Skip to main content

McMaster Project

Project PI: Jagdish Patel

This research project is to identify the correct bound conformations of veranamine in signma-1 and 5HT2B receptors. Crystal coordinates of sigma-1 and 5HT2B receptors will be downloaded from the Protein Data Bank. Both of the receptor structures will then be used to perform ordinary molecular dynamics to sample various conformations available. Docking software will be used to dock veranamine to 100 snapshots obtained from molecular dynamics simulations of each receptor to determine the pocket heavily populated with high scoring poses. Highly populated pose of veranamine in both receptors will be refined using flexible docking to obtain the final best bound conformations. Structural analogs will be docked directly to the predicted binding site of veranamine using a docking software.

Using Biophysical Protien Models to Map Genetic Variation to Phenotypes

Project Team: Marty Ytreberg (PI), Craig Miller, Brandon Ogbunugafor (University of Vermont), Daniel Weinreich (Brown University)

Understanding how genotypes map to organismal phenotypes is one of the great unsolved problems in modern biology. This research is built on the principle that the biophysical properties of proteins represent general, computationally-predictable intermediaries for mapping genotypes to phenotypes. The central scientific hypothesis for the project is that protein biophysical models provide an efficient framework for predicting how mutations – alone, in combination, and in different environments -influence protein stability, affinity for substrates and partners, and the mappings to higher-level phenotypes. To test this hypothesis, an interdisciplinary team was assembled spanning three jurisdictions (Idaho, Rhode Island, and Vermont) with the necessary expertise in computational protein modeling, diverse biological systems, measurement of protein biophysical properties, and mathematical modeling.

The methodological hypothesis for the project is that a biophysics-first framework can be applied to diverse experimental systems to reveal general underlying principles of genotype to phenotype mapping, uncover gaps in understanding, and suggest potential generic and system-specific extensions. Initially, beta-lactamase and the respiratory syncytial virus F protein will be used as testbed systems because they represent two fundamental interaction types: protein-substrate and protein-protein. The objectives during the first 1.5 years are to investigate how single mutations (Objective 1), and combinations of mutations (Objective 2), affect biophysical phenotypes and their mappings to higher-level phenotypes. In years 2-4, lessons from Objectives 1 and 2 will be applied to other model systems (Objective 3), to consideration of larger, naturally-selected, mutational combinations (Objective 4), and to the role of the environment in modifying genotype to phenotype mappings (Objective 5).

To the team’s knowledge, this is the first attempt to test the ability of protein models to predict how mutations affect biophysical and higher-level phenotypes across such a broad range of systems. Among the vast number of possible mutational combinations, only a small fraction are expected to be viable. The proposed research has the potential to develop tools to predict these rare combinations, which would be nearly impossible to identify empirically. The research will identify generic and system-specific lessons about the mapping of genotypes to phenotypes, such as how often do biophysical and higher-level phenotypes show epistasis and how well can models predict deviations from additivity.

The research has the potential to benefit society by leading to advances in biotechnology and health sciences. For example, the work on protein-protein binding will improve the ability to predict which mutant pathogens will evade therapy.

The team consists of ten faculty members (four early-career), four postdoctoral associates, eight graduate students, and six undergraduate students. Goals and activities for each of these groups will lead to a diverse and vibrant workforce, with mentoring and resources to support early-career faculty in achieving tenure and promotion, and professional development for postdocs. Interactive animations will be created to engage the general public in research findings. A website will aid in dissemination of project results, tools, activities, and broader impacts. Presentations will be created and delivered at diverse venues, local science centers and schools, and regional Tribes.

Long-term sustainability of the interjurisdictional collaborations will be supported by establishing an infrastructure for project coordination, supporting team collaboration, and reducing barriers to participation.

Stay completely up-to-date on the RII Track-2 FEC: Using Biophysical Protein Models to Map Genetic Variation to Phenotypes (National Science Foundation award OIA-1736253) via the Genotypes to Phenotypes website.

Comparison of Two Phasitron Designs for IPV

Project Team: Tao Xing (PI), Gordon Murdoch (co-PI) and Rabijit Dutta (co-PI)

With support from Percussionaire Corp based in Sagle, Idaho, the team will test the new Phasitron design. They will compare the pressure and tidal volumes and the net flow entertainment under various lung conditions. They will also explore computational fluid dynamics (CFD) simulations of the two Phasitron devices. This project and funding grew from the Pilot Project, Multi Scale Model of Interactions Between Lung and Pulmonary Ventilation.

Collaborative Research: A Mathematical Theory of Transmissible Vaccines

Project Team: Scott Nuismer (PI), Chris Remien (Co-PI), James Bull (Co-PI), Andrew Basinski

Viral vaccines have had remarkable and long-lasting impacts on human health, resulting in the worldwide eradication of smallpox, the elimination of polio within much of the developed world, and the effective control of many other diseases. Although great strides have been made in the development and production of vaccines since Edward Jenner’s first vaccinations with cowpox in the early 1800’s, little has changed in the way vaccines are delivered. Even today, virtually every vaccine must be given directly to the patient. Recent advances in molecular biology suggest that the centuries-old method of individual-based vaccine delivery could be on the cusp of a major revolution.  Specifically, genetic engineering brings to life the possibility of a “transmissible vaccine.” Rather than directly vaccinating every individual within a population, a transmissible vaccine would allow large swaths of the population to be vaccinated effortlessly by releasing an infectious agent that is genetically engineered to be benign yet infectious. In fact, some existing vaccines are transmissible to a limited extent, and transmissible vaccines have already been developed and deployed in wild animal populations. Remarkably enough, however, no theory exists to guide the safe and effective use of this revolutionary new type of vaccine. We will develop a mathematical framework for understanding the ecology and evolution of transmissible vaccines, and test the emerging mathematical results using an experimental viral system. Epidemiological efficacy will be assessed by calculating the gains in disease protection conferred by a transmissible vaccine relative to a traditional vaccine. Evolutionary robustness will be explored using models that predict the rate at which a genetically engineered vaccine will lose its efficacy or increase its virulence. In both cases, models will be analyzed using a combination of direct and asymptotic solutions, approximations, numerical solutions, and individual-based simulations. Key mathematical results will be tested experimentally using interactions between bacteria and viruses that infect them.

NIH R00 Project: Causal Inference of Gene Regulatory Networks With Application to Breast Cancer

Project Team: Audrey Fu (PI), Bhadur Badsha, Evan Martin

In the investigation of the mechanisms behind gene regulation and its impact on diseases, two lines of research have been largely separately carried out in recent years. On the one hand, gene regulatory networks and protein interaction networks have been under extensive study, especially in systems biology, where genetic variation is usually ignored. On the other hand, mutations, indels (insertions and deletions), and copy number variants have been identified for many diseases in genome-wide association studies. It is therefore of immense interest to understand how genetic variation influences disease through gene regulatory networks.

To construct these networks, at least three key pieces of information are important: gene expression, transcription factor binding, and genotypes (especially at expression quantitative trait loci; that is, eQTLs). In particular, the latter two enable causal inference in the network construction, although how to use them in a probabilistic and rigorous way has not been systematically explored. This project aims to develop statistical models and efficient computational strategies, drawing on recent advances in graphical models and causal inference, to construct causal regulatory networks involving genetic variation and TF binding. This project will use breast cancer as a disease model and apply the proposed methodologies to different subtypes. Topological features of the inferred regulatory networks may suggest potentially different mechanisms in breast cancer subtypes.