Coarse-Grained Protein Models and Their Applications.

By Sebastian Kmiecik, Dominik Gront, Michal Kolinski, L. Wieteska, A. Dawid, Andrzej Kolinski. Chemical Reviews, 2016

952 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/0168aeaab05cc3ec7f9d6e78b570be5efb30384a

How researchers see this paper

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

In Materials Chemistry

1. Cecilia Clementi
2. Frank No'e
3. Alexandre M. J. J. Bonvin
4. Brooke E. Husic
5. Jeffrey J. Gray
6. Jiang Wang
7. Rodrigo Vargas Honorato
8. Guillaume Pagès

In Molecular Biology

1. Frank No'e
2. Cecilia Clementi
3. Brooke E. Husic
4. Jeffrey J. Gray
5. Alexandre M. J. J. Bonvin
6. Sai Pooja Mahajan
7. Rodrigo Vargas Honorato
8. Jorge Roel‐Touris

In Spectroscopy

1. Frank No'e
2. Cecilia Clementi
3. G. de Fabritiis
4. Jeffrey J. Gray
5. Alexandre M. J. J. Bonvin
6. Sebastian Kmiecik
7. Wenhao Gao
8. Andrzej Kolinski

Citations in the corpus

• Efficient Flexible Backbone Protein-Protein Docking for Challenging Targets (2017, neutral): “…r g e t s a r e c o m b i n e d , c o m p r i s i n g a ll t a r g e t s w i t hou t a t l e a s t on e h i g h - qu a li t y p r e d i c t i on b y a n y p r e d i c t o r . f L e n s i nk e t a l. ( 2016 ) . g H w a n g e t a l. ( 2010 ) . h V r e v e n e t a l. ( 2015 ) . granted stock options.”
• Prediction of protein disorder based on IUPred (2018, background): “31 However, such applications were limited to cases when a structural model was available.”
• Coarse graining molecular dynamics with graph neural networks. (2020, background): “…B.E.H., N.E.C., and D.L. contributed equally to this work. b) B.E.H., N.E.C., and D.L. contributed equally to this work. c) Electronic mail: gianni.defabritiis@upf.edu d ) Electronic mail: frank.noe@fu-berlin.de e ) Electronic mail: cecilia.clementi@fu-berlin.de and protein interactions 7 .”
• Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview (2018, background): “A first example consists of the Coarse-Grained MD simulations, in which groups of atoms are condensed into spheres, reducing the degrees of freedom of the system (Kmiecik et al., 2016).”
• Smooth orientation-dependent scoring function for coarse-grained protein quality assessment (2018, background): “A possibility of working in a simplified coarse-grained representation of amino acids, as in (14), overcomes this issue and also reduces the overall computational complexity.”
• Protein storytelling through physics (2020, background): “Coarse-grained modeling lumps together atoms into larger rigid units ( 23 – 26 ).”
• Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development (2020, positive): “The Martini force field, one of the first force fields for CGMD, has seen important applications and developments since its first installment and is now also employed for the analysis of lipid membrane properties, protein–lipid interactions, oligomerization of membrane proteins, the self-assembly of”
• CABS-flex 2.0: a web server for fast simulations of flexibility of protein structures (2018, methodology): “gine (the CABS model design and applications have been recently reviewed (1)).”
• MARTINI-Based Protein-DNA Coarse-Grained HADDOCKing (2019, background): “To this end, CG approaches group several atoms (either a few atoms or entire side chains) into larger “pseudo-atoms” or “beads,” which results into a reduction in the number of degrees of freedom of the system (Kmiecik et al., 2016).”
• Deep Learning in Protein Structural Modeling and Design (2020, background): “Coarse graining smoothens out the energy landscape, and thereby helps avoid trapping in local minima and speeds up conformational sampling.(135) One can learn the atomic-level properties to construct a fast and accurate neural coarse-grained model once the coarse-grained mapping is given.”