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Items: 1 to 20 of 99

1.

Investigating Cryptic Binding Sites by Molecular Dynamics Simulations.

Kuzmanic A, Bowman GR, Juarez-Jimenez J, Michel J, Gervasio FL.

Acc Chem Res. 2020 Mar 17;53(3):654-661. doi: 10.1021/acs.accounts.9b00613. Epub 2020 Mar 5.

PMID:
32134250
2.

Investigating drug-target association and dissociation mechanisms using metadynamics-based algorithms.

Cavalli A, Spitaleri A, Saladino G, Gervasio FL.

Acc Chem Res. 2015 Feb 17;48(2):277-85. doi: 10.1021/ar500356n. Epub 2014 Dec 12. Review.

PMID:
25496113
3.

Understanding Cryptic Pocket Formation in Protein Targets by Enhanced Sampling Simulations.

Oleinikovas V, Saladino G, Cossins BP, Gervasio FL.

J Am Chem Soc. 2016 Nov 2;138(43):14257-14263. Epub 2016 Oct 20.

4.

Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012).

Foffi G, Pastore A, Piazza F, Temussi PA.

Phys Biol. 2013 Aug;10(4):040301. Epub 2013 Aug 2.

PMID:
23912807
5.

CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites.

Cimermancic P, Weinkam P, Rettenmaier TJ, Bichmann L, Keedy DA, Woldeyes RA, Schneidman-Duhovny D, Demerdash ON, Mitchell JC, Wells JA, Fraser JS, Sali A.

J Mol Biol. 2016 Feb 22;428(4):709-719. doi: 10.1016/j.jmb.2016.01.029. Epub 2016 Feb 5.

6.

Cosolvent-Enhanced Sampling and Unbiased Identification of Cryptic Pockets Suitable for Structure-Based Drug Design.

Schmidt D, Boehm M, McClendon CL, Torella R, Gohlke H.

J Chem Theory Comput. 2019 May 14;15(5):3331-3343. doi: 10.1021/acs.jctc.8b01295. Epub 2019 May 6.

PMID:
30998331
7.
8.

Structure-Based Analysis of Cryptic-Site Opening.

Sun Z, Wakefield AE, Kolossvary I, Beglov D, Vajda S.

Structure. 2020 Feb 4;28(2):223-235.e2. doi: 10.1016/j.str.2019.11.007. Epub 2019 Dec 3.

PMID:
31810712
9.

Cryptic binding sites on proteins: definition, detection, and druggability.

Vajda S, Beglov D, Wakefield AE, Egbert M, Whitty A.

Curr Opin Chem Biol. 2018 Jun;44:1-8. doi: 10.1016/j.cbpa.2018.05.003. Epub 2018 May 23. Review.

10.

Exploring Cryptic Pockets Formation in Targets of Pharmaceutical Interest with SWISH.

Comitani F, Gervasio FL.

J Chem Theory Comput. 2018 Jun 12;14(6):3321-3331. doi: 10.1021/acs.jctc.8b00263. Epub 2018 May 25.

PMID:
29768914
11.

PlayMolecule CrypticScout: Predicting Protein Cryptic Sites using Mixed-Solvent Molecular Simulations.

Martinez-Rosell G, Lovera S, Sands ZA, De Fabritiis G.

J Chem Inf Model. 2020 Mar 16. doi: 10.1021/acs.jcim.9b01209. [Epub ahead of print]

PMID:
32175736
12.

Exploring the structural origins of cryptic sites on proteins.

Beglov D, Hall DR, Wakefield AE, Luo L, Allen KN, Kozakov D, Whitty A, Vajda S.

Proc Natl Acad Sci U S A. 2018 Apr 10;115(15):E3416-E3425. doi: 10.1073/pnas.1711490115. Epub 2018 Mar 26.

13.
14.

Deciphering Cryptic Binding Sites on Proteins by Mixed-Solvent Molecular Dynamics.

Kimura SR, Hu HP, Ruvinsky AM, Sherman W, Favia AD.

J Chem Inf Model. 2017 Jun 26;57(6):1388-1401. doi: 10.1021/acs.jcim.6b00623. Epub 2017 Jun 8.

PMID:
28537745
15.

A Collective Variable for the Rapid Exploration of Protein Druggability.

Cuchillo R, Pinto-Gil K, Michel J.

J Chem Theory Comput. 2015 Mar 10;11(3):1292-307. doi: 10.1021/ct501072t.

PMID:
26579775
16.

Molecular Dynamics Simulation and Prediction of Druggable Binding Sites.

Feng T, Barakat K.

Methods Mol Biol. 2018;1762:87-103. doi: 10.1007/978-1-4939-7756-7_6.

PMID:
29594769
17.

Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites.

Bowman GR, Geissler PL.

Proc Natl Acad Sci U S A. 2012 Jul 17;109(29):11681-6. doi: 10.1073/pnas.1209309109. Epub 2012 Jul 2.

18.

Designing small molecules to target cryptic pockets yields both positive and negative allosteric modulators.

Hart KM, Moeder KE, Ho CMW, Zimmerman MI, Frederick TE, Bowman GR.

PLoS One. 2017 Jun 1;12(6):e0178678. doi: 10.1371/journal.pone.0178678. eCollection 2017.

19.

Computational approaches to identifying and characterizing protein binding sites for ligand design.

Henrich S, Salo-Ahen OM, Huang B, Rippmann FF, Cruciani G, Wade RC.

J Mol Recognit. 2010 Mar-Apr;23(2):209-19. doi: 10.1002/jmr.984.

PMID:
19746440
20.

Site Identification by Ligand Competitive Saturation (SILCS) simulations for fragment-based drug design.

Faller CE, Raman EP, MacKerell AD Jr, Guvench O.

Methods Mol Biol. 2015;1289:75-87. doi: 10.1007/978-1-4939-2486-8_7.

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