Jinan Wang

Dr. Jinan Wang

University of Kansas
orcid

Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2021 in WIREs Computational Molecular Science
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Journal Article
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JWJinan Wang
Jinan Wang
PAPablo R. Arantes
Pablo R. Arantes
ABApurba Bhattarai
Apurba Bhattarai
RHRohaine V. Hsu
Rohaine V. Hsu
SPShristi Pawnikar
Shristi Pawnikar
YMYinglong Miao
Yinglong Miao

7 August 2020 in Journal of Chemical Theory and Computation
Verified
Journal Article
Closed access
YMYinglong Miao
Yinglong Miao
ABApurba Bhattarai
Apurba Bhattarai
JWJinan Wang
Jinan Wang

8 September 2021 in Nature
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Journal Article
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CDChristopher J. Draper-Joyce
Christopher J. Draper-Joyce
RBRebecca Bhola
Rebecca Bhola
JWJinan Wang
Jinan Wang
ABApurba Bhattarai
Apurba Bhattarai
ANAnh T. N. Nguyen
Anh T. N. Nguyen
ACArthur Christopoulos
Arthur Christopoulos

19 October 2020 in The Journal of Chemical Physics
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Journal Article
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JWJinan Wang
Jinan Wang
YMYinglong Miao
Yinglong Miao
Peptides mediate up to 40% of known protein–protein interactions in higher eukaryotes and play an important role in cellular signaling. However, it is challenging to simulate both binding and unbinding of peptides and calculate peptide binding free energies through conventional molecular dynamics, due to long biological timescales and extremely high flexibility of the peptides. Based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique, we have developed a new computational method “Pep-GaMD,” which selectively boosts essential potential energy of the peptide in order to effectively model its high flexibility. In addition, another boost potential is applied to the remaining potential energy of the entire system in a dual-boost algorithm. Pep-GaMD has been demonstrated on binding of three model peptides to the SH3 domains. Independent 1 µs dual-boost Pep-GaMD simulations have captured repetitive peptide dissociation and binding events, which enable us to calculate peptide binding thermodynamics and kinetics. The calculated binding free energies and kinetic rate constants agreed very well with available experimental data. Furthermore, the all-atom Pep-GaMD simulations have provided important insights into the mechanism of peptide binding to proteins that involves long-range electrostatic interactions and mainly conformational selection. In summary, Pep-GaMD provides a highly efficient, easy-to-use approach for unconstrained enhanced sampling and calculations of peptide binding free energies and kinetics.

8 July 2019 in The Journal of Physical Chemistry B
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Journal Article
Closed access
JWJinan Wang
Jinan Wang
YMYinglong Miao
Yinglong Miao

29 March 2023 in Journal of Chemical Theory and Computation
Verified
Journal Article
Closed access
JWJinan Wang
Jinan Wang
HDHung N. Do
Hung N. Do
KKKushal Koirala
Kushal Koirala
YMYinglong Miao
Yinglong Miao