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BE Seminar: “Using Computers to Derive Protein Structure from Sparse Data – A Case Study for Mass Spectrometry” (Steffen Lindert, Ohio State)
April 18 at 3:30 PM - 4:30 PM
Mass spectrometry-based methods such as covalent labeling, surface induced dissociation (SID) or ion mobility (IM) are increasingly used to obtain information about protein structure. However, in contrast to other high-resolution structure determination methods, this information is not sufficient to deduce all atom coordinates and can only inform on certain elements of structure, such as solvent exposure of individual residues, properties of protein-protein interfaces or protein shape. Computational methods are needed to predict high-resolution protein structures from the mass spectrometry (MS) data. Our group develops algorithms within the Rosetta software package that use mass spectrometry data to guide protein structure prediction. These algorithms can incorporate several different types of mass spectrometry data, such as covalent labeling, surface induced dissociation, and ion mobility. We developed scoring functions that assess the agreement of residue exposure with covalent labeling data, the agreement of protein-protein interface energies with SID data and the agreement of protein model shapes with collision cross section (CCS) IM measurements. We subsequently rescored Rosetta models generated with de novo protein folding and protein-protein docking and we were able to accurately predict protein structure from MS labeling, SID and IM data. Future work is focusing on developing custom machine learning models to predict protein structure from MS data.
Steffen Lindert, Ph.D.
Associate Professor, Department of Chemistry and Biochemistry, Ohio State University
Steffen Lindert received his M.Sc. in Physics from the University of Leipzig in 2006 and his Ph.D. in Chemical and Physical Biology (Molecular Biophysics track) from Vanderbilt University in 2011. Co- advised by Prof. Jens Meiler and Prof. Phoebe Stewart, he worked on a combined experimental and computational project developing a program – EM-Fold – which folds proteins into medium resolution cryoEM density maps. After finishing his Ph.D., he joined the laboratory of Prof. Andy McCammon at the University of California, San Diego, as a postdoctoral fellow. His research focused on macromolecular simulations of proteins involved in cardiomyocyte contraction and computer-aided drug discovery. He was awarded a prestigious postdoctoral fellowship from the American Heart Association. He started his research group at OSU in August of 2015.