2114 Newman & Wolfrom Laboratory
100 W 18th Avenue
Columbus, OH 43210
Areas of Expertise
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.
Research in the lab focuses on the development and application of computational techniques for modeling biological systems, with the goal of gaining a deeper understanding of biomolecular processes, predicting protein structure de novo with the use of sparse experimental data, and discovering new drugs. Specific research areas include:
Computational Protein Structure Prediction from Sparse Experimental Data:
Computational methods that predict a protein’s tertiary structure from the primary sequence are becoming very successful and offer an alternative in cases where experimental methods fail to determine the protein structure. De novo structure prediction has been most successful when combined with sparse experimental data, forming a symbiosis where the combination of methods is more powerful than the sum of the individuals. The lab works on combining medium resolution cryoEM data and de novo protein structure prediction. One avenue of research that we are pursuing is the combination of Rosetta and Molecular Dynamics (both methods guided by medium-resolution cryoEM density maps) to develop a more powerful protein structure refinement tool. While Rosetta uses knowledge-based energy functions, MD draws on physics-based potentials to describe and predict protein structure. A combination of the two methods may help overcome some of the current sampling and scoring limitations in computational protein structure prediction. In collaboration with the Wysocki lab, we are also working on using covalent labeling mass spectrometry data to guide de novo protein structure prediction.
Simulations of Biomolecules:
The lab is interested in understanding the relationship between dynamics, structure and function in proteins. For many biological systems the dynamics are crucial to the functions performed by these complexes. We use Molecular Dynamics (MD) simulations to investigate transitions between different conformational states, elucidate the dynamics of binding pockets (with implications for drug discovery) and gain insight into free energy barriers between states. Enhanced sampling methods, such as accelerated MD (aMD) can provide a means of extending the sampling of the conformational space beyond the current limits of nanosecond to microsecond timescales. The lab’s focus is on simulations of the contraction in muscle cells. Both in cardiac and skeletal muscle, thin and thick filaments slide past each other to achieve muscle contraction. The process of contraction is regulated by calcium and encompasses multiple molecular events. Thus our simulations target different proteins involved in contraction and also look at different scales (from a single protein to macromolecular complexes). Particular focus is on MD simulations of the cardiac troponin complex as well as myosin, and using the results of these simulations to aid the identification of drugs targeting heart failure.
Computer-Aided Drug Discovery:
Computational methods can play an important role in the discovery of drugs. Structure-based drug discovery methods computationally dock chemical compounds into protein models and predict binding affinities. The lab computationally screens huge compound libraries (“virtual high throughput screening”) and collaborates with experimental groups to test only the best predicted compounds. The lab is working on a variety of drug discovery efforts. We are interested in finding molecules that can modulate the calcium sensitivity of the thin filament with applications in treatment of heart failure. Another route that we are actively pursuing is finding cures for neglected tropical diseases. In one project we are looking for compounds that can inhibit vital enzymes in the liver fluke parasite. Lastly, we are also working with several groups to selectively target enzymes and receptors which are upregulated in cancer. In addition to our applied drug discovery efforts, computational methods for effective virtual screening are continuously being improved and developed further within the group. One focus is on the incorporation of receptor flexibility by using representative snapshots from molecular dynamics simulations.
Dr. Lindert is also accepting enthusiastic and motivated graduate and undergraduate students interested in using computational methods to investigate biological systems.
1. Coldren, W.H.; Tikunova, S.B.; Davis, J.P.; Lindert, S. (2020), Discovery of Novel Small-Molecule Calcium Sensitizers for Cardiac Troponin C: A Combined Virtual and Experimental Screening Approach. J Chem Inf Model 60 (7), 3648-3661.
2. Kim, S.S.; Alves, M.J.; Gygli, P.; Otero, J.; Lindert, S. (2019), Identification of novel cyclin A2 binding site and nanomolar inhibitors of cyclin A2-CDK2 complex. Curr Comput Aided Drug Des. In press.
3. Leelananda, S.P.; Lindert, S. (2020), Using NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD Protein Structure Refinement. J Chem Inf Model 60 (5), 2522-2532.
4. Seffernick, J.T.; Harvey, S.R.; Wysocki, V.H.; Lindert, S. (2019), Predicting Protein Complex Structure from Surface-Induced Dissociation Mass Spectrometry Data. ACS Cent Sci 5 (8), 1330-1341.
5. Bowman, J.D.; Lindert, S. (2019), Computational Studies of Cardiac and Skeletal Troponin. Front Mol Biosci 6:68.
6. Seffernick, J.T.; Ren, H.; Kim, S.S.; Lindert, S. (2019), Measuring Intrinsic Disorder and Tracking Conformational Transitions Using Rosetta ResidueDisorder. J Phys Chem B 123 (33), 7103-7112.
7. Bowman, J.D.; Coldren, W.H.; Lindert, S. (2019), Mechanism of Cardiac Troponin C Calcium Sensitivity Modulation by Small Molecules Illuminated by Umbrella Sampling Simulations. J Chem Inf Model 59 (6), 2964-2972.
8. Aprahamian, M.L.; Lindert, S. (2019), Utility of Covalent Labeling Mass Spectrometry Data in Protein Structure Prediction with Rosetta. J Chem Theory Comput 15 (5), 3410-3424.
9. Harvey, S.R.; Seffernick, J.T.; Quintyn, R.S.; Song, Y.; Ju, Y.; Yan, J.; Sahasrabuddhe, A.N.; Norris, A.; Zhou, M.; Behrman, E.J.; Lindert, S.; Wysocki, V.H. (2019), Relative interfacial cleavage energetics of protein complexes revealed by surface collisions. Proc Natl Acad Sci U S A 116 (17), 8143-8148.
10. Kim, S.S.; Aprahamian, M.L.; Lindert, S. (2019), Improving inverse docking target identification with Z-score selection. Chem Biol Drug Des 93 (6), 1105-1116.
11. Bowman, J.D.; Lindert, S. (2018), Molecular Dynamics and Umbrella Sampling Simulations Elucidate Differences in Troponin C Isoform and Mutant Hydrophobic Patch Exposure. J Phys Chem B 122 (32), 7874–7883.
12. Aprahamian, M.L.; Chea, E.E.; Jones, L.M.; Lindert, S. (2018), Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data. Anal Chem 90 (12), 7721–7729.
13. Kim, S.S.; Seffernick, J.T.; Lindert, S. (2018), Accurately Predicting Disordered Regions of Proteins Using Rosetta ResidueDisorder Application. J Phys Chem B 122 (14), 3920-30.
14. Aprahamian, M.L.; Tikunova, S.B.; Price, M.V.; Cuesta, A.F.; Davis, J.P.; Lindert, S. (2017), Successful Identification of Cardiac Troponin Calcium Sensitizers Using a Combination of Virtual Screening and ROC Analysis of Known Troponin C Binders. J Chem Inf Model 57 (12), 3056-3069.
15. Leelananda, S.P.; Lindert, S. (2017), Iterative Molecular Dynamics-Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities. J Chem Theory Comput 13 (10), 5131-5145.
16. Leelananda, S.P.; Lindert, S. (2016), Computational methods in drug discovery. Beilstein J Org Chem 12, 2694–2718.
17. Dewan, S.; McCabe, K. J.; Regnier, M.; McCulloch, A. D.; Lindert, S. (2016), Molecular Effects of cTnC DCM Mutations on Calcium Sensitivity and Myofilament Activation – An Integrated Multi-Scale Modeling Study. J Phys Chem B 120 (33), 8264-75.