Literature DB >> 20421891

Network models for molecular kinetics and their initial applications to human health.

Gregory R Bowman1, Xuhui Huang, Vijay S Pande.   

Abstract

Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models (MSMs), are typically built from physical simulations. Thus, they are capable of quantitative prediction of experiments and can also provide an intuition for complex conformational changes. Their primary application has been to protein folding; however, these technologies and the insights they yield are transferable. For example, MSMs have already proved useful in understanding human diseases, such as protein misfolding and aggregation in Alzheimer's disease.

Entities:  

Mesh:

Year:  2010        PMID: 20421891      PMCID: PMC4441225          DOI: 10.1038/cr.2010.57

Source DB:  PubMed          Journal:  Cell Res        ISSN: 1001-0602            Impact factor:   25.617


  58 in total

1.  High-resolution x-ray crystal structures of the villin headpiece subdomain, an ultrafast folding protein.

Authors:  Thang K Chiu; Jan Kubelka; Regine Herbst-Irmer; William A Eaton; James Hofrichter; David R Davies
Journal:  Proc Natl Acad Sci U S A       Date:  2005-05-13       Impact factor: 11.205

2.  Validation of Markov state models using Shannon's entropy.

Authors:  Sanghyun Park; Vijay S Pande
Journal:  J Chem Phys       Date:  2006-02-07       Impact factor: 3.488

3.  Coarse master equations for peptide folding dynamics.

Authors:  Nicolae-Viorel Buchete; Gerhard Hummer
Journal:  J Phys Chem B       Date:  2008-01-31       Impact factor: 2.991

4.  Simulation of the pressure and temperature folding/unfolding equilibrium of a small RNA hairpin.

Authors:  Angel E Garcia; Dietmar Paschek
Journal:  J Am Chem Soc       Date:  2007-12-23       Impact factor: 15.419

Review 5.  Long-timescale molecular dynamics simulations of protein structure and function.

Authors:  John L Klepeis; Kresten Lindorff-Larsen; Ron O Dror; David E Shaw
Journal:  Curr Opin Struct Biol       Date:  2009-04-08       Impact factor: 6.809

6.  Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint.

Authors:  Sergio Bacallado; John D Chodera; Vijay Pande
Journal:  J Chem Phys       Date:  2009-07-28       Impact factor: 3.488

Review 7.  The protein folding problem.

Authors:  Ken A Dill; S Banu Ozkan; M Scott Shell; Thomas R Weikl
Journal:  Annu Rev Biophys       Date:  2008       Impact factor: 12.981

8.  Constructing multi-resolution Markov State Models (MSMs) to elucidate RNA hairpin folding mechanisms.

Authors:  Xuhui Huang; Yuan Yao; Gregory R Bowman; Jian Sun; Leonidas J Guibas; Gunnar Carlsson; Vijay S Pande
Journal:  Pac Symp Biocomput       Date:  2010

9.  Kinetic analysis of molecular dynamics simulations reveals changes in the denatured state and switch of folding pathways upon single-point mutation of a beta-sheet miniprotein.

Authors:  Stefanie Muff; Amedeo Caflisch
Journal:  Proteins       Date:  2008-03

10.  The roles of entropy and kinetics in structure prediction.

Authors:  Gregory R Bowman; Vijay S Pande
Journal:  PLoS One       Date:  2009-06-09       Impact factor: 3.240

View more
  14 in total

1.  Simbios: an NIH national center for physics-based simulation of biological structures.

Authors:  Scott L Delp; Joy P Ku; Vijay S Pande; Michael A Sherman; Russ B Altman
Journal:  J Am Med Inform Assoc       Date:  2011-11-10       Impact factor: 4.497

2.  Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites.

Authors:  Gregory R Bowman; Phillip L Geissler
Journal:  Proc Natl Acad Sci U S A       Date:  2012-07-02       Impact factor: 11.205

Review 3.  Taming the complexity of protein folding.

Authors:  Gregory R Bowman; Vincent A Voelz; Vijay S Pande
Journal:  Curr Opin Struct Biol       Date:  2011-02       Impact factor: 6.809

4.  Atomistic folding simulations of the five-helix bundle protein λ(6−85).

Authors:  Gregory R Bowman; Vincent A Voelz; Vijay S Pande
Journal:  J Am Chem Soc       Date:  2011-02-02       Impact factor: 15.419

5.  The protein folding network indicates that the ultrafast folding mutant of villin headpiece subdomain has a deeper folding funnel.

Authors:  Hongxing Lei; Changjun Chen; Yi Xiao; Yong Duan
Journal:  J Chem Phys       Date:  2011-05-28       Impact factor: 3.488

6.  Discovery of multiple hidden allosteric sites by combining Markov state models and experiments.

Authors:  Gregory R Bowman; Eric R Bolin; Kathryn M Hart; Brendan C Maguire; Susan Marqusee
Journal:  Proc Natl Acad Sci U S A       Date:  2015-02-17       Impact factor: 11.205

7.  Improved coarse-graining of Markov state models via explicit consideration of statistical uncertainty.

Authors:  Gregory R Bowman
Journal:  J Chem Phys       Date:  2012-10-07       Impact factor: 3.488

8.  A chemical group graph representation for efficient high-throughput analysis of atomistic protein simulations.

Authors:  Noah C Benson; Valerie Daggett
Journal:  J Bioinform Comput Biol       Date:  2012-06-22       Impact factor: 1.122

9.  Quantitative comparison of alternative methods for coarse-graining biological networks.

Authors:  Gregory R Bowman; Luming Meng; Xuhui Huang
Journal:  J Chem Phys       Date:  2013-09-28       Impact factor: 3.488

10.  Mechanistic and structural insight into the functional dichotomy between IL-2 and IL-15.

Authors:  Aaron M Ring; Jian-Xin Lin; Dan Feng; Suman Mitra; Mathias Rickert; Gregory R Bowman; Vijay S Pande; Peng Li; Ignacio Moraga; Rosanne Spolski; Engin Ozkan; Warren J Leonard; K Christopher Garcia
Journal:  Nat Immunol       Date:  2012-10-28       Impact factor: 25.606

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.