Demo Data from VLDB 2026 / reviewers in the wild / expert
Dominik Schwarz
dblp:310/6537
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0003-3558-8396ORCID · reported
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › protein structure prediction
inter-residue distance prediction |
0.5 | 1 | 2021 | Co-evolutionary distance predictions contain flexibility information · Bioinform. 2021 |
Bioinformatics and computational biology › protein structure analysis › protein flexibility
protein flexibility prediction |
0.5 | 1 | 2021 | Co-evolutionary distance predictions contain flexibility information · Bioinform. 2021 |
Bioinformatics and computational biology
protein structure prediction |
0.5 | 1 | 2021 | Co-evolutionary distance predictions contain flexibility information · Bioinform. 2021 |
Methods — techniques the papers use, named apart from their topics
machine learning · 0.5co-evolution analysis · 0.5DMPfold · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Co-evolutionary distance predictions contain flexibility informationabstractMOTIVATION: Co-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predict distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. With AlphaFold2 lifting the accuracy of some predicted protein models close to experimental levels, structure prediction research will move on to other challenges. One of those areas is the prediction of more than one conformation of a protein. Here, we examine the potential of residue-residue distance predictions to be informative of protein flexibility rather than simply static structure. RESULTS: We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. We found that rigid residue pairs tended to have only a single local maximum in their predicted distance distributions while flexible residue pairs more often had multiple local maxima. These results suggest that the shape of predicted distance distributions contains information on the rigidity or flexibility of a protein and its constituent residues. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Dominik Schwarz, Guy Georges, Sebastian Kelm, Jiye Shi, Anna Vangone, Charlotte M. Deane |
Bioinform. | 1 |