Literature DB >> 15260581

Importance of chirality and reduced flexibility of protein side chains: a study with square and tetrahedral lattice models.

Jinfeng Zhang1, Yu Chen, Rong Chen, Jie Liang.   

Abstract

Side chains of amino acid residues are the determining factor that distinguishes proteins from other unstable chain polymers. In simple models they are often represented implicitly (e.g., by spin states) or simplified as one atom. Here we study side chain effects using two-dimensional square lattice and three-dimensional tetrahedral lattice models, with explicitly constructed side chains formed by two atoms of different chirality and flexibility. We distinguish effects due to chirality and effects due to side chain flexibilities, since residues in proteins are L residues, and their side chains adopt different rotameric states. For short chains, we enumerate exhaustively all possible conformations. For long chains, we sample effectively rare events such as compact conformations and obtain complete pictures of ensemble properties of conformations of these models at all compactness region. This is made possible by using sequential Monte Carlo techniques based on chain growth method. Our results show that both chirality and reduced side chain flexibility lower the folding entropy significantly for globally compact conformations, suggesting that they are important properties of residues to ensure fast folding and stable native structure. This corresponds well with our finding that natural amino acid residues have reduced effective flexibility, as evidenced by statistical analysis of rotamer libraries and side chain rotatable bonds. We further develop a method calculating the exact side chain entropy for a given backbone structure. We show that simple rotamer counting underestimates side chain entropy significantly for both extended and near maximally compact conformations. We find that side chain entropy does not always correlate well with main chain packing. With explicit side chains, extended backbones do not have the largest side chain entropy. Among compact backbones with maximum side chain entropy, helical structures emerge as the dominating configurations. Our results suggest that side chain entropy may be an important factor contributing to the formation of alpha helices for compact conformations. (c) 2004 American Institute of Physics.

Entities:  

Mesh:

Substances:

Year:  2004        PMID: 15260581     DOI: 10.1063/1.1756573

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  9 in total

1.  The role of protein homochirality in shaping the energy landscape of folding.

Authors:  Vikas Nanda; Aina Andrianarijaona; Chitra Narayanan
Journal:  Protein Sci       Date:  2007-06-28       Impact factor: 6.725

2.  Discrete state model and accurate estimation of loop entropy of RNA secondary structures.

Authors:  Jian Zhang; Ming Lin; Rong Chen; Wei Wang; Jie Liang
Journal:  J Chem Phys       Date:  2008-03-28       Impact factor: 3.488

3.  Multiscale Modeling of Cellular Epigenetic States: Stochasticity in Molecular Networks, Chromatin Folding in Cell Nuclei, and Tissue Pattern Formation of Cells.

Authors:  Jie Liang; Youfang Cao; Gamze Gursoy; Hammad Naveed; Anna Terebus; Jieling Zhao
Journal:  Crit Rev Biomed Eng       Date:  2015

4.  Rapid sampling of all-atom peptides using a library-based polymer-growth approach.

Authors:  Artem B Mamonov; Xin Zhang; Daniel M Zuckerman
Journal:  J Comput Chem       Date:  2010-08-23       Impact factor: 3.376

5.  Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity.

Authors:  Jie Liang; Hong Qian
Journal:  J Comput Sci Technol       Date:  2010-01       Impact factor: 1.571

6.  Statistical geometry of lattice chain polymers with voids of defined shapes: sampling with strong constraints.

Authors:  Ming Lin; Rong Chen; Jie Liang
Journal:  J Chem Phys       Date:  2008-02-28       Impact factor: 3.488

7.  On side-chain conformational entropy of proteins.

Authors:  Jinfeng Zhang; Jun S Liu
Journal:  PLoS Comput Biol       Date:  2006-12-08       Impact factor: 4.475

8.  Fast protein loop sampling and structure prediction using distance-guided sequential chain-growth Monte Carlo method.

Authors:  Ke Tang; Jinfeng Zhang; Jie Liang
Journal:  PLoS Comput Biol       Date:  2014-04-24       Impact factor: 4.475

9.  Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo.

Authors:  Kaixian Yu; Zihan Cui; Xin Sui; Xing Qiu; Jinfeng Zhang
Journal:  Front Genet       Date:  2021-11-29       Impact factor: 4.599

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.