Abstract by Wendy Billings

Personal Infomation

Presenter's Name

Wendy Billings

Degree Level



Bryce Hedelius
Todd Millecam

Abstract Infomation


Physics and Astronomy

Faculty Advisor

Dennis Della Corte
David Wingate


ProSPr: Protein Structure Prediction using Deep Learning


Deep convolutional neural networks have recently enabled spectacular progress in predicting protein structures, as demonstrated by DeepMind’s winning AlphaFold entry in the latest Critical Assessment of Structure Prediction experiment (CASP13). The best protein prediction pipeline leverages intermolecular distance predictions to assemble a final protein model, but the resources required to create these predictions for arbitrary sequences are not publicly available. Here we present ProSPr, a trained implementation of a comparable network, and make it freely available to the scientific community. We benchmark ProSPr predictions in the related task of contact prediction against the CASP13 contact prediction winner TripletRes. Further, we investigate the dependence of prediction quality on information derived from multiple sequence alignments and perform ablation tests. Access to ProSPr will enable other labs to build on best-in-class protein distance predictions and engineer superior protein reconstruction methods.