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Interface of Evolution and Structure Based Drug Design

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Constraining evolution and avoiding drug resistance

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Drug resistance occurs when, through evolution, a disease no longer responds to medications. Resistance impacts the lives of millions, limiting the effectiveness of many of our most potent drugs. This often happens under the selective pressure of therapy in bacterial, viral and fungal infections and cancer due to their rapid evolution.

We combine a variety of experimental and computational techniques to understand the molecular basis of drug resistance. Our new paradigm of drug design minimizes chances of resistance. Realizing that disrupting the drug target’s activity is necessary but not sufficient for developing a robust drug that avoids resistance.

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Research Focus

Strategies and Systems


We use multidisciplinary approaches, combining crystallography, enzymology, molecular dynamics and organic chemistry, to elucidate the molecular mechanisms of drug resistance. Resistance occurs when a heterogeneous populations of a drug target is challenged by the selective pressure of a drug. In cancer and viruses this heterogeneity is partially caused APOBEC3’s. We discovered resistance mutations occur either where drugs physically contact regions of the drug target that are not essential for substrate recognition or alter the ensemble dynamics of the drug target favoring substrate. We leverage these insights into a new strategies in structure-based drug design to minimize the likelihood for resistance by designing inhibitors to stay within the substrate envelope. This strategy not only describes most of the primary drug resistance for HIV, Hepatitis C viral protease inhibitors and influenza neuraminidase, but is generally applicable in the development of novel drugs that are less susceptible to resistance.

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  • Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

    Author(s): Whitfield TW, Ragland DA, Zeldovich KB, Schiffer CA
    Related Articles

    Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

    J Chem Theory Comput. 2020 Jan 16;:

    Authors: Whitfield TW, Ragland DA, Zeldovich KB, Schiffer CA

    Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few closely related systems has guided our understanding of the mechanisms by which changes such as ligand binding or mutation can alter the function. The general problem of detecting and interpreting such mechanisms from simulations of many related systems, however, remains a challenge. This problem is addressed here by applying supervised and unsupervised machine learning techniques to a variety of thermodynamic observables extracted from molecular dynamics simulations of different systems. As an important test case, these methods are applied to understand the evasion by human immunodeficiency virus type-1 (HIV-1) protease of darunavir, a potent inhibitor to which resistance can develop via the simultaneous mutation of multiple amino acids. Complex mutational patterns have been observed among resistant strains, presenting a challenge to developing a mechanistic picture of resistance in the protease. In order to dissect these patterns and gain mechanistic insight into the role of specific mutations, molecular dynamics simulations were carried out on a collection of HIV-1 protease variants, chosen to include highly resistant strains and susceptible controls, in complex with darunavir. Using a machine learning approach that takes advantage of the hierarchical nature in the relationships among the sequence, structure, and function, an integrative analysis of these trajectories reveals key details of the resistance mechanism, including changes in the protein structure, hydrogen bonding, and protein-ligand contacts.

    PMID: 31877249 [PubMed - as supplied by publisher]

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Lazare Research Building 828
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508-856-8008 (office)


Mailing Address:
University of Massachusetts Medical School
Attn: Dr. Celia Schiffer/BMP department
364 Plantation St LRB828
Worcester, MA 01605

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We are always interested in applications from qualified candidates at postdoctoral and research associate levels.

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Undergraduates interested in pursuing a PhD at UMass Medical School should apply directly to the Graduate School of Biomedical Sciences Program.