9. A third, low-throughput approach (not described above) introduces mutations alchemically, one or two at a time, during
explicit solvent MD. It does not require any parameter adjustment. It was applied to Tiam1–peptide binding and gave even
higher accuracy [44].
References
1. Malisi C, Schumann M, Toussaint NC et al
(2012) Binding pocket optimization by
computational protein design. PLoS One 7:
e52505–e52505
2. Feldmeier K, Ho ¨cker B (2013) Computational
protein design of ligand binding and catalysis.
Curr Opin Chem Biol 17:929–933
3. Tinberg CE, Khare SD, Dou J et al (2013)
Computational design of ligand-binding proteins with high affinity and selectivity. Nature
501:212–216
4. Stoddard BL (ed) (2016) Methods in molecular biology: design and creation of ligand binding proteins. Springer Verlag, New York
5. Srinivasan J, Cheatham TE, Cieplak P et al
(1998) Continuum solvent studies of the stability of DNA, RNA, and phosphoramidateÀDNA helices. J Am Chem Soc 120
(37):9401–9409
6. Jorgensen WL (2004) The many roles of computation
in
drug
discovery.
Science
303:1813–1818
7. Brandsdal BO, O ¨ sterberg F, Almlo ¨f M et al
(2003) Free energy calculations and ligand
binding. Adv Protein Chem 66:123–158
8. Carlsson J, Ande ´r M, Nervall M et al (2006)
Continuum solvation models in the linear
interaction energy method. J Phys Chem B
110:12034–12041
9. Simonson T (2007) Free energy calculations:
approximate methods for biological macromolecules. Springer Ser Chem Phys 86:423–461
10. Gallicchio E, Levy RM (2011) Recent theoretical and computational advances for modeling
protein-ligand binding affinities. Adv Protein
Chem Struct Biol 85:27–80
11. Simonson T (2013) Protein: ligand recognition: simple models for electrostatic effects.
Curr Pharm Des 19:4241–4256
12. Harris RC, Mackoy T, Fenley MO (2015) Problems of robustness in Poisson-Boltzmann
binding free energies. J Chem Theory Comput
11:705–712
13. Wang C, Nguyen PH, Pham K et al (2016)
Calculating protein-ligand binding affinities
with MMPBSA: method and error analysis. J
Comput Chem 37:2436–2446
14. Chakavorty A, Li L, Alexov E (2016) Electrostatic component of binding energy: interpreting predictions from Poisson–Boltzmann
equation and modeling protocols. J Comput
Chem 37:2495–2507
15. Katkova EV, Onufriev AV, Aguilar B et al
(2017) Accuracy comparison of several common implicit solvent models and their implementations in the context of protein-ligand
binding. J Mol Graph Model 72:70–80
16. Simonson T, Gaillard T, Mignon D et al (2013)
Computational protein design: the proteus
software and selected applications. J Comput
Chem 34:2472–2484
17. Simonson T (2019) The Proteus software for
computational protein design, Ecole Polytechnique, Paris. https://proteus.polytechnique.fr
18. Li Z, Yang Y, Zhan J et al (2013) Energy functions in de novo protein design: current challenges and future prospects. Annu Rev Biophys
42:315–335
19. Alford RF, Leaver-Fay A, Jeliazkov JR et al
(2017) The Rosetta all-atom energy function
for macromolecular modeling and design. J
Chem Theory Comput 13:3031–3048
20. Simonson T (2015) The physical basis of ligand
binding. In: Cavasotto CN (ed) In silico drug
discovery and design: theory, methods, challenges, and applications. CRC Press
21. Roux B, Simonson T (1999) Implicit solvent
models. Biophys Chem 78:1–20
22. Villa F, Panel N, Chen X et al (2018) Adaptive
landscape flattening in amino acid sequence
space for the computational design of protein:
peptide
binding.
J
Chem
Phys
149:072302–072302
23. Panel N, Sun YJ, Fuentes EJ et al (2017) A
simple PB/LIE free energy function accurately
predicts the peptide binding specificity of the
Tiam1 PDZ domain. Front Mol Biosci
4:65–65
24. Saven JG (2010) Computational protein
design: advances in the design and redesign of
biomolecular nanostructures. Curr Opin Colloid Interface Sci 15:13–17
Computational Design of Binding
253
explicit solvent MD. It does not require any parameter adjustment. It was applied to Tiam1–peptide binding and gave even
higher accuracy [44].
References
1. Malisi C, Schumann M, Toussaint NC et al
(2012) Binding pocket optimization by
computational protein design. PLoS One 7:
e52505–e52505
2. Feldmeier K, Ho ¨cker B (2013) Computational
protein design of ligand binding and catalysis.
Curr Opin Chem Biol 17:929–933
3. Tinberg CE, Khare SD, Dou J et al (2013)
Computational design of ligand-binding proteins with high affinity and selectivity. Nature
501:212–216
4. Stoddard BL (ed) (2016) Methods in molecular biology: design and creation of ligand binding proteins. Springer Verlag, New York
5. Srinivasan J, Cheatham TE, Cieplak P et al
(1998) Continuum solvent studies of the stability of DNA, RNA, and phosphoramidateÀDNA helices. J Am Chem Soc 120
(37):9401–9409
6. Jorgensen WL (2004) The many roles of computation
in
drug
discovery.
Science
303:1813–1818
7. Brandsdal BO, O ¨ sterberg F, Almlo ¨f M et al
(2003) Free energy calculations and ligand
binding. Adv Protein Chem 66:123–158
8. Carlsson J, Ande ´r M, Nervall M et al (2006)
Continuum solvation models in the linear
interaction energy method. J Phys Chem B
110:12034–12041
9. Simonson T (2007) Free energy calculations:
approximate methods for biological macromolecules. Springer Ser Chem Phys 86:423–461
10. Gallicchio E, Levy RM (2011) Recent theoretical and computational advances for modeling
protein-ligand binding affinities. Adv Protein
Chem Struct Biol 85:27–80
11. Simonson T (2013) Protein: ligand recognition: simple models for electrostatic effects.
Curr Pharm Des 19:4241–4256
12. Harris RC, Mackoy T, Fenley MO (2015) Problems of robustness in Poisson-Boltzmann
binding free energies. J Chem Theory Comput
11:705–712
13. Wang C, Nguyen PH, Pham K et al (2016)
Calculating protein-ligand binding affinities
with MMPBSA: method and error analysis. J
Comput Chem 37:2436–2446
14. Chakavorty A, Li L, Alexov E (2016) Electrostatic component of binding energy: interpreting predictions from Poisson–Boltzmann
equation and modeling protocols. J Comput
Chem 37:2495–2507
15. Katkova EV, Onufriev AV, Aguilar B et al
(2017) Accuracy comparison of several common implicit solvent models and their implementations in the context of protein-ligand
binding. J Mol Graph Model 72:70–80
16. Simonson T, Gaillard T, Mignon D et al (2013)
Computational protein design: the proteus
software and selected applications. J Comput
Chem 34:2472–2484
17. Simonson T (2019) The Proteus software for
computational protein design, Ecole Polytechnique, Paris. https://proteus.polytechnique.fr
18. Li Z, Yang Y, Zhan J et al (2013) Energy functions in de novo protein design: current challenges and future prospects. Annu Rev Biophys
42:315–335
19. Alford RF, Leaver-Fay A, Jeliazkov JR et al
(2017) The Rosetta all-atom energy function
for macromolecular modeling and design. J
Chem Theory Comput 13:3031–3048
20. Simonson T (2015) The physical basis of ligand
binding. In: Cavasotto CN (ed) In silico drug
discovery and design: theory, methods, challenges, and applications. CRC Press
21. Roux B, Simonson T (1999) Implicit solvent
models. Biophys Chem 78:1–20
22. Villa F, Panel N, Chen X et al (2018) Adaptive
landscape flattening in amino acid sequence
space for the computational design of protein:
peptide
binding.
J
Chem
Phys
149:072302–072302
23. Panel N, Sun YJ, Fuentes EJ et al (2017) A
simple PB/LIE free energy function accurately
predicts the peptide binding specificity of the
Tiam1 PDZ domain. Front Mol Biosci
4:65–65
24. Saven JG (2010) Computational protein
design: advances in the design and redesign of
biomolecular nanostructures. Curr Opin Colloid Interface Sci 15:13–17
Computational Design of Binding
253