MD-LAIs Software: Computing Whole-Sequence and Amino Acid-Level "Embeddings" for Peptides and Proteins.

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 () level. 4) They lack flexibility in computing specific MDs. To address these issues, we developed (olecular escriptors from ocal mino acid nvariant), Java-based software designed to compute both whole-sequence and -level MDs for peptides and proteins. These MDs are generated by applying aggregation operators () to macromolecular vectors containing the chemical-physical and structural properties of . The set of includes both nonclassical (e.g., Minkowski norms) and classical (e.g., Radial Distribution Function). Classical capture neighborhood structural information at different levels, while nonclassical are applied using a sliding window to generalize the -level output. A weighting system based on fuzzy membership functions is also included to account for the contributions of individual . 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 -level MDs. Overall, we expect that 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).