John Karanicolas
John Karanicolas
Molecular Therapeutics Program, Fox Chase Cancer Center
Verified email at - Homepage
Cited by
Cited by
ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules
A Leaver-Fay, M Tyka, SM Lewis, OF Lange, J Thompson, R Jacak, ...
Methods in enzymology 487, 545-574, 2011
MMTSB Tool Set: enhanced sampling and multiscale modeling methods for applications in structural biology
M Feig, J Karanicolas, CL Brooks III
Journal of Molecular Graphics and Modelling 22 (5), 377-395, 2004
Macromolecular modeling and design in Rosetta: recent methods and frameworks
JK Leman, BD Weitzner, SM Lewis, J Adolf-Bryfogle, N Alam, RF Alford, ...
Nature methods 17 (7), 665-680, 2020
Structure-based design of non-natural amino-acid inhibitors of amyloid fibril formation
SA Sievers, J Karanicolas, HW Chang, A Zhao, L Jiang, O Zirafi, ...
Nature 475 (7354), 96-100, 2011
The 3D profile method for identifying fibril-forming segments of proteins
MJ Thompson, SA Sievers, J Karanicolas, MI Ivanova, D Baker, ...
Proceedings of the National Academy of Sciences 103 (11), 4074-4078, 2006
The origins of asymmetry in the folding transition states of protein L and protein G
J Karanicolas, CL Brooks III
Protein Science 11 (10), 2351-2361, 2002
Atomic accuracy in predicting and designing noncanonical RNA structure
R Das, J Karanicolas, D Baker
Nature methods 7 (4), 291-294, 2010
Allostery in its many disguises: from theory to applications
SJ Wodak, E Paci, NV Dokholyan, IN Berezovsky, A Horovitz, J Li, ...
Structure 27 (4), 566-578, 2019
Improved Gō-like models demonstrate the robustness of protein folding mechanisms towards non-native interactions
J Karanicolas, CL Brooks III
Journal of molecular biology 334 (2), 309-325, 2003
Musashi RNA-binding proteins as cancer drivers and novel therapeutic targets
AE Kudinov, J Karanicolas, EA Golemis, Y Boumber
Clinical Cancer Research 23 (9), 2143-2153, 2017
Targeting CDK9 reactivates epigenetically silenced genes in cancer
H Zhang, S Pandey, M Travers, H Sun, G Morton, J Madzo, W Chung, ...
Cell 175 (5), 1244-1258. e26, 2018
A de novo protein binding pair by computational design and directed evolution
J Karanicolas, JE Corn, I Chen, LA Joachimiak, O Dym, SH Peck, ...
Molecular cell 42 (2), 250-260, 2011
Computational design of affinity and specificity at protein–protein interfaces
J Karanicolas, B Kuhlman
Current opinion in structural biology 19 (4), 458-463, 2009
Machine learning classification can reduce false positives in structure-based virtual screening
YO Adeshina, EJ Deeds, J Karanicolas
Proceedings of the National Academy of Sciences 117 (31), 18477-18488, 2020
The structural basis for biphasic kinetics in the folding of the WW domain from a formin-binding protein: lessons for protein design?
J Karanicolas, CL Brooks III
Proceedings of the National Academy of Sciences 100 (7), 3954-3959, 2003
Natural product (−)-gossypol inhibits colon cancer cell growth by targeting RNA-binding protein Musashi-1
L Lan, C Appelman, AR Smith, J Yu, S Larsen, RT Marquez, H Liu, X Wu, ...
Molecular oncology 9 (7), 1406-1420, 2015
Isothermal analysis of ThermoFluor data can readily provide quantitative binding affinities
N Bai, H Roder, A Dickson, J Karanicolas
Scientific reports 9 (1), 2650, 2019
Druggable protein interaction sites are more predisposed to surface pocket formation than the rest of the protein surface
DK Johnson, J Karanicolas
PLoS computational biology 9 (3), e1002951, 2013
Rationalizing PROTAC-mediated ternary complex formation using Rosetta
N Bai, SA Miller, GV Andrianov, M Yates, P Kirubakaran, J Karanicolas
Journal of chemical information and modeling 61 (3), 1368-1382, 2021
Targeting the interaction between RNA-binding protein HuR and FOXQ1 suppresses breast cancer invasion and metastasis
X Wu, G Gardashova, L Lan, S Han, C Zhong, RT Marquez, L Wei, ...
Communications biology 3 (1), 193, 2020
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