Neural network for screening active sites on proteins

Johanna Bustamante-Torres, Samantha Pardo, Moises Bustamante-Torres

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


The study and understanding of proteins fields are excellent in the biosciences field. The interactions of proteins provide essential information about life. Therefore, many techniques have been developed for this analysis, such as in vitro, in vivo, and in silico. Despite each technique having advantages, in silico methods are a terrific alternative for analyzing the proteins and their interactions using computer tools by its versatility through algorithms. The active sites are of great interest because of their significance in the structure of the protein to interact with another molecule. This chapter details some of the main techniques currently applied to study the active sites on proteins, the database where the information is available, such as Protein Data Bank (PDB), Dali server, structural alignment program (SSAP), structural alignment of multiple proteins (STAMP), catalytic site atlas (CSA), or protein families' database (Pfam). Besides, it describes relevant information about some algorithms that have been developed based on machine learning, such as PDBSiteScan program, patterns in nonhomologous tertiary structures (PINTS), genetic active site search (GASS), site map, computed atlas of surface topography of proteins (Castp), etc. These programs allow getting trustful information about the site actives and other interactions.

Original languageEnglish
Title of host publicationDrug Design using Machine Learning
Number of pages22
ISBN (Electronic)9781394167258
ISBN (Print)9781394167234
StatePublished - 7 Oct 2022

Bibliographical note

Publisher Copyright:
© 2022 Scrivener Publishing LLC.


  • Active sites
  • In silico techniques
  • In vitro techniques
  • In vivo techniques
  • Machine learning
  • Proteins


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