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Wei Tian 0003

dblp:56/3860-3 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-2146-1717ORCID · verified

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

Applied, interdisciplinary, general and emerging computing · 2 · 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 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › protein structure analysis
beta barrel membrane protein
0.312017
Efficient computation of transfer free energies of amino acids in beta-barrel membrane proteins · Bioinform. 2017
Bioinformatics and computational biology › protein structure analysis › membrane protein analysis
membrane protein structure
0.312017
Efficient computation of transfer free energies of amino acids in beta-barrel membrane proteins · Bioinform. 2017

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

approximation method · 0.3
YearPublicationVenuePosition
2023 Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations
abstract
We report the structure-based pathogenicity relationship identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.
Boshen Wang, Xue Lei, Wei Tian 0003, Alan Perez-Rathke, Jie Liang 0002
Briefings Bioinform.3
2017 Efficient computation of transfer free energies of amino acids in beta-barrel membrane proteins
abstract
MOTIVATION: Transmembrane beta-barrel proteins (TMBs) serve a multitude of essential cellular functions in Gram-negative bacteria, mitochondria and chloroplasts. Transfer free energies (TFEs) of residues in the transmembrane (TM) region provides fundamental quantifications of thermodynamic stabilities of TMBs, which are important for the folding and the membrane insertion processes, and may help in understanding the structure-function relationship. However, experimental measurement of TFEs of TMBs is challenging. Although a recent computational method can be used to calculate TFEs, the results of which are in excellent agreement with experimentally measured values, this method does not scale up, and is limited to small TMBs. RESULTS: We have developed an approximation method that calculates TFEs of TM residues in TMBs accurately, with which depth-dependent transfer free energy profiles can be derived. Our results are in excellent agreement with experimental measurements. This method is efficient and applicable to all bacterial TMBs regardless of the size of the protein. AVAILABILITY AND IMPLEMENTATION: An online webserver is available at http://tanto.bioe.uic.edu/tmb-tfe . CONTACT: : [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Wei Tian 0003, Meishan Lin, Hammad Naveed, Jie Liang 0002
Bioinform.1