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.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Wehrle, Janna P
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Stanford University
Schools of Arts and Sciences
United States
Zip Code
McGibbon, Robert T; Husic, Brooke E; Pande, Vijay S (2017) Identification of simple reaction coordinates from complex dynamics. J Chem Phys 146:044109
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
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; Sultan, Mohammad M; Hernández, Carlos X et al. (2017) MSMBuilder: Statistical Models for Biomolecular Dynamics. Biophys J 112:10-15
Liu, Bowen; Ramsundar, Bharath; Kawthekar, Prasad et al. (2017) Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models. ACS Cent Sci 3:1103-1113
Husic, Brooke E; McGibbon, Robert T; Sultan, Mohammad M et al. (2016) Optimized parameter selection reveals trends in Markov state models for protein folding. J Chem Phys 145:194103
Shukla, Diwakar; Peck, Ariana; Pande, Vijay S (2016) Conformational heterogeneity of the calmodulin binding interface. Nat Commun 7:10910
Schwantes, Christian R; Shukla, Diwakar; Pande, Vijay S (2016) Markov State Models and tICA Reveal a Nonnative Folding Nucleus in Simulations of NuG2. Biophys J 110:1716-1719
Harrigan, Matthew P; Shukla, Diwakar; Pande, Vijay S (2015) Conserve Water: A Method for the Analysis of Solvent in Molecular Dynamics. J Chem Theory Comput 11:1094-101
Weber, Jeffrey K; Pande, Vijay S (2015) Entropy-production-driven oscillators in simple nonequilibrium networks. Phys Rev E Stat Nonlin Soft Matter Phys 91:032136

Showing the most recent 10 out of 117 publications