Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Liang Rao

dblp:262/6214 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › molecular informatics › molecular modeling › molecular docking
protein-ligand docking
0.612022
ATPdock: a template-based method for ATP-specific protein-ligand docking · Bioinform. 2022
Bioinformatics and computational biology › molecular informatics › molecular modeling › molecular docking
template-based docking
0.612022
ATPdock: a template-based method for ATP-specific protein-ligand docking · Bioinform. 2022
Bioinformatics and computational biology › gene regulation
binding site prediction
0.212022
ATPdock: a template-based method for ATP-specific protein-ligand docking · Bioinform. 2022
Bioinformatics and computational biology
protein structure prediction
0.212022
ATPdock: a template-based method for ATP-specific protein-ligand docking · Bioinform. 2022

Methods — techniques the papers use, named apart from their topics

structural alignment · 0.6monte carlo simulation · 0.6energy function · 0.6
YearPublicationVenuePosition
2022 ATPdock: a template-based method for ATP-specific protein-ligand docking
abstract
MOTIVATION: Accurately identifying protein-ATP binding poses is significantly valuable for both basic structure biology and drug discovery. Although many docking methods have been designed, most of them require a user-defined binding site and are difficult to achieve a high-quality protein-ATP docking result. It is critical to develop a protein-ATP-specific blind docking method without user-defined binding sites. RESULTS: Here, we present ATPdock, a template-based method for docking ATP into protein. For each query protein, if no pocket site is given, ATPdock first identifies its most potential pocket using ATPbind, an ATP-binding site predictor; then, the template pocket, which is most similar to the given or identified pocket, is searched from the database of pocket-ligand structures using APoc, a pocket structural alignment tool; thirdly, the rough docking pose of ATP (rdATP) is generated using LS-align, a ligand structural alignment tool, to align the initial ATP pose to the template ligand corresponding to template pocket; finally, the Metropolis Monte Carlo simulation is used to fine-tune the rdATP under the guidance of AutoDock Vina energy function. Benchmark tests show that ATPdock significantly outperforms other state-of-the-art methods in docking accuracy. AVAILABILITY AND IMPLEMENTATION: https://jun-csbio.github.io/atpdock/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Liang Rao, Ning-Xin Jia, Jun Hu 0010, Dongjun Yu, Guijun Zhang
Bioinform.1