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Constraining evolution → avoiding drug resistance: lessons from viruses
Author(s) -
Schiffer Celia
Publication year - 2020
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2020.34.s1.00171
Subject(s) - computational biology , proteases , drug resistance , drug discovery , biology , protease , drug , genetics , function (biology) , genome , gene , bioinformatics , enzyme , biochemistry , pharmacology
Drug resistance negatively impacts the lives of millions of patients and costs our society billions of dollars by limiting the longevity of many of our most potent drugs. Drug resistance occurs when heterogeneity exists within a genome and selection weakens inhibitor binding but maintains the biological function of the target. Genome diversity occurs both from transcriptional infidelity and the specific activity of human APOBEC3’s. To avoid drug resistance, the structure at atomic resolution and evolutionarily constraints on its variation is required. This rationale was derived from our lab’s experience with substrate recognition both of human APOBEC3s and of antiviral drug targets. In particular we have acquired a rich and versatile experimental dataset of viral proteases altered by the selective pressures of inhibitors. With this data we are integrating alterations in both the protein sequence and the inhibitor with changes in potency and we correlate this data to our co‐crystal structures and our strategy of parallel molecular dynamics (pMD) to elucidate specificity and the molecular mechanisms of drug resistance. pMD is a strategy we have developed to collectively analyze a series of MD simulations of similar yet distinct molecular complexes to decipher conformational and dynamic differences responsible for changes in molecular recognition. We perform pMD simulations on complexes with varying protein sequence and ligand identity to unravel structural and dynamic properties that underlie coupled changes in molecular recognition and resistance. We have applied this pMD strategy to protein‐ligand complexes for series of natural substrates, inhibitors, and protease mutations. Combined with experimental potency data, we determine which physical properties or molecular interactions are key to molecular recognition for a given system. These changes in structure and dynamics dictate the interdependency of molecular mechanism of resistance and areprincipals that are generally applicable to other quickly evolving diseases where drug resistance is quickly evolving. Support or Funding Information NIH P01 GM109767, R01 AI085051, R01AI150478