An in silico pipeline for enzyme-substrate modelling using arthropod P450s

Area of Science:

• Structural biology
• Computational biology
• Biochemistry

Background:

• Artificial intelligence (AI) has revolutionized protein structure prediction, impacting drug development and functional validation.
• While AI models benefit human proteome studies, they also offer insights into less-represented organisms like arthropods, which have extensive protein sequences but lack experimental structures.
• Existing in silico tools are accessible, but a lack of specific guidance hinders scientists without deep structural biology expertise.

Purpose of the Study:

• To provide a step-by-step guide for generating and interpreting in silico cytochrome P450 models and small molecule interactions.
• To demonstrate the utility of AI-driven structural biology in explaining experimental observations for insect proteins.
• To showcase a pipeline for augmenting laboratory experiments using publicly available tools.

Main Methods:

• Generation of three-dimensional protein models using artificial intelligence (AI).
• In silico analysis of small molecule interactions with cytochrome P450 models.
• Application of publicly available online tools for structural biology analysis.

Main Results:

• Successful generation and interpretation of in silico cytochrome P450 models for specific insect proteins.
• In silico explanation for experimentally observed biochemical results related to nicotine and neonicotinoid metabolism.
• Demonstration of AI models' utility in functional validation of insect proteins.

Conclusions:

• AI-generated protein models offer a powerful approach for studying organisms with limited structural data.
• The provided pipeline effectively uses publicly available tools to explain biochemical phenomena in insect cytochrome P450s.
• In silico structural biology can significantly augment and guide laboratory-based experimental research.