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Pramesh Singh

dblp:205/2436 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
protein structure analysis
0.912025
An interpretable alphabet for local protein structure search based on amino acid neighborhoods · Bioinform. 2025
Bioinformatics and computational biology › structural bioinformatics › protein structure representation
structural alphabet
0.912025
An interpretable alphabet for local protein structure search based on amino acid neighborhoods · Bioinform. 2025
Bioinformatics and computational biology
protein structure prediction
0.312025
An interpretable alphabet for local protein structure search based on amino acid neighborhoods · Bioinform. 2025

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

3dn structural alphabet · 0.93di alphabet · 0.9
YearPublicationVenuePosition
2025 An interpretable alphabet for local protein structure search based on amino acid neighborhoods
abstract
MOTIVATION: Recent advancements in protein structure prediction methods have vastly increased the size of databases of protein structures, necessitating fast methods for protein structure comparison. Search methods that find structurally similar proteins can be applied to find remote homologs, study the functional relationships among proteins, and aid in protein engineering tasks. RESULTS: We design a "3Dn" structural alphabet that encodes the local neighborhoods around each amino acid in an interpretable way. In a search benchmark task, a combination of our alphabet and Foldseek's 3Di alphabet, outperforms each alphabet individually and ranks best among local search methods that do not require amino acid identity information. We provide software tools that enable the exploration of novel alphabets and combinations of alphabets for protein structure search. AVAILABILITY AND IMPLEMENTATION: The code is freely available at https://github.com/spetti/structure_comparison and at Zenodo https://doi.org/10.5281/zenodo.15734371.
Saba Zerefa, Jesse Cool, Pramesh Singh, Samantha Petti
Bioinform.3