PTM-Psi on the Cloud: A Cloud-Compatible Workflow for Scalable, High-Throughput Simulation of Post-Translational...

Area of Science:

• Computational biology
• Biochemistry
• Cloud computing

Background:

• Post-translational modifications (PTMs) significantly impact protein structure and function.
• Investigating the combinatorial effects of PTMs, especially thiol PTMs, presents a significant computational challenge.
• Cyanobacteria's Calvin-Benson cycle relies on protein complexes whose PTMs are crucial for regulating light-dependent sugar production.

Purpose of the Study:

• To develop an advanced computational framework for accelerating the study of PTMs on protein structures and interactions.
• To enhance the PTM-Psi Python package by refactoring it into a cloud-compatible library for broader accessibility and capability.
• To enable high-throughput analysis of thiol PTMs on protein megacomplexes using cloud computing resources.

Main Methods:

• Developed an advanced computational framework leveraging asynchronous, loosely coupled workflows on the Azure Quantum Elements Cloud platform.
• Implemented a "workflow of workflows" approach for optimized resource allocation and management of child workflows.
• Refactored the PTM-Psi Python package into a cloud-compatible library, integrating emerging cloud computing assets.
• Utilized the cloud's heterogeneous architecture for computational investigation of thiol PTMs on a cyanobacterial protein megacomplex.

Main Results:

• Successfully transformed the thiol PTM analysis pipeline into a high-throughput system by utilizing cloud service strengths.
• Demonstrated the framework's capability to handle the combinatorial explosion of thiol PTMs on a key protein megacomplex.
• Optimized resource allocation and leveraged cloud architecture for efficient computational investigation.
• Achieved reduced operational complexity and lowered entry barriers for data interpretation and structural modeling.

Conclusions:

• The cloud-based PTM-Psi framework significantly accelerates the study of PTM impacts on protein structure and interactions.
• This approach enhances high-throughput analysis of thiol PTMs, making complex structural modeling more accessible to redox proteomics specialists.
• The "workflow of workflows" strategy effectively utilizes cloud heterogeneous architecture for complex biological investigations.