Lecture 5: Dr. Julia Köhler Leman (Jeff Gray Lab)

Computational membrane protein structure prediction

Membrane proteins are critical functional molecules in the human body, constituting more than 30% of open reading frames in the human genome. As we also learned in Lecture 4, the determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1–2% for the last decade. In contrast, over half of all drugs targetMPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated.

Computational tools are instrumental to membrane protein structure prediction, consequently increasing our understanding of membrane protein function and their role in disease. In their recent article, Dr. Julia Kohler Leman and her colleagues describe a general framework facilitating membrane protein modeling and design that combines the scientific principles for membrane protein modeling with the flexible software architecture of Rosetta3. This new framework, called RosettaMP, provides a general membrane representation that interfaces with scoring, conformational sampling, and mutation routines that can be easily combined to create new protocols. To demonstrate the capabilities of this implementation, the researchers developed four proof-of-concept applications for (1) prediction of free energy changes upon mutation; (2) high-resolution structural refinement; (3) protein-protein docking; and (4) assembly of symmetric protein complexes, all in the membrane environment. The preliminary results show that these algorithms can produce meaningful scores and structures. The data also suggest needed improvements to both sampling routines and score functions. Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design.

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Dr. Julia Koehler Leman received her Bachelors and Masters of Science degrees in physics from the University of Leipzig, Germany. Her doctoral research revolved around membrane protein structure determination using NMR spectroscopy and computational techniques and she received her Ph.D. in Chemical and Physical Biology in 2012 while working with Drs. Jens Meiler and Charles Sanders at Vanderbilt University. After graduation, she worked as a postdoctoral fellow under the supervision of Dr. Jeff Gray at Hopkins on the projects that involve development and application of Rosetta software, focusing on creating and improving applications for structure prediction, docking, and design. Very recently, she started working as a research fellow at Simons Foundation. 

Dr. Koehler Leman has published 7 first-author papers and co-authored 6 papers in peer-reviewed international journals. Her current research interests lie in computational methods development in modeling membrane proteins with and without restraints from NMR spectroscopy.