Ameya Harmalkar

This paper introduces Rosetta-MPDock, a membrane protein docking method that improves prediction accuracy by capturing conformational changes and integrating AlphaFold2-multimer, achieving better success rates in predicting membrane protein complex structures.

GeoDock predicts protein-protein docked structures using a multi-track iterative transformer, achieving high success rates and fast inference speeds for rigid and flexible targets.

This paper develops machine learning models to predict antibody thermostability using sequence and structure features, showing better generalization with a CNN over pre-trained language models.

The paper introduces ReplicaDock 2.0, a method that improves protein complex structure prediction by simulating induced-fit dynamics, achieving better accuracy for flexible targets.

The paper presents a framework for ensuring reproducibility in bio-macromolecular modeling through automated benchmarks and a test server, demonstrating its utility for Rosetta and other tools.