John Lamb

This paper introduces PconsDock, a fold-and-dock method that predicts tertiary and quaternary structures of protein pairs without prior structural information, achieving comparable performance to traditional methods.

The paper identifies novel topologies in CPA/AT transporters using an integrated method and presents a database with structural classifications.

pyconsFold models and docks proteins using distance predictions, outperforming contact prediction methods and being 20 times faster than pyRosetta.

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

This study identifies a novel reentrant-helix-reentrant fold in CPA/AT transporters using ab-initio structure prediction, revealing structural adaptations and the utility of deep-learning-based modeling.