Side‐chain modeling with an optimized scoring function
By Shide Liang, Nick V. Grishin. 2002
140 citations recorded by the source corpus.
Subfields: 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 Materials Chemistry
- David Baker
- Jeffrey J. Gray
- Tanja Kortemme
- Brian Kuhlman
- Roland L. Dunbrack
- Andrew Leaver-Fay
- Yaoqi Zhou
- Yuedong Yang
In Molecular Biology
- David Baker
- Jeffrey J. Gray
- Tanja Kortemme
- Brian Kuhlman
- Roland L. Dunbrack
- Jens Meiler
- Andrew Leaver-Fay
- Yaoqi Zhou
Citations in the corpus
- Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, methodology): “Variants of the rotamer recovery test have long been used to evaluate molecular structure energy functions (Liang & Grishin, 2002; Petrella, Lazaridis, & Karplus, 1998), including extensive use to evaluate the Rosetta energy function (Dantas et al., 2007; Dobson, Dantas, Baker, & Varani, 2006;…”
- Cyclic coordinate descent: A robotics algorithm for protein loop closure (2003, background): “2000) and side-chain prediction methods (Bower et al. 1997; Dunbrack Jr. 1999; Mendes et al. 2001; Xiang and Honig 2001; Liang and Grishin 2002) have been significant in recent years, loop modeling remains a difficult task for several reasons.”
- A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions (2011, unknown): “844 Structure 19, 844–858, June 8, 2011 a2011 Elsevier Ltd All rights a term in scoring functions (Canutescu et al., 2003; Krivov et al., 2009; Liang and Grishin, 2002; Rohl et al., 2004b).”
- On the role of the crystal environment in determining protein side-chain conformations. (2002, background): “Prior treatments of solvation have been limited largely to the low accuracy but computationally convenient distance-dependent dielectric models,(20) – 26 and atomic solvation parameters or other simple surface-area dependent terms.(19,27) Our choice of energy function is capable of modeling all of t”
- Prediction of amino acid side chain conformation using a deep neural network (2017, methodology): “Second, a physics-based scoring function is used to evaluate the likelihood of the sampled conformations(8-11).”
- A graph‐theory algorithm for rapid protein side‐chain prediction (2003, unknown): “…as well as molecular mechanics potential energy functions with complex solvation free energy terms (Mendes et al. 2001a; Xiang and Honig 2001; Liang and Grishin 2002) For some emerging uses of side-chain prediction, fast calculations are a requirement These include Web-based homolog and…”
- Rapid Protein Side-Chain Packing via Tree Decomposition (2005, unknown): “Many side-chain prediction methods have been proposed and implemented in the past two decades [12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27].”
- Approximate Inference and Protein-Folding (2002, background): “However, in the last couple of years several groups reported better results due to more accurate energy function [7], better searching algorithm [8] , or extended rotamer library [14].”
- Energy functions in de novo protein design: current challenges and future prospects. (2013, background): “E surface and E volume are the contacting surface area and overlapping volume between a rotamer and surrounding protein atoms (68), respectively.”