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.

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.

Title: The modeling of the HIV Capsid with novel binding sites on Nucleoporin 153

Project Team: Paul Rowley, Shunji Li, Jagdish Patel

Start Date: August 2021

A molecular modeling approach will be used to assess whether homologs of NUP153 motif-1 interact with the HIV-1 capsid. We have preliminary binding data which suggest at least one of the motifs interacts with capsid. Physical binding measurement revealed that Motif-1 homologs, Motif-4, and Motif-5 had a measurable KD, suggesting that they directly bind capsid. What is unknown is if these peptides are interfacing with the same binding site as Motif-1.

Title: Myofilament Cooperative Activation in Human and Porcine Myocardium

Project Team: Daniel Fitzsimons, Tuan Phan

Start Date: September 2021

Contraction and relaxation of the mammalian heart is a cooperative process involving the binding of Ca2+ and myosin cross-bridges to the thin filament. While the cooperative effects of Ca2+ and myosin cross-bridge binding to the thin filament have been well-characterized in the rodent myocardium, the relative contributions of these molecular processes remained unexamined in human and porcine myocardium. Given the nearly ten-fold difference in resting heart rates (i.e., mouse: 600 beats/min vs. human: 60-80 beats/min) and myocardial twitch kinetics between small and large mammals, it is unlikely that the relative contributions of Ca2+ and myosin cross-bridges in the cooperative activation are the same. Therefore, using contractile data collected in murine, human, and porcine hearts as a starting point, we propose a mathematical model to describe molecular mechanisms of force generation in human/porcine cardiac muscle. The purpose of this project is two-fold: (i) to ascertain the effects of nearest-neighbor molecular interactions along the thin filament on force development and relaxation in human myocardium; (ii) to determine whether thick filament proteins have a greater contribution to the cooperative response in human myocardium compared to rodents.

Title: Optimizing spatiotemporal modeling for public health risk analysis

Project Team: Helen Brown, Christopher Murphy, Chris Williams, Erich Seamon, Mohamed Megheib

Start Date: September 2021

This is a continuation of small area estimate (SAE) modeling of Idaho Behavioral Risk Factor Surveillance System (BRFSS) health indicators. In 2019 the Pl sought MAG support to model obesity indicators. A model was created but the person modeling the work left the UI unexpectedly and did not leave behind reproducible code. Brown, Pl, sought funding support from IDHW/BRFSS to engage IMCI modelers Seamon and Megheib to develop a methodology to create county level measures for three BRFSS health indicators. This work was completed and a publication is in process.

This project will continue the work initiated by Seamon and Megheib to optimize the modeling techniques used to arrive at county level health estimates. This is critically important as the BRFSS data is currently only available at a Public Health District (PHO) level. Each PHO comprises 5-7 counties and the lack of data granularity does not allow for clear assessment of critical public health concerns, nor targeted and strategic public health interventions and evaluation. This work also has the potential to inform BRFSS sampling strategies, increase the capacity of the state to generate county level estimates, and initiate new techniques for combining BRFSS with other existing data sets.

The prime areas of model optimization include: 1) expanding upon existing BRFSS model development, by extending the approach to a larger range of health indicators; 2) automating the transformation and constraint selection via more sophisticated algorithmic evaluation; and 3) modularizing the model pipeline to allow for dynamic running and model parameter selection thru the development of an R-specific dashboard (only accessible to researchers and other policy staff – not public)