Abstract
Understanding the relationship between protein structure and function remains a central challenge in molecular biology, biomedicine, and biotechnology, particularly in the context of rapidly expanding experimental and computational datasets. Advances in artificial intelligence (AI) and machine learning (ML) have significantly improved protein structure prediction and functional annotation; however, existing approaches often operate in isolated computational environments and lack scalable, collaborative, and context-aware infrastructures. This thesis research proposes an AI- and machine learning–driven distributed intelligence framework for comprehensive protein structure–function analysis that integrates distributed systems, geospatial technologies, and advanced communication infrastructures.
The proposed framework leverages deep learning architectures—including graph neural networks, transformer-based protein language models, and geometric deep learning—to model protein folding, conformational dynamics, and structure–function relationships. These models are deployed across cloud–edge–high-performance computing environments using distributed and federated learning paradigms, enabling scalable analysis of large-scale protein datasets derived from cryo-electron microscopy, X-ray crystallography, NMR spectroscopy, and multi-omics sources. By incorporating geospatial technologies, the framework supports geographically distributed laboratories, instruments, and data repositories, allowing spatially aware data management, provenance tracking, and collaborative model training across global research networks.
Communication technologies, including high-throughput data streaming, secure communication protocols, and interoperable APIs, are integrated to ensure real-time data exchange, model synchronization, and collaborative inference. This design enhances reproducibility, reduces data silos, and supports privacy-preserving learning across institutional and national boundaries. The framework also enables dynamic resource orchestration, adaptive workload distribution, and fault-tolerant protein analytics, thereby improving computational efficiency and robustness.
The expected outcomes of this research include improved accuracy and interpretability of protein structure–function predictions, enhanced scalability of AI-based protein analysis pipelines, and a novel digital infrastructure for global protein science collaboration. Ultimately, this work aims to establish a next-generation, intelligent, and distributed platform that accelerates biological discovery, supports translational research in drug discovery and protein engineering, and advances the integration of emerging digital technologies into protein structure and function research.
Keywords
Protein Structure and Function; Artificial Intelligence; Machine Learning; Distributed Intelligence; Deep Learning; Geometric Deep Learning; Federated Learning; Geospatial Technology; Communication Technology; Cloud–Edge Computing; Protein Structure Prediction; Functional Annotation; Systems Biology