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Jian Zhang 0037

dblp:07/314-37 · DBLP profile ↗
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6ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-6558-791XORCID · verified

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

Applied, interdisciplinary, general and emerging computing · 6 · 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
5 papers
Bioinformatics and computational biology · 100%

Topics — the 8 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › proteomics
post-translational modification
0.712023
PTMint database of experimentally verified PTM regulation on protein-protein interaction · Bioinform. 2023
Bioinformatics and computational biology
drug discovery
0.332016
ASBench: benchmarking sets for allosteric discovery · Bioinform. 2015
Alloscore: a method for predicting allosteric ligand-protein interactions · Bioinform. 2016
Allosite: a method for predicting allosteric sites · Bioinform. 2013
Bioinformatics and computational biology › drug discovery › computational drug discovery
protein-ligand interaction prediction
0.212016
Alloscore: a method for predicting allosteric ligand-protein interactions · Bioinform. 2016
Bioinformatics and computational biology › structural bioinformatics › ligand binding site analysis
allosteric site prediction
0.212013
Allosite: a method for predicting allosteric sites · Bioinform. 2013
Bioinformatics and computational biology
protein function prediction
0.212013
Allosite: a method for predicting allosteric sites · Bioinform. 2013
Bioinformatics and computational biology › computational neuroscience › neural coding
olfactory coding
0.112011
ODORactor: a web server for deciphering olfactory coding · Bioinform. 2011
Bioinformatics and computational biology › drug discovery
virtual screening
0.112016
Alloscore: a method for predicting allosteric ligand-protein interactions · Bioinform. 2016
Bioinformatics and computational biology
protein structure analysis
0.112015
ASBench: benchmarking sets for allosteric discovery · Bioinform. 2015

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

manual curation · 0.7database integration · 0.7machine learning · 0.2benchmarking · 0.2web server · 0.2molecular structure analysis · 0.1
YearPublicationVenuePosition
2023 PTMint database of experimentally verified PTM regulation on protein-protein interaction
abstract
MOTIVATION: Post-translational modification (PTM) is an important biochemical process. which includes six most well-studied types: phosphorylation, acetylation, methylation, sumoylation, ubiquitylation and glycosylation. PTM is involved in various cell signaling pathways and biological processes. Abnormal PTM status is closely associated with severe diseases (such as cancer and neurologic diseases) by regulating protein functions, such as protein-protein interactions (PPIs). A set of databases was constructed separately for PTM sites and PPI; however, the resource of regulation for PTM on PPI is still unsolved. RESULTS: Here, we firstly constructed a public accessible database of PTMint (PTMs that are associated with PPIs) (https://ptmint.sjtu.edu.cn/) that contains manually curated complete experimental evidence of the PTM regulation on PPIs in multiple organisms, including Homo sapiens, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae and Schizosaccharomyces pombe. Currently, the first version of PTMint encompassed 2477 non-redundant PTM sites in 1169 proteins affecting 2371 protein-protein pairs involving 357 diseases. Various annotations were systematically integrated, such as protein sequence, structure properties and protein complex analysis. PTMint database can help to insight into disease mechanism, disease diagnosis and drug discovery associated with PTM and PPI. AVAILABILITY AND IMPLEMENTATION: PTMint is freely available at: https://ptmint.sjtu.edu.cn/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Xiaokun Hong, Ningshan Li, Jiyang Lv, Yan Zhang 0124, Jing Li 0107, Jian Zhang 0037, Hai-Feng Chen
Bioinform.6
2016 Alloscore: a method for predicting allosteric ligand-protein interactions
abstract
UNLABELLED: Allosteric ligands have increasingly gained attention as potential therapeutic agents due to their higher target selectivity and lower toxicity compared with classic orthosteric ligands. Despite the great interest in the development of allosteric drugs as a new tactic in drug discovery, the understanding of the ligand-protein interactions underlying allosteric binding represents a key challenge. Herein, we introduce Alloscore, a web server that predicts the binding affinities of allosteric ligand-protein interactions. This method exhibits prominent performance in describing allosteric binding and could be useful in allosteric virtual screening and the structural optimization of allosteric agonists/antagonists. AVAILABILITY AND IMPLEMENTATION: The Alloscore server and tutorials are freely available at http://mdl.shsmu.edu.cn/alloscore CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Qiancheng Shen, Minyi Su, Xinyi Liu 0003, Shaoyong Lu, Zhongjie Chen, Renxiao Wang, Jian Zhang 0037
Bioinform.8
2015 ASBench: benchmarking sets for allosteric discovery
abstract
Abstract Summary: Allostery allows for the fine-tuning of protein function. Targeting allosteric sites is gaining increasing recognition as a novel strategy in drug design. The key challenge in the discovery of allosteric sites has strongly motivated the development of computational methods and thus high-quality, publicly accessible standard data have become indispensable. Here, we report benchmarking data for experimentally determined allosteric sites through a complex process, including a ‘Core set’ with 235 unique allosteric sites and a ‘Core-Diversity set’ with 147 structurally diverse allosteric sites. These benchmarking sets can be exploited to develop efficient computational methods to predict unknown allosteric sites in proteins and reveal unique allosteric ligand–protein interactions to guide allosteric drug design. Availability and implementation: The benchmarking sets are freely available at http://mdl.shsmu.edu.cn/asbench. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online
Wenkang Huang, Guanqiao Wang, Qiancheng Shen, Xinyi Liu 0003, Shaoyong Lu, Lv Geng, Zhimin Huang, Jian Zhang 0037
Bioinform.8
2014 The Structural Basis of ATP as an Allosteric Modulator
abstract
Adenosine-5'-triphosphate (ATP) is generally regarded as a substrate for energy currency and protein modification. Recent findings uncovered the allosteric function of ATP in cellular signal transduction but little is understood about this critical behavior of ATP. Through extensive analysis of ATP in solution and proteins, we found that the free ATP can exist in the compact and extended conformations in solution, and the two different conformational characteristics may be responsible for ATP to exert distinct biological functions: ATP molecules adopt both compact and extended conformations in the allosteric binding sites but conserve extended conformations in the substrate binding sites. Nudged elastic band simulations unveiled the distinct dynamic processes of ATP binding to the corresponding allosteric and substrate binding sites of uridine monophosphate kinase, and suggested that in solution ATP preferentially binds to the substrate binding sites of proteins. When the ATP molecules occupy the allosteric binding sites, the allosteric trigger from ATP to fuel allosteric communication between allosteric and functional sites is stemmed mainly from the triphosphate part of ATP, with a small number from the adenine part of ATP. Taken together, our results provide overall understanding of ATP allosteric functions responsible for regulation in biological systems.
Shaoyong Lu, Wenkang Huang, Qiancheng Shen, Ruth Nussinov, Jian Zhang 0037
PLoS Comput. Biol.7
2013 Allosite: a method for predicting allosteric sites
abstract
MOTIVATION: The use of allosteric modulators as preferred therapeutic agents against classic orthosteric ligands has colossal advantages, including higher specificity, fewer side effects and lower toxicity. Therefore, the computational prediction of allosteric sites in proteins is receiving increased attention in the field of drug discovery. Allosite is a newly developed automatic tool for the prediction of allosteric sites in proteins of interest and is now available through a web server. AVAILABILITY: The Allosite server and tutorials are freely available at http://mdl.shsmu.edu.cn/AST CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Wenkang Huang, Shaoyong Lu, Zhimin Huang, Xinyi Liu 0003, Linkai Mou, Yaqin Liu, Zhongjie Chen, Tingjun Hou, Jian Zhang 0037
Bioinform.11
2011 ODORactor: a web server for deciphering olfactory coding
abstract
SUMMARY: ODORactor is an open access web server aimed at providing a platform for identifying odorant receptors (ORs) for small molecules and for browsing existing OR-ligand pairs. It enables the prediction of ORs from the molecular structures of arbitrary chemicals by integrating two individual functionalities: odorant verification and OR recognition. The prediction of the ORs for several odorants was experimentally validated in the study. In addition, ODORactor features a comprehensive repertoire of olfactory information that has been manually curated from literature. Therefore, ODORactor may provide an effective way to decipher olfactory coding and could be a useful server tool for both basic olfaction research in academia and for odorant discovery in industry. AVAILABILITY: Freely available at http://mdl.shsmu.edu.cn/ODORactor CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Xinyi Liu 0003, Xubo Su, Fei Wang 0012, Zhimin Huang, Ruina Zhang, Lifang Wu, Yingyi Chen, Hanyi Zhuang, Jian Zhang 0037
Bioinform.14