Smiles2Monomers: a link between chemical and biological structures for polymers

Area of Science:

• Computational chemistry
• Bioinformatics
• Polymer science

Background:

• Monomeric composition is crucial for polymer structure comparison and synthetic biology.
• Existing databases often lack monomeric polymer structures, hindering research.
• The Smiles2Monomers (s2m) tool was developed to address this gap.

Purpose of the Study:

• To design and implement an efficient and accurate algorithm for inferring polymer monomeric structures.
• To enable de novo reconstruction of polymer structures from chemical data.
• To facilitate the integration of automated monomeric annotation into polymer databases.

Main Methods:

• A two-step strategy involving subgraph isomorphism for monomer mapping and dynamic programming for Markovian index construction.
• A greedy algorithm for combining mapped monomers into a consistent structure.
• Refinement using a local branch and cut algorithm.

Main Results:

• The s2m tool achieved over 90% sensitivity in de novo polymer structure reconstruction.
• Average computation time per polymer was as low as 2 seconds.
• The method successfully identified annotation errors in existing polymer databases.

Conclusions:

• s2m provides efficient and sensitive de novo monomeric annotations for polymers.
• The tool can improve the accuracy and speed of polymer database curation.
• s2m is available as a downloadable tool and a web service for peptide-like polymers.