Integrating Molecular Similarity and AlphaFold-Based Structural Alignment for Target Discovery in Trypanosoma cruzi

Area of Science:

• Computational biology
• Drug discovery
• Parasitology

Background:

• Chagas disease, caused by *Trypanosoma cruzi*, is a neglected tropical disease affecting millions globally.
• Current treatments for Chagas disease are limited, especially in chronic infections, necessitating new therapeutic strategies.
• Early-stage drug discovery faces bottlenecks in target identification, traditionally relying on slow and expensive experimental methods.

Purpose of the Study:

• To develop and validate an integrated computational pipeline for prioritizing molecular targets against *Trypanosoma cruzi*.
• To accelerate early-stage drug discovery for Chagas disease by offering a cost-effective and rapid alternative to experimental methods.
• To identify potential drug targets for compounds with known or unknown anti-*T. cruzi* activity.

Main Methods:

• An in silico pipeline combining ligand-based and structure-based computational approaches was developed.
• Ligand-based methods involved similarity searches in bioactivity databases.
• Structure-based methods utilized pairwise structural alignment against the *T. cruzi* proteome predicted by AlphaFold.

Main Results:

• The pipeline successfully identified known targets for six out of eight validated compounds.
• Potential targets were hypothesized for two anti-*T. cruzi* compounds with previously unknown mechanisms of action.
• The computational approach demonstrated moderate success in target prioritization.

Conclusions:

• The developed pipeline offers a flexible and cost-effective framework for early-stage target prioritization in drug discovery.
• Limitations include challenges with novel chemical structures and poorly annotated targets.
• The pipeline's modular design facilitates adaptation for other neglected tropical diseases.