Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction

By Payel Das, Mark Moll, Hernan F. Stamati, Lydia E. Kavraki, Cecilia Clementi. 2006

317 citations recorded by the source corpus.

The definition of reaction coordinates for the characterization of a protein-folding reaction has long been a controversial issue, even for the "simple" case in which one single free-energy barrier separates the folded and unfolded ensemble. We propose a general approach to this problem to obtain a few collective coordinates by using nonlinear dimensionality reduction. We validate the usefulness of this method by characterizing the folding landscape associated with a coarse-grained protein model of src homology 3 as sampled by molecular dynamics simulations. The folding free-energy landscape projected on the few relevant coordinates emerging from the dimensionality reduction can correctly identify the transition-state ensemble of the reaction. The first embedding dimension efficiently captures the evolution of the folding process along the main folding route. These results clearly show that the proposed method can efficiently find a low-dimensional representation of a complex process such as protein folding.

Subfields: Condensed Matter Physics, Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Condensed Matter Physics

1. Ronald R. Coifman
2. Stéphane Lafon
3. Boaz Nadler
4. Stanley J. Osher
5. Guy Gilboa
6. Amit Singer
7. T. Sauer
8. Tyrus Berry

In Materials Chemistry

1. Xin Gao
2. Yu Li
3. Ioannis G. Kevrekidis
4. Steven L. Brunton
5. Igor Mezi'c
6. J. Nathan Kutz

In Molecular Biology

1. Xin Gao
2. Ronald R. Coifman
3. Boaz Nadler
4. Chao Huang
5. Yu Li
6. Ioannis G. Kevrekidis
7. Stéphane Lafon
8. Lizhong Ding

Citations in the corpus

• Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, methodology): “In addition to gene expression data [117], this example can be adopted to other applications, which encounter the high dimensionality issue, such as protein fingerprints [26].”
• Diffusion Maps, Reduction Coordinates, and Low Dimensional Representation of Stochastic Systems (2008, methodology): “Our work is also related to the application of other dimensionality reduction techniques to data from molecular dynamics simulations, such as [14].”
• Enhanced Sampling in Molecular Dynamics Using Metadynamics, Replica-Exchange, and Temperature-Acceleration (2013, unknown): “, [169–171]).”