Demo Data from EDBT 2026 / reviewers in the wild / expert
João M. C. Teixeira
dblp:311/9306
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-9113-0622ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 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. | 2 |
| 2020 | Insights on cross-species transmission of SARS-CoV-2 from structural modelingabstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the ongoing global pandemic that has infected more than 31 million people in more than 180 countries worldwide. Like other coronaviruses, SARS-CoV-2 is thought to have been transmitted to humans from wild animals. Given the scale and widespread geographical distribution of the current pandemic and confirmed cases of cross-species transmission, the question of the extent to which this transmission is possible emerges, as well as what molecular features distinguish susceptible from non-susceptible animal species. Here, we investigated the structural properties of several ACE2 orthologs bound to the SARS-CoV-2 spike protein. We found that species known not to be susceptible to SARS-CoV-2 infection have non-conservative mutations in several ACE2 amino acid residues that disrupt key polar and charged contacts with the viral spike protein. Our models also allow us to predict affinity-enhancing mutations that could be used to design ACE2 variants for therapeutic purposes. Finally, our study provides a blueprint for modeling viral-host protein interactions and highlights several important considerations when designing these computational studies and analyzing their results. João P. G. L. M. Rodrigues, Susana Barrera-Vilarmau, João M. C. Teixeira, Marija Sorokina, Elizabeth Seckel, Panagiotis L. Kastritis, Michael Levitt 0001 |
PLoS Comput. Biol. | 3 |