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Jerome Nilmeier is currently a postdoctoral researcher in the Computational Materials Science Group. His original appointment was in the Biosciences and Biotechnology division, where he developed and implemented a variety of techniques for automatically detecting the function of a protein based on given sequence. This included structure prediction and refinement, as well as docking and molecular dynamics studies. From this work, an automated in-house catalytic site identification procedure was developed using a combination of graph theoretic search techniques and machine learning approaches.
He has developed and implemented variety of Monte Carlo techniques, primarily in the context of biochemical simulation. The original approaches included a combination of exact and approximate methods designed to address specific issues arising in sampling of proteins, which include loop closure algorithms, rotamer sampling methods, as well as surface generalized Born models of protein solvation. From this work, exact Monte Carlo approaches which have broader applicability and that rely on nonequilibrium theorems were developed, which can be used not only to develop efficient simulation procedures, but also to analyze nonequilibrium data. He is currently developing parallelization techniques for kinetic Monte Carlo algorithms with applications to lattice dynamics in materials.
My general interests are in developing new high performance algorithms and theoretical approaches for simulating and understanding complex molecular and biomolecular systems.
Nilmeier, J. P., Kirshner, D.A., Wong, S.E., Lightstone, F. C. (2013). "Rapid Catalytic Template Searching as an Enzyme Function Prediction Procedure." PLoS ONE, 8(5): p.e62535
Nilmeier, J. P., Kirshner, D.A., Lightstone, F. C. (2013). "Catalytic site identification - a web server to identify catalytic site structural matches throughout PDB" (2013) Nucleic Acids Research. (Current url: http://catsid.llnl.gov/ )
Nilmeier, J. P., G. E. Crooks, et al. (2011). "Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation." Proceedings of the National Academy of Sciences.
Nilmeier, J., L. Hua, et al. (2011). "Assessing Protein Loop Flexibility by Hierarchical Monte Carlo Sampling." Journal of chemical theory and computation.
Sellers, B. D., J. P. Nilmeier, et al. (2010). "Antibodies as a model system for comparative model refinement." Proteins: Structure, Function, and Bioinformatics 78(11): 2490-2505.
Nilmeier, J. and M. P. Jacobson (2009). "Monte Carlo Sampling with Hierarchical Move Sets: POSH Monte Carlo." Journal of chemical theory and computation 5(8): 1968-1984.
Nilmeier, J. and M. Jacobson (2008). "Multiscale Monte Carlo sampling of protein sidechains: Application to binding pocket flexibility." Journal of chemical theory and computation 4(5): 835-846.