UML for peptides has been created to develop a method for clustering peptides with combinations of amino acids with respect to two target properties: number of acidic and basic groups. The main objective of this source code is to use machine learning techniques to develop a model of interactions at the interface of proteins and gold nanoclusters (AuNC). The target properties have been chosen to ensure the generalisability of the model. With this approach, no dependency with the AuNC structure and solvation effects were created. By selecting features of the amino acids that correlate with the target properties associated with the interaction, clusters containing molecular structures with similar interaction behaviour with the AuNC can be generated. This allows an interaction model to be created by grouping structures with relevant chemical structures for good or poor interaction with the AuNC structure. This is the metadata for the source code version 1.1.0 deposited in GitLab. Further updates and corrections may be released under other versions, but the version linked to this metadata (1.1.0) is the one used in the project "Development of an Interaction Model of the Protein-Nanocluster Interface by Machine Learning-Assisted Clustering of Amino Acids". The source code is published at: https://gitlab.jyu.fi/brdesouz/uml-for-peptides

2025