Skip to main content

Standards of Evidence Working Group

Working Group Leader: Bert Baumgaertner

Group Members: Florian Justwan, Kendal Mitton, Chenangnon Tovissode

Originated: January 2023


This working group uses both modeling and empirical investigations to understand the role that evidentiary standards play when individuals evaluate claims. We are interested in the systematic ways standards of evidence differ across various types of claims (e.g. political vs scientific) and the determinants of these systematic differences, including base rates (priors) vs likelihoods, in-group vs outgroup, confirmation vs disconfirmation, prospective vs retrospective, and general public vs political elites.

Mathematical Immunology

Working Group Leader: Tanya Miura

Group members: Esteban Hernandez Vargas, Rodolfo Blanco Rodriguez

Originated: November 8, 2022


The Mathematical Immunology Working Group will develop mathematical models to explain the roles of immune responses during respiratory viral infections.

PIPP Molecular Modeling (MoMo)

Working Group Leader: Marty Ytreberg

Group Members: Jonathan Barnes, Jagdish Patel, America Chi

Originated: December 15, 2022


MoMo will brainstorm ideas to participate in the pandemic preparedness proposal that will be led by University of Texas, Austin.

Paul Rowley Awarded Modeling Access Grant

Title: Using molecular modeling to assess structural conservation of KP4-Like proteins and their potential as antifungals

Project Team: Paul Rowley, Jonathan Barnes

Start Date: May 2022

KP4 protein has been used as a potent antifungal drug to reduce the spoilage of commodity crops. While the potential of KP4 has not been fully realized due to its narrow spectrum of activity, there are at least 500 more KP4-like (KP4L) homologs that we have identified that are encoded by fungal genomes. The central hypothesis is that these proteins represent an untapped resource of novel antifungal proteins that could be leveraged against important fungal diseases. To that end, we propose to initiate a molecular modeling project to determine if the low sequence homology of KP4L proteins to KP4 translates into structural conservation. This work will complement empirical studies that are underway in the Rowley lab. We anticipate that this project will be completed and published before the end of 2022.

Molecular dynamic simulations previously collected by Dr. Jagdish Patel from a prior MAG demonstrated that three KP4L proteins adopt a stable structure similar to the structural model of KP4 that was determined by X-ray crystallography. This MAG would follow up this work by using AlphaFold2 and molecular dynamics simulations to predict the structure of 20 KP4L proteins that represent the known diversity of these proteins. Furthermore, the application of in silico mutagenesis by FoldX will enable us to draw conclusions about the structural plasticity of these proteins and their ability to tolerate mutations. In silico mutagenesis will be complemented by site-saturated empirical mutagenesis to determine the accuracy of the computational models and to identify amino acids that are critical for the toxicity of KP4L proteins.

Tanya Miura Awarded Modeling Access Grant

Title: Synergistic mutations allow for antibody resistance in respiratory syncytial virus

Project Team: Tanya Miura, Jonathan Barnes

Start Date: June 2022

This project was initially funded by the NSF EPSCoR Track II Geno-Pheno Grant to use molecular modeling to predict antibody escape variants of respiratory syncytial virus (RSV). In that work, we identified five novel mutations in the RSV fusion protein that imparted resistance to the antibody motavizumab. Interestingly, none of these mutations arise when the virus is under selective pressure, only one mutation (K272E) arises under motavizumab selection. We engineered a virus with methionine at position 272 (K272M), which is sensitive to motavizumab and requires two nucleotide changes to mutate to glutamic acid (E). When this variant was grown under motavizumab selection, a resistant variant arose, K272M/N262K. Interestingly, neither the K272M or N262K mutations individually impart motavizumab resistance.
We propose to use molecular modeling to explain how interactions between these mutations may impart resistance. This would entail three classical molecular dynamics simulations. K272M, N262K, and K272M+N262K. Trajectory analysis would follow to ascertain possible mechanisms and interactions that differ between the mutations on their own and in combination. The results of this analysis should shed light on how K272M+N262K imparts resistance when acting in concert with no observed effect on their own.