling, loop modeling, single template approach, segment based approach.

INTRODUCTION Modeling of signaling pathways leading to wrinkle formation have concluded that ultraviolet radiation, stress and pollution activates five different types of receptors (EGFR, PAF, TNFR, PDGFR and ILR1), which then activates corresponding mitogen activated protein kinases (MAPK 1, 8 and 9) and activator protein-1 (AP-1) transcription factor. AP-1 transcription factor transcribes the production of matrix metalloproteinases (mmps), collagen degradation and leads to wrinkle formation. AP-1 factor is the only common protein involved in every pathway. Therefore AP-1 (c-jun) can be a potent target against wrinkle formation (Chauhan et al., 2009). Transcription factor AP-1 also regulates a variety of cellular processes, including proliferation, differentiation and apoptosis, and contributes to both basal and stimulus-activated gene expression (Leppa et al., 1999). The binding activity of AP-1 on DNA depends upon the redox status of oxidant-sensitive cysteines in their structures. It is one mechanism by * Corresponding author: Pallavi Chauhan, Ph.D. Scholar, Pallavi Chauhan, Department of Bioinformatics, MANIT, Bhopal-462051, India Email: [email protected]

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which cells transduce oxidative stress into the inducible expression of a wide variety of genes implicated in cellular changes (Klatt et al., 1999). AP-1 can be produced by 18 different dimeric combinations of proteins Jun (c-jun, JunB and JunD) and Fos (c-fos, FosB, Fra-1 and Fra-2) families, including Jun homodimers and Jun–Fos heterodimers. The Jun and Fos proteins contain a basic-region leucine zipper (bZIP) domain. Like all bZIP transcription factors, AP1 proteins have to dimerize before they can bind to their DNA target sites identified by the sequences TGACTCA, TGACGTCA, or variants thereof (Angel et al., 1991). The activity of AP-1 encoding genes is tightly regulated: c-jun is the best-characterized examples of this group. Other AP-1 coding-genes, such as junD, c-fos and ATF-2, are expressed at fairly constant levels (De Groot et al., 1991; Gupta et al.,1995; Fisher et al. 2002). The expression of c-jun is subjected to regulation by a large number of stimuli and signaling pathways (Whitmarsh et al., 1996). Any aberrant regulation of this protein can not only cause wrinkles but also malignant transformation and carcinogenesis. Therefore it can be concluded that preventing activation of c-jun will hinder the formation of AP-1 since it will not be able to dimerize with c-fos. Consequently it will result in blockage of expression of gene induced by oxidative stress, which might cause abnormalities in cell. c-jun has already been specifically targeted to prevent cancer induction in mouse models. In c-jun the residues 31-57 constitute IJIB, 2009, Vol. 8, No. 1, 25


Figure 1: Putative domains of protein c-jun.

Fig. 1 the first domain ranging from amino acid 1-240 belongs to jun superfamily domain. The second domain constitute leucine-zipper superfamily domain ranging from 250-314 amino acid. To annotate c-jun with known structural templates in PDB, BLAST and PSIBLAST was used (Altschul et al., 1997).

Model construction For model construction two different types of homology based approach was employed. First approach employs, use of single template for construction of model. This approach was further divided into two categories based on the different methods of loop modeling. In first category loop was modeled by modeler whereas in second category loop was modeled using fold recognition approach.

transcription repression domain, residues from 91-186 forms transcription activation domain, 241-252 residues provide c-jun bending on DNA and residue 252-281 are responsible for sequence specific recognition on DNA (Krebs et al., 1995; Kerppola et al., 1997). Before inhibiting the expression of c-jun through insilico drug design against wrinkle formation and other disorders 3D structure of c-jun should be available. Only a part of c-jun was present in PDB (Protein Databank). In this paper an attempt has been made to provide complete 3D structure of protein c-jun by homology modeling, as it is the only method which provides RMS error less than 0.2 nm (Sanchez et al., 1999). Moreover two different types of approaches of homology modeling, namely single template approach and segment based approach was used to model the protein.

MATERIALS AND METHODS Template selection for c-jun c-jun protein sequence was obtained from NCBI database. The protein is 331amino acid long. c-jun protein consists of two domains as shown in Fig. 1. In International Journal of Integrative Biology ©IJIB, All rights reserved

In second approach i.e. segment based homology modeling more than one template was used. This approach was also further divided into two categories based on the percentage identity between target and the template and on the basis of loop modeling methods. In first category templates having more than 25% identity were used and loop was modeled using modeller. Whereas in second category templates having more than 40% identity was used (Sali A 1995). In this category loop was modeled using fold recognition approach. Details of all the templates used for modeling is shown in Table-1 [Supplementary data]. A fragment of c-jun is present in PDB but was not used as a template for construction of model as it is very small fragment instead JNM was used as a template as it covers more sequence length than c-jun. Moreover JNM has completely same secondary structure (helix through the length) as that of c-jun and belongs to same bZIP-1 superfamily. In both the approaches the target and template sequences were aligned using MODELLER 9v5which executes a global dynamic programming method for comparison between the target-template sequences (Sali et al., 1994). The loops modeled using fold recognition approach by LOMETS (Levefelt et al., 2006). LOMETS (Local Meta Threading Server) was used for fold assignment. The server comprises of several programs that identify the fold not only on the IJIB, 2009, Vol. 8, No. 1, 26


basis of secondary structure but also considers mutations, solvent accessibility and pairwise residue contacts (Wu et al., 2007).

Energy refinement and model evaluation The constructed 3D-models were energy minimized in GROMACS force field using steepest descent minimization Algorithms (Van et al., 2005). RMSD and RMSF were calculated for each model. RMSD is a good indicator of the uncertainty in the atomic coordinates (Laskowski RA, 2003). The RMSF is calculation of atomic positions after (optionally) fitting to a reference frame. PROCHECK was used to validate the model via Parameters like the covalent bond distances and angles, stereochemical validation, atom nomenclature (Laskowski et al., 1996). What check was used to evaluate the folding pattern of the models (Hooft et al., 1996). A systematic representation of method used in construction and evaluation of models are shown in Fig. 2.

RESULTS The 3D models constructed by above mentioned approaches are shown in Fig. 3. In all the figures A stands for model 1, B for model 2, C for model 3 and D for model 4. The Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) were calculated for each model. Lowest value for both these calculations proves the best model. The RMSD value was calculated for model backbone after 1st fit to backbone at 9000 cycles (9 ps time). Model A attained a constant deviation at 0.1 nm, model B at 0.15 nm, model C at 0.125 nm and model D showed constant deviations at point less than 0.125 nm. Among the four constructed models; model A and D showed less deviation. However RMSD value for each model built was between 0.1 - 0.15 nm which is under acceptable range. A comparative figure of RMSD calculation for all the models are shown in Fig. 4. RMSF also showed good results for model A and D. Model A and D have fluctuations within 0.1 nm whereas Model B and C exceeded the range of 0.1 nm. A comparative chart for RMSF values for all the four models is shown in Fig. 5. All the four models build were evaluated with PROCHECK, Model A and D showed best results. The comparative study of Ramachandran plot for all the four models is shown in Fig. 6. Other parameters like Main chain, Side chain, Bond length, Bond angle and list of all planar groups within limits obtained for all four models is shown in Table 2 [Supplementary data].


In order to determine the folding pattern of the protein c-jun secondary structure of the protein was found out using various tools like GOR, JFRED, HNN, NN predict, PORTER, SOPMA and SSpro. After performing a comparative study from the results of all the above mentioned tools, the most predictive secondary structure composition for protein c-jun obtained is shown in Fig. 7. Here H stands for helix, E stands for strand and C stands for sheet, coil, bend and turns. The models constructed must follow this folding pattern to be a true model. For each constructed model the folding pattern was confirmed using software WHAT CHECK. The comparative study of all the four models by WHAT CHECK is shown in Fig. 8. In Fig. 8, H stands for helix, S for strand, T for turn and rest empty space is for coil. Model D completely obeys the folding pattern predicted for protein c-jun.

DISCUSSION In the present study two different approaches of homology modeling was used. The aim was to identify the approach that yields the most accurate model. If we have a look at the models, model A constructed using single template however passed the criteria for RMSD and RMSF but failed the criteria for core residue in Ramachandran plot i.e. have less than 90% residue in core region and do not acquire the desired folds. Model B developed using single template and loop modeling done by fold recognition using single template, too passed the criteria for RMSD and RMSF but failed again as have only 78.7% residues in core region and a single template used for loop modeling would not make the model to acquire the desired folds. Model C constructed using multiple templates having more than 25% identity again failed as have less than 90% residue in core region and partially but not completely acquired desired folds. Model D constructed using multiple templates having more than 40% identity and loop modeled by fold recognition, successfully passed all the parameters for RMSD and RMSF, have more than 90% residue in core region and completely acquire desired folds. The calculated RMSD and RMSF values for all models were under favorable range i.e. under 0.2 nm. Therefore by RMSD and RMSF no clear distinction would be made on the maximum suitability of the model. This may be due to the fact that the templates selected for model construction have more than 25% identity. When all the results obtained from RMSD, RMSF, PROCHECK and WHAT CHECK were assembled; model D gave the best results. Therefore model D proves to be the best model among all the four constructed models. Hence model D may be further used for in silico designing of inhibitors for c-jun. Moreover the technique applied in the paper can be

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further used with certain modifications for construction of other models by homology modeling.

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