PT  - JOURNAL ARTICLE
AU  - Jessie L. Gan
AU  - Dhruv Kumar
AU  - Cynthia Chen
AU  - Bryn C. Taylor
AU  - Benjamin R. Jagger
AU  - Rommie E. Amaro
AU  - Christopher T. Lee
TI  - Benchmarking ensemble docking methods as a scientific outreach project
AID  - 10.1101/2020.10.02.324343
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.10.02.324343
4099  - http://biorxiv.org/content/early/2020/10/04/2020.10.02.324343.short
4100  - http://biorxiv.org/content/early/2020/10/04/2020.10.02.324343.full
AB  - The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that CatS is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.Competing Interest StatementThe authors have declared no competing interest.