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Ligand-protein docking and rational drug design Terry P Lybrand University of Washington, Seattle, USA Over the past year there have been some interesting and significant advances in computer-based ligand-protein docking techniques and related rational drug-design tools, including flexible ligand docking and better estimation of binding free energies and solvation energies. As a result, the successful use of computational tools to help generate interesting new guide (lead) compounds for targeted receptors is becoming more commonplace. Current Opinion in Structural Biology 1995, 5:224-228 Introduction Computational tools Ligand-binding interactions (i.e. formation of a complex between two molecules) are central to numerous biological processes such as signal transduction, physiological regulation, gene transcription, and enzymatic reactions. Ligand-binding interactions encompass both macromolecular complexes (e.g. protein-protein and protein-DNA) and complexes of small molecules with macromolecules. As many proteins regulate key biological functions via interactions with small molecules, these receptor proteins are often prime targets for therapeutic agents. A detailed understanding of interactions between small molecules and proteins may therefore form the basis for a rational drug-design strategy. Rational drug design is attractive as a drug development paradigm for two reasons: it offers some hope for reduction of the enormous costs and time required in traditional random screening protocols for drug discovery, and may facilitate the development of more selective therapeutic agents with fewer undesirable side effects. Interactive molecular graphics Numerous computational tools have evolved to investigate ligand-receptor complexes and to develop new ligands [5]. Interactive molecular graphics methods have long been used to analyze receptor crystal structures and to manually dock candidate ligands in the binding site [6]. The interactive graphics approach is extremely labor intensive, primarily qualitative rather than quantitative in nature, and subject to personal biases. On the other hand, it takes advantage of the knowledge and intuition possessed by experienced structural biologists in a way that no strictly computational techniques have yet been able to do. As a result, interactive molecular graphics methods are used extensively and remain the principal tool for ligand design in many cases. Developments in molecular biology over the past 15 years make it possible to obtain experimentally useful quantities of numerous receptor proteins of potential therapeutic importance. Technical advances in X-ray crystallography, multidimensional NMtL spectroscopy, and other structural characterization methods have made it easier to obtain high-resolution structural data for many important ligand-protein complexes. Still, there is at present no structural information for the vast majority of therapeutically interesting receptor proteins. In favorable cases, computational procedures such as homology model building may be used to generate approximate three-dimensional models for receptor proteins [1,2]. Development of various molecular modeling tools and ready availability of high-performance computing resources make possible detailed computational analyses and design projects for ligand-receptor complexes, provided suitable structural models are available for the receptors [3,4]. Over the past year, there have been some significant advances and improvements in computational tools used for ligand-protein docking and rational drug-design applications, and these developments are the focus of this review. 224 Binding energy calculations Graphics model-building methods are often combined with energy calculations based on potential energy functions (see also this issue, Halgren, pp 205-210 and Sippl, pp 229-235 for a general discussion of current issues related to potential energy functions). Energy calculations used routinely in rational drug-design applications include energy minimization, molecular dynamics, Poisson-Boltzmann electrostatics, and free energy perturbation methods [7",8]. In principle, these methods should provide quite detailed and definitive information for rational drug-design projects, and there are many recent examples where these methods have been used to good effect [9-13,14°,15]. Inadequacies in potential energy functions and conformational sampling, however, restrict the power of these methods in modeling ligand-receptor complexes. These methods are also computationally quite expensive, which limits their practical utility in many cases. In some recent ligand-binding studies, attempts have been made to overcome certain limitations relating to potential functions and computational expense through the use of empirical free energy functions or free energy estimations [16,17]. These approaches often estimate the free energy of ligand-receptor interactions as a function © Current Biology Ltd ISSN 0959-440X Ligand-protein docking and rational drug design Lybrand 225 of hydrophobic contact surface area, number of hydrogen bonds, buried polar surface area, and similar terms. As a result, these methods tend to be much less computationaUy demanding than methods involving potential energy functions (free energy interactions may be analyzed using static crystal structures, for example), and, unlike free energy perturbation methods, they are not dependent on traditional potential energy functions or molecular dynamics configurational sampling. These empirical free energy function approaches may offer some promise for improved ligand-receptor binding free energy estimates, though they too are subject to inaccuracies in calibration of the functions. Improved calculation of ligand-binding energies may also be possible through a combined use of PoissonBoltzmann electrostatics calculations with systematic conformational search, as has been reported recently for several systems [18°,19]. This approach ensures that all significant conformational states are sampled via a systematic variation of all important rotatable bonds in the molecules, and utilizes Poisson-Boltzmann electrostatics calculations (which have proven to be quite reliable for energetic calculations in many examples) to compute relative energies. This approach is quite computationally expensive, however, and offers little advantage over free energy perturbation methods in that regard. Furthermore, this approach has been tested for only a few systems thus far, so it is not clear whether it will constitute a general strategy for calculating the free energy of formation of ligand-receptor complexes. Docking programs Many recent modeling programs entail some type of docking strategy for ligand discovery and design. Some programs consider only structural or steric aspects of ligand-receptor interactions, whereas others also include chemical complementarity [20°]. These algorithms attempt to match or fit a candidate ligand to a target binding site. They may be used to locate plausible binding pockets in a protein for a specific ligand, but more often in rational drug-design projects are used to screen a database of small molecules to identify those that best fit a target receptor site. This approach has yielded a number of documented successes in lead compound discovery [21°°,22], but there are still a number of weaknesses with general docking methods. For example, some recent crystal structures reveal that successful lead compounds identified by docking searches bind rather differently to the target receptor than predicted by the docking programs [23]. All docking strategies incorporate some type of scoring scheme to rank the quality of the ligand-receptor site complexes they discover. At present, results using these scoring schemes do not correlate well with experimental relative binding affinities. For example, the best-binding molecule from a series of compounds (as determined by experimental measurements) will frequently not receive the top score in a docking search, though it is often among the top candidates. Much work currently focuses on the ira- provement of scoring schemes, but it seems likely that other factors, such as the rigid molecule approximation or lack of structural refinement for docked complexes, may contribute substantially to the problem, and thus the scoring scheme itself (which is usually some sort of simplified potential energy function) may not be seriously flawed. Several issues have been addressed recently in order to improve docking algorithms. Some docking programs now incorporate an energy minimization procedure to facilitate the identification of complexes with good geometries and plausible ligand-receptor interactions, with some apparent improvement of results [24]. Most docking algorithms at present utilize rigid ligand and receptor-site geometries; in the best of cases this is a serious oversimplification, but inclusion of flexibility via a minimization or molecular dynamics method or via a systematic conformational search procedure has been computationally unfeasible for most problems. Recently, new docking programs that include at least limited conformational flexibility have started to appear. A number of methods now consider conformational flexibility for the ligands. In some cases, ligand flexibility is incorporated via inclusion of multiple conformers of flexible ligands in databases [25°,26]. During the docking search, each unique conformer is considered as a separate molecule. Although effective, this approach requires that all relevant conformations for all molecules of interest be identified and archived in the data base before docking searches. Some approaches utilize Monte Carlo and/or multiple copy simultaneous search techniques to enhance orientational sampling and incorporate ligand flexibility [27,28°,29]. Multiple copy simultaneous search involves placement of numerous copies of the ligand in the binding pocket, each with a different initial orientation and position. Then, an energy minimization or molecular dynamics calculation can be used to optimize the interactions of each copy of the ligand with the receptor simultaneously. During the energy calculations, the nmltiple copies of ligand do not interact with each other, and thus produce no adverse effects in the calculation. As each copy of the ligand is positioned differently at the beginning of the calculations, this method should provide a much more thorough exploration of all possible binding modes than could be obtained by conventional energy-calculation methods. Monte Carlo and multiple copy simultaneous search techniques exact much greater computational requirements than simple rigid molecule docking methods, but can be more effective for large, flexible ligands such as peptides [28°,29]. Several recent approaches also include at least limited flexibility for the receptor site as well as for the ligand. In one recent study, limited receptor-site flexibility was included via systematic variation of amino acid side chains over a tabulated set of allowable conformations, with standard systematic conformational search 226 Theory and simulation used to introduce ligand flexibility [30"]. In another method, systematic conformational search is used to account for ligand flexibility, with energy minimization of candidate docked complexes to permit receptor site flexibility [31°,32",33]. To offset the enormous increase in computational effort required by this strategy, trial ligand-binding orientations are prescreened to select complexes that maximize hydrogen-bonding contacts before the energy minimization calculation is initiated. This approach has yielded some impressive results in several tests for receptor sites with significant polar character [31°,32"]. As pointed out recently by Blaney and Dixon [20°], however, this approach will probably be less effective for docking predominantly non-polar ligands and receptor sites, as prescreening based on hydrogen-bonding contacts or other polar interactions tends to de-emphasize steric and hydrophobic complementarity too much. An alternative strategy to include ligand flexibility involves docking of molecular fragments independently to the receptor site, with subsequent (re)connection of the fragments to form the full ligand. This strategy has been used for a number of years in many different contexts. It has the additional advantage that it can be extended to become a de novo rational ligand design procedure; functional groups and other assorted molecular fragnmnts are placed at logical positions in the receptor site and appropriate connector fragments are chosen to link all pieces together in a contiguous ligand. The CAVEAT [34] and H O O K [35] programs are typical recent examples of this pseudo-docking/ligand design strategy. Another serious weakness in current scoring schemes for most ligand-docking programs is the complete neglect of different solvation (desolvation) energies for various ligands. Within a closely related series ofligand molecules, the intrinsic ligand-receptor interactions may be nearly indistinguishable, although the solvation energies vary more significantly. In such cases, the ease of desolvation for the various ligand molecules may play the major role in governing relative ligand-binding affinity. A variety of methods are available to estimate solvation energies for small molecules. In principle, it is possible to calculate relative solvation energies for a series of ligands using the free energy perturbation techniques discussed above. These calculations are extremely computationally intensive, however, and in some cases may not yield completely rehable results for ligands with subtle solvation differences [19]. In recent years, many ab initio and semiempirical quantum mechanical methods have been modified to permit solvation energy calculations by the use of continuum solvent models, that is, the solvent is represented as a continuous, homogeneous medium rather than as numerous explicit solvent molecules [36]. These quantum mechanical approaches are also computationally expensive when many ligands are to be considered, and in some cases solvation energy estimations are based on reaction field methods, thus limiting their applicability to polar molecules (non-polar solute molecules, i.e. molecules without permanent dipole moments, do not induce dipoles in the solvent medium, and thus do not give rise to a reaction field that interacts with the solute). Much work has also focused in recent years on the development o f empirical functions based on accessible surface area and/or molecular volume to estimate solvation energies. Encouraging results have been obtained recently with continuum methods that use Poisson-Boltzmann calculations to deal with electrostatic effects, and molecular surface or volume terms to account for non-polar contributions to solvation energy [37",38]. These methods are much more efficient computationally than methods that include explicit solvent molecules or quantum mechanical reaction field techniques, and appear to provide quite accurate and reliable results for many classes of small molecules. The computational efficiency and reliability of these new methods may make them suitable for incorporation in general ligand docking scoring schemes. In addition to these specific hmitations in docking scoring schemes and algorithms, there are other areas where important improvements have been made over the past year. Nearly all docking algorithms rely at least in part on some type o f surface matching operation to position ligands in binding sites, and to evaluate the quality of the steric fit. Accurate representation of molecular geometries and shapes can sometimes require a large number of surface points, which in turn increases the computational requirements for docking algorithms significantly. More efficient molecular surface representations based on limited numbers of surface points have been refined [39]. Improved algorithms to identify good surface matches have also been reported recently [40,41]. These enhancements in surface representation and surface matching can be coupled with other docking algorithm enhancements discussed above to yield better performance and reliability. Conclusions Significant advances have been made in ligand-docking methods and other rational drug-design tools over the past year or so. Advances in computer hardware capabilities must also be considered. It is now possible to obtain affordable desktop workstations whose processor power rivals that of many supercomputers. As a result, individual researchers can now perform detailed energy calculations or ligand-docking sinmlations with complex surface representations and full flexibihty, tasks that were practical a few years ago only with extensive access to large supercomputers. It is surely not a coincidence that the number of 'successful' rational drug (or, more accurately, lead compound) design efforts reported in the literature and at conferences seems to have increased noticeably over the past year. Although there is still nmch room for improvement in methods and software, it appears that computer-aided rational drug design is poised to make far more significant contributions to drug development than has been possible in the past. Ligand-protein dockingand rational drug design Lybrand 227 References and recommended reading 17. Papers of particular interest, published within the annual period of review, have been highlighted as: • of special interest •o of outstanding interest 18. • 1. Johnson MS, Srinivasan N, Sowdhamini R, Blundell TL: Knowledge-based protein modeling. Crit Rev Biochem Mol Biol 1994, 29:1-68. 2. Kontoyianni M, Lybrand TP: Three-dlmensional models for integral membrane proteins: possibilities and pitfalls. Perspect Drug Discov Des 1993, 1:291-300. 3. Guida w e : Software for structure-based drug design. Curr Opin Struct Biol 1994, 4:777-781. 4. Spel[meyer DC, Swope WC: The effect of workstation technology on methods in drug design and discovery. Perspect Drug Discov Des 1993, 1:359-370. 5. Whittle PJ, Blundell TL: Protein structure-based drug design. Annu Rev Biophys Biomol Struct 1994, 23:349-37S. 6. Olson AJ, Morris GM: Seeing our way to drug design. Perspect Drug Discov Des 1993, 1:329-344. 7. • Lesyng B, McCammon JA: Molecular modeling methods: basic techniques and challenging problems. Pharmacol Ther 1993, 60:149-167. This paper provides a succint overview of a number of computational techniques based on potential energy functions that are used routinely in rational drug-design applications. 8. Wendoloski Jl, Shen J, Oliva MT, Weber PC: Biophysical lools for structure-based drug design. Pharmacol Ther 1993, 60:169-183. 9. 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Biophys Chem 1994, 50:237-248. 14. • Gilson MK, Straatsma TP, McCammon JA, Ripoll DR, Faerman CH, Axelson PH, Silman I, Sussman JL: "Open back door" in a molecular dynamics simulation of acetylcholinesterase. Science 1994, 263:1276-1278. This paper illustrates nicely the power of computational methods in the study of ligand-protein complexes. Using Poisson-Boltzmann electrostatics calculations and molecular dynamics simulations, the authors propose the presence of an alternative entrance to the active site of acetylcholinesterase, and suggest a mutation to test this hypothesis. Results from enzyme kinetics studies of acetylcholinesterase are better understood in the context of this alternative active-site entrance, and it is unlikely than an alternative active-site entrance would have been considered without the molecular modeling results. 15. DiNola A, Roccatano D, Berendsen HJC: Molecular dynamics simulation of the docking of substrates to proteins. Proteins 1994, 19:174-182. 16. Krystek S, Stouch T, Novotny J: Affinity and specificity of serine endopeptidase protein inhibitor interactions - - empirical free energy calculations based on X-ray crystallographic structures. J Mol Biol 1993, 234:661-679. Aqvist J, Medina C, Samuelsson JE: New method for predicling binding affinity in computer-aided drug design. Protein Eng 1994, 7:385-391. Zacharias M, Luty BA, Davis ME, McCammon JA: Combined conformational search and finite-difference Poisson-Boltzmann approach for flexible docking: application to an operator mutation in the ~. repressor-operator complex. ] Mo/ Bio/ 1994, 238:455--465. These authors report on the use of systematic conformational search and Poisson-Boltzmann electrostatics calculations to compute energies of complex formation for the lambda repressor with its DNA operator sequence. This is one of the first examples of the application of this technique to estimate energies of complex formation. 19. Ewing TJA, Lybrand TP: A comparison of perturbation methods and Poisson-Boltzmann electrostatics calculations for estimation of relative solvation free energies. J Phys Chem 1994 98:1748-1752. Blaney JM, Dixon JS: A good ligand is hard to find: automated docking methods. Perspect Drug Discov Des 1993, 1:301-319. This manuscript is an excellent recent review of ligand-docking techniques now in common use. Different algorithms are described, and the strengths and limitations of docking methods are discussed. 20. 21. "• Ring CS, Sun E, McKerrow JH, Lee GK, Rosenthal PJ, Kuntz ID, Cohen FE: Structure-based inhibitor design by using protein models for the development of antiparasitic agents. Proc Natl Acad Sci USA 1993, 90:3583-3587. Standard ligand-docking methods were used to probe the Fine Chemicals Directory database for candidate inhibitors of cercarial elastase from schistosomes and cysteine protease from malarial trophozoite. An inhibitor of modest affinity was identified for each enzyme, serving as a suitable lead compound for further development in each case. This paper nicely illustrates the power of homology model building (for construction of the receptor-site models) and ligand docking, coupled with classic enzymology, to discover novel enzyme inhibitors with therapeutic potential. 22. Bodian DL, Yamasaki RB, Buswell RL, Stearns JF, White JM, Kuntz ID: Inhibition of the fusion-inducing conformational change of influenza hemagglutinin by benzoquinones and hydroquinones. Biochemistry 1993, 32:2967-2978. 23. Rutenber E, Fauman EB, Keenan RJ, Fong S, Furth PS, DeMontellano PRO, Meng E, Kuntz ID, DeCamp DL, Salto R et al.: Structure of a non-peplide inhibitor complexed with HIV-1 protease. Developing a cycle of structure-based drug design. J Biol Chem 1993, 268:15343-15346. 24. Meng EC, Gschwend DA, Blaney JM, Kuntz ID: Orientational sampling and rigid-body minimization in molecular docking. Proteins 1993, 17:266-278. Miller MD, Kearsley SK, Underwood DJ, Sheridan RP: FLOG: a system to select 'quasi-flexible' ligands complementary to a receptor of known three-dimenslonal structure. J Comput Aided Mol Des 1994, 8:153-174. This paper describes the use of databases containing multiple conformations of a particular ligand as a simple strategy to address ligand flexibility in standard docking algorithms. 2S. • 26. Kearsley SK, Underwood DJ, Sheridan RP, Miller MD: Flexibases: a way to enhance the use of molecular docking methods. J Comput Aided Mol Des 1994, 8:565-$82. 27. Lunney EA, Hagen SE, Domagala JM, Humblet C, Kosinski J, Tait BD, Warmus JS, Wilson M, Ferguson D, Hupe D et al.: A novel nonpeptide HIV-1 protease inhibitor: elucidation of the binding mode and its application in the design of related analogs. J Med Chem 1994, 37:2664-2677. Rosenfeld R, Zheng Q, Vajda S, DeLisi C: Computing the struclure of bound peptides - - application to antigen recognition by class-I major histocompatibility complex receptors. J Mol Biol 1993, 234:515-521. Multiple copy simultaneous search techniques are used to identify the optimal binding mode for peptide ligand in a class I major histocompatibility complex receptor. In this study, the receptor is fixed in a conformation that was observed crystallographically, and multiple peptide fragments were docked simultaneously. 28. • 228 Theory and simulation 29. Sezerman U, Vajda S, Cornette J, DeLisi C: Toward computational determination of peptide-receptor structure. Protein 5ci ~993, 2:1827-1843. 36. 30. LeachAR: Ligand docking to proteins with discrete side chain 37. • • flexibility. J Mol Biol 1994, 235:345-356. This work incorporates both ligand flexibility (via a systematic conformational search) and limited receptor flexibility via sampling of allowable side-chain conformations of amino acids in the binding pocket. This strategy affords a reasonably efficient mechanism to include modest flexibility in a traditional ligand-protein docking study. Yamada N, Itai A: Development of an efficient automated docking method. Chem Pharm Bull (Tokyo) 1993, 41:1197-1202. See annotation [32"], 31. , Yamada N, Itai A: Application and evaluation of the automated docking method. Chem Pharm Bull (Tokyo) 1993, 41:1203-1206. This paper and [31"] describe an alternative approach to account for ligand and receptor flexibility in a docking search. This method uses a systematic conformational search for the ligand, then prescreens docked ligand-receptor complexes for those with optimal hydrogen-bonded contacts before energy minimization of the complex is performed to allow for flexibility of the receptor site. 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Protein Eng 1994, 7:39--46. 41. Helmer-Citterich M, Tramontano A: PUZZLE: a new method for automated protein docking based on surface complementarity. J Mol Biol 1994, 235:1021-1031. 32. • 33. Mizutani MY, Tomioka N, Itai A: Rational automatic search method for stable docking models of protein and ligand, j Mol Biol 1994, 243:310-326. 34. Lauri G, Bartlett PA: CAVEAT: a program to facilitate the design of organic molecules. J Comput Aided/viol Des 1994, 8:51-66. 35. Eisen MB, Wiley DC, Karplus M, Hubbard RE: HOOK: a program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecular binding site. Proteins 1994, 19:199-221. Cramer CJ, Truhlar DG (Eds): Structure and reactivity in aqueous solution. ACS Symposium Series 568. Washington DC: American Chemical Society; 1994. TP Lybrand, University of Washington, Molecular Bioengineering Program, BF-10, Seattle, WA 98195, USA. E-mail: [email protected]