Molecular dynamics simulations generate large volumes of high-resolution data describing biomolecular structure and dynamics. While simulation engines are optimized for numerical integration, post-simulation analysis is often implemented through impromptu workflows that lack formal computational structure and offer limited scalability and efficiency. This thesis reframes hydrogen-bond interaction analysis as a problem in algorithmic design and high-performance computing.

A modular computational pipeline is developed to convert post-trajectory-derived hydrogen-bond interaction data into interpretable metrics through a structured sequence of stages. Central to this framework is a novel unified single-pass streaming algorithm that computes biologically relevant metrics: specific interactions, exhaustive hydrogen bond sums and residence time, simultaneously during one traversal of the data, minimizing redundancy and ensuring linear scaling with respect to input size.

The pipeline is evaluated using molecular dynamics simulations of the tumor suppressor protein p53, including multiple isoform splice variants and the cancer-associated mutation Y220C. By treating isoforms as controlled structural perturbations, the analysis allows for a systematic comparison of interaction behavior across conditions. Moreover, the computational performance of the pipeline is characterized through profiling and parallelization experiments. Results show that while the workload is embarrassingly parallel at a file level, practical speedup is constrained by overhead and I/O costs, which is consistent with Amdahl’s Law.

This work shows that post-simulation analysis in molecular dynamics can be treated as rigorously defined computational systems. By integrating high-performance computing principles, the proposed framework provides a scalable and reproducible approach to large-scale interaction analysis, bridging the gap between computing and molecular biophysics.