- Email: [email protected]
Probing active site surfaces: a “groping” experience
The first algorithm, selection-mutation-focusing (SMF), is based on a technique for optimization and learning, termed a genetic algorithm. This approach allows a set of quasioptimal solutions to be obtained. The second algorithm, sparse matrix driven @MD), is heuristic and allows a single solution to be obtained very quickly. It relies on the fact that each side chain interacts strongly only with a subset of the remaining side chains or backbone groups in the protein. Successful applications of these algorithms to the prediction of known protein conformations are presented. This approach also appears to be useful for the analysis of side-chain connectivity, starting from the quasi-optimal conformations of a given protein, and may lead to the definition of a subdivision of the protein into connected regions.
by terminating the expansion of a poured cast at a constant distance from the cavity wall atoms. This added distance compensates for the vdW radius of the ligand atoms. As such, steric contact is approximated with vector model penetration of the cast, circumventing the need to constantly recalculate it. This allows interactive manipulation of a ligand within a binding cavity while maintaining continuous feedback on steric interference. This work was supported by the National Institutes of Health (grant GM24483) and the Medical Scientist Training Program in the Division of Biology and Biomedical Sciences at Washington Unive~ity (training grant GMO72~).
Probing Active Site Surfaces: A “Groping” Experience
REFERENCES 1 Tuffery, P., Etchebest, C., Hazout, S., and Lavery, J. Biomol. Strut. Dynam. in press
R,
Cavity Search: An Algorithm for the Isolation and Display of Cavity-fike Binding Regions Chris M.W. Ho and Garland R. Marshall A set of algorithms designed to enhance the display and study of protein binding cavities is presented. These algorithms, collectively entitled CAVITY SEARCH, allow the user to isolate and fully define the extent of a cavity. Solid modeling techniques are employed to produce a detailed cast of the region. Mathematically, a filler solid is “poured” into the void of a cavity. Using a flood-fill algorithm, a thr~-dimensional search is performed, allowing the solid to fill every continuous crack and crevice within this region. A Boolean operation subtracting the volume of the atoms comprising the cavity wall from the volume of the filler solid produces a perfect reproduction of the interior cavity volume. The cast is then contoured for display. When viewed with appropriate Z-clipping, the cast appears as a hollow shell whose walls form a grid-like mesh that adheres strictly to the vdW surface of the cavity. This technique enables one to greatly simplify the display and study of a ligand-receptor system. The cast isolates all molecular surfaces in direct contact with the receptor void, circumventing the need to display any receptor atoms. The full attention of the investigator may then be directed toward the interactive fitting of a ligand within it. To aid in this process, other algorithms have been developed to color code the walls of the cast by various properties, including (1) (2) (3)
the electrostatic charge of either the ligand or the receptor the ligand-receptor electrostatic complementarity the ligand-receptor gap distance.
Recently, methods have been developed to produce cavity casts that display the region within which a vector (i.e., stick) model of a binding ligand must reside. This is achieved
36
J. Mol. Graphics,
1992, Vol. 10, March
Vivian Codv Medical Foundation 14203, USA
of Buffalo, 73 High St., Buffalo, NY
A new force feedback molecular modeling system, GROP, developed at the University of North Carolina, Chapel Hill, was used to model interactions of three lipophilic antifolate inhibitors in the active site of avian dihydrofoIate reductase (DHFR), and the hormone thyroxine and a bromoflavone inhibitor in the binding site of human transthyretin (TTR), a thyroid hormone transport protein. The results of these force feedback experiments, which use an AMBER force field to calculate the active site zone, are compared with antifolate modeling results from the molecular mechanics force field program YETI, which contains explicit parameterization for directional hydrogen bonds, and with crystallographic data of TTR-inhibitor complexes. During the docking experiment using the GROP system, remote manipulator technology enables the user to “feel” the molecular forces. The forces and torque applied to the user’s hand in controlling the movement of a drug with the Argonne Remote Manipulator (ARM) are calculated in real time against a precalculated AMBER force field of the active site zone of the system under study. Therefore the protein active site is static. These forces supplement the three-dimensional image of the active site displayed with a Connelly surface. There are dials on the arm which permit rotation of flexible bonds in the drug molecule and the energy of a particular conformation can be monitored independently. An energy scale shown on the screen monitors the energy of the drug itself and the interactive energy of the docking process.
DIHYDROFOLATE REDUCTASEANTIFOLATE BINDING Three lipophilic antifolates, 2,4-diamino-5-adamantyl-6methylpy-rimidine (DAMP), 2,4-diamino-4-adam~tylyp~ teridine (DAPT), and its 4-0~0 folate analogue (DOPT), ail potent inhibitors of DHFR, were modeled using the force feedback system. Crystallographic data for the avian DHFR complex reveal that DAMP binds in the active site by interacting with GLU 30, which hydrogen bonds to the antifolate N(2) and protonates N( 1). This a~tz~ofate orientation
is characteristic of all DHFR antifolate structural data. On the other hand, folate substrate analogues that have a 4-0~0 substituent interact with GLU 30 via N(2) and N(3) and are unprotonated; e.g., thefolute orientation. In these two orientations, the adamantyl group occupies different regions of the active site. The antifolate orientation of DAMP and DAPT were modeled and the folate orientation of DOPT. The results of these force feedback experiments revealed several energy minima in which the inhibitor maintained its hydrogen bonding interactions. However, comparison of these minima with the results of the YETI calculations showed that the inhibitor orientations were significantly different for the two systems, in p~icular for the folate models. The YETI energy minimizations permit movement of the residue side chains within the active site zone. These calculations showed that there is significant movement of the phenylalanine and tyrosyl residues that form the bottom of the active site pocket and little movement of the cofactor NADPH that forms part of the top surface of the active site. In all cases, these residue movements relieve short intermolecular contacts caused by the steric bulk of the adamantyl group and the side chains. As demonstrated by these force feedback examples, the antifolates were pushed deeper into the active site and closer to the cofactor than their YET1 counterparts. Some of these models showed that the antifolate was tilted such that the pyrimidine ring was pushed toward the bottom of the active site and the adamantyl group toward the cofactor while still maintaining reasonable hydrogen bonding interactions with Glu-30. These data, which contained no information on structural waters in the active site, suggested that there is more room for movement of the antifolates than is suggested from either the structural data or the YET1 calculations.
TRANSTHYRETIN-INHIBITOR
COMPLEXES
Transthyretin is a serum transport protein that accounts for about 20% of the circulating thyroxine (Ta). In addition, hormone binding can be competitively displaced by a number of pharmacological agents. One of the most potent competitive inhibitors is the bromoflavone, 3-methyl-4’,6dihydroxy-3’,5’-dibromoflavone (EMD21388). The results of the force feedback studies are compared with crystallographic data for the flavone TTR complex. Structural data for thyroxine TTR complex showed that the hormone phenolic ring is bound deep in the hormone channel with its amino-acid side chain near the channel entrance (“forward” mode) and that the iodine atoms occupy hydrophobic pockets in the channel. However, structural data for the bromoflavone revealed that there is a second o~entation for the inhibitor in which the bromophenolic ring lies near the channel entrance, in the “reverse” mode of binding compared to the substrate T4. CROP force feedback modeling studies of TTR and T4 indicated that small shifts in the position of the starting crystallographic model resulted in a lower energy. In probing alternate orientations for the binding of T.,, these modeling studies revealed the surprising result that T4 could also occupy the binding channel in the reverse mode with the phenolic ring bound at the channel entrance. Modeling the bromo~avone in the forward and reverse
modes showed signi~~~t differences in the energy minima compared to the structural data. In one model an energy minima was found for the flavone bound along one side of the channel rather than in the center, and in another the bromoflavone was found to fill the active site differently while still maintaining the same relative halogen binding sites. Again, the force feedback models revealed that both the forward and reverse modes of binding are energy minima. Therefore, the basic conclusions from this modeling experience with these two different protein systems are that the GROP force feedback results are in genera1 agreement with data calculated with other force field methods and with structural observation. Thus, for the antifolate study several alternative binding orientations were evaluated, which revealed that there is more flexibility in the active site than was first considered, and in the transthyretin study these calculations showed that the unusual reverse mode of competitor binding was a feasible model for thyroxine binding as well. Furthermore, these studies showed that a large number of potential inhibitor binding models could be quickly assessed and their coordinates stored for further analysis. In addition, this method also revealed the ease with which unusual modes of inhibitor binding could be generated and their results analyzed. For example, it was a simple matter to reverse the inhibitor direction to binding in the hormone channel and to explore the effects of reverse-mode binding for the hormone. By interpreting the response of the force feedback, the user is also given directional information, as well as the feasibility of the proposed interactions. The ability to “feel” the degree of the resistance and the torque met when the intermolecular interactions are unfavorable permits the user to evaluate how much effort is needed to overcome this local minima. Coupled with the user’s experience and innate pattern-recognition skills, this method could become a powerful tool in the arsenal of drug design studies. These results suggest that further study is warranted to enhance the potential usefufness of this method in the rapid screening of binding interactions between a drug and its target. Supported in part by NCI-34714 and DK-41009.
Ribbons 2.0 Mike Carson University of Alabama at Bi~ingham, Center for Macromolecular C~stallography, 2.52 BHS, 79 THT University Station, Birmingham, AL 35294, USA Ribbon models are the best way to view the folding and secondary structure of a polypeptide chain. Color coding allows encapsulation of information relevant to the overall structure of the macromolecule. The program Ribbons 2.0 has been developed to allow the interactive viewing of solid, shaded molecular models in real time on the Silicon Graphics 4D family of workstations. (A port to the Evans and Sutherland ESV workstation employing PEX and Motif is in progress.) The principal
J. Mol. Graphics, 1992, Vol. 10, March
37
by terminating the expansion of a poured cast at a constant distance from the cavity wall atoms. This added distance compensates for the vdW radius of the ligand atoms. As such, steric contact is approximated with vector model penetration of the cast, circumventing the need to constantly recalculate it. This allows interactive manipulation of a ligand within a binding cavity while maintaining continuous feedback on steric interference. This work was supported by the National Institutes of Health (grant GM24483) and the Medical Scientist Training Program in the Division of Biology and Biomedical Sciences at Washington Unive~ity (training grant GMO72~).
Probing Active Site Surfaces: A “Groping” Experience
REFERENCES 1 Tuffery, P., Etchebest, C., Hazout, S., and Lavery, J. Biomol. Strut. Dynam. in press
R,
Cavity Search: An Algorithm for the Isolation and Display of Cavity-fike Binding Regions Chris M.W. Ho and Garland R. Marshall A set of algorithms designed to enhance the display and study of protein binding cavities is presented. These algorithms, collectively entitled CAVITY SEARCH, allow the user to isolate and fully define the extent of a cavity. Solid modeling techniques are employed to produce a detailed cast of the region. Mathematically, a filler solid is “poured” into the void of a cavity. Using a flood-fill algorithm, a thr~-dimensional search is performed, allowing the solid to fill every continuous crack and crevice within this region. A Boolean operation subtracting the volume of the atoms comprising the cavity wall from the volume of the filler solid produces a perfect reproduction of the interior cavity volume. The cast is then contoured for display. When viewed with appropriate Z-clipping, the cast appears as a hollow shell whose walls form a grid-like mesh that adheres strictly to the vdW surface of the cavity. This technique enables one to greatly simplify the display and study of a ligand-receptor system. The cast isolates all molecular surfaces in direct contact with the receptor void, circumventing the need to display any receptor atoms. The full attention of the investigator may then be directed toward the interactive fitting of a ligand within it. To aid in this process, other algorithms have been developed to color code the walls of the cast by various properties, including (1) (2) (3)
the electrostatic charge of either the ligand or the receptor the ligand-receptor electrostatic complementarity the ligand-receptor gap distance.
Recently, methods have been developed to produce cavity casts that display the region within which a vector (i.e., stick) model of a binding ligand must reside. This is achieved
36
J. Mol. Graphics,
1992, Vol. 10, March
Vivian Codv Medical Foundation 14203, USA
of Buffalo, 73 High St., Buffalo, NY
A new force feedback molecular modeling system, GROP, developed at the University of North Carolina, Chapel Hill, was used to model interactions of three lipophilic antifolate inhibitors in the active site of avian dihydrofoIate reductase (DHFR), and the hormone thyroxine and a bromoflavone inhibitor in the binding site of human transthyretin (TTR), a thyroid hormone transport protein. The results of these force feedback experiments, which use an AMBER force field to calculate the active site zone, are compared with antifolate modeling results from the molecular mechanics force field program YETI, which contains explicit parameterization for directional hydrogen bonds, and with crystallographic data of TTR-inhibitor complexes. During the docking experiment using the GROP system, remote manipulator technology enables the user to “feel” the molecular forces. The forces and torque applied to the user’s hand in controlling the movement of a drug with the Argonne Remote Manipulator (ARM) are calculated in real time against a precalculated AMBER force field of the active site zone of the system under study. Therefore the protein active site is static. These forces supplement the three-dimensional image of the active site displayed with a Connelly surface. There are dials on the arm which permit rotation of flexible bonds in the drug molecule and the energy of a particular conformation can be monitored independently. An energy scale shown on the screen monitors the energy of the drug itself and the interactive energy of the docking process.
DIHYDROFOLATE REDUCTASEANTIFOLATE BINDING Three lipophilic antifolates, 2,4-diamino-5-adamantyl-6methylpy-rimidine (DAMP), 2,4-diamino-4-adam~tylyp~ teridine (DAPT), and its 4-0~0 folate analogue (DOPT), ail potent inhibitors of DHFR, were modeled using the force feedback system. Crystallographic data for the avian DHFR complex reveal that DAMP binds in the active site by interacting with GLU 30, which hydrogen bonds to the antifolate N(2) and protonates N( 1). This a~tz~ofate orientation
is characteristic of all DHFR antifolate structural data. On the other hand, folate substrate analogues that have a 4-0~0 substituent interact with GLU 30 via N(2) and N(3) and are unprotonated; e.g., thefolute orientation. In these two orientations, the adamantyl group occupies different regions of the active site. The antifolate orientation of DAMP and DAPT were modeled and the folate orientation of DOPT. The results of these force feedback experiments revealed several energy minima in which the inhibitor maintained its hydrogen bonding interactions. However, comparison of these minima with the results of the YETI calculations showed that the inhibitor orientations were significantly different for the two systems, in p~icular for the folate models. The YETI energy minimizations permit movement of the residue side chains within the active site zone. These calculations showed that there is significant movement of the phenylalanine and tyrosyl residues that form the bottom of the active site pocket and little movement of the cofactor NADPH that forms part of the top surface of the active site. In all cases, these residue movements relieve short intermolecular contacts caused by the steric bulk of the adamantyl group and the side chains. As demonstrated by these force feedback examples, the antifolates were pushed deeper into the active site and closer to the cofactor than their YET1 counterparts. Some of these models showed that the antifolate was tilted such that the pyrimidine ring was pushed toward the bottom of the active site and the adamantyl group toward the cofactor while still maintaining reasonable hydrogen bonding interactions with Glu-30. These data, which contained no information on structural waters in the active site, suggested that there is more room for movement of the antifolates than is suggested from either the structural data or the YET1 calculations.
TRANSTHYRETIN-INHIBITOR
COMPLEXES
Transthyretin is a serum transport protein that accounts for about 20% of the circulating thyroxine (Ta). In addition, hormone binding can be competitively displaced by a number of pharmacological agents. One of the most potent competitive inhibitors is the bromoflavone, 3-methyl-4’,6dihydroxy-3’,5’-dibromoflavone (EMD21388). The results of the force feedback studies are compared with crystallographic data for the flavone TTR complex. Structural data for thyroxine TTR complex showed that the hormone phenolic ring is bound deep in the hormone channel with its amino-acid side chain near the channel entrance (“forward” mode) and that the iodine atoms occupy hydrophobic pockets in the channel. However, structural data for the bromoflavone revealed that there is a second o~entation for the inhibitor in which the bromophenolic ring lies near the channel entrance, in the “reverse” mode of binding compared to the substrate T4. CROP force feedback modeling studies of TTR and T4 indicated that small shifts in the position of the starting crystallographic model resulted in a lower energy. In probing alternate orientations for the binding of T.,, these modeling studies revealed the surprising result that T4 could also occupy the binding channel in the reverse mode with the phenolic ring bound at the channel entrance. Modeling the bromo~avone in the forward and reverse
modes showed signi~~~t differences in the energy minima compared to the structural data. In one model an energy minima was found for the flavone bound along one side of the channel rather than in the center, and in another the bromoflavone was found to fill the active site differently while still maintaining the same relative halogen binding sites. Again, the force feedback models revealed that both the forward and reverse modes of binding are energy minima. Therefore, the basic conclusions from this modeling experience with these two different protein systems are that the GROP force feedback results are in genera1 agreement with data calculated with other force field methods and with structural observation. Thus, for the antifolate study several alternative binding orientations were evaluated, which revealed that there is more flexibility in the active site than was first considered, and in the transthyretin study these calculations showed that the unusual reverse mode of competitor binding was a feasible model for thyroxine binding as well. Furthermore, these studies showed that a large number of potential inhibitor binding models could be quickly assessed and their coordinates stored for further analysis. In addition, this method also revealed the ease with which unusual modes of inhibitor binding could be generated and their results analyzed. For example, it was a simple matter to reverse the inhibitor direction to binding in the hormone channel and to explore the effects of reverse-mode binding for the hormone. By interpreting the response of the force feedback, the user is also given directional information, as well as the feasibility of the proposed interactions. The ability to “feel” the degree of the resistance and the torque met when the intermolecular interactions are unfavorable permits the user to evaluate how much effort is needed to overcome this local minima. Coupled with the user’s experience and innate pattern-recognition skills, this method could become a powerful tool in the arsenal of drug design studies. These results suggest that further study is warranted to enhance the potential usefufness of this method in the rapid screening of binding interactions between a drug and its target. Supported in part by NCI-34714 and DK-41009.
Ribbons 2.0 Mike Carson University of Alabama at Bi~ingham, Center for Macromolecular C~stallography, 2.52 BHS, 79 THT University Station, Birmingham, AL 35294, USA Ribbon models are the best way to view the folding and secondary structure of a polypeptide chain. Color coding allows encapsulation of information relevant to the overall structure of the macromolecule. The program Ribbons 2.0 has been developed to allow the interactive viewing of solid, shaded molecular models in real time on the Silicon Graphics 4D family of workstations. (A port to the Evans and Sutherland ESV workstation employing PEX and Motif is in progress.) The principal
J. Mol. Graphics, 1992, Vol. 10, March
37