The soybean cyst nematode (SCN) remains the most serious pathogen problem of US soybean
production, demonstrating that the industry is in need of novel management tools. Studying SCN basic
biology is the most promising lead to identifying vulnerabilities in its life cycle that can be exploited.
ISU research has generated unprecedented ‘omic’ resources for SCN, and these resources are ready for
mining. Conventional molecular biology approaches will NOT suffice to fully explore the wealth of
available knowledge. Instead, we have reached a critical point in time where it is vital to now use
computational biology and emerging artificial intelligence tools to take the next steps and glean critical
insights for our fight against SCN.

Pathogens secrete proteins, so-called effectors, into their hosts to enable parasitism. We have identified more than 1,000 separate SCN effector proteins. Dauntingly, the molecular functions of these proteins remain unexplored, which means that their potential towards controlling SCN remains unexploited. Detailed study of each of these proteins is prohibitively complex and not practical. Instead, we need to perform sophisticated high throughput analyses to i) pinpoint effectors whose exploration is particularly promising and ii) specifically define intervention strategies. This research is such a high throughput approach that will fast-track key discoveries which then can be exploited. Successful completion of the proposed research will propel fundamental knowledge towards translational research.
We will perform a multi-pronged computational strategy to predict complex protein structures, model the dynamics of interaction interfaces, investigate the nature of binding interactions, and classify key partners involved during early infection stages to uncover molecular targets for the engineering of novel resistance traits. Furthermore, we can specifically interrogate effector collections bioinformatically to identify the proteins responsible for parasitism functions that have been empirically proven. We have developed three Specific Aims that are critical steps in opening a new research direction at ISU. All three aims are entirely doable within the proposal time frame and using the proposed funding.
Aim 1: Predict Molecular Interaction Interfaces and Binding Mechanisms of SCN Effectors and their Plant Targets: Understanding exactly where and how SCN effectors bind soybean proteins at the structural level is crucial to disrupting infection. We have in hand confirmed cyst nematode-host protein binding pairs. We will model effector–host target complexes using AlphaFold-Multimer and specialized docking tools to map contact residues, define interaction-driving forces (electrostatic, hydrophobic, pi-stacking, etc.), and explore the spatial and temporal dynamics of critical binding interfaces across SCN strains and soybean variants. These analyses will identify precise molecular weak points where defense strategies can be focused.
Aim 2: Classify SCN Effector-Soybean Target Interactions to Expose High-Impact Partners: Using protein-protein interaction pairs reported in the literature and identified in our labs, we have a critical mass of confirmed interactions. Grouping effectors and their host targets based on interaction mechanisms and functional roles will reveal the potential vulnerabilities SCN exploits during soybean infection. Using sequence conservation, predicted molecular interactions and structural alignment, we can group effectors and host proteins to identify convergence points, shared binding interfaces and defense mechanisms. This classification will prioritize the most impactful effector-host pairs for durable resistance:

ISU has the critical expertise in place to conduct this research. Drs. Thomas Baum and Steve Whitham are experts in pathogen effector biology who have been instrumental in producing a wealth of biological, molecular, and genomic resources. The ISU Genome Informatics Facility (GIF), led by Dr. Andrew Severin, provides key expertise in protein structural analyses.
We expect to identify key effector functions and to define the structural and dynamic features of effector interaction interfaces. This will reveal critical points of vulnerability within the SCN pathosystem, prioritize host genes for resistance breeding or gene editing, and provide a foundation for developing targeted interventions to disrupt nematode infection at its earliest stages. Most importantly, however, this work will set in motion the much-needed shift from wet-lab bench work to powerful computational approaches to exploit the wealth of data generated at ISU with commodity support.

The soybean cyst nematode (SCN) is the most damaging pest of soybean in the United States, responsible for an estimated more than $1 billion in annual yield losses. The work proposed here is the mandatory next step in translating data generated mostly by soybean checkoff funds into actionable discoveries that allow clearly articulated anti-nematode interventions. These research aims are critically and directly relevant to future projects targeting SCN by new molecular approaches. This project will lead to powerful preliminary data, which will enable the submission of competitive research proposals to federal funding outlets and industry partners. Successful completion of the proposed work will set in motion the development of a completely novel computational research field in the analyses of nematode pathosystems. This research will jump-start efforts to focus cutting-edge AI tools on research towards solving the #1 soybean pathogen problem.