MD-LAIs Software: Computing Whole-Sequence and Amino Acid-Level “Embeddings” for Peptides and Proteins
Journal
Journal of Chemical Information and Modeling
ISSN
1549-960X
Publisher
American Chemical Society
Date Issued
2024
Author(s)
Torres García, José Ernesto
Type
text::journal::journal article
Abstract
Several computational tools have been developed to calculate sequence-based molecular descriptors (MDs) for peptides and proteins. However, these tools have certain limitations: 1) They generally lack capabilities for curating input data. 2) Their outputs often exhibit significant overlap. 3) There is limited availability of MDs at the amino acid (aa) level. 4) They lack flexibility in computing specific MDs. To address these issues, we developed MD-LAIs (Molecular Descriptors from Local Amino acid Invariants), Java-based software designed to compute both whole-sequence and aa-level MDs for peptides and proteins. These MDs are generated by applying aggregation operators (AOs) to macromolecular vectors containing the chemical-physical and structural properties of aas. The set of AOs includes both nonclassical (e.g., Minkowski norms) and classical AOs (e.g., Radial Distribution Function). Classical AOs capture neighborhood structural information at different k levels, while nonclassical AOs are applied using a sliding window to generalize the aa-level output. A weighting system based on fuzzy membership functions is also included to account for the contributions of individual aas. MD-LAIs features: 1) a module for data curation tasks, 2) a feature selection module, 3) projects of highly relevant MDs, and 4) low-dimensional lists of informative global and aa-level MDs. Overall, we expect that MD-LAIs will be a valuable tool for encoding protein or peptide sequences. The software is freely available as a stand-alone system on GitHub (https://github.com/Grupo-Medicina-Molecular-y-Traslacional/MD_LAIS). ©The authors © American Chemical Society ©Journal of Chemical Information and Modeling.
License
Acceso Abierto
How to cite
Contreras-Torres, E., & Marrero-Ponce, Y. (2024). MD-LAIs Software: Computing Whole-Sequence and Amino Acid-Level “Embeddings” for Peptides and Proteins. Journal of Chemical Information and Modeling, 64(23), 8665–8672. https://doi.org/10.1021/acs.jcim.3c01189