The development of new approaches to treat rapidly evolving viral infections is an important area of research. This project will pursue general methods for the development of drug therapies that lead to inhibitors that avoid resistance in the context of AIDS, yet broadly applicable to infectious disease and cancer. We will continue to develop and improve the substrate envelope hypothesis, will develop and apply inverse computational design methods for small-molecule ligands, will study failures of the substrate envelope hypothesis to improve the approach, and will develop alternative approaches related to the substrate envelope hypothesis in the context of HIV-1 protease through a collaborative effort with experimental groups expert in organic and medicinal chemistry, enzyme assays, protein crystallography, and virology. Because essentially all therapies developed for infectious disease and cancer are limited by the selection of resistant variants, strategies for avoiding or a least delaying resistance that are generally applicable would have tremendous impact on drug development. This project will understand and improve the substrate envelope and related approaches in the well studied HIV-1 protease, but the methods developed will be broadly applicable to target-based infectious disease and cancer drug resistance. The substrate envelope hypothesis maintains that inhibitors that reside within the volume shared by substrates are less susceptible to resistance mutations, because such mutants must still process substrates. Preliminary work has demonstrated some success and shown some limitations of the substrate envelope hypothesis, and the proposed project will further test and develop the substrate envelope hypothesis in the context of HIV-1 protease. Extensions of the substrate envelope hypothesis include other modes of being substrate-like besides the geometric criteria of occupying the common substrate volume. Our previous work includes successes and failures of the substrate envelope hypothesis. The failures are molecules we designed that when synthesized bind wholly within the substrate envelope but fail to bind robustly across a panel of drug-resistant variants. By studying these molecules and substrates bound to wild type and drug-resistant variants, we will seek a mechanistic understanding of failures of the substrate envelope hypothesis, which will use to develop improved versions of the substrate envelope hypothesis. Finally, advances in deep sequencing technology make it possible to consider mapping the functional mutational space of candidate targets, and using the functional mutational space as a guide to the development of inhibitors that are not susceptible to resistance mutations. This project will develop and study methodology for using the functional mutational space as a basis for the design of inhibitors that avoid resistance. We will compare the success of this approach to the substrate envelope approach, noting that the new approach is applicable to targets whether they are enzymes or not, whereas the current state of the substrate envelope hypothesis is applicable to enzymes only.

Public Health Relevance

Current medical drug therapy for infectious disease and cancer is limited by the emergence of resistance, in which a previously effective therapy loses its effectiveness, often through mutations in the target. This project aims to study methods for developing new therapies that prevent, or at least significantly delay, the emergence of resistance. Initial work will target the HIV protease, which is the target of some current therapies, but for which the emergence of resistant strains remains a significant problem.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM082209-08
Application #
9280974
Study Section
Special Emphasis Panel (ZRG1-MSFD-N (08)F)
Program Officer
Sakalian, Michael
Project Start
2007-08-01
Project End
2018-05-31
Budget Start
2017-06-01
Budget End
2018-05-31
Support Year
8
Fiscal Year
2017
Total Cost
$432,533
Indirect Cost
$85,407
Name
Massachusetts Institute of Technology
Department
Type
Schools of Engineering
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02142
Bonk, Brian M; Tarasova, Yekaterina; Hicks, Michael A et al. (2018) Rational design of thiolase substrate specificity for metabolic engineering applications. Biotechnol Bioeng 115:2167-2182
Zhao, Boyang; Sedlak, Joseph C; Srinivas, Raja et al. (2016) Exploiting Temporal Collateral Sensitivity in Tumor Clonal Evolution. Cell 165:234-246
Vincent, Benjamin M; Langlois, Jean-Baptiste; Srinivas, Raja et al. (2016) A Fungal-Selective Cytochrome bc1 Inhibitor Impairs Virulence and Prevents the Evolution of Drug Resistance. Cell Chem Biol 23:978-991
Traxlmayr, Michael W; Kiefer, Jonathan D; Srinivas, Raja R et al. (2016) Strong Enrichment of Aromatic Residues in Binding Sites from a Charge-neutralized Hyperthermostable Sso7d Scaffold Library. J Biol Chem 291:22496-22508
Shen, Yang; Radhakrishnan, Mala L; Tidor, Bruce (2015) Molecular mechanisms and design principles for promiscuous inhibitors to avoid drug resistance: lessons learned from HIV-1 protease inhibition. Proteins 83:351-72
Shen, Yang; Altman, Michael D; Ali, Akbar et al. (2013) Testing the substrate-envelope hypothesis with designed pairs of compounds. ACS Chem Biol 8:2433-41
Silver, Nathaniel W; King, Bracken M; Nalam, Madhavi N L et al. (2013) Efficient Computation of Small-Molecule Configurational Binding Entropy and Free Energy Changes by Ensemble Enumeration. J Chem Theory Comput 9:5098-5115
Nalam, Madhavi N L; Ali, Akbar; Reddy, G S Kiran Kumar et al. (2013) Substrate envelope-designed potent HIV-1 protease inhibitors to avoid drug resistance. Chem Biol 20:1116-24
King, Bracken M; Silver, Nathaniel W; Tidor, Bruce (2012) Efficient calculation of molecular configurational entropies using an information theoretic approximation. J Phys Chem B 116:2891-904
Shen, Yang; Gilson, Michael K; Tidor, Bruce (2012) Charge Optimization Theory for Induced-Fit Ligands. J Chem Theory Comput 8:4580-4592

Showing the most recent 10 out of 18 publications