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Making Virtual Screening a Reality Making virtual screening a reality John T. Koh* Department of Chemistry and Biochemistry, University of Delaware, Newark, DE 19716 he discovery of new bioactive interactions for the target receptor, the to evaluate their methods in identifying compounds for specific biomo- complexity of the problem is in reality new thyroid hormone receptor (TR) lecular targets represents a sig- far greater. For example, the ligand and antagonists. nificant hurdle in the early the receptor may exist in a different set Although the structure of the TR Tstages of drug discovery. Advances in of conformations when in free solution bound to its natural agonist triiodothy- automation and bioanalytical methods than when bound. The entropy of the ronine (T3) is known (9), antagonists of have provided high-throughput screen- unassociated ligand and receptor is gen- TR that may be useful for the treatment ing (HTS) techniques that can perform erally higher than that of the complexes, of hyperthyroidism are believed to bind individual biochemical assays on as and favorable interactions with water TR in a different but related conforma- many as a million compounds or more. are lost on binding. These energetic tion. By structural analogy to known Even with HTS, the discovery of new costs of association must be offset by antagonist-bound structures of related lead compounds largely remains a mat- the gain of favorable intermolecular hormone receptors, a hypothetical ter of trial and error. Although the protein–ligand interactions. The magni- model of the antagonist-bound form of number of compounds that can be eval- tude of the energetic costs and gains is the receptor was created through molec- uated by HTS methods is seemingly typically much larger than their differ- ular modeling (10). This model was used large, these numbers are small in com- ence, and, therefore, potency is ex- to virtually screen drug-like molecules parison to the astronomical number of tremely difficult to predict even when from the available chemicals directory possible molecular structures that might (ACD) as potential TR antagonists. The represent potential drug-like molecules selected candidate compounds could be (1). Often, far more compounds exist or further narrowed to just a few hundred can be synthesized by combinatorial The ability to design compounds by using virtual screening to methods than can be reasonably and eliminate the compounds that bind the affordably evaluated by HTS. As the a molecule to bind, agonist-bound form of the receptor. Of costs of computing decreases and as 75 analogs that were purchased and computational speeds increase, many inhibit, or activate a evaluated in cell-based assays, 14 (19%) researchers have directed efforts to de- biomolecular target showed low micromolar antagonist activ- velop computational methods to per- ity, far more than would typically be form ‘‘virtual screens’’ of compounds remains challenging. obtained from random-based screening (2–4). Because the cost of performing or screening methods that only consider screens in silico can be faster and less the structure of known ligands. Future expensive than HTS methods, virtual studies will ultimately determine screening methods may provide the key relative errors are small. Although sev- whether such high hit rates are general to limit the number of compounds to eral methods have been developed to for different receptor types. However, be evaluated by HTS to a subset of more accurately predict the strength of even hit rates below 1% can represent a molecules that are more likely to yield molecular association events by account- substantial savings of time and costs for ‘‘hits’’ when screened. For the practical ing for entropic and solvation effects (6, lead identification compared with blind advantages of virtual screening to be 7), these methods are costly in terms of HTS. realized, computational methods must computational time and are inappropri- Several groups have used structure- excel in speed, economy, and accuracy. ate for the virtual screening of large based design to convert known hormone Striking the right balance of these cri- compound databases. The challenge in receptor agonists to antagonists. Be- teria with existing tools presents a for- developing practical virtual screening cause the difference between the midable challenge. In this issue of methods is to develop an algorithm that agonist-bound and unliganded or PNAS, Schapira et al. (5) present an is fast enough to rapidly evaluate poten- antagonist-bound receptor structures inspiring example of structure-based tially millions of compounds while main- primarily involves the displacement of virtual screening applied to a challeng- taining sufficient accuracy to success- helix-12 of nuclear receptors, analogs of ing problem of developing new thyroid fully identify a subset of compounds ligands that bear molecular extensions hormone receptor antagonists when that is significantly enriched in hits. Ac- that prevent helix-12 from adopting an only a related receptor structure is cordingly, structure-based screening agonist-like conformation often behave available. methods typically use a minimalist as antagonists (11). Although this ap- Receptor-based virtual screening uses ‘‘grid’’ representation of the receptor proach has been successfully applied to knowledge of the target protein’s struc- properties and an empirical or semiem- the discovery of other hormone receptor ture to select candidate compounds with pirically derived scoring function to esti- antagonists, including TR, it limits the which it is likely to favorably interact. mate the potency of the bound complex discovery of new antagonists to direct Even when the structure of the target (8). Several programs now employ a analogs of known agonists (12). Virtual molecule is known, the ability to design range of scoring functions, but it is of- screening based on receptor structure a molecule to bind, inhibit, or activate a ten difficult to assess their effectiveness therefore has the distinct advantage of biomolecular target remains a daunting on difficult ‘‘real-world’’ problems. In aiding the discovery of new antagonist challenge. Although the fundamental this study, computational chemists Mat- structural classes or pharmacophores goals of screening methods are to iden- thieu Schapira and Ruben Abagyan tify those molecules with the proper team up with molecular endocrinologists complement of shape, hydrogen bond- and organic chemists from Herbert See companion article on page 7354. ing, and electrostatic and hydrophobic Samuels’ and Stephen Wilson’s groups *E-mail: [email protected]. 6902–6903 ͉ PNAS ͉ June 10, 2003 ͉ vol. 100 ͉ no. 12 www.pnas.org͞cgi͞doi͞10.1073͞pnas.1332743100 Downloaded by guest on October 1, 2021 COMMENTARY that may ultimately yield compounds nists (12). To help improve the potency Virtual screening methods continue to with improved pharmacokinetic proper- of their best hit, Schapira et al. (5) syn- develop in sophistication and accuracy. ties or help in the discovery of leads thesized a series of analogs to optimize These results serve to illustrate that that are not restricted by existing pat- their lead compound by using virtual structure-based virtual screening pro- ents. The 14 compounds identified by screening to select from among the pos- vides a rapid and powerful tool for the virtual screening importantly represent sible compounds that could be synthe- discovery of new bioactive pharmacoph- new TR antagonist pharmacophores; sized directly from commercially avail- ores and may additionally benefit the however, these potential lead com- able reagents. The eight new analogs process of lead optimization, even when pounds have notably lower potency than were all active antagonists, some of the available structure represents an al- antagonists previously identified by ‘‘ex- which showed modest improvement over ternative conformational state of the tension’’ modification of known TR ago- the initial lead. biomolecular target. 1. Bohacek, R. S., McMartin, C. & Guida, W. C. Sci. USA 100, 7354–7359. 10. Schapira, M., Raake, B. M., Samuels, H. H. & (1996) Med. Res. Rev. 16, 3–50. 6. Reynolds, C. A., King, P. M. & Richards, W. G. Abagyan, R. (2000) Proc. Natl. Acad. Sci. USA 97, 2. Bajorath, J. (2002) Nat. Rev. Drug Discovery 1, (1992) Mol. Phys. 76, 251–275. 1008–1013. 882–894. 7. Zhang, L. Y., Gallicchio, E., Friesner, R. A. 11. Apriletti, J. W., Ribeiro, R. C. J., Wagner, R. L., 3. Abagyan, R. & Totrov, M. (2001) Curr. Opin. & Levy, R. M. (2001) J. Comput. Chem. 22, Feng, W., Webb, P., Kushner, P. J., West, B. L., Chem. Biol. 5, 375–382. 591–607. Nilsson, S., Scanlan, T. S., Fletterick, R. J. & 4. Lyne, P. D. (2002) Drug Discovery Today 7, 1047– 8. Schulz-Gasch, T. & Stahl, M. (2003) J. Mol. Model. Baxter, J. D. (1998) Clin. Exp. Pharmacol. Physiol. 1055. 9, 47–57. 25, S2–S11. 5. Schapira, M., Raaka, B. M., Das, S., Fan, L., 9. Wagner, R. L., Apriletti, J. W., McGrath, M. E., 12. Nguyen, N., Apriletti, J. W., Cunha-Lima, S. T., Totrov, M., Zhou, Z., Wilson, S. R., Abagyan, West, B. L., Baxter, J. D. & Fletterick, R. J. (1995) Webb, P., Baxter, J. D. & Scanlan, T. S. (2002) R. & Samuels, H. H. (2003) Proc. Natl. Acad. Nature 378, 690–697. J. Med. Chem. 45, 3310–3320. Koh PNAS ͉ June 10, 2003 ͉ vol. 100 ͉ no. 12 ͉ 6903 Downloaded by guest on October 1, 2021.
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