Thomas Bailly

dblp:425/7991 · DBLP profile ↗
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1ranked-venue papers
1as 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 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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
protein structure analysis
0.912025
ASMC: investigating the amino acid diversity of enzyme active sites · Bioinform. 2025
Bioinformatics and computational biology › protein structure analysis
structural alignment
0.912025
ASMC: investigating the amino acid diversity of enzyme active sites · Bioinform. 2025

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

pocket prediction · 0.9multiple sequence alignment · 0.9clustering · 0.9
YearPublicationVenuePosition
2025 ASMC: investigating the amino acid diversity of enzyme active sites
abstract
MOTIVATION: The analysis of enzyme active sites is essential for understanding their activity in terms of catalyzed reaction and substrate specificity, providing insights for engineering to obtain targeted properties or modify the substrate scope. In 2010, a first version of the Active Site Modeling and Clustering (ASMC) workflow was published. ASMC predicts isofunctional clusters from enzyme families, based on structural modeling and clustering of active sites. Since then, structure- and sequence-based methods have developed considerably. RESULTS: We present here a redesign of the ASMC workflow. This new major version includes recent pocket prediction, structural alignment and clustering methods, as well as a refined amino acid distance matrix, thereby improving the relevance of results and reducing the need for laborious manual analysis to obtain relevant clusters. In addition, we have implemented multiple sequence alignment as a possible input for the clustering step, along with an additional script to compare 2D and 3D active sites. Finally, the code has been unified from three to one programming language (Python) to facilitate its installation and maintenance. This new version of ASMC was evaluated on a set of protein families, resulting in overall better performances compared to its original version. AVAILABILITY AND IMPLEMENTATION: ASMC is supported on Linux operating system and freely available at https://github.com/labgem/ASMC, along with a complete documentation (wiki, tutorial).
Thomas Bailly, Eddy Elisée, David Vallenet
Bioinform.1