Due the limitations of both simulation and experiment, an ultimate understanding of protein folding will come from a coupled approach of detailed simulations extensively validated and tested by experiment. However, developing simulation methodology which can quantitatively connect with experimental kinetics still remains a great theoretical challenge, due to the long timescales involved and the difficulties and complexities of detailed, atomistic models. Here, we propose new, third generation distributed computing methods to tackle these challenges and the application of these methods to questions related to how proteins self-assemble in solution as well as in the biologically relevant contexts. While protein folding has itself been studied computationally for many years, our work differs from other approaches in (1) its use of innovative distributed computing methods for simulating long, biologically relevant time scale kinetics (on the millisecond to second timescale - dramatically longer than the previous state of the art) and for large and complex proteins (on the 80 to 150 amino acid length scale) using detailed, fully atomistic, explicit solvent models and (2) the application of these detailed models to address questions of folding in the biological contexts of different environments in the cell. Moreover, we are able to perform a quantitative comparison to experiment, which is critical for both the testing and greater impact of our computational methods;indeed, key experimental collaborations using cutting edge methods are proposed to make direct connections to our proposed simulations. Finally, the proposed work would have an impact on our basic understanding of several protein-related diseases, such protein misfolding diseases, such as Alzheimer's Disease and Huntington's Disease. Indeed, methodology from the previous project period has already lead to advances in the simulation of peptide aggregation in Alzheimer's and Huntington's Disease. Also, by understanding the nature of folding in biological contexts, such as in the presence of membranes, in biologically confined spaces, and with crowding agents, and by directly comparing those simulations to novel experiments of folding in the cell, we would gain insight into the nature of protein folding in vivo, which is the next important step in our understanding of protein folding and its connection to biology and biomedical questions.

Public Health Relevance

The process by which proteins (key building blocks in our body) assemble (or """"""""fold"""""""") is a critical part of the central dogma of life, but yet is still poorly understood due to immense challenges both experimentally and theoretically. Moreover, numerous diseases, such as Alzheimer's Disease and Huntington's Disease, result from protein misfolding. Here, we propose novel methods to tackle the protein folding problem, at an unprecedented scale, using novel theoretical methods, new analysis tools, and the most powerful computer cluster in the world, Folding@home.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM062868-11
Application #
8447492
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Wehrle, Janna P
Project Start
2002-07-01
Project End
2015-03-31
Budget Start
2013-04-01
Budget End
2014-03-31
Support Year
11
Fiscal Year
2013
Total Cost
$366,328
Indirect Cost
$131,906
Name
Stanford University
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Feinberg, Evan N; Sur, Debnil; Wu, Zhenqin et al. (2018) PotentialNet for Molecular Property Prediction. ACS Cent Sci 4:1520-1530
Sinitskiy, Anton V; Pande, Vijay S (2018) Computer Simulations Predict High Structural Heterogeneity of Functional State of NMDA Receptors. Biophys J 115:841-852
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Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N et al. (2018) MoleculeNet: a benchmark for molecular machine learning. Chem Sci 9:513-530
McKiernan, Keri A; Husic, Brooke E; Pande, Vijay S (2017) Modeling the mechanism of CLN025 beta-hairpin formation. J Chem Phys 147:104107
McGibbon, Robert T; Husic, Brooke E; Pande, Vijay S (2017) Identification of simple reaction coordinates from complex dynamics. J Chem Phys 146:044109
Sinitskiy, Anton V; Stanley, Nathaniel H; Hackos, David H et al. (2017) Computationally Discovered Potentiating Role of Glycans on NMDA Receptors. Sci Rep 7:44578
Harrigan, Matthew P; McKiernan, Keri A; Shanmugasundaram, Veerabahu et al. (2017) Markov modeling reveals novel intracellular modulation of the human TREK-2 selectivity filter. Sci Rep 7:632
Husic, Brooke E; Pande, Vijay S (2017) Note: MSM lag time cannot be used for variational model selection. J Chem Phys 147:176101
Lopez, Tom; Dalton, Kevin; Tomlinson, Anthony et al. (2017) An information theoretic framework reveals a tunable allosteric network in group II chaperonins. Nat Struct Mol Biol 24:726-733

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