{"id":270,"date":"2015-01-20T07:40:06","date_gmt":"2015-01-20T07:40:06","guid":{"rendered":"http:\/\/biomedpharmajournal.org\/?p=270"},"modified":"2020-04-23T11:22:40","modified_gmt":"2020-04-23T11:22:40","slug":"interaction-of-ribosome-inactivating-proteins-with-growth-factor-receptors-a-cheminformatic-approach","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol1no1\/interaction-of-ribosome-inactivating-proteins-with-growth-factor-receptors-a-cheminformatic-approach\/","title":{"rendered":"Interaction of Ribosome Inactivating Proteins with Growth Factor Receptors – a Cheminformatic Approach"},"content":{"rendered":"

Introduction<\/strong><\/p>\n

Many plants contain proteins that are capable of inactivating eukaryotic ribosomes, these are termed as \u201cRibosome-Inactivating Proteins\u201d (RIPs)1<\/sup>. Ricin from Ricinus communis<\/em> and Abrin from Abrus precatorius<\/em> belong to type II RIPs, both have got molecular similarity and similar mechanism of action 2,3<\/sup>. They are composed of\u00a0 2 chains A and B. The B chain helps in binding of proteins to cell surface receptors which are terminated with galactose containing oligosaccharides. The A chain is taken up into the cell and has N-glycosidase activity bringing about depurination of a specific adenine residue from the GAGA loop of 28S rRNA from 60S ribosome, thereby halting protein synthesis and leading to cell death.<\/p>\n

Type-2 RIPs have a possible implication in therapeutics since these are more toxic to tumor cells than normal cells4<\/sup>. Hence these molecules were chosen for cheminformatic studies to understand protein-protein interactions, which offer a theoretical opportunity to develop antitumor drugs.<\/p>\n

Cheminformatic\u00a0 study is based on\u00a0 computer guided system which is\u00a0 capable of varying parameters. Modern drug designing efforts are exploiting at least three core technologies aimed at increasing the efficiency of finding drug leads viz.<\/em>, Genomics, High throughput Screening and Combinatorial Chemistry. Computational modeling has been developed and widely applied in better understanding of protein-protein interactions5,6 <\/sup>. Computer simulations are done by studying macromolecular dynamics and free energy calculation methods7<\/sup>. Detailed energetics and structural knowledge of interactions between biomolecules is fundamental to understand the complex interactive mechanism that takes place in living organism and also to design drugs for blocking or modifying these interactions using molecular docking8,9<\/sup>. In our study, we have used Rapid prototyping computational simulations using software tool Hex4.2. Using molecular dynamics and free energy calculations, the docking of ricin,and abrin was done with EGFR and FGFR. Growth factor receptors were selected for this insilico<\/em> study because, in cancer, aberrant signaling can cause constitutive activation of growth factor receptors which may in turn influence key steps in the process of tumor invasion and metastasis. Involvement of EGFR and FGFR in tumor spread has indicated them as potential target receptors for antimetastatic studies.<\/p>\n

Experimental<\/strong><\/p>\n

For the present study, Hex4.2 program was used which is an interactive molecular graphics program used to calculate the feasible docking pairs such as protein-protein (of our choice).\u00a0 In Hex docking calculations, 3D parametric functions were used to encode both surface shape and electrostatic charge and their potential distribution.\u00a0 Each property was represented by a vector of coefficients.\u00a0 From Hex\u2019s surface skin\u00a0 model of protein\u00a0 topology,\u00a0 an\u00a0 expression\u00a0 for\u00a0 a\u00a0 docking\u00a0 score\u00a0 as\u00a0 a function\u00a0 of\u00a0 six\u00a0 degrees\u00a0 of\u00a0 freedom\u00a0 in\u00a0 a\u00a0 rigid\u00a0 body\u00a0 docking\u00a0 search with\u00a0 suitable\u00a0 scaling\u00a0 factors was derived.\u00a0 This docking score was interpreted as interaction energy.\u00a0 This is a spherical polar approach where\u00a0 the\u00a0 molecules\u00a0 were\u00a0 relatively\u00a0 rotated\u00a0 to\u00a0 generate\u00a0 and\u00a0 evaluate\u00a0 good\u00a0 docking\u00a0 orientations\u00a0 which\u00a0 were effectively\u00a0 a\u00a0 six\u00a0 dimensional\u00a0\u00a0 Fourier\u00a0 Correlations.<\/p>\n

Hex program was started by downloading and displaying the PDB structure of Ricin, Abrin and Epidermal growth factor receptor and fibroblast growth factor receptor. Docking was separately done with each RIP with EGFR and FGFR respectively. This procedure was repeated three times to ensure reproducible results.<\/p>\n\n\n\n
\"Figure<\/td>\n\n

Figure 1: Docking of Ricin with EGFR and FGFR<\/strong><\/p>\n

Click here to View figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Figure<\/td>\n\n

Figure 2: Docking of Abrin with EGFR and FGFR<\/strong><\/p>\n

Click here to View figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

There\u00a0 were\u00a0 four\u00a0 stages\u00a0 or\u00a0 controls\u00a0 in\u00a0 the\u00a0 program. The first control was the \u2018orientation\u2019.\u00a0 Here\u00a0 the\u00a0 two molecules\u00a0 were\u00a0 oriented\u00a0 with\u00a0 their\u00a0 favorable\u00a0 positions\u00a0 for\u00a0 docking.\u00a0 This was\u00a0 followed\u00a0 by\u00a0 the\u00a0 second\u00a0 control,\u00a0 \u2018clustering\u2019 where\u00a0 the\u00a0 program\u00a0 uses\u00a0 a\u00a0 simple\u00a0 clustering\u00a0 algorithm\u00a0 to\u00a0 group\u00a0 spatially\u00a0 similar\u00a0 docking\u00a0 orientations.\u00a0 The\u00a0 third\u00a0 control\u00a0 of\u00a0 this\u00a0 program\u00a0 was\u00a0 \u2018matching\u2019 in which there\u00a0 was\u00a0 superposition\u00a0 of\u00a0 pair\u00a0 of\u00a0 proteins\u00a0 ie,\u00a0 RIP\u00a0 and\u00a0 the\u00a0 receptor\u00a0 and\u00a0 a\u00a0 search\u00a0 for\u00a0 maximum\u00a0 similarity.\u00a0 The\u00a0 final\u00a0 stage\u00a0 was\u00a0 \u2018docking\u2019\u00a0 which\u00a0 was\u00a0 much\u00a0 similar\u00a0 to\u00a0 matching. At this stage, the\u00a0 surface\u00a0 skin\u00a0 coefficients\u00a0 followed by docking\u00a0 correlation\u00a0 scores\u00a0 at\u00a0 each\u00a0 of\u00a0 the\u00a0 specified\u00a0 angular\u00a0 and\u00a0 intermolecular\u00a0 increment were calculated.<\/p>\n

Results and Discussions <\/strong><\/p>\n

The interaction studies reveal that the best docking results were for interaction of the pairs, Ricin B chain – Epidermal growth factor receptor,\u00a0 Ricin B chain – Fibroblast growth factor receptor, Abrin – Epidermal growth factor receptor, Abrin – Fibroblast growth factor receptor with an Etotal<\/sub> values of\u00a0 -562.43\u00a0 -427.77, -511.13 and -490.48 respectively as shown in the Table1.<\/p>\n

Table 1: Docking Results<\/strong><\/p>\n\n\n\n\n\n\n\n
RIP<\/strong><\/td>\nReceptor<\/strong><\/td>\nEnergy values<\/strong><\/td>\nBumps<\/strong><\/td>\n<\/tr>\n
Ricin<\/td>\nEGFR<\/td>\n-562.43<\/td>\n-1<\/td>\n<\/tr>\n
Ricin<\/td>\nFGFR<\/td>\n-427.77<\/td>\n-1<\/td>\n<\/tr>\n
Abrin<\/td>\nEGFR<\/td>\n-511.13<\/td>\n-1<\/td>\n<\/tr>\n
Abrin<\/td>\nFGFR<\/td>\n-490.48<\/td>\n-1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

A large number of protein structures have been deposited into PDB, however only a small fraction of protein-protein complexes has been experimentally characterized so far. In this context theoretical prediction of protein-protein interactions is becoming critically important in structural biology. Using Hex4.2, we have used the 2 different RIPs as lead compounds which are prototype compounds having biological and pharmacological activities.\u00a0 It is evident that the best interactive pattern is shown by ricin with EGFR with a very minimal energy value. The bumps of all the 4 interactions are \u20131 indicating that the stearic clashes are very minimal and thereby\u00a0 better interaction. All these results\u00a0 indicate that both the growth factor receptor can be targeted by these lead compounds in treatment of cancer.<\/p>\n

Conclusion<\/strong><\/p>\n

The computational docking techniques are promising to be an essential tool in predicting the interactions of ligand-receptor complexes since it is cumbersome and very laborious to obtain crystal structures of protein complexes.\u00a0 Ricin and Abrin can be powerful lead molecules which interact strongly with Epidermal and Fibroblast growth factor receptors as evidenced by the docking results.\u00a0 The newly emerging field of cheminformatics with variety of software tools is addressing the needs of a broad community of scientists in First Approximation studies before venturing into invitro<\/em> and invivo<\/em> studies. Subsequently, these insilico<\/em> data can be further extended into invitro studies and drug designing by targeting these lead molecules to the specific receptors which may have a positive implication in cancer therapy.<\/p>\n

References<\/strong><\/p>\n

    \n
  1. Barbieril, M.G. Batteli and F.Stirpe, Biochem Biophys\u00a0 Acta<\/em>\u00a0 1154, 237 \u2013 282 (1993).<\/li>\n
  2. .Endo and K.Tsurugi, J Biol chem <\/em>\u00a0262\u00a0 8128 \u2013 8130 (1987).<\/li>\n
  3. r.hartley, j.a.chaddock, Trendes plant sci<\/em> 1 254-260 (1996)<\/li>\n
  4. Sumasridhar, L.M.S. Rudrammaji ,B.S..Raghavendra and T.R.S..Chouhan, Cytol. genet<\/em>. 8 (ns) 53-58 (2007).<\/li>\n
  5. Vajda, M. Sippl, and J. Novotny,\u00a0\u00a0 Curr. Opin. Struct. Biol.<\/em> 7: 222\u2013228.(1997).<\/li>\n
  6. J.E Sternberg, H.A. Gabb, and\u00a0 R.M. Jackson, Curr. Opin. Struct. Biol.<\/em> 8: 250\u2013256.(1998)<\/li>\n
  7. A McCammon,. . Curr. Opin. Struct. Biol.<\/em> 8: 245\u2013249 (1998)<\/li>\n
  8. Sumasridhar, L.M.S.Rudrammaji and T.R.S.Chouhan. Accepted for publication in proceedings of All India seminar on Vistas of Nano Applications<\/em>, Bangalore (2006).<\/li>\n
  9. Botta, F. Corelli, F. Manetti, A. Tafi, Farmaco<\/em> 57(2) 153-165 (2002).<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Introduction Many plants contain proteins that are capable of inactivating […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-270","post","type-post","status-publish","format-standard","hentry","category-vol1no1"],"_links":{"self":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/270","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/comments?post=270"}],"version-history":[{"count":5,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/270\/revisions"}],"predecessor-version":[{"id":32479,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/270\/revisions\/32479"}],"wp:attachment":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/media?parent=270"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/categories?post=270"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/tags?post=270"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}