William G. Noid

William G. Noid

Main Content

  • Professor of Chemistry
Office:
528 Chemistry Building
University Park, PA 16802
Email:
(814) 867-2387

Education:

  1. B.S. University of Tennessee, Knoxville 2000.
  2. Ph.D. Cornell University, 2005.

Honors and Awards:

  1. Camille Dreyfus Teacher-Scholar Award 2012
  2. NSF Career Award 2011
  3. Alfred P. Sloan Foundation Research Fellow 2011
  4. Penn State Institute for CyberScience Faculty Fellow 2011
  5. HP Outstanding Junior Faculty Award from the ACS Division of Computers in Chemistry 2010
  6. NIH Ruth L. Kirschstein NRSA postdoctoral fellow 2006-2007
  7. Tunis Wentink Prize (co-recipient) 2005
  8. Wachter award in Theoretical/Physical Chemistry 2003
  9. NSF Graduate Research Fellow 2002-2005
  10. NSF IGERT Fellow in nonlinear dynamics and complex systems 2000-2002

Selected Publications:

Kathryn M. Lebold, and William G. Noid. “Dual approach for effective potentials that accurately model structure and energetics.” J Chem Phys 150 : 234107 (2019).

Nicholas J.H. Dunn, Kathryn M. Lebold, Michael R. DeLyser, Joseph F. Rudzinski, and William George Noid. “BOCS: Bottom-Up Open-Source Coarse-Graining Software.” J Phys Chem B 122 : 3363 (2018).

Yi-Ting Liao,* Anthony C. Manson,* Michael R. DeLyser,* William G. Noid, and Paul S. Cremer  (2017).  “Trimethylamino N-oxide (TMAO) stabilizes proteins via a distinct mechanism compared with betaine and glycine.”  Proc. Natl. Acad. Sci. USA.  114: 2479 (2017).

Nicholas J.H. Dunn,* Thomas T. Foley,* and W. G. Noid (2016). “van der Waals perspective on coarse-graining: Progress toward solving representability and transferability problems.”  Acct. Chem. Res. 49 2832-2840.  DOI: 10.1021/acs.accounts.6b00498

Joseph F. Rudzinski and William G. Noid. “Bottom-up coarse-graining of peptide ensembles and helix-coil transitions.” J. Chem. Theor. Comput. 11 1278-91 (2015).

Chad W. Lawrence, Sushant Kumar, William G. Noid, and Scott A. Showalter. “The role of ordered proteins in the folding-upon-binding of intrinsically disordered proteins.” J. Phys. Chem. Lett. 5 833-8 (2014).

W. G. Noid “Perspective: Coarse-grained models for biomolecular systems.” J. Chem. Phys. 139 090901 (2013).

Christopher R. Ellis, Buddhadev Maiti, and W. G. Noid.  "Specific and Nonspecific Effects of Glycosylation."  J. Am. Chem. Soc. 134 8184-93 (2012).

Joseph F. Rudzinski and W. G. Noid "Coarse-graining entropy, forces, and structures." J. Chem. Phys. 135 214101 (2011).

J.W. Mullinax and W.G. Noid.  “Recovering physical potentials from a model protein databank.” Proc. Natl. Acad. Sci. USA 107 (46) 19867-72. (2010).

J. W. Mullinax and W. G. Noid, “Generalized Yvon-Born-Green theory for molecular systems.” Phys. Rev. Lett. 103 198104 (2009).

* Equal contributing authors

Information:

Theories and computational methods for multiscale modeling of soft materials, such as liquids, peptides, and polymers; Statistical mechanical theories for chemical biology and materials science; Non-equilibrium theories for active materials; Theory and simulation for complex protein and polymer solutions.

Multiscale modeling

Our group primarily focuses on developing rigorous statistical mechanical theories and computational methodologies for multiscale modeling.

Despite remarkable advances in computational methods and resources, atomically detailed simulations remain prohibitively time-consuming for modeling many processes of interest, e.g., protein-protein interactions or self-assembly. These considerations have motivated tremendous interest in developing much more efficient “coarse-grained (CG) models.”  These CG models typically represent each molecule with relatively few interaction sites that each correspond to groups of one or more atoms.  By employing such a reduced representation of the system, CG models provide the computational efficiency that is necessary for addressing the physically relevant time- and length scales, as well as for providing adequate statistical sampling.  Furthermore, CG approaches also provide important conceptual advantages by eliminating “unnecessary” atomic details and focusing attention on the essential features of a system.  Consequently, CG models hold great promise for modeling many important “soft” materials, including liquids, surfactants, bilayers, polymers, proteins, nucleic acids, etc.

Of course, the utility of CG models fundamentally depends upon their ability to accurately describe the “correct physics” governing the phenomenon of interest.  However, it remains challenging to determine the appropriate interactions between the CG sites. Moreover, a CG model that is parameterized to accurately model a particular protein, may not be “transferable” to other proteins, i.e., a CG model optimized for a particular protein may not be an accurate model for other proteins. Furthermore, CG models that are parameterized to reproduce structural properties often provide a poor description of thermodynamic properties.

Our research group derives and implements rigorous multiscale methods for addressing these challenges by developing accurate CG models on the basis of detailed simulations or, ultimately, experimental data.  Our recent generalization of the Yvon-Born-Green theory provides the first variational framework for directly (i.e., noniteratively) determining, from structural information alone, the set of potentials that provide an optimal approximation to the many-body potential of mean force for complex molecular systems.   We have introduced an extended ensemble framework as a variational theory for optimizing the transferability of CG potentials for modeling multiple systems.  We have established an information theoretic basis for this framework and have provided basic insight into the role of resolution for CG models.  We have developed theory and computational methods for accurately modeling thermodynamic properties, such as pressures and energetics.  Furthermore, we have extended these approaches for modeling inhomogeneous systems.  These advances direct our current work in attempting to realize the promise of multiscale modeling methods for complex systems.

Theory and simulation for biophysics

We also collaborate with experimentalists to address interesting questions regarding biochemistry and active materials.  Most recently, we have been developing theory and performing simulations to interpret experimental measurements by the Cremer laboratory at PSU.  In particular, we have been examining the effects of osmolytes (small neutral organic molecules) upon the thermodynamic stability of proteins and other macromolecules.  Previously, we have collaborated with the Showalter lab to investigate the biophysical properties of intrinsically disordered proteins and have also performed simulations to elucidate the effects of glycosylation upon protein folding.

Research Interests:

Biological

Theories of statistical mechanics applied to investigate structural biology

Computational / Theoretical

Statistical mechanics of unfolded proteins

Physical

Theories of statistical mechanics applied to investigate structural biology