version date: 1 December 2006 EXERC|sEⅢ.1 STRUCTURAL DATA: THE BASIS FOR MOLECULAR MODELING Francesca Spyrakis', Laura Giurato2, Salvatore Guccione, and pietro cozzini Department of Biochemistry and Molecular Biology, University of Parma, 43100 Parma, Italy, Department of Pharmaceutical Sciences, University of Catania, 1-95125 Catania, italy Laboratory of Molecular Modeling, Department of General and Inorganic Chemistry, Chemical-Physics and Analytical Chemistry, University of Parma, 43100 Parma, italy Theoretical Introduction Proteins can properly and correctly work only when they fold into their native structures. The possibility of observing the three-dimensional structure of a protein, through X-ray diffraction or NMR techniques, is essential to understand how a protein works and to rationally design potential ligands, capable of inhibiting or activating a biolog ical target. We can, in fact, assume that structural data represent the milestone for reliable molecular modeling and computer-aided drug design approaches. Nevertheless, determining the exact structure into which a protein naturally folds is a very complex and time-consuming procedure. Thus, the molecule has to be purified crystallized, exposed to X-radiation, and the diffraction diagram must be transformed into an intelligible electron density map. Unfortunately, some proteins are not easy to crystallize and others are not able to generate clear diffraction patterns after X-ray exposure, so, different computational approaches and simulations must be applied, in order to find new potential lead compounds Actually, there are two different methods used to design new drugs the ab initio approach and the in silico virtual screening. The first is based on the knowledge of the three-dimensional structure of the target binding pocket and aims to design drugs sterically and physicochemically complementary to the active site properties and shape. The second one is able to screen thousand of compounds belonging to a large database of known molecules, and to computationally evaluate their binding affinity toward the target protein(Lengauer 2003) If we are very lucky and we know both the three-dimensional structure of our biological target and of the potential lead compound, or at least of the potential chemical scaffold, we can simply apply structure-based drug design procedures, trying to improve the inhibition potency of the potential new drugs. If the protein structure is known, but we do not have any clue about the structure of a possible inhibitor, or activator, we can use combinatorial chemistry techniques to generate millions of molecules, to be subsequently virtually tested, with docking and scorin simulations. Knowing the structure of the receptor and the shape and chemical nature of the binding pocket, it is also possible to model a lead compound, in order to achieve an almost perfect geometrical and chemical complementarity, thus combining random virtual screening and rational drug design processes, or to choose a complete de novo design approach. On the contrary, ligand based design can be used when only the ligand configuration is known, but the target structure not available. Quantitative structure-activity relationship(QSAR)investigation could represent a useful tool to improve the inhibition properties of a lead compound, computationally modifying its chemical properties. Moreover, QSAR techniques could elucidate also the chemical peculiarities of the binding pocket, driving to the prediction of a pharmacophore model. At last, when no structural information is available on either the biological target or the ligands, the unique possibility is to generate thousands of different chemical compounds through combinatorial chemistry and to experimentally screen them. Virtual screening is the computational in silico variant of high throughput screening, and even if the accuracy of virtual predictions is usually lower than the synthesized aper ano neasurements, virtual screening presents some benefits. The calculations accuracy of experimental er, and it is possible to test and model compounds not yet purchased or <www.iupac.org/publications/cd/medicinal_chemistry/>
1 EXERCISE III.1 STRUCTURAL DATA: THE BASIS FOR MOLECULAR MODELING Francesca Spyrakis1 , Laura Giurato2 , Salvatore Guccione2 , and Pietro Cozzini3 1 Department of Biochemistry and Molecular Biology, University of Parma, 43100 Parma, Italy; 2 Department of Pharmaceutical Sciences, University of Catania, I-95125 Catania, Italy; 3 Laboratory of Molecular Modeling, Department of General and Inorganic Chemistry, Chemical-Physics and Analytical Chemistry, University of Parma, 43100 Parma, Italy Theoretical Introduction Proteins can properly and correctly work only when they fold into their native structures. The possibility of observing the three-dimensional structure of a protein, through X-ray diffraction or NMR techniques, is essential to understand how a protein works and to rationally design potential ligands, capable of inhibiting or activating a biological target. We can, in fact, assume that structural data represent the milestone for reliable molecular modeling and computer-aided drug design approaches. Nevertheless, determining the exact structure into which a protein naturally folds is a very complex and time-consuming procedure. Thus, the molecule has to be purified, crystallized, exposed to X-radiation, and the diffraction diagram must be transformed into an intelligible electron density map. Unfortunately, some proteins are not easy to crystallize and others are not able to generate clear diffraction patterns after X-ray exposure, so, different computational approaches and simulations must be applied, in order to find new potential lead compounds. Actually, there are two different methods used to design new drugs: the ab initio approach and the in silico virtual screening. The first is based on the knowledge of the three-dimensional structure of the target binding pocket and aims to design drugs sterically and physicochemically complementary to the active site properties and shape. The second one is able to screen thousand of compounds belonging to a large database of known molecules, and to computationally evaluate their binding affinity toward the target protein (Lengauer 2003). If we are very lucky and we know both the three-dimensional structure of our biological target and of the potential lead compound, or at least of the potential chemical scaffold, we can simply apply structure-based drug design procedures, trying to improve the inhibition potency of the potential new drugs. If the protein structure is known, but we do not have any clue about the structure of a possible inhibitor, or activator, we can use combinatorial chemistry techniques to generate millions of molecules, to be subsequently virtually tested, with docking and scoring simulations. Knowing the structure of the receptor and the shape and chemical nature of the binding pocket, it is also possible to model a lead compound, in order to achieve an almost perfect geometrical and chemical complementarity, thus combining random virtual screening and rational drug design processes, or to choose a complete de novo design approach. On the contrary, ligandbased design can be used when only the ligand configuration is known, but the target structure is not available. Quantitative structure–activity relationship (QSAR) investigation could represent a useful tool to improve the inhibition properties of a lead compound, computationally modifying its chemical properties. Moreover, QSAR techniques could elucidate also the chemical peculiarities of the binding pocket, driving to the prediction of a pharmacophore model. At last, when no structural information is available on either the biological target or the ligands, the unique possibility is to generate thousands of different chemical compounds through combinatorial chemistry and to experimentally screen them. Virtual screening is the computational in silico variant of highthroughput screening, and even if the accuracy of virtual predictions is usually lower than the accuracy of experimental measurements, virtual screening presents some benefits. The calculations, in fact, are cheaper and faster, and it is possible to test and model compounds not yet purchased or synthesized. <www.iupac.org/publications/cd/medicinal_chemistry/> version date: 1 December 2006
version date: 1 December 2006 Three-dimensional modeling, even though it is still far from complete exactness, represents a seful tool for the prediction of unknown pharmaceutical target structures. Amino acid residues are characterized by specific electrostatic fields, and used to assume particular shapes. Through several techniques, such as force field and molecular mechanics calculations, homology modeling, and fold recognition, it is sometimes possible to model and predict the folding pathway that a specific protein might follow and also the behavior and dynamics of the folded protein. Proteins sharing similar sequences or functions often adopt the same overall fold. Nevertheless, it is interesting to observe that proteins with no sequence similarity and even with different functions, may also exhibit similar foldings. Thus, it has been suggested that there is a limited number of 1000-7000 different families, to which almost all proteins may belong, that have been adapted by duplication mutation, or natural selection processes, to perform all the existent biological functions(Lengauer same general structure. Usually, the tertiary structures of evolutionary related proteins are better conserved than their primary sequences. However, differences between three-dimensional structures are known to increase with decreasing sequence identity, thus leading to corresponding model accuracy fall-off These observations constitute the basis of the two most successful, and widely applied protocols for protein modeling: homology modeling and protein threading Homology modeling combines two different computational tools: sequence analysis and molecular modeling. This approach is based on the assumption that, usually, homologous proteins share very similar structures. Therefore, given the amino acid primary sequence of an unknown structure and the experimentally solved structure of a homologous protein, homology modeling uses the known protein as a template to model the new structure, mutating each different amino acid in the solved structure. Whereas, when no homologous structures are available, protein threading endeavors to find a protein fold compatible with the model sequence The basic protocol followed by homology modeling approaches is made up of four steps: (i)a template or parent structure, related to the unknown one, is identified and the two sequences are aligned; (ii) the backbone coordinates of the well-conserved regions are borrowed from the template structure;(iii)non-conserved sequences, like loop or regions in which several amino acids have been inserted or deleted, are virtually modeled using methods based on the knowledge of protein structure determinants; (iv)side chains are added, in according with the backbone modulation and the generation of a low-energy model Even when the three-dimensional structure of the pharmaceutical target has been determined through experimental techniques, such as X-ray diffraction, the computational modelers must face different structural problems: (i) hydrogen atoms position and (ii) water molecules orientation. In fact, being hydrogens too small to be clearly identified, these atoms must be computationally added and minimized, to avoid steric clashes and negative repulsive interactions. Similarly, only the water oxygen position can be unambiguously determined, so important water molecules must be optimized, in order to clearly investigate their potential role of protein-ligand bridges (Ladbury 1996: Cozzini et al. 2004). Another big problem is represented by the resolution of the crystallographic structure, which can significantly affect the quality of models and simulations. At high resolution values, lower than 1.5 A, the model is probably more than 95 %a consequence of the observed data, while at lower resolution values, bigger than 2.5 A, the model is much more subjective than widely appreciated(Davis et al. 2003; Kleiwegt et al. 1999). As a consequence, unless the resolution is high, the presence or absence of water molecules cannot be determined with certainty, and, sometimes, the addition of waters is used to artificially reduce the differences between observed and calculated structure-factors amplitude(davies et al. 2003) Usually, quick methods treat the receptor as a rigid object, thus neglecting any kind of protein flexibility. This represents one of the most common errors occurring in computational studies ( Carlson 2002a). The binding of a ligand within an enclosed binding site, in fact, requires that part <www.iupac.org/publications/cd/medicinal_chemistry/>
2 Three-dimensional modeling, even though it is still far from complete exactness, represents a useful tool for the prediction of unknown pharmaceutical target structures. Amino acid residues are characterized by specific electrostatic fields, and used to assume particular shapes. Through several techniques, such as force field and molecular mechanics calculations, homology modeling, and fold recognition, it is sometimes possible to model and predict the folding pathway that a specific protein might follow and also the behavior and dynamics of the folded protein. Proteins sharing similar sequences or functions often adopt the same overall fold. Nevertheless, it is interesting to observe that proteins with no sequence similarity and even with different functions, may also exhibit similar foldings. Thus, it has been suggested that there is a limited number of 1000–7000 different families, to which almost all proteins may belong, that have been adapted by duplication, mutation, or natural selection processes, to perform all the existent biological functions (Lengauer 2003). Members belonging to the same family and functionally analogous sites assume the same folding patterns. This makes sense, since proteins performing the same functions should have the same general structure. Usually, the tertiary structures of evolutionary related proteins are better conserved than their primary sequences. However, differences between three-dimensional structures are known to increase with decreasing sequence identity, thus leading to corresponding model accuracy fall-off. These observations constitute the basis of the two most successful, and widely applied, protocols for protein modeling: homology modeling and protein threading. Homology modeling combines two different computational tools: sequence analysis and molecular modeling. This approach is based on the assumption that, usually, homologous proteins share very similar structures. Therefore, given the amino acid primary sequence of an unknown structure and the experimentally solved structure of a homologous protein, homology modeling uses the known protein as a template to model the new structure, mutating each different amino acid in the solved structure. Whereas, when no homologous structures are available, protein threading endeavors to find a protein fold compatible with the model sequence. The basic protocol followed by homology modeling approaches is made up of four steps: (i) a template or parent structure, related to the unknown one, is identified and the two sequences are aligned; (ii) the backbone coordinates of the well-conserved regions are borrowed from the template structure; (iii) non-conserved sequences, like loop or regions in which several amino acids have been inserted or deleted, are virtually modeled, using methods based on the knowledge of protein structure determinants; (iv) side chains are added, in according with the backbone modulation and the generation of a low-energy model. Even when the three-dimensional structure of the pharmaceutical target has been determined through experimental techniques, such as X-ray diffraction, the computational modelers must face different structural problems: (i) hydrogen atoms position and (ii) water molecules orientation. In fact, being hydrogens too small to be clearly identified, these atoms must be computationally added and minimized, to avoid steric clashes and negative repulsive interactions. Similarly, only the water oxygen position can be unambiguously determined, so important water molecules must be optimized, in order to clearly investigate their potential role of protein-ligand bridges (Ladbury 1996; Cozzini et al. 2004). Another big problem is represented by the resolution of the crystallographic structure, which can significantly affect the quality of models and simulations. At high resolution values, lower than 1.5 Å, the model is probably more than 95 % a consequence of the observed data, while at lower resolution values, bigger than 2.5 Å, the model is much more subjective than widely appreciated (Davis et al. 2003; Kleiwegt et al. 1999). As a consequence, unless the resolution is high, the presence or absence of water molecules cannot be determined with certainty, and, sometimes, the addition of waters is used to artificially reduce the differences between observed and calculated structure-factors amplitude (Davies et al. 2003). Usually, quick methods treat the receptor as a rigid object, thus neglecting any kind of protein flexibility. This represents one of the most common errors occurring in computational studies (Carlson 2002a). The binding of a ligand within an enclosed binding site, in fact, requires that part <www.iupac.org/publications/cd/medicinal_chemistry/> version date: 1 December 2006
version date: 1 December 2006 of the receptor is flexible, in order to allow the ligand access. For this reason, several binding sites are characterized by large flexible regions, able to open and close, in according with molecule entrance or release(Carlson 2002b). The capability of properly modeling protein flexibility is also extremely important to understand the real inhibition activity of a drug. Exploiting the possible conformations induced by the ligand entrance in the binding site could prevent several prediction errors and the elimination of potentially active compounds(Teague 2003) A case study: The estrogen receptor (green boxes outline practical sessions) To execute the following exercise, you are required to download a graphic molecular modeling program (e.g, Pymol, RasMol, RasTop, Swiss-PDB Viewer, or Chime), which are freely available on the web at the following addresses Pymol http:/pvmol.sourceforge.net Ras mol Rast http://www.geneinfinitv.org/rastop/ Swiss-pdbViewerhttp://www.expasv.ch/spdbv/ Chime http://www.mdli.com Using one of the listed programs, you will be able to directly observe the reported protein structures, identify the critical amino acids, and observe the different shapes assumed by the flexible binding pocket of the studied protein. all structures are freely availableontheProteinDataBankwebsite(www.pdb.org The fundamental role played by estrogens in reproductive endocrinology has been known from the middle of the 20 century. Through the interaction with the two different receptors ERa and ERB, belonging to the big nuclear receptor superfamily, estrogens participate in the regulation of the female reproductive system, and in particular, of the uterus, ovaries, and breast(Jordan 2003 Amari et al. 2004). Estrogens and progestins have been used as oral contraceptives, and are also applied in estrogen replacement therapy, to alleviate symptoms and urogenital atrophy, during menopause. Jensen and Jacobson hypothesized, in the late 1950s, that an estrogen receptor (Er), able to start all the biochemical processes associated with estrogen actions should be present in the target tissues (Jensen et al. 1960; 1962). The Er was then isolated and characterized, and subsequently found also in breast tumors, providing the base for the development of antiestrogenic therapy as an alternative to ablative surgery. The first molecules developed to contrast the estrogen- simulated growth of breast cancer were tamoxifen( Wakeling et al. 1983)and raloxifen. These two molecules are currently on the market for the treatment of hormone-dependent breast cancer and for the prevention and cure of osteoporosis(Dutertre et al. 2000). The actions of estrogen and antiestrogen compounds are mediated by the interaction with ERa and ERB, respectively identified in 1966 and 1999(Toft et al. 1966; Gustafsson 1999). The two receptors share a similar size and also a similar structure, formed by a variable amino terminal region involved in transactivation processes, a central dNa binding domain (dnb), a ligand binding domain(lBd) characterized by a 53% identity in ERo and ERB, and a carboxy terminal region (Jordan 2003). When natural estrogens bind to the ERs, inducing the dissociation from heat shock protein, the dimerization, the binding to a specific DNA region, and the transcription of responsive genes Tamoxifen and raloxifen belong to the SErMs drug category (selective ER modulators), which comprises ligands able to act both as estrogen and as antiestrogen compounds. SERMs may, in fact show agonistic or antagonistic activity, according to the cellular and promoter contest and also to the receptor isoform(Pike et al. 2001). It is generally accepted that different ligands induce <www.iupac.org/publications/cd/medicinal_chemistry/>
3 of the receptor is flexible, in order to allow the ligand access. For this reason, several binding sites are characterized by large flexible regions, able to open and close, in according with molecule entrance or release (Carlson 2002b). The capability of properly modeling protein flexibility is also extremely important to understand the real inhibition activity of a drug. Exploiting the possible conformations induced by the ligand entrance in the binding site could prevent several prediction errors and the elimination of potentially active compounds (Teague 2003). A case study: The estrogen receptor (green boxes outline practical sessions) To execute the following exercise, you are required to download a graphic molecular modeling program (e.g., Pymol, RasMol, RasTop, Swiss-PDB Viewer, or Chime), which are freely available on the web at the following addresses: Pymol: http://pymol.sourceforge.net/ RasMol: http://www.bernstein-plus-sons.com/software/rasmol/ RasTop: http://www.geneinfinity.org/rastop/ Swiss-PDB Viewer: http://www.expasy.ch/spdbv/ Chime http://www.mdli.com/ Using one of the listed programs, you will be able to directly observe the reported protein structures, identify the critical amino acids, and observe the different shapes assumed by the flexible binding pocket of the studied protein. All structures are freely available on the Protein Data Bank web site (www.pdb.org). The fundamental role played by estrogens in reproductive endocrinology has been known from the middle of the 20th century. Through the interaction with the two different receptors ERα and ERβ, belonging to the big nuclear receptor superfamily, estrogens participate in the regulation of the female reproductive system, and in particular, of the uterus, ovaries, and breast (Jordan 2003; Amari et al. 2004). Estrogens and progestins have been used as oral contraceptives, and are also applied in estrogen replacement therapy, to alleviate symptoms and urogenital atrophy, during menopause. Jensen and Jacobson hypothesized, in the late 1950s, that an estrogen receptor (ER), able to start all the biochemical processes associated with estrogen actions, should be present in the target tissues (Jensen et al. 1960; 1962). The ER was then isolated and characterized, and subsequently found also in breast tumors, providing the base for the development of antiestrogenic therapy as an alternative to ablative surgery. The first molecules developed to contrast the estrogensimulated growth of breast cancer were tamoxifen (Wakeling et al. 1983) and raloxifen. These two molecules are currently on the market for the treatment of hormone-dependent breast cancer and for the prevention and cure of osteoporosis (Dutertre et al. 2000). The actions of estrogen and antiestrogen compounds are mediated by the interaction with ERα and ERβ, respectively identified in 1966 and 1999 (Toft et al. 1966; Gustafsson 1999). The two receptors share a similar size and also a similar structure, formed by a variable amino terminal region involved in transactivation processes, a central DNA binding domain (DNB), a ligand binding domain (LBD) characterized by a 53 % identity in ERα and ERβ, and a carboxy terminal region (Jordan 2003). When natural estrogens bind to the ERs, inducing the dissociation from heat shock protein, the dimerization, the binding to a specific DNA region, and the transcription of responsive genes. Tamoxifen and raloxifen belong to the SERMs drug category (selective ER modulators), which comprises ligands able to act both as estrogen and as antiestrogen compounds. SERMs may, in fact, show agonistic or antagonistic activity, according to the cellular and promoter contest and also to the receptor isoform (Pike et al. 2001). It is generally accepted that different ligands induce <www.iupac.org/publications/cd/medicinal_chemistry/> version date: 1 December 2006
version date: 1 December 2006 different conformational changes in the ERs, able to interfere with its ability to interact with other proteins, such as coactivators and corepressors The first attempt to model the structure of ERa was made by Holtje and Dall in 1993(Holtje et al. 1993). The binding site was identified through homology modeling approach, comparing the er sequence with the sequences of other steroid receptors and steroid binding proteins. The sequent between Leu379 and Met388 was identified as a region of particular interest. Analogous sequences were localized into several related proteins, like the progesterone receptor, the androgen receptor, the glucocorticoid receptor, the mineralcorticoid receptor, the steroid dehydrogenase, and the Na/K-ATPase. A conserved triptophan (Trp383) was identified in all the compared macromolecules. This residue could favorably interact with the hydrophobic scaffold of steroid skeleton, while Glu380, sometimes substituted by GIn380, could easily act as a hydrogen bond donor, capable of interacting with the estradiol hydroxyl group. These chemical and structural properties are usually shared by common SERMs. This sequence was identified as a part of the binding site arranged in an a-helix secondary structure. A second conserved region was located in the a-helix formed by the peptide comprised between Cys447 and Ser456. The two helixes were joined together, and the estradiol was docked into them. In the proposed model, Glu380, Trp383 Leu387 on the first helix and Lys449, Ile452, and Leu453 on the second seemed to be really important for the complex formation Download from the Protein Data Bank the structure of ERa complexed with estradiol (lere): observe the complex and the secondary structure of the receptor. Identify the region delimited by Leu379 and Met388. Identify the region delimited by Cys447 and Ser456 Localize the following residues: Glu 380, Trp383, Leu387, Lys449, Ile452, and Leu453 Yo The ER case represents a typical examp le of computational failure. Four years later, in fact, in 7, Brzozowski and co-workers identified through crystallography the real structure and the real binding pocket of the LBD, solving the structures of the receptor complexed with estradiol and raloxifen( Brzozowski et al. 1997). The LBD is formed by 2 B-sheets and 12 a-helixes, organized in 2 layers(Hl-H4 and H7, H8, HI l)surrounding a central core(H5, H6, H9, and H1O). H12 flanks the three-layer motif and is characterized by high mobility. Estradiol occupies diagonally the binding site, forming two H-bonds with its two hydroxyl groups. The phenolic hydroxyl interacts with Glu353, Arg394, and a water molecule, the 17-B hydroxyl contacts His 524(Figs 2a and 3a) while the lipophilic estradiol portion occupies the LBD hydrophobic core, formed by segments of helixes H3, H6, H8, HIl, and H12. The recognition of the receptor and the stabilization of the complex are obtained through a strong chemical and structural complementarity between the ligand and the binding pocket Identify residues Glu353, Arg 394, and His524 in lere pdb. Highlight the H-bonds formed by the estradiol molecules with these three residues Identify the hydrophobic amino acids surrounding the hydrophobic core of the ligand: Ala350, Trp383, Leu346, Leu387, Leu391, Ile 424, Leu428, Leu525 Leu540 Compare the experimentally determined location of the binding site with the model proposed by holtje and Dall in 1993 ww.iupac.org/publications/cd/medicinal_chemistry/>
4 different conformational changes in the ERs, able to interfere with its ability to interact with other proteins, such as coactivators and corepressors. The first attempt to model the structure of ERα was made by Höltje and Dall in 1993 (Höltje et al. 1993). The binding site was identified through homology modeling approach, comparing the ER sequence with the sequences of other steroid receptors and steroid binding proteins. The sequence between Leu379 and Met388 was identified as a region of particular interest. Analogous sequences were localized into several related proteins, like the progesterone receptor, the androgen receptor, the glucocorticoid receptor, the mineralcorticoid receptor, the steroid dehydrogenase, and the Na+ /K+ -ATPase. A conserved triptophan (Trp383) was identified in all the compared macromolecules. This residue could favorably interact with the hydrophobic scaffold of steroid skeleton, while Glu380, sometimes substituted by Gln380, could easily act as a hydrogen bond donor, capable of interacting with the estradiol hydroxyl group. These chemical and structural properties are usually shared by common SERMs. This sequence was identified as a part of the binding site arranged in an α-helix secondary structure. A second conserved region was located in the α-helix formed by the peptide comprised between Cys447 and Ser456. The two helixes were joined together, and the estradiol was docked into them. In the proposed model, Glu380, Trp383, Leu387 on the first helix and Lys449, Ile452, and Leu453 on the second, seemed to be really important for the complex formation. • Download from the Protein Data Bank the structure of ERα complexed with estradiol (1ere): observe the complex and the secondary structure of the receptor. • Identify the region delimited by Leu379 and Met388. • Identify the region delimited by Cys447 and Ser456. • Localize the following residues: Glu 380, Trp383, Leu387, Lys449, Ile452, and Leu453. The ER case represents a typical example of computational failure. Four years later, in fact, in 1997, Brzozowski and co-workers identified through crystallography the real structure and the real binding pocket of the LBD, solving the structures of the receptor complexed with estradiol and raloxifen (Brzozowski et al. 1997). The LBD is formed by 2 β-sheets and 12 α-helixes, organized in 2 layers (H1-H4 and H7, H8, H11) surrounding a central core (H5, H6, H9, and H10). H12 flanks the three-layer motif and is characterized by high mobility. Estradiol occupies diagonally the binding site, forming two H-bonds with its two hydroxyl groups. The phenolic hydroxyl interacts with Glu353, Arg394, and a water molecule, the 17-β hydroxyl contacts His524 (Figs. 2a and 3a), while the lipophilic estradiol portion occupies the LBD hydrophobic core, formed by segments of helixes H3, H6, H8, H11, and H12. The recognition of the receptor and the stabilization of the complex are obtained through a strong chemical and structural complementarity between the ligand and the binding pocket. • Identify residues Glu353, Arg 394, and His524 in 1ere.pdb. • Highlight the H-bonds formed by the estradiol molecules with these three residues. • Identify the hydrophobic amino acids surrounding the hydrophobic core of the ligand: Ala350, Trp383, Leu346, Leu387, Leu391, Ile 424, Leu428, Leu525, Leu540… • Compare the experimentally determined location of the binding site with the model proposed by Höltje and Dall in 1993. <www.iupac.org/publications/cd/medicinal_chemistry/> version date: 1 December 2006
version date: 1 December 2006 R394 E353 R394 E353 H524 a H524 Fig. 1(a) Estradiol in ERa binding pocket (lere).(b) Raloxifen in ERa binding pocket (lerr). The ligands are displayed in ball and stick, while the protein residues in capped stick. H-bonds are identified by yellow dotted nes Nevertheless, several cavities still remain unoccupied after the estradiol binding. It is, in fact well known, that LBD, being extremely flexible, can accept a number of very different hydrophobic groups, adapting its shape to the ligand conformation(Anstead et al. 1997). The raloxifen lateral chain is, in fact, really too long to be contained in the estradiol binding pocket, thus, H12 displaced from its natural position and forced to protrude from the pocket formed by H3 and Hll ll steroidal and non-steroidal antiestrogen compounds, characterized by volum displace H12, inducing the same different binding pocket conformation(Fig. y/ nous side chains Fig 2(a) ERa complexed with estradiol (lere). (b) ERo complexed with raloxifen(lerr).(c)ERa complexed with 4- OH tamoxifen(Bert). The receptor is represented in ribbon tube, while the ligands are shown in spacefill cartoons. The helix 12 is highlighted by yellow circles The antagonist properties of raloxifen and other antiestrogenic compounds are specifically based on the induced movement of H12, which disrupts the surface topography of AF-2, the LBDs transcriptional activation function, usually able to interact with putative transcriptional coactivatots in a ligand-dependent manner(Beato et al. 1996; Cavailles et al. 1994). The coactivator recruitment <ww.iupac. org/publications/cd/medicinal chemistry/>
5 Fig. 1 (a) Estradiol in ERα binding pocket (1ere). (b) Raloxifen in ERα binding pocket (1err). The ligands are displayed in ball and stick, while the protein residues in capped stick. H-bonds are identified by yellow dotted lines. Nevertheless, several cavities still remain unoccupied after the estradiol binding. It is, in fact, well known, that LBD, being extremely flexible, can accept a number of very different hydrophobic groups, adapting its shape to the ligand conformation (Anstead et al. 1997). The raloxifen lateral chain is, in fact, really too long to be contained in the estradiol binding pocket, thus, H12 is displaced from its natural position and forced to protrude from the pocket formed by H3 and H11. All steroidal and non-steroidal antiestrogen compounds, characterized by voluminous side chains, displace H12, inducing the same different binding pocket conformation (Fig. 2b). Fig. 2 (a) ERα complexed with estradiol (1ere). (b) ERα complexed with raloxifen (1err). (c) ERα complexed with 4- OH tamoxifen (3ert). The receptor is represented in ribbon tube, while the ligands are shown in spacefill cartoons. The helix12 is highlighted by yellow circles. The antagonist properties of raloxifen and other antiestrogenic compounds are specifically based on the induced movement of H12, which disrupts the surface topography of AF-2, the LBD’s transcriptional activation function, usually able to interact with putative transcriptional coactivatots, in a ligand-dependent manner (Beato et al. 1996; Cavailles et al. 1994). The coactivator recruitment a b c H524 H524 a b R394 R394 E353 E353 D351 <www.iupac.org/publications/cd/medicinal_chemistry/> version date: 1 December 2006