End-to-End Optimized Pipeline for Prediction of Protein Folding Kinetics
Vijay Arvind. R Haribharathi Sivakumar Brindha. R
Protein folding is the intricate process by which a linear sequence of amino
acids self-assembles into a unique three-dimensional structure. Protein folding
kinetics is the study of pathways and time-dependent mechanisms a protein
undergoes when it folds. Understanding protein kinetics is essential as a
protein needs to fold correctly for it to perform its biological functions
optimally, and a misfolded protein can sometimes be contorted into shapes that
are not ideal for a cellular environment giving rise to many degenerative,
neuro-degenerative disorders and amyloid diseases. Monitoring at-risk
individuals and detecting protein discrepancies in a protein's folding kinetics
at the early stages could majorly result in public health benefits, as
preventive measures can be taken. This research proposes an efficient pipeline
for predicting protein folding kinetics with high accuracy and low memory
footprint. The deployed machine learning (ML) model outperformed the
state-of-the-art ML models by 4.8% in terms of accuracy while consuming 327x
lesser memory and being 7.3% faster.