Literature DB >> 21743800

Assessing protein loop flexibility by hierarchical Monte Carlo sampling.

Jerome Nilmeier1, Lan Hua, Evangelos A Coutsias, Matthew P Jacobson.   

Abstract

Loop flexibility is often crucial to protein biological function in solution. We report a new Monte Carlo method for generating conformational ensembles for protein loops and cyclic peptides. The approach incorporates the triaxial loop closure method which addresses the inverse kinematic problem for generating backbone move sets that do not break the loop. Sidechains are sampled together with the backbone in a hierarchical way, making it possible to make large moves that cross energy barriers. As an initial application, we apply the method to the flexible loop in triosephosphate isomerase that caps the active site, and demonstrate that the resulting loop ensembles agree well with key observations from previous structural studies. We also demonstrate, with 3 other test cases, the ability to distinguish relatively flexible and rigid loops within the same protein.

Entities:  

Year:  2011        PMID: 21743800      PMCID: PMC3129859          DOI: 10.1021/ct1006696

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  32 in total

1.  Cyclic coordinate descent: A robotics algorithm for protein loop closure.

Authors:  Adrian A Canutescu; Roland L Dunbrack
Journal:  Protein Sci       Date:  2003-05       Impact factor: 6.725

2.  A kinematic view of loop closure.

Authors:  Evangelos A Coutsias; Chaok Seok; Matthew P Jacobson; Ken A Dill
Journal:  J Comput Chem       Date:  2004-03       Impact factor: 3.376

3.  A hierarchical approach to all-atom protein loop prediction.

Authors:  Matthew P Jacobson; David L Pincus; Chaya S Rapp; Tyler J F Day; Barry Honig; David E Shaw; Richard A Friesner
Journal:  Proteins       Date:  2004-05-01

4.  Structure of yeast triosephosphate isomerase at 1.9-A resolution.

Authors:  E Lolis; T Alber; R C Davenport; D Rose; F C Hartman; G A Petsko
Journal:  Biochemistry       Date:  1990-07-17       Impact factor: 3.162

Review 5.  Protein structure prediction.

Authors:  B Al-Lazikani; J Jung; Z Xiang; B Honig
Journal:  Curr Opin Chem Biol       Date:  2001-02       Impact factor: 8.822

6.  On the structure of the inverse kinematics map of a fragment of protein backbone.

Authors:  R J Milgram; Guanfeng Liu; J C Latombe
Journal:  J Comput Chem       Date:  2008-01-15       Impact factor: 3.376

7.  Multiscale Monte Carlo Sampling of Protein Sidechains: Application to Binding Pocket Flexibility.

Authors:  Jerome Nilmeier; Matt Jacobson
Journal:  J Chem Theory Comput       Date:  2008-05       Impact factor: 6.006

Review 8.  Progress in protein structure prediction.

Authors:  D T Jones
Journal:  Curr Opin Struct Biol       Date:  1997-06       Impact factor: 6.809

9.  Structure of the triosephosphate isomerase-phosphoglycolohydroxamate complex: an analogue of the intermediate on the reaction pathway.

Authors:  R C Davenport; P A Bash; B A Seaton; M Karplus; G A Petsko; D Ringe
Journal:  Biochemistry       Date:  1991-06-18       Impact factor: 3.162

10.  Small molecule recognition of c-Src via the Imatinib-binding conformation.

Authors:  Arvin C Dar; Michael S Lopez; Kevan M Shokat
Journal:  Chem Biol       Date:  2008-10-20
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  7 in total

Review 1.  Constraint methods that accelerate free-energy simulations of biomolecules.

Authors:  Alberto Perez; Justin L MacCallum; Evangelos A Coutsias; Ken A Dill
Journal:  J Chem Phys       Date:  2015-12-28       Impact factor: 3.488

2.  Exhaustive Conformational Sampling of Complex Fused Ring Macrocycles Using Inverse Kinematics.

Authors:  Evangelos A Coutsias; Katrina W Lexa; Michael J Wester; Sara N Pollock; Matthew P Jacobson
Journal:  J Chem Theory Comput       Date:  2016-08-04       Impact factor: 6.006

3.  Force distribution in a semiflexible loop.

Authors:  James T Waters; Harold D Kim
Journal:  Phys Rev E       Date:  2016-04-18       Impact factor: 2.529

4.  Distance-Guided Forward and Backward Chain-Growth Monte Carlo Method for Conformational Sampling and Structural Prediction of Antibody CDR-H3 Loops.

Authors:  Ke Tang; Jinfeng Zhang; Jie Liang
Journal:  J Chem Theory Comput       Date:  2016-12-20       Impact factor: 6.006

Review 5.  Computational design of structured loops for new protein functions.

Authors:  Kale Kundert; Tanja Kortemme
Journal:  Biol Chem       Date:  2019-02-25       Impact factor: 4.700

Review 6.  Conformational sampling in template-free protein loop structure modeling: an overview.

Authors:  Yaohang Li
Journal:  Comput Struct Biotechnol J       Date:  2013-02-25       Impact factor: 7.271

7.  Segmenting Proteins into Tripeptides to Enhance Conformational Sampling with Monte Carlo Methods.

Authors:  Laurent Denarie; Ibrahim Al-Bluwi; Marc Vaisset; Thierry Siméon; Juan Cortés
Journal:  Molecules       Date:  2018-02-09       Impact factor: 4.411

  7 in total

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