Interdiscip Sci Comput Life Sci (2017) 9:214–223 DOI 10.1007/s12539-016-0145-z

ORIGINAL RESEARCH ARTICLE

Comparative Modeling, Molecular Docking, and Revealing of Potential Binding Pockets of RASSF2; a Candidate Cancer

1 1 1 1,2,3 Sonia kanwal • Farrukh Jamil • Ahmad Ali • Sheikh Arslan Sehgal

Received: 15 August 2015 / Revised: 4 December 2015 / Accepted: 6 January 2016 / Published online: 18 January 2016 Ó International Association of Scientists in the Interdisciplinary Areas and Springer-Verlag Berlin Heidelberg 2016

Abstract RASSF2, potential tumor suppressor gene, acts and analysis of binding pockets are pivotal to drug dis- as a KRAS-specific effectors and may promote covery. It proposed that predicted structure is reliable for apoptosis and cell cycle arrest. It stabilizes STK3/MST2 by the structural insights and functional studies. The predicted protecting it from proteasomal degradation. RASSF2 plays binding pockets may lead to further analysis (drug dis- a significant role against the inhibition of cancer. MOD- covery), used against cancer study. ELLER (9v15) and online servers (I-Tasser, SwissModel, 3D-JigSaw, ModWeb) were utilized to generate 3D struc- Keywords RASSF2 Á Homology modeling Á Cancer Á tures of the RASSF2 based on homology modeling. A Molecular docking analyses Á RASSF2 Á Binding domains comparison between models predicted by MODELLER (9v15) and Web servers had been checked through utilized evaluation tools. The most potent model for RASSF2 was 1 Introduction analyzed and selected for molecular docking studies. The binding pockets were revealed for binding studies through Cancer is termed as malignant tumor and neoplasm tumor Site Hound. AutoDock Vina and AutoDock4 were utilized that involved in irregular cell growth having potential to for molecular docking, and the attempt of this experiment spread in other parts of body [1]. There are about 100 dif- was to identify the ligands for RASSF2. The selected ferent known cancers that affect humans [2] and form a compounds may act as regulators and regulate the normal subset of neoplasms that is a group of cells involved in activity of RASSF2. It was also analyzed and observed that unregulated growth, and form a mass or lump, but may be the selected compounds showed least binding energy and spread diffusely [3]. The cancer has characteristics of self- high-affinity binding in predicted top binding domain. The reliance in growth signaling, coldness to anti-growth sig- determination of protein function is based on accurate nals, elusion of apoptosis, enabling of a boundless replica- identification of binding sites in protein structures. The tive latent, stimulation and sustainment of angiogenesis, binding site is known, and it may allow the ligand type and commencement of metastasis, and incursion of tissues [4]. protein function to be determined by performing in silico The succession from normal cells to cells that can form a and experimental procedures. The detection, comparison, visible mass to absolute cancer involves multiple steps known as malignant progression [5]. The development of human cancer is a multistep process, involving the assis- & Sheikh Arslan Sehgal tance of mutations in signaling, cell cycle, cell death path- [email protected] ways, and interactions between the tumor and the tumor 1 Department of Biosciences, COMSATS Institute of microenvironment [6]. Information Technology, Sahiwal, Pakistan The tumor metabolism has waxed and waned over the 2 State Key Laboratory of Biomembrane and Membrane past century of cancer research. The early hypotheses that Biotechnology, Institute of Zoology, Chinese Academy of were based on annotations and proved insufficient to elu- Sciences, Beijing, China cidate tumor genesis and oncogenesis revolt pushed tumor 3 University of Chinese Academy of Sciences, Beijing, China metabolism to the limitations of cancer research. In recent 123 Interdiscip Sci Comput Life Sci (2017) 9:214–223 215 years, concentration has been reformed that many of the the nucleus and nuclear localization of RASSF2 considered signaling pathways affected by genetic mutations and the as necessary for tumor suppressor function. The activity of tumor microenvironment have a deep effect on hub meta- the kinases ERK2 and MST1/2 is critical for cytoplasmic bolism [7]. Abnormal splicing of mRNA precursors is a delocalization of RASSF2. Activation of RASSF2 tumor nearly ubiquitous and extremely flexible point of gene suppressor pathway may originate from the nucleus by control in humans. It provides cells with the opportunity to following ERK2 and MST1/2 activation that allowed create protein isoforms of differing, even opposing, func- RASSF2 to accumulate in the cytoplasm where it may tions from a single gene. Cancer cells take the advantage of interact with another interacting partner K-Ras GTP [18]. the flexibility to produce that promote growth and RASSF2 binds directly to K-Ras in a GTP-dependent survival. The isoforms developmentally regulated and manner through the Ras effector domain. RASSF2 induces preferentially re-expressed in tumors. Rising insights into apoptosis and cell cycle arrest [19]. The MST1 (mam- the pathways that frequently are deregulated in cancer malian sterile 20-like kinase 1) regulates RASSF2 protein often play important roles in promoting irregular splicing stability. The reduced level of MST1 in cancer cells and revolve contributes to all aspects of tumor biology [8]. noticeably destabilizes RASSF2, and Mst1-deficient mice The RASSF family of tumor suppressor (TSG) showed reduced RASSF2 protein levels in several organs. encodes Ras super family effector proteins which mediate RASSF2 activates MST1 kinase activity through formation growth inhibitory functions of Ras proteins. Human of a RASSF2–MST1 complex, which inhibits the MST– RASSF2 has highly conserved orthologues crossways in FOXO3 signaling pathway. RASSF2 engages the JNK many species. The main features of RASSF2 are conserved pathway and induces apoptosis in an MST1-independent across species including RA, SARAH domain, NLS, and manner. RASSF2 binds with RAS effector to form com- the sequence important for nuclear export. Ras-association plex with MST1 for the induction of apoptosis and cell domain family (RASSF) consists of RASSF1-10, and cycle arrest. The RASSF2 plays an important role against RASSF2 is one among 10 members of RASSF. RASSF1–6 the inhibition of cancer [16]. The expression of RASSF2 is termed as ‘classical’ RASSF family and contains suppresses the tumor growth as similar to other RASSF C-terminal RA and SARAH domains. Consequently, family members. RASSF2 involved in suppression of col- RASSF1–6 is most similar in sequence within their orectal, lungs, breast, gastric, nasopharyngeal, and oral C-termini. RASSF7–10 represents evolutionarily con- squamous cell carcinomas (OSCC) cell lines in vitro using served but structurally dissimilar. The RASSF7–10 mem- colony formation, growth curve, and soft agar growth bers lack the SARAH domains and contain N-terminal RA assays [16, 17, 19–23]. The cells are subcutaneously domains. RASSF7–10 are termed the ‘‘N-terminal’’ injected into severe combined immunodeficiency (SCID) RASSF family. RASSF2 is most similar to RASSF4 and mice. RASSF2 re-expression in breast tumor cells inhibits RASSF6, both showed epigenetically inactivation in can- in vivo tumor growth [23]. Numerous studies demonstrated cer, contribute in K-Ras signaling to inhibit tumor cell that tumor-suppressive properties are likely to arise from growth, and induce apoptosis [9–13]. Several members of the ability of RASSF2 to regulate apoptosis and cell cycle RASSF family are inactivated by promoter DNA hyper- progression [16, 17, 20, 21]. The cells growth suppression methylation in a wide range of cancers, and inactivation of (breast cancer cells and Cos-7) by RASSF2 is dependent on RASSF2 has been described in a rising number of tumor the nuclear localization signal (NLS) which located at types [14]. RASSF2 regulates apoptosis and cell cycle amino acids 151–167 [17, 21]. For tumor-suppressive progression through interactions of various downstream function in OSCC and gastric cancer cells, the C-terminal effectors (MST1 and MST2) [15]. RASSF2 functions as portion of RASSF2 (RASSF2 [163–326]), containing the K-Ras adaptor protein and mediates growth inhibitory RA domain, is critical [21]. In OSCC, the C-terminal of properties of K-Ras [16]. The Northern blotting analysis RASSF2 exhibited enhanced growth suppression relative to showed that RASSF2 mRNA is highly expressed in many full-length RASSF2 [21]. RASSF2 [163–326] also disrupts normal tissues including brain, thymus, spleen, liver, small the NLS (leaves the sequence required for whole nuclear intestines, placenta, lungs, and peripheral blood [16]. export). In colorectal cancer cells, RASSF2 truncations Currently, there has been limited analysis of expression (RASSF2 [1–163] and RASSF2 [163–326]) exhibited patterns and distribution of the different RASSF2 isoforms. reduced growth suppression compared to full-length RASSF2A variants 1 and 2 are ubiquitously expressed in a RASSF2 [20]. In gastric cancer, transfection of RASSF2 range of normal tissues including colon, stomach, heart, with deletion of NLS [RASSF2deltaNLS] increased the bone marrow, kidney, ovary, lungs, liver, breast, testis, and percentage of apoptotic cells relative to full-length pancreas. Expression of RASSF2B and RASSF2C isoforms RASSF2 [17]. was almost untraceable in a range of normal tissues [17]. Despite its clinical importance in diagnosis and as a RASSF2–MST1/2 complex may incessantly cycle through crucial cancer causing protein, no direct involvement of 123 216 Interdiscip Sci Comput Life Sci (2017) 9:214–223

RASSF2 in inhibition of cancer has as yet been reported. generate 3D structure by using MODELLER 9v15 [27], Regulating the RASSF2 activity directly is quite difficult I-Tasser [28], SwissModel [29], 3D-JigSaw [30], and because of its high level of structural complexity. Various ModWeb [31], which relies on the basis of spatial restraint. studies have been aimed at direct RASSF2 regulation The detection of errors in experimental and hypothetical mechanisms and indirect regulation of RASSF2 activity by models of protein structures is a main problem in structural targeting proteins. RASSF2 is a cancer-inhibiting protein, bioinformatics. There is no single technique that continu- and it may regulate by forming heterodimers and homod- ally and exactly predicts the errors in 3D structure [32]. imers through Ras effector domains. There is no direct Different evaluation tools were used for the evaluation of evidence of activating of RASSF2 and only one example of protein structure. Models are generated by MODELLER direct in vivo regulation of RASSF2. Bioinformatics is an 9v15 and other online Web servers; the model was selected interdisciplinary field which solves the biological problems on the basis of ERRAT and Rampage results. The quality by using computational, mathematical, and statistical of 3D model was confirmed by ERRAT [33]. ERRAT approaches. A large number of protein structure predictions generated a plot indicating the confidence and overall through X-ray crystallography and NMR methods are very quality of models. Rampage [34] generated Ramachandran time consuming and expensive. Bioinformatics provides plot for the assessment of models along with allocation of different tools to predict three-dimensional (3D) structures residues in favored, allowed, and outlier regions. The and revealing binding regions of proteins. Homology binding pockets were explored by Site Hound [35]. The modeling, threading, and ab initio approaches were used to materials and methods are aided by tutorials of different predict 3D structure of proteins. The 3D structure of tools that are used for structure prediction (Supplementary RASSF2 is not reported yet in PDB. In present study, we file). AutoDock tools 4.2 were utilized for molecular searched for direct activators of RASSF2 by employing in docking (protein–ligand docking) studies. The endeavor of silico approaches, including molecular docking studies. A this experiment was to identify the regulator of RASSF2 computational approach is utilized to predict 3D structure which can bind with tumor suppressor protein RASSF2 and and to reveal binding pockets of RASSF2. Structure pre- regulate the normal RASSF2 activity. A computational diction is very important because without 3D structure it is approach was used to analyze the activation of RASSF2 by quite difficult to find out binding sites against inhibitors. The interacting with two ligands (ANP and GNP) through selected compounds could act as a template and provide a AutoDock4 and AutoDock Vina. The scrutinized com- basis for designing direct RASSF regulators. Computational pounds were subjected to energy minimization through 3D studies have had effective success in increasing our under- ChemDraw Ultra, UCSF Chimera 1.8, and Torch. Docking standing of complex biological processes (fold kinetics, analyses were carried out by AutoDock Vina and Auto- structural dynamics and signaling pathways). Due to the Dock tools 4.2. UCSF Chimera 1.8 visualizing tool was effective advancement in technology, new algorithms are used for interaction and binding analyses among the atoms being developed for docking analyses, which have increased of receptor and ligand proteins. The results were analyzed the search space for discovery of bioactive compounds. The by using AutoDock tools and UCSF Chimera 1.8. docking studies enable prediction of the structure of a pro- tein–inhibitor complex under equilibrium conditions. The pose score measures the ability of a ligand to fit into the active site of a protein, while the rank score is more com- 3 Results and Discussion plex but attempts to estimate binding energies. This in silico analysis may provide evidence for reliable work for drug The aim of present research was based on the relationship discovery against cancer. between RASSF2 and cancer. This research was aimed to predict RASSF2 3D structure and bioinformatics analyses for identifying, and evaluating novel inhibitors or regula- 2 Materials and Methods tors. In current study, in silico methodologies (homology modeling, binding site revealing and docking analyses) The canonical amino acid (326 a.a) sequence of RASSF2- were carried out. The 3D structure of RASSF2 was mod- encoding protein was retrieved from Uniprot Knowledge- eled by employing a crystal structure template. The pre- base database having accession number P50749 [24]in dicted structure has a good degree of accuracy, especially FASTA format. NCBI Basic Local Alignment Search Tool at the active site of the protein. Comparative docking was (Psi-BLAST) [25] was used against Protein Data Bank experienced by automated docking analysis by using (PDB) [26] for suitable template search with query AutoDock4 and AutoDock Vina. A detailed comparative sequence. The six templates were observed as a result of docking analysis of the interactions between RASSF2 and Psi-BLAST. Comparative modeling was employed to selected ligands has pointed out the effective interactions 123 Interdiscip Sci Comput Life Sci (2017) 9:214–223 217 with the lowest binding energy that can be taken into A comparison between the predicted structures by account for inhibiting cancer. MODELLER 9v15 and Web servers had been checked The 3D structure of RASSF2 is not known yet. The through Ramachandran plots and ERRAT overall quality amino acid sequence of RASSF2 in FASTA format was factor values. Complete analysis results of predicted 68 retrieved from Uniprot having accession number P50749 models were plotted and most potent model for RASSF2 (Table 1) lists the six templates 3DDC, 3MMG, 4OH8, was observed and selected from graph (Fig. 1) and visu- 2F6R, 2PNO, and 2REG with optimal alignment of the first alized through UCSF Chimera 1.8 (Fig. 2). template and good alignment for the others, sorted by For evaluation of the predicted 3D structure, models overall quality, query coverage, similarity and E-values. were submitted into RAMPAGE tool. Ramachandran plots The structure predicted by MODELLER 9v15 and online showed U and W distributions of non-glycine, non-proline Web servers (SwissModel, I-Tasser, 3D-JigSaw) were residues (Fig. 4) and showed residues distribution (Fig. 5). validated by evaluation tools (ERRAT and Rampage). The phi and psi angles were plotted against each other to

Table 1 Templates for Templates Max score Total score Query coverage (%) E-value Identity (%) RASSF2 sorted by their overall quality query coverage, identity 3DDC-B 51.2 51.2 26 1e-07 34 and E-values 3MMG-A 32.0 32.0 15 0.59 30 4OH8-B 28.9 28.9 17 1.3 28 2F6R-A 30.8 30.8 14 1.4 29 2PNO-A 29.3 29.3 11 2.5 39 2REG-A 28.9 28.9 11 7.1 41

Fig. 1 Comparative model assessment plot showed Ramachandran favored, allowed and outlier regions

123 218 Interdiscip Sci Comput Life Sci (2017) 9:214–223 differentiate the favorable and unfavorable regions. These The top 10 binding pockets of RASSF2 were recognized angles were used to evaluate the quality of regions. The 3D which ranked on the basis of energy (Table 2). The volume predicted model of RASSF2 was subjected to ERRAT of the binding pockets was also analyzed in x-axis, y-axis, protein structure verification server. ERRAT provided an and z-axis. The binding residues of the binding cavities overall quality factor of model as 80.126 % (Fig. 3). explored for abundant binding of novel ligands. On the error axis, two lines were drawn to indicate the The binding pockets of RASSF2 were not reported yet. confidence with which it was possible to reject the regions So, the in silico approaches utilized for the prediction of that exceed the error value. It expressed as the percentage binding pockets. The energy range of predicted cavities of the protein for the calculated error value falls below the also elaborates the efficiency of binding pockets. The 95 % of rejection limit. Good high-resolution structure mutational study of binding residues suggested that these generally produces values around 95 % higher. For lower residues could be used as clinical prospectus against cancer resolutions (2.5–3.0 A), the average overall quality factor is studies. RASSF2 is a novel tumor suppressor gene that around 91 %. ERRAT produced the value of error function regulates Ras signaling and plays a crucial role in the early and showed confidence limits by comparing with statistical stages of tumor genesis [20]. The RASSF family proteins analysis from highly refined predicted structures. So, involved in major signaling apoptotic pathway. 80.126 % overall value is considered as much reliable. The tumor-suppressive hippo pathway controls tissue homeostasis all the way through balancing cell prolifera- tion and apoptosis. The hippo pathway is a main regulatory pathway of cell proliferation and survival. Deregulations of this pathway lead to cancer. The activation of kinases Mst1/2 has significance in hippo pathway. Mst1/2 and its regulator RASSFs contain Salvador/RASSF1A/Hippo (SARAH) domains which can homo- and heterodimerize Mst2, which undergoes activation through transautophos- phorylation at its activation loop. RASSF5 disrupts Mst2 homodimer and blocks Mst2 auto-activation. The binding of RASSF5 to activated Mst2 is unable to inhibit its kinase activity. The RASSF5 can act as an inhibitor or a potential positive regulator of Mst2. It depends on binding to Mst2 before or after activation loop phosphorylation. RASSFs can either function as inhibitors or activators of hippo pathway [36, 37]. The active site of Mst2-RASSF5 and RAS-NORE1 is well defined for selected compounds (ANP and GNP) and clearly visible in Fig. 2 3D Structure of RASSF2 selected from the generated graph the electron density map. These ligands may play an

Fig. 3 ERRAT gave a quality factor of 80.126 % to the RASSF2 model 123 nedsi c optLf c 21)9:214–223 (2017) Sci Life Comput Sci Interdiscip

Table 2 Top 10 binding pockets with binding residues, energy, and volume of RASSF2 Rank Energy (kcal/mol) Energy range Volume (A˚ ) Center x Center y Center z Residues

1 -758.76 (-14.48, -8.92) 71.00 62.554 71.560 59.607 His-43, Phe-50, Ser-83, Trp-86, His-87, Cys-90, Met-199, Gln- 203, Lys-206, Leu-207, Asn-210, Val-298, Leu-301, Met- 302, Tyr-305 2 -702.97 (-17.89, -8.93) 64.00 56.667 75.737 74.434 Leu-39, Gln-40, Arg-42, His-43, Arg-44, Glu-45, Glu-49, Phe- 50, Met-141, Arg-142, Thr-143, Ile-312, Arg-315, Leu-316, Ile-326 3 -687.20 (-15.67, -8.93) 66.00 61.275 84.585 69.436 Ala-114, Pro-115, Pro-116, Arg-128, Lys-131, Pro-132, Glu- 135, Arg-142, Arg-309, Ile-312, Arg-313, Leu-316, Glu-317 4 -674.85 (-16.10, -8.91) 60.00 72.275 57.380 53.401 Phe-212, Lys-213, Ile-214, Glu-215, Asn-216, Val-225, Val- 226, His-227, Thr-220, Glu-261, Lys-262, Leu-283, Ile-287 5 -639.99 (-16.38, -9.05) 49.00 61.610 76.706 48.884 Cys-90, Asn-91, Ala-94, Thr-98, Leu-102, Glu-292, Glu-293, Glu-294, Asp-295, Val-298, Lys-299, Met-302 6 -625.45 (-12.98, -8.93) 61.00 68.237 77.825 62.872 Ile-77, Pro-80, Pro-81, Ser-83, Ser-84, His-87, Lys-106, Ser- 124, Thr-125, Asp-126, Arg-128, Gly-129, Pro-132, Leu- 133, Met-302, Tyr-305 7 -547.85 (-19.44, -9.00) 46.00 51.993 66.647 45.491 Gln-14, Asp-15, Lys-16, Tyr-17, Ile-18, Gln-288, Lys-289, Gln-291, Glu-294, Arg-296, Glu-297, Lys-300 8 -504.07 (-18.91, -9.26) 39.00 72.207 56.888 63.827 Leu-56, Ile-58, Met-170, Arg-78, Val-155, Val-190, Asn-192, Arg-194, Ile-214, Phe-221, Tyr-224, Val-226, His-227, Thr- 228, Ser-229, Leu-259 9 -501.82 (-15.81, -8.93) 43.00 75.863 70.381 57.894 Arg-78, Pro-81, Ser-82, Ser-84, Ser-85, Trp-86, Ser-88, Thr- 125, Glu-215, Ser-217, Ala-218 10 -477.40 (-19.04, -8.95) 40.00 45.597 68.052 57.073 Leu-9, Gly-13, Gln-14, Tyr-17, Lys-20, Lys-28, Asn-31, Leu- 32, Tyr-33, Tyr-34, Thr-200, Thr-201 123 219 220 Interdiscip Sci Comput Life Sci (2017) 9:214–223

Fig. 4 Ramachandran plot showed separate U and W distributions of residues in General, Glycine, Pre-pro and Proline

important role in regulation or activation of hippo signaling pathway that may leads to inhibition of cancer. There is no evidence of a direct inhibitor of RASSF2 in known biological databases and literature. In present work, phosphoaminophosphonic acid adenylate ester (ANP) and phosphoaminophosphonic acid guanylate ester (GNP) were selected to validate the predicted binding pockets of Fig. 5 Graph of RASSF2 3D structure having favored, allowed, and outlier region RASSF2. GNP and ANP were the first molecules designed

Fig. 6 2D structures of selected ligands, a Phosphoaminophosphonic acid adenylate ester (ANP), b Phosphoaminophosphonic acid guanylate (GNP)

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Fig. 7 Binding interactions of selected compounds with RASSF2 by utilizing AutoDock Vina. a ANP binding with RASSF2, b GNP interactions with RASSF2

Fig. 8 ANP and GNP docking studies by AutoDock4 and AutoDock Vina present in different colors and showed interactions at the top ranked binding pocket. Blue color highlights the top ranked binding domain to directly dock with RASSF5 in mouse and human. The important to make sure about compounds for comparative direct binding domain and active site of RASSF2 are still docking experiments, which use same compounds for unknown. In this analysis, we primarily targeted RASSF2, selected docking tools. Docking through AutoDock Vina so that selected ligands could interact with binding and AutoDock4 was applied on selected compounds (ANP domains predicted by utilizing in silico approaches. The and GNP) and ranked complexes on the basis of highest docking step was performed to identify the ligands showing binding affinities. The 2D structures (Fig. 6) of the selected maximum interactions with regard to protein residues. It is compounds generated and minimized from ChemDraw

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Table 3 Interacting residues of RASSF2 by utilizing AutoDock Vina Ligands Binding energy (kcal/mol) Residues

ANP -6.5 Pro-80, Ser-83, Ser-84, His-87, Gln-203, Lys-206, Leu-207, Asn-210, Leu-301, Met-302, Tyr-305 GNP -7.8 Ser-83, Ser-84, Tyr-86, His-87, Ser-124, Gln-203, Asn-210, Leu-301, Tyr-305

[39] showed least binding energy. The complex was domains, and selected molecules will help to design effective selected on the basis of highest binding affinity and further potential drugs against cancer by targeting RASSF2. Selec- evaluated on the basis of the interactions between RASSF2 ted compounds accomplished the predicted binding domains residues. It was observed that AutoDock4 and AutoDock and readily interacted with RASSF2. These results suggest Vina showed interactions in same binding pocket and that selected inhibitors may serve as a lead compound residues analyzed by UCSF Chimera 1.8 [38]. It was also for further screening, inhibitor searching, and experimen- observed that the ANP and GNP showed least binding tation with RASSF2. The proposed results have led to a energy and highest binding affinity in the first-ranked simplification of the process of predicting the binding binding pocket (Table 2) of RASSF2. The interacting domains biological investigation. Predicted protein model residues of RASSF2-ANP and RASSF2-GNP analyzed described in this work may be further used for finding through AutoDock Vina were visualized by UCSF Chimera interactions with other proteins involved in different cancers. 1.8 (Fig. 7). The ANP and GNP docking studies by AutoDock4 and AutoDock Vina showed interactions at the top ranked 4 Conclusion binding pocket (Fig. 8). It was observed and analyzed that both selected compounds would bind at the binding resi- In conclusion, 3D model of RASSF2 was predicted by dues Ser-83, Ser-84, His-87, Gln-203, Asn-210, Leu-301, modeling tools and evaluated by evaluation tools observed and Tyr-305 in AutoDock Vina docking analyses (Table 3). as most potent 3D models. The top 10 binding pockets of The same residues were also observed in AutoDock4 RASSF2 were identified which ranked on the basis of docking analyses. The similar residues were observed from energy. This analysis suggested that the predicted binding both the tools analyses and validate the docking studies. It pockets could be used against cancer studies. The predicted was also observed that the residues observed in docking binding residues may lead to the drug designing of lead studies were similar to the first-ranked predicted binding compounds against cancer. Results concluded that selected domain. The reconciling of residues observed from dock- compounds (ANP and GNP) stabilize RASSF2 and they ing analyses (AutoDock4 and AutoDock Vina) and pre- could serve as a lead compounds for further studies on dicted binding domains validated both the in silico RASSF family. experiments. The field of bioinformatics and cancer genetics are Acknowledgments The work is sponsored by CAS-TWAS Presi- blooming and the potential in cancer treatments is glowing. dent’s Fellowship for International Ph.D. Students. Authors are thankful to Zunera Khalid, Mirza A. Hammad, and Rana Adnan Besides the research resources being devoted for the Tahir, Bioinformatics Research Laboratory Department of Bio- understanding of cancer and numerous scientists are trying science, COMSATS Institute of Information Technology, Sahiwal, to explore the cure of cancer. Drug designing is not the Pakistan, for their kind guidance in interpreting and suggestions for only costly procedure but also time consuming. Therefore, improving the results. the bioinformatics approaches and methodologies were Compliance with Ethical Standards applied for drug discovery procedure. This capable affinity has huge significance in minimizing the time phase and Conflict of Interest The authors declare that they have no com- amplifying the selected compounds having minimal side peting interests. effects for specific disease targets and better biological activity. The protein function determined from the structure of the References protein. The potential drug designing is unfeasible without the 3D structure of protein and interacting binding domain. 1. Keyvani V, Kerachian MA (2014) The effect of fasting on the The in silico approaches helped to reveal the 3D structure and important molecular mechanisms related to cancer treatment. J Fasting Health 2(3):113–118 binding domain. The followed methodology in current study 2. Yun YH, Kwak M, Park SM, Kim S, Choi JS, Lim HY (2007) will lead to predict effective 3D structures and binding Chemotherapy use and associated factors among cancer patients domains. The RASSF2-predicted 3D structure, binding near the end of life. Oncology 72(3–4):164–171

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