James Ferguson
Staff Scientist Structural Biology The Scripps Research Institute
James Ferguson is a Staff Scientist at Scripps Research, where he leverages structural biology, biochemistry, and computational approaches to advance the understanding of protein structure and function in drug discovery. Since joining Scripps Research in 2015, he has progressed from graduate student to postdoctoral associate and now staff scientist, contributing to cutting-edge research at the interface of structural biology and therapeutic development. James earned his Master’s degree in Biochemistry from the University of Oxford, where he built a strong foundation in molecular science. His expertise spans protein characterization, data analysis, and structure-based approaches to addressing challenging biological questions.
Seminars
Accurately predicting how molecules bind to proteins remains one of the major unsolved challenges in structure-based drug design, with current approaches struggling to rank binding affinities, predict accurate poses and design proteins with sufficiently high success rates. This technical workshop will explore how AI, physics-based modeling and experimental validation can be combined to improve predictive performance by:
- Improving small-molecule docking and binding pose prediction by examining the limitations of current approaches, including challenges with homologous versus novel structures and the difficulty of accurately modeling protein-ligand interactions
- Advancing affinity prediction and free-energy calculations to better rank compounds by exploring how AI and physics-based approaches can address complex parameters such as protein flexibility, solvent and water interactions that limit current predictive accuracy
- Strengthening de novo protein design and computationally designed binders by examining how prediction models can be improved, experimentally validated and coupled with high-throughput screening to increase the success rate of designed proteins
- Benchmarking predictive models against experimental data to identify which AI/ML approaches genuinely improve SBDD outcomes and where expert oversight and wet-lab validation remain essential
- Leveraging automated structure-building tools to enable high-throughput Cryo-EM map analysis for antibody discovery
- Validating and characterizing antibody leads to assess binding, specificity, and functional activity
- Designing Cryo-EMPEM-inspired yeast display libraries to accelerate functional and epitope-specific antibody discovery
