Computational Biophysics and Molecular Design (Kireev lab) develops and applies computational tools to advance our understanding of complex biological systems and discover therapies for unmet medical needs.
We are involved in multiple translational projects in collaboration with biomedical scientists in academia and small companies. Current and past collaborators include S. Frye (UNC) and Meryx, on inhibitors of protein kinases Axl, Mer and FLT3; P. Blancafort (Univ. West. Australia), on peptide modulators of transcription factors EN1/2; L. Parise (UNC) and Reveris, on antagonists of Calcium- and Integrin-binding protein 1 (CIB1); R. Hromas (U. Florida), on Metnase inhibitors; G. Johnson and L. Graves (UNC) and KinoDyn, on modulation of superenhancers; and with Sirga on small-molecule inhibitors of host tRNA/HIV protein interactions.
The most advanced of our projects focuses on the discovery of new therapeutics to treat Acute Lymphoblastic Leukemia (ALL). ALL is a deadly disease that strikes predominantly children between ages 2-5. It can be treated by a combination of chemo- and radio-therapy, but most survivors suffer from life-long irreversible damage caused by these regimens. Our project started with a rationale that inhibitors of Mer tyrosine kinase, a proto-oncogene ectopically expressed in childhood ALL, would allow to substantially reduce radio- and chemotherapies. From the beginning of the project, we applied computer-aided techniques to guide the synthetic effort, which eventually resulted in highly potent and selective Mer kinase inhibitors. The lead compounds have shown excellent survival benefit in multiple animal models and appropriate safety level in preclinical studies. Clinical trials are expected to start in 2017. The current and future focus of computer-aided design is to further exploit the invented series in order to develop compounds with specific selectivity profiles, such as dual Mer/Axl or dual Mer/Flt3, in order to target new therapeutic indications.
Selected publications and patents:
- Zhang W, DeRyckere D, Hunter D, Liu J, Stashko MA, Minson KA, Cummings CT, Lee M, Glaros TG, Newton DL, Sather S, Zhang D, Kireev D, Janzen WP, Earp HS, Graham DK, Frye SV and Wang X (2014) UNC2025, a potent and orally bioavailable MER/FLT3 dual inhibitor. J Med Chem, 57 (16):7031-7041. doi:10.1021/jm500749d; PMCID: PMC4148167
- Da, C. and Kireev D., Discovery of Mer kinase inhibitors by virtual screening using Structural Protein–Ligand Interaction Fingerprints. Bioorganic & Medicinal Chemistry, 2015. 23(5): p. 1096-1101.
- Zhang, W. H., Zhang, D. H., Stashko, M. A., DeRyckere, D., Hunter, D., Kireev, D., Miley, M. J., Cummings, C., Lee, M., Norris-Drouin, J., Stewart, W. M., Sather, S., Zhou, Y. Q., Kirkpatrick, G., Machius, M., Janzen, W. P., Earp, H. S., Graham, D. K., Frye, S. V., and Wang, X. D. (2013) Pseudo-Cyclization through Intramolecular Hydrogen Bond Enables Discovery of Pyridine Substituted Pyrimidines as New Mer Kinase Inhibitors. Journal of Medicinal Chemistry 56, 9683-9692
- International patent No. WO 2014/062774 A1, filed on April 24, 2014, Pyrazolopyrimidine Compounds for the Treatment of Cancer
- US Patent 20,150,291,606, filed on October 16, 2013, MERTK-specific pyrrolopyrimidine compounds
Novel tools for Computer-Aided Drug Design (CADD)
- Empirical rescoring functions for structure-based VS. Accurate and affordable assessment of ligand-protein affinity for structure-based virtual screening (SB-VS) is a standing challenge in drug discovery. Hence, efficient post-docking filters based upon various types of structure-activity information may prove useful. Recently, we introduced one such filter based upon three-dimensional Structural Protein-Ligand Interaction Fingerprints (SPLIF). SPLIF enables a quantitative score of whether a docking pose interacts with the protein target similarly to a known ligand, which helps to improve both sensitivity and specificity of SB-VS. In our initial study, we evaluated the method performance on 10 public datasets from the DUD-E database. We have also applied SPLIF to several ongoing projects including Mer kinase, EndA (a novel antibiotic target) and Metnase (a lysine methyltransferase and a potential cancer target). Nanomolar to micromolar hits have been identified for these targets. Future directions include methodological improvements, such as parameter optimization and applications of SPLIF as descriptors in QSAR and chemogenomics.
- Data-driven design of bioactive compounds with controlled polypharmacology. The idea of improving biological activity of small-molecule ligands through adding “good” or removing “bad” fragments is central to medicinal chemistry. Over the last decade, this idea has been technologically upgraded with the advent of Fragment-Based Discovery (FBD) fueled by the high-throughput structural biology. While FBD has proven extremely useful in finding ligands for novel targets, where other strategies failed, its use in selectivity optimization is still uncommon, mainly because of a high cost due to multiple targets involved. We are currently developing a novel fragment-based strategy for multitarget lead design, which makes use of data-driven mathematical models to alleviate the experimental effort. The key component of the approach involves FRAgments in Structural Environments (FRASE’s), which can be learned by mining large structural and biochemical databases. We have also developed a computational model that makes use of FRASEs to predict the likelihood of whether a given ligand would be active against a given target. Importantly, the activities can be predicted for “orphan” targets. Eventually, FRASEs can be used as components for building multi-target inhibitors with controlled polypharmacology. The approach was applied to develop inhibitors of closely related immunotherapeutic cancer targets MER, AXL and Tyro3. The efficacy and specificity of the new leads have been investigated and confirmed in multiple biochemical, cellular and animal models, including kinome-wide polypharmacology profiling, acute lymphoblastic leukemia (ALL), melanoma and NSCLC cell lines, as well as a pharmacodynamic study in mice. Structural validity of the approach was corroborated by x-ray crystallography.
- Da C, Kireev D (2014) Structural Protein–Ligand Interaction Fingerprints (SPLIF) for Structure-Based Virtual Screening: Method and Benchmark Study. Journal of Chemical Information and Modeling 54 (9):2555-2561. doi:10.1021/ci500319f
Da, C. and Kireev D., Discovery of Mer kinase inhibitors by virtual screening using Structural Protein–Ligand Interaction Fingerprints. Bioorganic & Medicinal Chemistry, 2015. 23(5): p. 1096-1101.
Simulations of ultra-large biological systems
What if computers were able to reproduce the workings of an entire human cell to molecular resolution? To mention but a few important breakthroughs, entirely new drug-discovery strategies, such as rational optimization of a drug’s pharmacokinetics and pharmacodynamics (through tracking drug concentrations in cell compartments), would become possible. We currently develop a computational approach for simulation of ultra-large biomolecular systems, up to a whole cell.
Our approach, called Molecular Biosystems (MB), combines strengths of physics-based molecular dynamics and data-driven systems biology. In MB, a physics-based core is used to simulate stochastic trajectories of molecules in the cytoplasm, while intermolecular interactions and biochemical reactions are implemented using data-driven algorithms connected to comprehensive biological databases, such as GenBank, Uniprot or ENCODE. Eventually, we expect MB to be able to handle systems involving millions of molecules of hundreds of types. The current MB implementation enables simulations of biological systems consisting of thousands of proteins of multiple types on an hour scale, that is, one billion time steps (equivalent of a 3.5 hour trajectory) per week.
Currently, MB are used to address a challenging question in the fields of epigenetics and regenerative biology. A major challenge in tissue regeneration is securing sustainable sources of pluripotent stem cells. Current techniques are ineffective. The molecular assembly called heterochromatin locks cells in their specialized states. Better strategies could be devised, if the structure of heterochromatin was known. Our focus is on OCT4, a key gene maintaining the cellular pluripotent state. A mechanistic understanding of how OCT4 gets repressed during cell development and differentiation would significantly advance the field of epigenetics and make it one step closer to growing critical human tissues from more abundant cells.
Structural mechanisms of the histone code
A pattern of posttranslational modifications (PTMs) on disordered histone tails, called the histone code, is a key component in regulation of gene expression. Hence, histone effectors – signaling proteins recruited by the histone PTMs – are of particular interest as potential therapeutic targets in cancer and other diseases. The human genome features hundreds of putative effectors. Most of these effectors are multi-domain proteins. They can interact with the cognate histones and other proteins in myriad of ways, which remain largely unknown. Because of their large sizes (up to 2,000 aa) and flexibility, the histone effectors are extremely challenging for structural biology approaches. We have previously shown that computational tools in combination with biochemical and biophysical experiments are able (i) to provide a detailed, time-resolved picture useful for understanding the mechanisms of the histone code and for motivating new experiments, and (ii) to guide the design of chemical probes that would switch off the effector proteins in cell and in vivo to more efficiently study their biological function and role in disease.
Our early work focused on understanding the molecular recognition between the Malignant Brain Tumor (MBT) domain, a methyllysine effector, and methylated histone substrates. In 2010, we performed a multidisciplinary study that involved computational modeling and design, chemical synthesis and biophysical experiments. In this study, we designed minimal small-molecule MBT binders and calculated their binding free energies using Free Energy Perturbation. Since the calculated free energies were corroborated by Isothermal Calorimetry measurements, we could reasonably assume that we can also use the simulations to decipher the structural details of histone-effector recognition. Eventually, we came up with a model explaining the intriguing selectivity profile of MBT domains and providing guidance for the design of more potent MBT antagonists. We have also explored the ways of more affordable free energy assessment for these promising therapeutic targets.
Later, we expanded our interest to studying the structure and dynamics of multidomain histone effectors (~700 such proteins are currently known) to better understand their function. Our multidisciplinary collaboration with the Strahl lab resulted in obtaining a unique insight into the structural mechanism of the histone H3 engagement by the Tandem Tudor-PHD fragment (TTD-PHD) of UHRF1. UHRF1 is an E3 ubiquitin-protein ligase overexpressed in many different forms of human cancers. Recently, the Strahl lab demonstrated that UHRF1 links the histone H3K9 methylation to DNA methylation and does so by binding to the chromatin in a multivalent fashion. A series of microsecond-scale molecular dynamics simulations has eventually prompted the structural mechanism of the bivalent UHRF1 engagement on the H3K9 methylated chromatin.
- Gao, C., et al., Biophysical Probes Reveal a ‘Compromise’ Nature of the Methyl-lysine Binding Pocket in L3MBTL1. J Am Chem Soc, 2011. 133(14): p. 5357-5362.
- Gao, C., J.M. Herold, and D. Kireev, Assessment of free energy predictors for ligand binding to a methyllysine histone code reader. J Comput Chem, 2012. 33(6): p. 659-665.
- Rothbart, S. B., Dickson, B. M., Ong, M. S., Krajewski, K., Houliston, S., Kireev, D. B., Arrowsmith, C. H., and Strahl, B. D. (2013) Multivalent histone engagement by the linked tandem Tudor and PHD domains of UHRF1 is required for the epigenetic inheritance of DNA methylation. Genes & Development 27, 1288-1298
- (1993) Ph.D.; Inst. Physiol. Active Comp., Russian Acad. Sci.
- (1993 – 97) Post-doctoral fellow; Université d’Orleans (France)
- (1997 – 2008) Head of Drug Design; Sanofi (Montpellier, France)
- (2008 – present) Professor, Medicinal Chemistry & Natural Products
- (2008 – present) Director, Computational Drug Discovery,
- Center for Integrative Chemical Biology and Drug Discovery