Molecular recognition is a highly interdependent process. Subsite couplings within the active site of proteases are most often revealed through conditional amino acid preferences in substrate recognition. However, the potential effect of these couplings on inhibition and thus inhibitor design is largely unexplored. The present study examines the interdependency of subsites in HIV-1 protease using a focused library of protease inhibitors, to aid in future inhibitor design. Previously a series of darunavir (DRV) analogs was designed to systematically probe the S1′ and S2′ subsites. Co-crystal structures of these analogs with HIV-1 protease provide the ideal opportunity to probe subsite interdependency. All-atom molecular dynamics simulations starting from these structures were performed and systematically analyzed in terms of atomic fluctuations, intermolecular interactions, and water structure. These analyses reveal that the S1′ subsite highly influences other subsites: the extension of the hydrophobic P1′ moiety results in 1) reduced van der Waals contacts in the P2′ subsite, 2) more variability in the hydrogen bond frequencies with catalytic residues and the flap water, and 3) changes in the occupancy of conserved water sites both proximal and distal to the active site. In addition, one of the monomers in this homodimeric enzyme has atomic fluctuations more highly correlated with DRV than the other monomer. These relationships intricately link the HIV-1 protease subsites and are critical to understanding molecular recognition and inhibitor binding. More broadly, the interdependency of subsite recognition within an active site requires consideration in the selection of chemical moieties in drug design; this strategy is in contrast to what is traditionally done with independent optimization of chemical moieties of an inhibitor.

Citrullination converts peptidyl-arginine to peptidyl-citrulline and is mediated by the enzymes peptidyl arginine deiminases (PADs), including PAD1–4 and PAD6. The physiological role of citrullination in immune cells is poorly understood. Here we report that suppression of PAD activity attenuates TLR-induced expression of IL-1β and TNFα by neutrophils in vivo and in vitro but not their global transcription activity. Mechanistically, PAD4 directly citrullinates NF-kB p65 and enhances the interaction of p65 with importin α3, which brings p65 into the nucleus. The citrullination-enhanced interaction of p65 with importin α3, and its nuclear translocation and transcriptional activity can be attributed to citrullination of four arginine residues located in the Rel homology domain of p65. Furthermore, a rheumatoid arthritis-prone variant of PAD4, carrying three missense mutations, is more efficient in interacting with p65 and enhancing NF-kB activity. Together, these data demonstrate a critical role of citrullination in NF-kB-dependent expression of IL-1β and TNFα.

Under the selective pressure of therapy, HIV-1 protease mutants resistant to inhibitors evolve to confer drug resistance. Such mutations can impact both the dynamics and structures of the bound and unbound forms of the enzyme. Flap+ is a multidrug-resistant variant of HIV-1 protease with a combination of primary and secondary resistance mutations (L10I, G48V, I54V, V82A) and a strikingly altered thermodynamic profile for darunavir (DRV) binding relative to the wild-type protease. We elucidated the impact of these mutations on protein dynamics in the DRV-bound state using molecular dynamics simulations and NMR relaxation experiments. Both methods concur in that the conformational ensemble and dynamics of protease are impacted by the drug resistance mutations in Flap+ variant. Surprisingly this change in ensemble dynamics is different from that observed in the unliganded form of the same variant (Cai, Y. et al. J. Chem. Theory Comput.2012, 8, 3452–3462). Our comparative analysis of both inhibitor-free and bound states presents a comprehensive picture of the altered dynamics in drug-resistant mutant HIV-1 protease and underlies the importance of incorporating dynamic analysis of the whole system, including the unliganded state, into revealing drug resistance mechanisms.

Oncogenic transcriptional coregulators C-terminal Binding Protein (CtBP) 1 and 2 possess regulatory D-isomer specific 2-hydroxyacid dehydrogenase (D2-HDH) domains that provide an attractive target for small molecule intervention. Findings that the CtBP substrate 4-methylthio 2-oxobutyric acid (MTOB) can interfere with CtBP oncogenic activity in cell culture and in mice confirm that such inhibitors could have therapeutic benefit. Recent crystal structures of CtBP 1 and 2 revealed that MTOB binds in an active site containing a dominant tryptophan and a hydrophilic cavity, neither of which are present in other D2-HDH family members. Here we demonstrate the effectiveness of exploiting these active site features for design of high affinity inhibitors. Crystal structures of two such compounds, phenylpyruvate (PPy) and 2-hydroxyimino-3-phenylpropanoic acid (HIPP), show binding with favorable ring stacking against the CtBP active site tryptophan and alternate modes of stabilizing the carboxylic acid moiety. Moreover, ITC experiments show that HIPP binds to CtBP with an affinity greater than 1000-fold over that of MTOB and enzymatic assays confirm that HIPP substantially inhibits CtBP catalysis. These results, thus, provide an important step, and additional insights, for the development of highly selective antineoplastic CtBP inhibitors.

Developing tools to accurately predict the clinical prevalence of drug-resistant mutations is a key step toward generating more effective therapeutics. Here we describe a high-throughput CRISPR-Cas9-based saturated mutagenesis approach to generate comprehensive libraries of point mutations at a defined genomic location and systematically study their effect on cell growth. As proof of concept, we mutagenized a selected region within the leukemic oncogene Using bulk competitions with a deep-sequencing readout, we analyzed hundreds of mutations under multiple drug conditions and found that the effects of mutations on growth in the presence or absence of drug were critical for predicting clinically relevant resistant mutations, many of which were cancer adaptive in the absence of drug pressure. Using this approach, we identified all clinically isolated BCR-ABL1 mutations and achieved a prediction score that correlated highly with their clinical prevalence. The strategy described here can be broadly applied to a variety of oncogenes to predict patient mutations and evaluate resistance susceptibility in the development of new therapeutics.