Member Biography
Jonah Vilseck, Ph.D.
410 W. 10th St.
HS 5017
Indianapolis, IN 46202
Phone: (317) 274-9626
Research Program Membership
Associate member:
Dr. Vilseck's research interests include:
Protein side chain mutations are fundamental to a variety of biological processes, including evolution, protein-engineering, and disease, and affect a protein’s shape, stability, and function. Loss-of-function or gain-of-function mutations are at the heart of several human diseases, including cancer. For disease targets for which three-dimensional structural data is available, rigorous physics-based computational methodologies, including molecular dynamics simulations and free energy calculations, can provide atomistic insight into (i) the mechanisms and thermodynamics of protein-ligand or protein-protein binding events, (ii) the stability of a protein’s folded state, and (iii) the conformational dynamics of a protein complex. This information, in turn, can be used to guide the discovery and design of novel small molecule or peptide-based therapeutics. However, little effort has been expended to use these computational techniques to understand and quantify the effects of point mutations on drug targets. My lab will develop and use computational tools to better understand the structural and functional effects of aberrant protein side chain mutations in disease targets and design novel pharmaceutical agents capable of addressing the ongoing healthcare concerns they cause. A major goal of my laboratory will be to understand drug resistance in cancer, specifically in multiple myeloma (MM) and in triple negative breast cancer (TNBC). In this pursuit, I would like to be a member of the Experimental and Developmental Therapeutics (EDT) Program of the Simon Cancer Center. Furthermore, I foresee establishing collaborations with current IU Simon Cancer Center investigators to address breast, lung, and pancreatic cancers in general. My lab will investigate the effects of point mutations in two main areas: (1) Address drug resistance by studying the effects of point mutations on small molecule–protein interactions. Acquired drug resistance is a growing concern for managing healthcare worldwide. This is especially true of multiple myeloma and triple negative breast cancers, where resistance mechanisms are a leading cause of patient relapse and lead to increased tumor metastasis and malignancies. For example, in MM, proteasome inhibitors (PI) have been observed to be susceptible to primary resistance through mutations to the ß5 proteasomal subunit, their main target. Clinically identified mutations, such as A49(S/T/V), A50V, M45(A/T/V/I), and C52F substitutions, are within the vicinity of the PI binding site and alter the structural and dynamic properties of the target. This reduces drug efficacy or eliminates drug binding altogether [1,2]. In TNBC, it has been shown that mutations in breast cancer associated protein 1 (BRCA1) can disrupt native DNA damage repair mechanisms and sensitize malignant cells to DNA-targeting chemotherapies. Chemical inhibition of BRCA1 thus offers a therapeutic strategy to increase the effectiveness of DNA-targeting chemotherapies towards TNBC. However, the development of BRCA1 C-terminal domain (BRCT) inhibitors has been complicated by the observation of multiple amino acid mutations lining the phosphopeptide binding cleft. It is hypothesized that these mutations alter the conformational shape of the binding cleft and induce drug resistance [3,4]. For cancer targets, like those describe above, my lab’s computational tools will be used to elucidate the structural and molecular mechanisms of small molecule drug resistance caused by point mutations. Mutations nearest the bound drugs will be investigated first, followed by mutations further away. Efforts will begin by performing a variety of separate computer simulations of native substrates and small molecule drugs bound to many protein variants, each featuring different side chain mutations. Simulation trajectories will be analyzed and compared to gain an atomistic picture of how drug resistance evolves. An innovative free energy method known as multisite ¿-dynamics (MS¿D), described in greater detail below, will then be used to optimize new anti-cancer therapeutics in a combinatorial and efficient manner. This work will take advantage of the growing ‘omics data at IUSM available through IUSCC’s Genomics and Bioinformatics Cores to understand which point mutations have been observed clinically for cancer targets and which should be included in my lab’s models. Targets for which structural data is available and drug resistance has been reported will be a focus. Promising lead compounds designed with MS¿D will then be evaluated experimentally in collaboration with future colleagues at IUSM and ultimately developed into novel anti-cancer agents. Inclusion into the EDT Program within the cancer center as a beginning Associate Member would help facilitate these goals and be appropriate for this research. Membership would also provide access to the Therapeutics, Proteomics, and X-ray diffraction cores or resources available through IUSCC to validate and sustain this research as it progresses. (2) Develop small molecule drugs to modulate protein-protein interaction networks in cancer. Protein–protein interactions (PPIs) play essential roles in oncogenic signaling networks, and as such, represent an important class of therapeutic targets susceptible to small molecule or monoclonal antibody drugs. However, the structurally diverse nature of protein-protein recognition interfaces makes PPIs challenging targets for drug design. Point mutations along the protein–protein interface of cancer related targets, for which structural data is available, will thus be explored computationally to better understand the mechanisms of protein-protein recognition and the effects of mutation on binding affinity, secondary structure, conformational dynamics, and thermal stability. Understanding the mechanisms of signal transduction through PPIs will fuel the discovery of novel anti-cancer small molecule and peptide-based drugs. To initiate this research I have already become involved in the RNA Cancer Biology group, and my inclusion within EDT would be a major benefit for achieving future goals of this research. Multisite ¿-Dynamics (MS¿D), a rigorous and physics-based free energy method, is uniquely suited to perform these research aims. MS¿D can evaluate whether a modification to a lead compound will increase or decrease its binding affinity to an oncoprotein target. Predicted increases in binding affinities generally translates well into observed increases in experimental inhibitory activities. Thus, utilization of MS¿D in structure-based drug design and lead optimization is highly beneficial because lead compound analogs can be rank ordered to predict the best inhibitors. MS¿D is unique, however, from other methods in its ability to explore many different substituent modifications at one or multiple sites around a biochemical system simultaneously. As a result, tens to hundreds of relative free energy differences can be readily computed and significant computational cost savings are possible over conventional methods (e.g. 10-30 times fewer resources are required). In my lab’s efforts to design novel anti-cancer drugs to overcome clinically observed drug resistance in cancer, including MM, TNBC, lung, and pancreatic cancers, MS¿D will be used to perform concerted side chain mutations cooperatively with ligand substituent modifications within a single molecular dynamics simulation. This will allow all combinatorial ligand states to “see” all mutational states of the protein target. Ligand modifications can then be rank ordered according to their affinity for each protein variant to identify novel lead compounds capable of maintaining binding efficacy across many protein variants. Exploring lead compound analogs in this fashion will solve a parallel optimization problem associated with drug resistance and identify small molecule anti-cancer drugs that maintain binding affinity for wild-type and resistant forms of an oncoprotein target. In a similar manner, side chain mutational scans of different amino acid residues along protein-protein interfaces will elucidate the effect of point mutations on protein-protein interactions and dynamics. Conventional computer-aided drug design will ensue to design small molecule anti-cancer drugs capable of inhibiting native target function or disrupting critical protein-protein complexes. These design strategies may also be applied to the design of monoclonal antibodies to develop peptide-based therapies. In conclusion, my proposed research will develop and validate ¿-dynamics based computational techniques for its use to study highly challenging disease targets in cancer. Simultaneous modeling of small molecule and protein side chain mutations will solve a parallel optimization problem, provide new understandings about the role of point mutations in disease on an atomistic level, and enable effective drug design pursuits. These research activities will utilize the growing ‘omics data about MM, TNBC, lung, and pancreatic cancers to understand the relationships between gene mutations and protein activity, especially as it relates to the development of drug resistance. State-of-the-art computational techniques will be used for the discovery and optimization of new anti-cancer drugs to overcome drug resistance or block critical oncogenic signals in protein-protein interaction networks. References (1) Huber, E.; et al. Structure 2015, 23, 407-417. (2) Robak, P.; et al. Cancer Treat. Rev. 2018, 70, 199-208. (3) Coquelle, N.; et al. Biochemistry 2011, 50, 4579-4589. (4) White, E. R.; et al. ACS Chem. Biol. 2015, 10, 1198-1208.
Ph.D. - Yale University, New Haven, CT 12/2016
Post-doctoral Fellowship - University of Michigan, Ann Arbor, MI 07/2019
