Demo Data from SIGIR 2026 / reviewers in the wild / expert
G. Neshich
dblp:180/9406
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
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-protein interaction prediction
antibody-antigen interface analysis |
0.2 | 1 | 2016 | EPI-peptide designer: a tool for designing peptide ligand libraries based on epitope-paratope interactions · Bioinform. 2016 |
Bioinformatics and computational biology › protein design
peptide design |
0.2 | 1 | 2016 | EPI-peptide designer: a tool for designing peptide ligand libraries based on epitope-paratope interactions · Bioinform. 2016 |
Bioinformatics and computational biology › protein analysis › protein-protein interaction
protein-protein interaction analysis |
0.2 | 1 | 2016 | EPI-peptide designer: a tool for designing peptide ligand libraries based on epitope-paratope interactions · Bioinform. 2016 |
Methods — techniques the papers use, named apart from their topics
molecular interaction computation · 0.2interface graph construction · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | EPI-peptide designer: a tool for designing peptide ligand libraries based on epitope-paratope interactionsabstractMOTIVATION: Antibodies are an important class of biological drugs, but with limitations, such as inadequate pharmacokinetics, adverse immunogenicity and high production costs. Synthetic peptides for the desired target represent an important alternative to antibodies. However, no computational tool exists to guide the design of these peptides. RESULTS: To identify the interacting residues in a given antibody-antigen (Ab-Ag) interface we used Interface Interacting Residue (I2R), a selection method based on computed molecular interactions. The aggregation of all the molecular interactions between epitope and paratope residues allowed us to transform the 3D Ab-Ag complex structures into interface graphs. Based on these data and the probability of molecular interaction we developed EPI-Peptide Designer tool that uses predicted paratope residues for an epitope of interest to generate targeted peptide ligand libraries. EPI-Peptide Designer successfully predicted 301 peptides able to bind to LiD1 target protein (65% of the experimentally tested peptides), an enrichment of 22% compared to randomly generated peptides. This tool should enable the development of a new generation of synthetic interacting peptides that could be very useful in the biosensor, diagnostic and therapeutic fields. AVAILABILITY AND IMPLEMENTATION: All software developed in this work are available at http://www.biocomp.icb.ufmg.br/biocomp/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Benjamin Thomas Viart, Camila Dias-Lopes, Edgar Ernesto Gonzalez Kozlova, C. F. B. Oliveira, C. Nguyen, G. Neshich, Carlos Chávez-Olórtegui, F. Molina, Liza Figueredo Felicori |
Bioinform. | 6 |