Demo Data from EDBT 2026 / reviewers in the wild / expert
Thomas E. Tsangaris
dblp:370/4877
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0003-3683-1183ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › computational structural biology
conformational ensemble generation |
0.7 | 1 | 2023 | Local Disordered Region Sampling (LDRS) for ensemble modeling of proteins with experimentally undetermined or low confidence prediction segments · Bioinform. 2023 |
Bioinformatics and computational biology › protein structure prediction
loop modeling |
0.7 | 1 | 2023 | Local Disordered Region Sampling (LDRS) for ensemble modeling of proteins with experimentally undetermined or low confidence prediction segments · Bioinform. 2023 |
Bioinformatics and computational biology › structural bioinformatics
protein structure |
0.7 | 1 | 2023 | Local Disordered Region Sampling (LDRS) for ensemble modeling of proteins with experimentally undetermined or low confidence prediction segments · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
side-chain entropy · 0.7monte carlo · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Local Disordered Region Sampling (LDRS) for ensemble modeling of proteins with experimentally undetermined or low confidence prediction segmentsabstractSUMMARY: The Local Disordered Region Sampling (LDRS, pronounced loaders) tool is a new module developed for IDPConformerGenerator, a previously validated approach to model intrinsically disordered proteins (IDPs). The IDPConformerGenerator LDRS module provides a method for generating all-atom conformations of intrinsically disordered protein regions at N- and C-termini of and in loops or linkers between folded regions of an existing protein structure. These disordered elements often lead to missing coordinates in experimental structures or low confidence in predicted structures. Requiring only a pre-existing PDB or mmCIF formatted structural template of the protein with missing coordinates or with predicted confidence scores and its full-length primary sequence, LDRS will automatically generate physically meaningful conformational ensembles of the missing flexible regions to complete the full-length protein. The capabilities of the LDRS tool of IDPConformerGenerator include modeling phosphorylation sites using enhanced Monte Carlo-Side Chain Entropy, transmembrane proteins within an all-atom bilayer, and multi-chain complexes. The modeling capacity of LDRS capitalizes on the modularity, the ability to be used as a library and via command-line, and the computational speed of the IDPConformerGenerator platform. AVAILABILITY AND IMPLEMENTATION: The LDRS module is part of the IDPConformerGenerator modeling suite, which can be downloaded from GitHub at https://github.com/julie-forman-kay-lab/IDPConformerGenerator. IDPConformerGenerator is written in Python3 and works on Linux, Microsoft Windows, and Mac OS versions that support DSSP. Users can utilize LDRS's Python API for scripting the same way they can use any part of IDPConformerGenerator's API, by importing functions from the "idpconfgen.ldrs_helper" library. Otherwise, LDRS can be used as a command line interface application within IDPConformerGenerator. Full documentation is available within the command-line interface as well as on IDPConformerGenerator's official documentation pages (https://idpconformergenerator.readthedocs.io/en/latest/). Zi Hao Liu, João M. C. Teixeira, Oufan Zhang, Thomas E. Tsangaris, Jie Li 0072, Claudiu C. Gradinaru, Teresa Head-Gordon, Julie D. Forman-Kay |
Bioinform. | 4 |