25. Pantazes RJ, Grisewood MJ, Maranas CD
(2011) Recent advances in computational protein design. Curr Opin Struct Biol 21:467–472
26. Der BS, Kuhlman B (2013) Strategies to control the binding mode of de novo designed
protein interactions. Curr Opin Struct Biol
23:639–646
27. Moal IH, Moretti R, Baker D et al (2013)
Scoring functions for protein-protein interactions. Curr Opin Struct Biol 23:862–867
28. Zanghellini A (2014) De novo computational
enzyme design. Curr Opin Biotechnol
29:132–138
29. Khoury GA, Smadbeck J, Kieslich CA et al
(2014) Protein folding and de novo protein
design for biotechnological applications.
Trends Biotechnol 32:99–109
30. Simonson T, Ye-Lehmann S, Palmai Z et al
(2016) Redesigning the stereospecificity of
tyrosyl-tRNA synthetase. Proteins 84:240–253
31. Cornell WD, Cieplak P, Bayly CI et al (1996) A
second generation force field for the simulation
of proteins, nucleic acids, and organic molecules
J.
Am.
Chem.
Soc.
1995,
117, 5179À5197. J Am Chem Soc
118:2309–2309
32. Michael E, Polydorides S, Simonson T et al
(2017) Simple models for nonpolar solvation:
parameterization and testing. J Comput Chem
38:2509–2519
33. Liu X, Shepherd TR, Murray AM et al (2013)
The structure of the Tiam1 PDZ domain/
phospho-syndecan1 complex reveals a ligand
conformation that modulates protein dynamics. Structure 21:342–354
34. Tuffery P, Etchebest C, Hazout S et al (1991)
A new approach to the rapid determination of
protein side chain conformations. J Biomol
Struct Dyn 8:1267–1289
35. Gaillard T, Panel N, Simonson T (2016) Protein side chain conformation predictions with
an MMGBSA energy function. Proteins
84:803–819
36. Druart K, Bigot J, Audit E et al (2016) A
hybrid Monte Carlo scheme for multibackbone
protein design. J Chem Theory Comput
12:6035–6048
37. Mignon D, Panel N, Chen X et al (2017)
Computational design of the Tiam1 PDZ
domain and its ligand binding. J Chem Theory
Comput 13:2271–2289
38. Polydorides S, Simonson T (2013) Monte
carlo simulations of proteins at constant pH
with generalized born solvent, flexible sidechains, and an effective dielectric boundary. J
Comput Chem 34:2742–2756
39. Villa F, Mignon D, Polydorides S et al (2017)
Comparing pairwise-additive and many-body
generalized born models for acid/base calculations and protein design. J Comput Chem
38:2396–2410
40. Frenkel D, Smit B (1996) Understanding
molecular simulation, Computational science
series, vol 1. Academic Press, New York
41. Mignon D, Simonson T (2016) Comparing
three stochastic search algorithms for computational protein design: Monte Carlo, replica
exchange Monte Carlo, and a multistart,
steepest-descent heuristic. J Comput Chem
37:1781–1793
42. Bhattacherjee A, Wallin S (2013) Exploring
protein-peptide binding specificity through
computational peptide screening. PLoS Comput Biol 9:e1003277
43. Shepherd TR, Hard RL, Murray AM et al
(2011) Distinct ligand specificity of the Tiam1
and Tiam2 PDZ domains. Biochemistry
50:1296–1308
44. Panel N, Villa F, Fuentes EJ et al (2018) Accurate PDZ/peptide binding specificity with
additive and polarizable free energy simulations. Biophys J 114:1091–1102
45. Songyang Z, Fanning AS, Fu C et al (1997)
Recognition of unique carboxyl-terminal
motifs by distinct PDZ domains. Science
275:73–77
46. Shepherd TR, Klaus SM, Liu X et al (2010)
The Tiam1 PDZ domain couples to syndecan1
and promotes cell-matrix adhesion. J Mol Biol
398:730–746
47. Liu X, Speckhard DC, Shepherd TR et al
(2016) Distinct roles for conformational
dynamics in protein-ligand interactions. Structure 24:2053–2066
48. Kuhn B, Kollman PA (2000) A ligand that is
predicted to bind better to avidin than biotin:
insights from computational fluorine scanning.
J Am Chem Soc 122:3909–3916
49. Wang L, Magliery TJ, Liu DR et al (2000) A
new functional suppressor tRNA/aminoacyltRNA synthetase pair for the in vivo incorporation of unnatural amino acids into proteins
[16]. J Am Chem Soc 122:5010–5011
50. Rastelli G, Del Rio A, Degliesposti G et al
(2010) Fast and accurate predictions of binding free energies using MM-PBSA and
MM-GBSA. J Comput Chem 31:797–810
51. Hou T, Wang J, Li Y et al (2011) Assessing the
performance of the MM/PBSA and
MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular
dynamics simulations. J Chem Inf Model
51:69–82
254
Nicolas Panel et al.
(2011) Recent advances in computational protein design. Curr Opin Struct Biol 21:467–472
26. Der BS, Kuhlman B (2013) Strategies to control the binding mode of de novo designed
protein interactions. Curr Opin Struct Biol
23:639–646
27. Moal IH, Moretti R, Baker D et al (2013)
Scoring functions for protein-protein interactions. Curr Opin Struct Biol 23:862–867
28. Zanghellini A (2014) De novo computational
enzyme design. Curr Opin Biotechnol
29:132–138
29. Khoury GA, Smadbeck J, Kieslich CA et al
(2014) Protein folding and de novo protein
design for biotechnological applications.
Trends Biotechnol 32:99–109
30. Simonson T, Ye-Lehmann S, Palmai Z et al
(2016) Redesigning the stereospecificity of
tyrosyl-tRNA synthetase. Proteins 84:240–253
31. Cornell WD, Cieplak P, Bayly CI et al (1996) A
second generation force field for the simulation
of proteins, nucleic acids, and organic molecules
J.
Am.
Chem.
Soc.
1995,
117, 5179À5197. J Am Chem Soc
118:2309–2309
32. Michael E, Polydorides S, Simonson T et al
(2017) Simple models for nonpolar solvation:
parameterization and testing. J Comput Chem
38:2509–2519
33. Liu X, Shepherd TR, Murray AM et al (2013)
The structure of the Tiam1 PDZ domain/
phospho-syndecan1 complex reveals a ligand
conformation that modulates protein dynamics. Structure 21:342–354
34. Tuffery P, Etchebest C, Hazout S et al (1991)
A new approach to the rapid determination of
protein side chain conformations. J Biomol
Struct Dyn 8:1267–1289
35. Gaillard T, Panel N, Simonson T (2016) Protein side chain conformation predictions with
an MMGBSA energy function. Proteins
84:803–819
36. Druart K, Bigot J, Audit E et al (2016) A
hybrid Monte Carlo scheme for multibackbone
protein design. J Chem Theory Comput
12:6035–6048
37. Mignon D, Panel N, Chen X et al (2017)
Computational design of the Tiam1 PDZ
domain and its ligand binding. J Chem Theory
Comput 13:2271–2289
38. Polydorides S, Simonson T (2013) Monte
carlo simulations of proteins at constant pH
with generalized born solvent, flexible sidechains, and an effective dielectric boundary. J
Comput Chem 34:2742–2756
39. Villa F, Mignon D, Polydorides S et al (2017)
Comparing pairwise-additive and many-body
generalized born models for acid/base calculations and protein design. J Comput Chem
38:2396–2410
40. Frenkel D, Smit B (1996) Understanding
molecular simulation, Computational science
series, vol 1. Academic Press, New York
41. Mignon D, Simonson T (2016) Comparing
three stochastic search algorithms for computational protein design: Monte Carlo, replica
exchange Monte Carlo, and a multistart,
steepest-descent heuristic. J Comput Chem
37:1781–1793
42. Bhattacherjee A, Wallin S (2013) Exploring
protein-peptide binding specificity through
computational peptide screening. PLoS Comput Biol 9:e1003277
43. Shepherd TR, Hard RL, Murray AM et al
(2011) Distinct ligand specificity of the Tiam1
and Tiam2 PDZ domains. Biochemistry
50:1296–1308
44. Panel N, Villa F, Fuentes EJ et al (2018) Accurate PDZ/peptide binding specificity with
additive and polarizable free energy simulations. Biophys J 114:1091–1102
45. Songyang Z, Fanning AS, Fu C et al (1997)
Recognition of unique carboxyl-terminal
motifs by distinct PDZ domains. Science
275:73–77
46. Shepherd TR, Klaus SM, Liu X et al (2010)
The Tiam1 PDZ domain couples to syndecan1
and promotes cell-matrix adhesion. J Mol Biol
398:730–746
47. Liu X, Speckhard DC, Shepherd TR et al
(2016) Distinct roles for conformational
dynamics in protein-ligand interactions. Structure 24:2053–2066
48. Kuhn B, Kollman PA (2000) A ligand that is
predicted to bind better to avidin than biotin:
insights from computational fluorine scanning.
J Am Chem Soc 122:3909–3916
49. Wang L, Magliery TJ, Liu DR et al (2000) A
new functional suppressor tRNA/aminoacyltRNA synthetase pair for the in vivo incorporation of unnatural amino acids into proteins
[16]. J Am Chem Soc 122:5010–5011
50. Rastelli G, Del Rio A, Degliesposti G et al
(2010) Fast and accurate predictions of binding free energies using MM-PBSA and
MM-GBSA. J Comput Chem 31:797–810
51. Hou T, Wang J, Li Y et al (2011) Assessing the
performance of the MM/PBSA and
MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular
dynamics simulations. J Chem Inf Model
51:69–82
254
Nicolas Panel et al.